UAV charging method and charging device

Abstract

The present invention relates to a UAV charging method and a charging device, and relates to the field of UAV charging control, including: at a predetermined time node, collecting battery operating parameters, real-time power and charging environment parameters through a sensor monitoring unit, obtaining charging operation data, power percentage and environmental data; using the charging parameter adjustment threshold as the optimization space, optimizing the charging parameters within a predetermined time window according to the battery health factor, charging operation data, power percentage and environmental data, and obtaining the optimal charging parameters; executing battery charging control within the predetermined time window. The present application can solve the technical problem that the existing UAV charging method cannot set matching charging parameters according to the actual battery state, resulting in low charging efficiency and insufficient charging safety, and can improve the scientificity and accuracy of battery charging parameter setting, thereby effectively improving the charging efficiency of the UAV battery, while ensuring battery charging safety.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle charging control, and in particular to a unmanned aerial vehicle charging method and a charging device. Background Art

[0002] With the widespread application of drone technology, batteries are the main energy source for drones, and their charging methods directly affect the drone's endurance and operating efficiency. At present, drone charging methods usually adopt constant current-constant voltage (CC-CV) mode or fixed charging parameters. There is a lack of real-time monitoring and evaluation of the actual state of the battery, which makes it impossible to accurately set the appropriate charging parameters. There are technical bottlenecks such as low charging efficiency, insufficient charging safety, and poor environmental adaptability, which makes it difficult to meet the drone's long-term, high-efficiency, safe and stable working requirements. Summary of the invention

[0003] The present invention aims to solve the technical problems in existing UAV charging methods, such as the inability to set matching charging parameters according to the actual state of the battery, resulting in low charging efficiency and insufficient charging safety, and provides a UAV charging method and a charging device to solve the problems.

[0004] The technical solution of the present invention to solve the above technical problems is as follows:

[0005] In a first aspect, the present invention provides a method for charging a drone, comprising: reading battery performance parameters of a drone battery, evaluating the battery health status according to the battery performance parameters, and obtaining a battery health coefficient; at a predetermined time node, collecting battery operating parameters, real-time power and charging environment parameters through a sensor monitoring unit, and obtaining charging operation data, power percentage and environmental data; using a charging parameter adjustment threshold as an optimization space, optimizing the charging parameters within a predetermined time window according to the battery health coefficient, charging operation data, power percentage and environmental data, and obtaining optimal charging parameters; and executing battery charging control within the predetermined time window based on the optimal charging parameters.

[0006] In a second aspect, the present invention provides a UAV charging device, comprising: a battery health status evaluation module, which is used to read the battery performance parameters of the UAV battery, perform battery health status evaluation according to the battery performance parameters, and obtain a battery health coefficient; a charging information acquisition module, which is used to collect battery operating parameters, real-time power and charging environment parameters through a sensor monitoring unit at a predetermined time node, and obtain charging operation data, power percentage and environmental data; a charging parameter optimization module, which is used to use the charging parameter adjustment threshold as the optimization space, and perform charging parameter optimization within a predetermined time window according to the battery health coefficient, charging operation data, power percentage and environmental data to obtain the optimal charging parameters; a battery charging control module, which is used to execute battery charging control within the predetermined time window based on the optimal charging parameters.

[0007] In a third aspect, the present invention further provides an electronic device, comprising:

[0008] At least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor so that the at least one processor can perform the steps of any one of the methods described in the first aspect above.

[0009] In a fourth aspect, a computer-readable storage medium is provided, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed, the steps of the method described in any one of the first aspects are implemented.

[0010] The beneficial effects of the present invention are as follows: by reading the battery performance parameters of the drone battery, the battery health status is evaluated according to the battery performance parameters to obtain the battery health coefficient; at a predetermined time node, the battery operation parameters, real-time power and charging environment parameters are collected through the sensor monitoring unit to obtain charging operation data, power percentage and environmental data; with the charging parameter adjustment threshold as the optimization space, the charging parameters within the predetermined time window are optimized according to the battery health coefficient, charging operation data, power percentage and environmental data to obtain the optimal charging parameters; based on the optimal charging parameters, the battery charging control within the predetermined time window is executed; the scientificity and accuracy of the battery charging parameter setting can be improved, thereby effectively improving the charging efficiency of the drone battery, while ensuring the battery charging safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic diagram of a flow chart of a UAV charging method provided by the present invention;

[0012] Figure 2 A schematic diagram of the structure of a UAV charging device provided by the present invention;

[0013] Figure 3 A schematic diagram of the structure of an electronic device provided by the present invention;

[0014] Figure 4 A schematic diagram of the structure of a computer-readable storage medium provided by the present invention.

[0015] In the accompanying drawings, the components represented by the reference numerals are described as follows:

[0016] Battery health status assessment module 01, charging information collection module 02, charging parameter optimization module 03, battery charging control module 04, electronic device 500, memory 510, processor 520, first computer program 511, computer readable storage medium 600, second computer program 611. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0018] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0019] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0020] Embodiment 1, as Figure 1 As shown, an embodiment of the present invention provides a method for charging a drone, which is applied to a drone charging device and specifically includes the following steps:

[0021] Specifically, an embodiment of the present invention provides a drone charging method, which is applied to a drone charging device, wherein the drone charging device includes a battery circuit board, a body plug-in circuit board and a battery charging circuit board, wherein the battery circuit board includes a battery voltage input pad, a balanced charging connector and a battery voltage output connector, the battery power line is welded to the battery voltage input pad, and the battery's built-in balanced charging head is welded to the balanced charging connector, and the battery voltage output connector is welded; the body plug-in circuit board includes a battery voltage output pad and a battery voltage input connector, the power line is welded to the battery voltage output pad, and the battery voltage input connector is welded; the battery charging circuit board includes a battery charging input pad, a battery voltage input connector and a balanced charging plug-in connector, the power line is welded to the battery charging input pad, and the battery voltage input connector and the balanced charging plug-in connector are welded.

[0022] Among them, after the battery voltage output connector on the battery circuit board is plugged into the battery voltage input connector on the body plug-in circuit board, the battery voltage is input from the battery circuit board battery voltage output connector to the body plug-in connector battery voltage input connector through the copper layer on the circuit board, and the battery starts to supply power to the entire UAV equipment. When the battery voltage consumption is close to the cut-off voltage, the battery circuit board is separated from the body plug-in circuit board. At this time, the battery voltage output connector on the battery circuit board is plugged into the battery voltage input connector on the battery charging circuit board, and the balance charging connector on the battery circuit board is plugged into the balance charging plug-in connector on the battery charging circuit board to realize the battery charging function. After charging is completed, the battery circuit board and the battery charging circuit board are separated.

[0023] Among them, the battery circuit board replaces the plug-in connector on the battery and is used for battery power supply. The body plug-in circuit board replaces the plug-in connector on the battery and is used for battery voltage distribution and power supply for the entire drone equipment. The battery charging circuit board is used for battery charging and single-cell battery voltage monitoring. This combination and design improves the reliability, electrical contact performance and assembly efficiency of the power supply system, while reducing production costs, and has broad application prospects in the field of drone power.

[0024] S100: Reading battery performance parameters of the drone battery, performing battery health status assessment according to the battery performance parameters, and obtaining a battery health factor.

[0025] Furthermore, step S100 of the present application also includes:

[0026] S110: Reading battery performance parameters of the drone battery, wherein the battery performance parameters include capacity attenuation ratio, internal resistance increment ratio, and power output ratio.

[0027] Specifically, first, read the battery performance parameters of the drone battery, where the battery performance parameters include capacity attenuation ratio, internal resistance increment ratio and power output ratio. The capacity attenuation ratio refers to the attenuation degree of the current actual available capacity of the battery compared to the rated capacity of the new battery, reflecting the decline in the battery's energy storage capacity; the internal resistance increment ratio refers to the increase in the internal resistance of the current battery compared to the initial internal resistance of the new battery, reflecting the change in the battery's conductive properties; the power output ratio refers to the ratio of the current actual output power of the battery to the rated power of the new battery, reflecting the battery's power supply capacity in actual operation.

[0028] S120: Input the capacity attenuation ratio, internal resistance increment ratio and power output ratio into a battery health status analyzer, and output the battery health coefficient.

[0029] Furthermore, step S120 of the present application also includes:

[0030] S121: Perform information retrieval with the battery model as a constraint, and collect sample capacity attenuation ratio sets, sample internal resistance increment ratio sets, sample power output ratio sets, and sample battery health coefficient sets; S122: Use the sample capacity attenuation ratio sets, sample internal resistance increment ratio sets, and sample power output ratio sets as input supervision data, and use the sample battery health coefficient set as output supervision data to perform supervised training on the BP neural network to obtain a battery health status analyzer that meets the expected convergence constraints.

[0031] Specifically, taking the battery model as the core constraint, the performance data of the corresponding battery model is collected through the database, test platform or experimental equipment to obtain the sample capacity attenuation ratio set, sample internal resistance increment ratio set, sample power output ratio set and sample battery health coefficient set.

[0032] Then, a battery health status analyzer is constructed based on the BP neural network, and the battery health status analyzer includes an input layer, a hidden layer and an output layer, wherein the input data of the input layer are the capacity attenuation ratio, the internal resistance increment ratio and the power output ratio, and the output data of the output layer is the battery health coefficient; then, the sample capacity attenuation ratio set, the sample internal resistance increment ratio set and the sample power output ratio set are used as input supervision data, and the sample battery health coefficient set is used as output supervision data to perform supervised training on the battery health status analyzer, and the training set samples are input into the model in batches, and the output value is calculated by forward propagation, the error is calculated according to the loss function, and the weight is updated by the back propagation algorithm. The above process is repeated until the verification set error converges to obtain a battery health status analyzer that meets the expected convergence constraint, wherein the expected convergence constraint means that the loss function is less than the expected index or the number of iterative training reaches a predetermined number of training times (such as 1000 iterative training times).

[0033] Finally, the capacity attenuation ratio, internal resistance increment ratio and power output ratio are input into a battery health status analyzer, and the battery health coefficient is output.

[0034] S200: At a predetermined time point, the battery operation parameters, real-time power and charging environment parameters are collected through the sensor monitoring unit to obtain charging operation data, power percentage and environmental data.

[0035] Furthermore, step S200 of the present application also includes:

[0036] S210: The battery operating parameters include voltage, current and battery temperature, and the environmental data include ambient temperature and ambient humidity.

[0037] Specifically, a predetermined time node is obtained, wherein the predetermined time node can be set according to monitoring requirements, such as setting the time interval between adjacent monitoring nodes to 10 seconds, and then collecting data every 10 seconds; at the predetermined time node, the battery operation parameters, real-time power and charging environment parameters are collected through the sensor monitoring unit, wherein the battery operation parameters include voltage, current and battery temperature, and the environmental data include the ambient temperature and ambient humidity of the battery charging area, and the charging operation data, power percentage and environmental data are obtained. By obtaining the charging operation data, power percentage and environmental data, a basis is provided for the next step of optimizing the battery charging parameters.

[0038] S300: Taking the charging parameter adjustment threshold as the optimization space, optimizing the charging parameters within a predetermined time window according to the battery health factor, charging operation data, power percentage and environmental data, and obtaining the optimal charging parameters.

[0039] Furthermore, step S300 of the present application also includes:

[0040] S310: Obtain charging parameter adjustment thresholds, wherein the charging parameter adjustment thresholds include charging voltage thresholds and charging current thresholds; S320: Construct an initial charging simulation space based on battery model simulation, render the battery health factor, charging operation data, power percentage and environmental data into the initial charging simulation space, and obtain a final charging simulation space.

[0041] Specifically, first, obtain the charging parameter adjustment threshold, where the charging parameter adjustment threshold includes the charging voltage threshold and the charging current threshold. The charging voltage threshold refers to the adjustment range of the charging voltage, and the charging current threshold refers to the adjustment range of the charging current, which can be set according to the specific charging scenario, such as the charging voltage threshold of 3.6V to 4.2V, and the charging current threshold of 500 mAh to 1000 mAh. Then, according to the specific battery model, simulate the physical properties, chemical properties and working conditions of the battery to generate the basic framework of the initial charging simulation space, and then render the battery health factor, charging operation data, power percentage and environmental data to the initial charging simulation space to obtain the final charging simulation space.

[0042] S330: Based on the final charging simulation space, with the charging voltage threshold and the charging current threshold as the optimization space, optimizing the charging parameters within a predetermined time window, and outputting the optimal charging parameters.

[0043] Furthermore, step S330 of the present application also includes:

[0044] S331: randomly select multiple voltage parameters in the charging voltage threshold, and randomly select multiple current parameters in the charging current threshold, and combine them to obtain multiple charging parameters; S332: utilize the final charging simulation space to perform charging simulation according to the multiple charging parameters to obtain multiple charging simulation results, wherein the charging simulation results include charging simulation time and battery simulation temperature; S333: perform charging fitness evaluation on the charging simulation results to obtain multiple charging fitness, wherein the charging fitness is negatively correlated with the charging simulation time and the battery simulation temperature; S334: based on the charging fitness, continue to optimize the charging parameters within the predetermined time window, and output the optimal charging parameters.

[0045] Specifically, first, a plurality of voltage parameters are randomly selected from the charging voltage threshold, a plurality of current parameters are randomly selected from the charging current threshold, and the plurality of voltage parameters and the plurality of current parameters are randomly combined to obtain a plurality of charging parameters. Then, the final charging simulation space is used to perform charging simulation according to the plurality of charging parameters to obtain a plurality of charging simulation results, wherein the charging simulation results include charging simulation time and battery simulation temperature. Then, the charging fitness is evaluated for the plurality of charging simulation results, wherein the charging fitness is negatively correlated with the charging simulation time and the battery simulation temperature, that is, the shorter the charging simulation time and the smaller the battery simulation temperature, the greater the corresponding charging fitness, which indicates that the battery charging effect is better, and a plurality of charging fitness of the plurality of charging simulation results are obtained. Finally, according to the charging fitness, the charging parameter optimization within the predetermined time window is continued, wherein the predetermined time window is a time period, which can be set according to the actual charging scenario, such as setting the predetermined time window to the next 10 minutes, that is, the charging parameter optimization within the next 10 minutes is performed, and the optimal charging parameters are output.

[0046] Furthermore, step S334 of the present application also includes:

[0047] S3341: Set the charging parameters as the initial solution, arrange the multiple charging parameters in descending order according to the charging fitness, and generate an initial solution sequence; S3342: Set the first N solutions of the initial solution sequence as optimal solutions, and set the last M solutions as inferior solutions, where the sum of N and M is the number of initial solutions, and M is Q times of N, and Q is an integer greater than or equal to 3 and less than or equal to 5; S3343: Randomly cluster the M inferior solutions with the N optimal solutions as the center to determine N areas, where the number of inferior solutions in each area is the same; S3 344: Taking the optimal solution in the field as the optimization direction, optimize and adjust the inferior solution in the field according to the predetermined step size to obtain N updated fields; S3345: Identify the N updated fields, if the fitness of the inferior solution in the same update field is greater than the fitness of the optimal solution, replace the superior solution with the inferior solution; S3346: Continue to iterate the optimization until the predetermined number of optimization times is reached, output N current fields, select the current field with the largest sum of fitness as the optimal field, and set the optimal solution of the optimal field as the optimal charging parameter.

[0048] Specifically, first, the charging parameters are set as the initial solution, and the multiple charging parameters are arranged from large to small according to the charging fitness to generate an initial solution sequence. Then, the first N solutions of the initial solution sequence are set as optimal solutions, and the last M solutions are set as inferior solutions, where the sum of N and M is the number of initial solutions, and M is Q times of N, and Q is an integer greater than or equal to 3 and less than or equal to 5. Then, with the N optimal solutions as the center, the M inferior solutions are equally randomly clustered to determine N areas, where the number of inferior solutions in each area is the same. Furthermore, taking the optimal solution in the field as the optimization direction, the inferior solution in the field is optimized and adjusted according to a predetermined step size, wherein the predetermined step size includes a voltage adjustment step size and a current adjustment step size, and N update fields are obtained; then the N update fields are identified, and if the fitness of the inferior solution in the same update field is greater than the fitness of the optimal solution, the superior solution is replaced by the inferior solution; the iterative optimization is continued until the predetermined number of optimization times is reached, and N current fields are output; the current field with the largest sum of fitness among the N current fields is further selected as the optimal field, and finally the optimal solution of the optimal field is set as the optimal charging parameter.

[0049] S400: Execute battery charging control within the predetermined time window based on the optimal charging parameters.

[0050] Specifically, finally, according to the optimal charging parameters, the charging control of the drone battery within the predetermined time window is performed. For example, assuming that the predetermined time window is 10 minutes, the drone battery charging control within the next 10 minutes is performed according to the optimal charging parameters.

[0051] The embodiment of the present invention provides a method for charging a drone, which has at least the following technical effects:

[0052] By reading the battery performance parameters of the drone battery, the battery health status is evaluated according to the battery performance parameters to obtain the battery health coefficient; at a predetermined time node, the battery operation parameters, real-time power and charging environment parameters are collected through the sensor monitoring unit to obtain charging operation data, power percentage and environmental data; with the charging parameter adjustment threshold as the optimization space, the charging parameters within the predetermined time window are optimized according to the battery health coefficient, charging operation data, power percentage and environmental data to obtain the optimal charging parameters; based on the optimal charging parameters, the battery charging control within the predetermined time window is executed; the scientificity and accuracy of the battery charging parameter setting can be improved, thereby effectively improving the charging efficiency of the drone battery, while ensuring the battery charging safety.

[0053] Embodiment 2, as Figure 2 As shown, based on the same inventive concept as the drone charging method provided in the first embodiment, the embodiment of the present invention further provides a drone charging device, including:

[0054] The battery health status assessment module 01 is used to read the battery performance parameters of the drone battery, perform battery health status assessment according to the battery performance parameters, and obtain the battery health coefficient; the charging information acquisition module 02 is used to collect battery operation parameters, real-time power and charging environment parameters through the sensor monitoring unit at a predetermined time node, and obtain charging operation data, power percentage and environmental data; the charging parameter optimization module 03 is used to use the charging parameter adjustment threshold as the optimization space, and perform charging parameter optimization within a predetermined time window according to the battery health coefficient, charging operation data, power percentage and environmental data to obtain the optimal charging parameters; the battery charging control module 04 is used to execute battery charging control within the predetermined time window based on the optimal charging parameters.

[0055] Furthermore, the drone charging device also includes: reading battery performance parameters of the drone battery, wherein the battery performance parameters include capacity attenuation ratio, internal resistance increment ratio and power output ratio; inputting the capacity attenuation ratio, internal resistance increment ratio and power output ratio into a battery health status analyzer, and outputting the battery health coefficient.

[0056] Furthermore, the unmanned aerial vehicle charging device also includes: performing information retrieval with the battery model as a constraint, collecting sample capacity attenuation ratio set, sample internal resistance increment ratio set, sample power output ratio set and sample battery health coefficient set; using the sample capacity attenuation ratio set, sample internal resistance increment ratio set, sample power output ratio set as input supervision data, and using the sample battery health coefficient set as output supervision data, supervised training is performed on the BP neural network to obtain a battery health status analyzer that meets the expected convergence constraints.

[0057] Furthermore, the drone charging device also includes: the battery operating parameters include voltage, current and battery temperature, and the environmental data includes ambient temperature and ambient humidity.

[0058] Furthermore, the drone charging device also includes: obtaining a charging parameter adjustment threshold, wherein the charging parameter adjustment threshold includes a charging voltage threshold and a charging current threshold; constructing an initial charging simulation space based on battery model simulation, rendering the battery health factor, charging operation data, power percentage and environmental data to the initial charging simulation space to obtain a final charging simulation space; based on the final charging simulation space, taking the charging voltage threshold and the charging current threshold as the optimization space, optimizing the charging parameters within a predetermined time window, and outputting the optimal charging parameters.

[0059] Furthermore, the drone charging device also includes: randomly selecting multiple voltage parameters in the charging voltage threshold, randomly selecting multiple current parameters in the charging current threshold, and combining them to obtain multiple charging parameters; using the final charging simulation space, performing charging simulation according to the multiple charging parameters, and obtaining multiple charging simulation results, wherein the charging simulation results include charging simulation time and battery simulation temperature; performing charging fitness evaluation on the charging simulation results to obtain multiple charging fitness, wherein the charging fitness is negatively correlated with the charging simulation time and the battery simulation temperature; based on the charging fitness, continuing to optimize the charging parameters within a predetermined time window, and outputting the optimal charging parameters.

[0060] Furthermore, the unmanned aerial vehicle charging device also includes: setting the charging parameters as the initial solution, arranging the multiple charging parameters from large to small according to the charging fitness, and generating an initial solution sequence; setting the first N solutions of the initial solution sequence as optimal solutions, and setting the last M solutions as inferior solutions, wherein the sum of N and M is the number of initial solutions, and M is Q times of N, and Q is an integer greater than or equal to 3 and less than or equal to 5; taking the N optimal solutions as the center, randomly clustering the M inferior solutions to determine N fields, wherein the number of inferior solutions in each field is the same; taking the optimal solution in the field as the optimization direction, optimizing and adjusting the inferior solutions in the field according to a predetermined step size to obtain N updated fields; identifying the N updated fields, if the fitness of the inferior solution in the same updated field is greater than the fitness of the optimal solution, replacing the optimal solution with the inferior solution; continuing to iterate the optimization until the predetermined number of optimizations is reached, outputting N current fields, selecting the current field with the largest sum of fitness as the optimal field, and setting the optimal solution of the optimal field as the optimal charging parameter.

[0061] For example 3, please refer to Figure 3 , Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides an electronic device 500, including a memory 510, a processor 520, and a first computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the first computer program 511, the following steps are implemented: reading battery performance parameters of the drone battery, performing battery health status evaluation according to the battery performance parameters, and obtaining a battery health coefficient; at a predetermined time node, collecting battery operating parameters, real-time power and charging environment parameters through a sensor monitoring unit, and obtaining charging operation data, power percentage and environmental data; taking the charging parameter adjustment threshold as the optimization space, optimizing the charging parameters within a predetermined time window according to the battery health coefficient, charging operation data, power percentage and environmental data, and obtaining the optimal charging parameters; and executing battery charging control within the predetermined time window based on the optimal charging parameters.

[0062] Example 4, please refer to Figure 4 , Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 4 As shown, this embodiment provides a computer-readable storage medium 600, on which a second computer program 611 is stored. When the second computer program 611 is executed by the processor, the following steps are implemented: reading the battery performance parameters of the drone battery, performing a battery health status assessment according to the battery performance parameters, and obtaining a battery health coefficient; at a predetermined time node, collecting battery operation parameters, real-time power and charging environment parameters through a sensor monitoring unit, and obtaining charging operation data, power percentage and environmental data; taking the charging parameter adjustment threshold as the optimization space, optimizing the charging parameters within a predetermined time window according to the battery health coefficient, charging operation data, power percentage and environmental data, and obtaining the optimal charging parameters; based on the optimal charging parameters, executing the battery charging control within the predetermined time window.

[0063] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0064] It should be understood by those skilled in the art that embodiments of the present invention may provide methods, devices or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0065] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0066] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0067] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0068] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0069] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A method for charging a drone, characterized in that: Applied to a drone charging device, the method includes: Read the battery performance parameters of the drone battery, evaluate the battery health status according to the battery performance parameters, and obtain the battery health coefficient; At a predetermined time point, the sensor monitoring unit collects battery operating parameters, real-time power and charging environment parameters to obtain charging operation data, power percentage and environmental data; Taking the charging parameter adjustment threshold as the optimization space, optimizing the charging parameters within a predetermined time window according to the battery health factor, charging operation data, power percentage and environmental data, and obtaining the optimal charging parameters; Based on the optimal charging parameters, executing battery charging control within the predetermined time window; Wherein, the obtaining of the optimal charging parameters includes: Acquire a charging parameter adjustment threshold, wherein the charging parameter adjustment threshold includes a charging voltage threshold and a charging current threshold; Constructing an initial charging simulation space based on battery model simulation, rendering the battery health factor, charging operation data, power percentage and environmental data into the initial charging simulation space, and obtaining a final charging simulation space; Based on the final charging simulation space, taking the charging voltage threshold and the charging current threshold as the optimization space, optimizing the charging parameters within a predetermined time window, and outputting the optimal charging parameters; Wherein, the outputting the optimal charging parameter includes: Randomly select multiple voltage parameters from the charging voltage threshold, randomly select multiple current parameters from the charging current threshold, and combine them to obtain multiple charging parameters; Using the final charging simulation space, performing charging simulation according to the multiple charging parameters to obtain multiple charging simulation results, wherein the charging simulation results include charging simulation time and battery simulation temperature; Performing charging fitness evaluation on the charging simulation result to obtain multiple charging fitnesses, wherein the charging fitnesses are negatively correlated with the charging simulation time and the battery simulation temperature; Based on the charging adaptability, continue to optimize the charging parameters within a predetermined time window and output the optimal charging parameters; The step of continuing to optimize the charging parameters within the predetermined time window includes: The charging parameters are set as initial solutions, and the plurality of charging parameters are arranged in descending order according to charging fitness to generate an initial solution sequence; The first N solutions of the initial solution sequence are set as optimal solutions, and the last M solutions are set as inferior solutions, where the sum of N and M is the number of initial solutions, and M is Q times N, and Q is an integer greater than or equal to 3 and less than or equal to 5; Taking N optimal solutions as the center, randomly cluster M inferior solutions and determine N areas, where the number of inferior solutions in each area is the same; Taking the best solution in the domain as the optimization direction, the inferior solution in the domain is optimized and adjusted according to the predetermined step size to obtain N updated domains; Identify the N update areas, and if the fitness of the inferior solution in the same update area is greater than the fitness of the superior solution, replace the superior solution with the inferior solution; Continue to iterate and optimize until the predetermined number of optimization times is reached, output N current fields, select the current field with the largest sum of fitness as the optimal field, and set the optimal solution of the optimal field as the optimal charging parameter.

2. A drone charging method according to claim 1, characterized in that: The battery health status is evaluated according to the battery performance parameters to obtain a battery health factor, including: Read the battery performance parameters of the drone battery, wherein the battery performance parameters include capacity attenuation ratio, internal resistance increment ratio and power output ratio; The capacity attenuation ratio, internal resistance increment ratio and power output ratio are input into a battery health status analyzer, and the battery health coefficient is output.

3. A UAV charging method according to claim 2, characterized in that: It also includes building a battery health status analyzer, including: Information retrieval is performed with battery model as constraint, and sample capacity attenuation ratio set, sample internal resistance increment ratio set, sample power output ratio set and sample battery health coefficient set are collected; With the sample capacity attenuation ratio set, sample internal resistance increment ratio set and sample power output ratio set as input supervision data and the sample battery health coefficient set as output supervision data, the BP neural network is supervised and trained to obtain a battery health status analyzer that meets the expected convergence constraints.

4. A drone charging method according to claim 1, characterized in that: The battery operating parameters include voltage, current and battery temperature, and the environmental data include ambient temperature and ambient humidity.

5. A UAV charging device, characterized in that: The device for implementing the steps of a drone charging method according to any one of claims 1 to 4 comprises: A battery health status assessment module is used to read the battery performance parameters of the drone battery, perform battery health status assessment based on the battery performance parameters, and obtain a battery health coefficient; The charging information collection module is used to collect battery operating parameters, real-time power and charging environment parameters through the sensor monitoring unit at a predetermined time node to obtain charging operation data, power percentage and environmental data; A charging parameter optimization module is used to optimize the charging parameters within a predetermined time window according to the battery health factor, charging operation data, power percentage and environmental data, using the charging parameter adjustment threshold as the optimization space to obtain the optimal charging parameters; The battery charging control module is used to perform battery charging control within the predetermined time window based on the optimal charging parameters.

6. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby implementing the steps of a drone charging method as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores a computer software program, and when the computer software program is executed by the processor, the steps of the drone charging method according to any one of claims 1 to 4 are implemented.

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