Wireless charging management system and method for unmanned aerial vehicle end

Abstract

The invention discloses a wireless charging management system and method for an unmanned aerial vehicle end, and relates to the technical field of unmanned aerial vehicle charging management, the system comprises a wireless charging station and an unmanned aerial vehicle, and the wireless charging management system provides high-precision relative position information through a UWB grid reference tag array to ensure that the unmanned aerial vehicle rapidly arrives at a charging area; the three-axis magnetometer is combined with the magnetic field gradient tensor algorithm, the pitch angle and yaw angle error is corrected in real time, energy transmission loss caused by attitude deviation is reduced, and the charging efficiency of the unmanned aerial vehicle is greatly improved. Through cooperation of the UWB base station and the magnetic induction coil array, accurate positioning and attitude correction of the unmanned aerial vehicle in the wireless charging station are realized, and the docking success rate and the charging efficiency of wireless charging of the unmanned aerial vehicle are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) charging management, and in particular to a wireless charging management system and method for an UAV. Background Art

[0002] With the widespread adoption of drone technology in logistics, agriculture, power inspections, emergency rescue, and other fields, insufficient battery life has become a key bottleneck restricting their continued operation. Traditional contact charging relies on manual intervention, is inefficient, and is susceptible to environmental interference. Wireless charging technology, however, achieves contactless energy transmission through electromagnetic induction and magnetic resonance, becoming a key breakthrough in improving drone automation.

[0003] Traditional electromagnetic induction charging relies on precise alignment of the transmitter and receiver coils. When a drone needs to charge while in flight, the transmitter at the charging station and the receiver at the drone must be precisely aligned. However, in reality, when landing at a charging station, drones are often affected by factors such as flight deviation and takeoff and landing angles, resulting in positional deviations. This in turn causes a sharp drop in wireless charging coupling efficiency, resulting in low energy transfer efficiency and failing to meet the requirements for efficient charging. Summary of the Invention

[0004] In response to the existing problem that when a drone lands on a charging station, the coupling efficiency of wireless charging drops sharply due to position deviation, resulting in low energy transmission efficiency, the present invention provides a wireless charging management system and method for drones, which can improve the coupling efficiency of the charging receiving end and improve charging efficiency. The specific technical solution is as follows: In a first aspect, the present invention provides a wireless charging management system for a drone, comprising: A wireless charging station is provided with a charging platform, and the wireless charging station comprises: A magnetic induction coil array, which is embedded in the wireless charging station, and each coil is passed through an alternating current of a specific frequency; UWB base stations are evenly arranged around the charging platform to form a positioning anchor point network. Reference tag arrays are deployed in a grid pattern within the UWB base stations, and the coordinates of each tag are pre-calibrated. They are used to transmit / receive UWB signals, obtain the original distance information between the drone and the wireless charging station, and send it; a first calculation module, configured to receive the initial distance information, calculate the relative position of the drone and the wireless charging station in combination with the calibrated coordinates of the reference tag array, and send first target position information based on the relative position; a second calculation module, configured to receive a magnetic field vector, calculate a magnetic field gradient tensor, and calculate adjustment values of a pitch angle and a yaw angle of the UAV according to the magnetic field gradient tensor and a preset target magnetic field gradient, and send a second target position adjustment instruction based on the adjustment values; a wireless communication module, configured to transmit data information between the UAV and the wireless charging station; a wireless charging transmission module, configured to transmit wireless electric energy; a UAV, comprising: a mobile UWB tag module, configured to transmit / receive UWB signals; a three-axis magnetometer module, installed at a bottom end of the UAV, configured to measure a magnetic field vector and send the magnetic field vector; a flight control adjustment module, configured to receive the first target position information and adjust the UAV to fly to an initial target position, and receive the second target position adjustment instruction and finely adjust the UAV to a final target position; a wireless charging receiving module, configured to receive wireless electric energy to charge a battery of the UAV.

[0005] Preferably, the first calculation module is specifically configured to: receive original distance measurement values of bidirectional ranging between each UWB base station and the mobile UWB tag module, and perform three-dimensional positioning calculation based on a least square method to obtain estimated coordinates of the UAV; receive ranging values of each tag in the reference tag array and the mobile UWB tag module, and calculate difference values between measured coordinates and true coordinates of each tag in combination with calibrated coordinates of each tag in the reference tag array; obtain corrected coordinates of the UAV by constructing a global error field model through a spatial interpolation algorithm based on the estimated coordinates and the difference values; calculate relative positions of the UAV and the wireless charging station based on the corrected coordinates and coordinate information of the wireless charging station, and send the first target position information based on the relative positions.

[0006] Preferably, after receiving the first target position information, the flight control adjustment module adjusts the UAV to fly to the initial target position and hovers at the initial target position.

[0007] Preferably, the second calculation module is specifically configured to: receive the magnetic field vector measured by the three-axis magnetometer module, and calculate the magnetic field gradient tensor; establish a loss function according to the magnetic field gradient tensor and a preset target magnetic field gradient, the target magnetic field gradient being obtained through experimental calibration in advance; obtain adjustment values of a pitch angle and a yaw angle of the UAV by adding a regular term of an attitude adjustment amount to the loss function to solve an optimal attitude. A second target position adjustment instruction is sent based on the adjustment values ​​of the pitch angle and yaw angle of the drone.

[0008] Preferably, the wireless charging transmitting module adopts the electromagnetic induction principle that matches the wireless charging receiving module, and is used to send electric energy to the wireless charging receiving module through wireless power, convert the DC power into wireless power and transmit it.

[0009] Preferably, the UWB base station includes a UWB transmitter and a UWB receiver for transmitting and receiving UWB signals; the UWB transmitter periodically transmits UWB signals, and when the transmitted UWB signal is received by the mobile UWB tag module, the UWB receiver receives the UWB signal transmitted by the mobile UWB tag module.

[0010] Preferably, the drone further includes a safety monitoring module, which includes: Infrared thermal imaging module, used to collect temperature information at various points on the surface of the drone's battery and generate temperature distribution images for monitoring the battery surface temperature field distribution; The overvoltage and overcurrent protector is used to monitor the voltage and current in the charging circuit in real time, and cut off the charging circuit when the voltage or current exceeds the set safety threshold.

[0011] Preferably, the wireless charging station further includes an energy management module: the energy management module includes: The integrated bidirectional DC-DC converter is used to dynamically adjust the output voltage and current according to the charging status of the drone's battery. The BMS battery management system is used to monitor the voltage, current and temperature parameters of the drone's battery in real time and dynamically adjust the charging mode.

[0012] Preferably, the wireless communication module is a Wi-Fi, Bluetooth or ZigBee communication module.

[0013] In a second aspect, the present invention further provides a wireless charging management method for a drone, which is applied to the aforementioned wireless charging management system for a drone, comprising: Obtaining original distance information between the drone and the wireless charging station, and calculating the relative position of the drone and the wireless charging station in combination with the calibrated coordinates of the reference tag array, and obtaining first target position information based on the relative position; According to the first target position information, adjust the drone to fly to the initial target position and start charging; Obtaining a magnetic field vector, calculating a magnetic field gradient tensor, and calculating adjustment values ​​for the pitch angle and yaw angle of the drone based on the magnetic field gradient tensor and a preset target magnetic field gradient, and obtaining a second target position adjustment instruction based on the adjustment values; Fine-tune the UAV to the final target position according to the second target position adjustment instruction; When the drone is adjusted to the final target position, the optimal charging state is activated for the drone based on the received radio energy.

[0014] Compared with the prior art, the present invention has the following beneficial effects: A wireless charging management system for drones, based on the present invention, uses a UWB gridded reference tag array to provide high-precision relative position information, ensuring the drone's rapid arrival at the charging area. A three-axis magnetometer, combined with a magnetic field gradient tensor algorithm, corrects pitch and yaw errors in real time, reducing energy transmission losses caused by attitude deviations and significantly improving the drone's charging efficiency. By synergizing a UWB base station with a magnetic induction coil array, the present invention achieves precise positioning and attitude correction for drones at wireless charging stations, significantly improving the drone's wireless charging docking success rate and charging efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0016] Fig. 1 This is a schematic diagram of a wireless charging management system for a drone according to an embodiment of the present invention.

[0017] Fig. 2 This is a flow chart of a wireless charging management method for a drone according to an embodiment of the present invention.

[0018] Fig. 3 This is a schematic diagram of a wireless charging station and a drone wirelessly charging according to an embodiment of the present invention.

[0019] The following are the descriptions of the reference numerals: 1-UAV; 2-Wireless charging station; 21-UWB base station. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] It should be understood that when used in this specification, the terms "include" and "comprising" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0022] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0023] It should be further understood that the term “and / or” used in the description of the present invention refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0024] See the following examples Figs. 1 to 3 , Fig. 3 The figure shows a schematic diagram of the wireless charging station and the wireless charging process of the drone.

[0025] The present invention provides a wireless charging management system for a drone, including: A wireless charging station is provided with a charging platform, and the wireless charging station comprises: A magnetic induction coil array, embedded within the wireless charging station, employs a 12×12 planar grid array (144 coils in total), with each coil having a diameter of 2 cm and a spacing of 3 cm. Each coil is supplied with an alternating current of a specific frequency, and its operating frequency is distinct from the UWB band to avoid interference with the UWB band. UWB base stations are evenly arranged around the charging platform to form a positioning anchor network, with no less than four UWB base stations and a coverage range of ≥ 20m × 20m. Reference tag arrays are deployed in a grid pattern within the UWB base stations, and the coordinates of each tag are pre-calibrated for transmitting / receiving UWB signals, obtaining the original distance information between the drone and the wireless charging station, and sending it; a first calculation module, configured to receive the initial distance information, calculate the relative position of the drone and the wireless charging station in combination with the calibrated coordinates of the reference tag array, and send first target position information based on the relative position; a second calculation module, configured to receive a magnetic field vector, calculate a magnetic field gradient tensor, and calculate adjustment values ​​for the pitch angle and yaw angle of the drone based on the magnetic field gradient tensor and a preset target magnetic field gradient, and send a second target position adjustment instruction based on the adjustment values; Wireless communication module, used to transmit data information between the drone and the wireless charging station; Wireless charging transmitter module, used to send wireless energy; A drone, comprising: Mobile UWB tag module, used to transmit / receive UWB signals; A three-axis magnetometer module is installed at the bottom of the drone, coaxial with the charging receiving coil, and is used to measure and transmit the magnetic field vector; a flight control adjustment module, configured to receive the first target position information and adjust the drone to fly to the initial target position, receive the second target position adjustment instruction, and fine-tune the drone to the final target position; Wireless charging receiving module, used to receive radio energy to charge the drone battery.

[0026] In this embodiment, the wireless charging receiving module receives radio energy from a wireless charging station and converts it into electrical energy to charge the drone's battery. Its operating principle is to utilize electromagnetic induction or magnetic resonance to convert the radio energy transmitted by the wireless charging station into DC power to charge the drone's battery. By optimizing the design of the receiving coil and matching circuit, the efficiency of radio energy reception is improved and energy loss is reduced. Wireless charging principles based on electromagnetic induction or magnetic resonance are well known in the art.

[0027] The UWB base station includes a UWB transmitter and a UWB receiver, which are used to transmit and receive UWB signals; the UWB transmitter periodically transmits UWB signals, and when the transmitted UWB signal is received by the mobile UWB tag module, the UWB receiver receives the UWB signal transmitted by the mobile UWB tag module.

[0028] It should be noted that the second computing module can use the ADuCM360 magnetic signal acquisition chip.

[0029] A wireless charging management system for drones, based on the present invention, uses a UWB gridded reference tag array to provide high-precision relative position information, ensuring the drone's rapid arrival at the charging area. A three-axis magnetometer, combined with a magnetic field gradient tensor algorithm, corrects pitch and yaw errors in real time, reducing energy transmission losses caused by attitude deviations and significantly improving the drone's charging efficiency. By synergizing a UWB base station with a magnetic induction coil array, the present invention achieves precise positioning and attitude correction for drones at wireless charging stations, significantly improving the drone's wireless charging docking success rate and charging efficiency.

[0030] Specifically, in a preferred embodiment of the present application, the first calculation module is specifically configured to: The original distance measurement values ​​of the two-way ranging between each UWB base station and the mobile UWB tag module are received, and the three-dimensional positioning solution is performed based on the least squares method to obtain the estimated coordinates of the drone; the two-way ranging design between the mobile UWB tag module and each UWB base station is used to offset the effects of clock synchronization errors and signal attenuation in one-way communication.

[0031] The UAV's mobile UWB tag module performs two-way distance measurement with each UWB base station at the charging station to obtain the original distance measurement value. ,in ; Perform three-dimensional positioning solution based on the least squares method and preliminarily estimate the coordinates of the drone : in, is the known coordinate of the j-th base station; The three-dimensional positioning solution based on the least squares method is optimized through iterative optimization of multi-UWB base station distance data to achieve rapid output of the drone's estimated coordinates.

[0032] Receiving the ranging values ​​of each tag in the reference tag array and the mobile UWB tag module, and calculating the difference between the measured coordinates and the true coordinates of each tag in combination with the calibrated coordinates of each tag in the reference tag array; For each reference tag, calculate its measured coordinates With real coordinates The difference: Based on the estimated coordinates and the difference, a global error field model is constructed by a spatial interpolation algorithm to solve the error field and obtain the corrected coordinates of the UAV; Construct a global error field model through spatial interpolation algorithm: in, is the weight coefficient, is the spatial correlation length, which is dynamically adjusted according to the environment and takes a typical value of 0.3; The coarse positioning result Input the error field model and obtain the compensation amount: The corrected coordinates of the drone are: Based on the corrected coordinates and the coordinate information of the wireless charging station, the relative position of the drone and the wireless charging station is calculated, and first target position information is sent based on the relative position.

[0033] After receiving the first target position information, the flight control adjustment module adjusts the drone to the initial target position and hovers at the initial target position. At this time, the drone is hovering over the charging platform, 10 cm away from the charging platform, and the drone's wireless charging state is activated.

[0034] In this embodiment, the difference between the known coordinates and the measured coordinates of the grid-like reference tags can be used to quantify the systematic deviation caused by environmental interference. Based on the constructed global error field model, the error characteristics of the discrete reference tags are extended to the entire charging platform space through an interpolation algorithm. When the drone is in any position, the error field model can be used to obtain real-time compensation, improving the drone's corrected coordinates. Accurate corrected coordinates ensure that the drone hovers stably above the charging platform, which not only avoids the interference of low-altitude airflow on positioning, but also provides a better measurement range for the three-axis magnetometer to collect magnetic field vectors.

[0035] When the drone hovers over the charging platform, its magnetometer module measures the composite magnetic field from the coil array. This magnetic field has a definite functional relationship with the drone's spatial position and attitude (pitch angle θ, yaw angle φ). Based on this principle, in a preferred embodiment of the present application, the second calculation module is specifically configured to: receiving the magnetic field vector measured by the three-axis magnetometer module and calculating the magnetic field gradient tensor; In the single-coil magnetic field model (Biot-Savart law), for the kth coil (center position is , the normal vector is ), the magnetic field generated at position P (observation point) is: in, is the vacuum permeability; is the coil current; is the vector of the observation point P relative to the center of the coil, ; is the magnetic moment of the kth coil, , is the coil area.

[0036] Based on the single-coil magnetic field model and the synthetic magnetic field model, since the drone makes a small movement at its current position, the magnetic field measurement values ​​at multiple locations on the drone can be recorded to construct a local magnetic field map. The synthetic magnetic field measured by the drone is the superposition of all coils: It should be noted that Indicates that in posture ( ) in the drone body coordinate system; when the drone attitude (θ, φ) changes, the coordinate system of the magnetometer module rotates with the drone, so the actual measurement value is the projection in the drone body coordinate system.

[0037] According to the magnetic field model, the magnetic field gradient tensor is defined as: In practical applications, since the charging platform is a two-dimensional plane, the magnetic field gradient tensor can be defined by focusing on the plane gradient (x, y direction).

[0038] A loss function is established based on the magnetic field gradient tensor and the preset target magnetic field gradient. The target magnetic field gradient is obtained in advance through experimental calibration. This position is represented as the ideal coupling position, that is, the point with the highest charging efficiency, which is recorded as .

[0039] The loss function is defined as the difference between the current measured gradient and the target gradient, and a regularization term for the posture adjustment is added: in, is the actual measured magnetic field gradient; is the ideal gradient expected at the current posture (calculated through a pre-established database or model); is the regularization coefficient to prevent over-adjustment of the posture.

[0040] Based on the regularization term of the attitude adjustment amount added to the loss function, the optimal attitude is solved to obtain the adjustment values ​​of the pitch angle and yaw angle of the drone; Solved by using a nonlinear optimization algorithm: The drone adjusts the pitch angle θ and yaw angle φ to the optimized angles. Repeat the above process until the gradient error is less than the threshold or the maximum number of iterations is reached.

[0041] A second target position adjustment instruction is sent based on the adjusted values ​​of the drone's pitch and yaw angles. Upon receiving the second target position adjustment instruction, the flight control adjustment module adjusts the drone's flight and landing to the final target position. At this point, the drone is landed on the charging platform and enters the optimal charging state.

[0042] In this embodiment, a synthetic magnetic field model based on the Biot-Savart law is constructed between the drone and the wireless charging station to quantify the multi-coil superposition effect into a mathematical relationship. Combined with the projection transformation of the body coordinate system, the optimal charging posture of the drone is accurately adjusted to improve the energy transmission efficiency of the wireless charging system.

[0043] Based on the wireless charging management system for a drone in the above embodiment, its working principle is to realize the wireless charging process control of the drone from approaching to accurately docking with the wireless charging station through the coordinated mechanism of UWB high-precision positioning and magnetic field gradient attitude calibration. The details are as follows: 1. UWB Positioning Anchor Network Construction and Initial Alignment The UWB base stations are evenly deployed around the charging platform of the wireless charging station. Fig. 3 Four (six or eight, etc.) UWB base stations are deployed to form a spatial positioning anchor network. Reference tags are deployed in a 5cm x 5cm grid inside the base stations, each with pre-calibrated coordinates. This calibration process is achieved through laser scanning combined with RTK-GNSS.

[0044] The UWB tag module carried by the drone periodically transmits a pulse signal. The surrounding UWB base stations receive the signal and record the arrival time. The distance from the tag to each base station is calculated using the TDOA algorithm. The first calculation module calculates the drone's initial position based on the distance data from each UWB base station and the reference tag coordinates.

[0045] An initial path is generated based on the current position of the drone and the center coordinates of the charging platform. The flight control adjustment module controls the drone to hover along the planned path to the initial target position 10 cm above the charging platform of the wireless charging station.

[0046] 2. Magnetic Field Gradient Tensor Attitude Calibration Mechanism After the drone hovers at its initial target position, the three-axis magnetometer module collects magnetic field vector data in real time, reflecting the relative attitude deviation between the drone and the magnetic induction coil array of the wireless charging station's charging platform. Based on this magnetic field vector data, the second calculation module calculates the magnetic field gradient tensor matrix. This matrix contains information about the rate of change of the magnetic field in three-dimensional space, reflecting the relative offset of the coils.

[0047] The real-time calculated magnetic field gradient tensor is compared with the preset target magnetic field gradient tensor (corresponding to the optimal coil coupling attitude). The least-squares method is used to determine the attitude adjustment, corresponding to the pitch, yaw, and roll angle adjustments. The flight control adjustment module adjusts the drone's quadrotor output based on the attitude adjustment commands, fine-tuning the attitude and bringing the drone to its final target position—the optimal charging position. After the final position adjustment is complete, the wireless charging transmitter module initiates an alternating current at a specific frequency, creating a resonant coupling between the magnetic induction coil array and the drone's receiver module, efficiently transferring power to the drone's battery.

[0048] It should be noted that the charging state can be turned on when the drone hovers at the initial target position, and the optimal charging state is turned on when the drone adjusts to the final target position.

[0049] Specifically, in a preferred embodiment of the present application, the drone further includes a safety monitoring module, and the safety monitoring module includes: Infrared thermal imaging module, used to collect temperature information at various points on the surface of the drone's battery and generate temperature distribution images for monitoring the battery surface temperature field distribution; The overvoltage and overcurrent protector is used to monitor the voltage and current in the charging circuit in real time, and cut off the charging circuit when the voltage or current exceeds the set safety threshold.

[0050] Specifically, the wireless charging station further includes an energy management module: the energy management module includes: The integrated bidirectional DC-DC converter is used to dynamically adjust the output voltage and current according to the charging status of the drone's battery. The BMS battery management system is used to monitor the voltage, current and temperature parameters of the drone's battery in real time and dynamically adjust the charging mode.

[0051] In this embodiment, the infrared thermal imaging module monitors the temperature field distribution on the battery surface. Through infrared thermal imaging technology, the temperature information of each point on the battery surface can be obtained in real time, and a temperature distribution image can be generated. When the temperature difference on the battery surface is detected to exceed the temperature threshold, it indicates that the battery may have abnormal conditions such as local overheating. At this time, the infrared thermal imaging module will transmit a signal to the energy management module. The energy management module will take corresponding protection measures based on the received signal, such as reducing the charging power, suspending charging, etc., to prevent thermal runaway of the battery and ensure charging safety; the overvoltage and overcurrent protector monitors the voltage and current in the charging circuit in real time. When the voltage or current exceeds the set safety threshold, the protector will act quickly to cut off the charging circuit and protect the drone battery and charging equipment from damage.

[0052] In specific implementations, once the drone's attitude is adjusted and the wireless charging receiver module and transmitter module achieve optimal coupling, the wireless charging station activates the transmitter module and transmits wireless power to the drone. During the charging process, the drone's safety monitoring unit monitors the battery's charging status in real time, including the battery surface temperature and the voltage and current in the charging circuit. Simultaneously, the wireless charging station's energy management module monitors the charging process in real time, dynamically adjusting the charging power and mode to ensure safe and efficient charging. When the drone's battery is fully charged or reaches a preset charge level, the energy management module detects a charging completion signal. Upon receiving this signal, the wireless charging station stops transmitting wireless power to the drone.

[0053] Preferably, the wireless communication module is a Wi-Fi, Bluetooth or ZigBee communication module.

[0054] The embodiment of the application further provides a wireless charging management method for a UAV end, applied to the wireless charging management system for the UAV end, and comprising the following steps: Obtaining original distance information between the UAV and the wireless charging station, and combining the calibration coordinates of the reference tag array, calculating the relative positions of the UAV and the wireless charging station, and obtaining first target position information based on the relative positions; According to the first target position information, adjusting the UAV to fly to an initial target position, and starting a charging state; Obtaining a magnetic field vector, calculating a magnetic field gradient tensor, and calculating adjustment values of a pitch angle and a yaw angle of the UAV according to the magnetic field gradient tensor and a preset target magnetic field gradient, and obtaining a second target position adjustment instruction based on the adjustment values; According to the second target position adjustment instruction, finely adjusting the UAV to a final target position; When the UAV is adjusted to the final target position, starting an optimal charging state for the UAV based on received wireless electric energy.

[0055] The wireless charging management method of the embodiment realizes full-process automation and high-precision alignment of the wireless charging of the UAV, first ensures an accurate initial position through UWB positioning combined with reference tag error compensation, and then further reduces coil alignment errors by dynamically correcting the attitude based on the magnetic field gradient tensor, solves the problem of low coupling efficiency caused by traditional position adjustment only, and improves the charging efficiency. Meanwhile, the wireless charging management method does not need manual intervention from position adjustment to attitude fine adjustment, and shortens the charging response time.

[0056] Those skilled in the art can appreciate that the units of the examples described in combination with the embodiments disclosed in the present application can be realized in electronic hardware, computer software or a combination of both, and the constitution of the examples has been described in general in the above description in order to clearly illustrate the interchangeability of hardware and software. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0057] In the embodiments provided in the present application, it should be understood that the division of units is only a logical functional division, and another division mode can be used in actual implementation, for example, a plurality of units can be combined into one unit, one unit can be split into a plurality of units, or some features can be ignored, etc.

[0058] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, 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 functional units.

[0059] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), a mobile hard drive, a magnetic disk, or an optical disk.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the specification of the present invention.

Claims

1. A wireless charging management system for drones, characterized in that: include: A wireless charging station is provided with a charging platform, and the wireless charging station comprises: A magnetic induction coil array, which is embedded in the wireless charging station, and each coil is passed through an alternating current of a specific frequency; UWB base stations are evenly arranged around the charging platform to form a positioning anchor point network. Reference tag arrays are deployed in a grid pattern within the UWB base stations, and the coordinates of each tag are pre-calibrated. They are used to transmit / receive UWB signals, obtain the original distance information between the drone and the wireless charging station, and send it; a first calculation module, configured to receive the initial distance information, calculate the relative position of the drone and the wireless charging station in combination with the calibrated coordinates of the reference tag array, and send first target position information based on the relative position; a second calculation module, configured to receive a magnetic field vector, calculate a magnetic field gradient tensor, and calculate adjustment values ​​for the pitch angle and yaw angle of the drone based on the magnetic field gradient tensor and a preset target magnetic field gradient, and send a second target position adjustment instruction based on the adjustment values; Wireless communication module, used to transmit data information between the drone and the wireless charging station; Wireless charging transmitter module, used to send wireless energy; A drone, comprising: Mobile UWB tag module, used to transmit / receive UWB signals; A three-axis magnetometer module, which is installed at the bottom of the drone and is used to measure and transmit the magnetic field vector; a flight control adjustment module, configured to receive the first target position information and adjust the drone to fly to the initial target position, receive the second target position adjustment instruction, and fine-tune the drone to the final target position; Wireless charging receiving module, used to receive radio energy to charge the drone battery.

2. A wireless charging management system for drones according to claim 1, characterized in that: The first calculation module is specifically configured to: Receive the original distance measurement values ​​of each UWB base station and the mobile UWB tag module for two-way ranging, and perform three-dimensional positioning solution based on the least squares method to obtain the estimated coordinates of the drone; Receiving the ranging values ​​of each tag in the reference tag array and the mobile UWB tag module, and calculating the difference between the measured coordinates and the true coordinates of each tag in combination with the calibrated coordinates of each tag in the reference tag array; Based on the estimated coordinates and the difference, a global error field model is constructed by a spatial interpolation algorithm to solve the error field and obtain the corrected coordinates of the UAV; Based on the corrected coordinates and the coordinate information of the wireless charging station, the relative position of the drone and the wireless charging station is calculated, and first target position information is sent based on the relative position.

3. The wireless charging management system for drones according to claim 2, characterized in that: After receiving the first target position information, the flight control adjustment module adjusts the UAV to fly to the initial target position and hovers at the initial target position.

4. The wireless charging management system for drones according to claim 1, characterized in that: The second calculation module is specifically configured to: receiving the magnetic field vector measured by the three-axis magnetometer module and calculating the magnetic field gradient tensor; Establishing a loss function based on the magnetic field gradient tensor and a preset target magnetic field gradient, wherein the target magnetic field gradient is obtained in advance through experimental calibration; By adding a regularization term of the attitude adjustment amount to the loss function, the optimal attitude is solved and the adjustment values ​​of the pitch angle and yaw angle of the drone are obtained; A second target position adjustment instruction is sent based on the adjustment values ​​of the pitch angle and yaw angle of the drone.

5. The wireless charging management system for drones according to claim 1, characterized in that: The wireless charging transmitting module adopts the electromagnetic induction principle that matches the wireless charging receiving module, and is used to send electric energy to the wireless charging receiving module through wireless power, converting DC power into wireless power and transmitting it out.

6. The wireless charging management system for drones according to claim 1, characterized in that: The UWB base station includes a UWB transmitter and a UWB receiver, which are used to transmit and receive UWB signals; the UWB transmitter periodically transmits UWB signals, and when the transmitted UWB signal is received by the mobile UWB tag module, the UWB receiver receives the UWB signal transmitted by the mobile UWB tag module.

7. The wireless charging management system for drones according to claim 1, characterized in that: The drone also includes a safety monitoring module, which includes: Infrared thermal imaging module, used to collect temperature information at various points on the surface of the drone's battery and generate temperature distribution images for monitoring the battery surface temperature field distribution; The overvoltage and overcurrent protector is used to monitor the voltage and current in the charging circuit in real time, and cut off the charging circuit when the voltage or current exceeds the set safety threshold.

8. The wireless charging management system for drones according to claim 7, characterized in that: The wireless charging station further includes an energy management module: the energy management module includes: The integrated bidirectional DC-DC converter is used to dynamically adjust the output voltage and current according to the charging status of the drone's battery. The BMS battery management system is used to monitor the voltage, current and temperature parameters of the drone's battery in real time and dynamically adjust the charging mode.

9. The wireless charging management system for drones according to claim 1, characterized in that: The wireless communication module is a Wi-Fi, Bluetooth or ZigBee communication module.

10. A wireless charging management method for a drone, characterized in that: A wireless charging management system for a drone as claimed in any one of claims 1 to 9, comprising: Obtaining original distance information between the drone and the wireless charging station, and calculating the relative position of the drone and the wireless charging station in combination with the calibrated coordinates of the reference tag array, and obtaining first target position information based on the relative position; According to the first target position information, adjust the drone to fly to the initial target position and start charging; Obtaining a magnetic field vector, calculating a magnetic field gradient tensor, and calculating adjustment values ​​for the pitch angle and yaw angle of the drone based on the magnetic field gradient tensor and a preset target magnetic field gradient, and obtaining a second target position adjustment instruction based on the adjustment values; Fine-tune the UAV to the final target position according to the second target position adjustment instruction; When the drone is adjusted to the final target position, the optimal charging state is activated for the drone based on the received radio energy.

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