Quick-charging unmanned aerial vehicle nest charging method and charging system

Abstract

The invention discloses a quick-charging unmanned aerial vehicle nest charging method and a charging system thereof. The charging system comprises a micro-control processing module, a charging output module and a feedback detection module. Real-time charging data and battery data of a target unmanned aerial vehicle in the charging process are collected through the feedback detection module, fuzzy control parameters are obtained through the micro-control processing module, and based on the fuzzy control parameters and feedback input variables, a fuzzy logic control algorithm is adopted to carry out data processing on the feedback input variables; according to the fast charging unmanned aerial vehicle nest charging system and the charging method thereof, the charging current value of the target unmanned aerial vehicle at the next moment is obtained, a charging control instruction is generated based on the charging current value, finally, the charging control instruction is responded through a charging output module, and charging output is correspondingly carried out on the target unmanned aerial vehicle in the unmanned aerial vehicle nest. The non-linear charging process of the unmanned aerial vehicle can be accurately controlled, and rapid charging is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle charging, and in particular relates to a fast-charging unmanned aerial vehicle nest charging method and a charging system. Background Art

[0002] A drone nest is a facility that integrates a high-tech automation system. It is designed specifically for drones and is used to store, charge, maintain and automatically dispatch drones. Its core function is to provide storage and charging functions for drones. When a drone is low on power while performing an inspection mission, it only needs to return to the nearest nest for charging and can continue to complete the inspection mission. There will be no problem of mission interruption and inability to recover in a short time. In other words, the existence of a drone nest ensures the integrity of the drone's mission.

[0003] However, the current charging system of the drone nest is often affected by many factors. For example, during the charging process, factors such as the voltage change rate of the drone battery will affect the charging process. This complex influence causes the simple charging control system to be unable to perform the charging control task; and, since the charging process of the drone is a nonlinear process, the existing charging system of the drone nest cannot accurately control this nonlinear charging process, and if a large current constant current is used for charging, the excessive current will affect the drone battery, causing the drone's service life to be greatly reduced. Therefore, in order to avoid damaging the drone battery, the existing charging system of the drone nest often uses a small current constant current charging, but the small current constant current charging will result in a very slow charging speed and cannot achieve fast charging.

[0004] Therefore, there is an urgent need to provide a drone nest charging method and a charging system that can quickly charge the drone. Summary of the invention

[0005] The purpose of the present invention is to provide a fast-charging drone nest charging method and charging system to solve the above-mentioned problems existing in the prior art.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for fast charging a drone nest, which includes:

[0008] Collect the real-time charging data and real-time battery data of the target UAV during the charging process at the current moment, and use the real-time charging data and real-time battery data as feedback input variables;

[0009] Obtaining fuzzy control parameters, and based on the fuzzy control parameters and feedback input variables, using a fuzzy logic control algorithm to perform data processing on the feedback input variables to obtain a charging current value of the target UAV at the next moment;

[0010] generating a charging control instruction based on the charging current value;

[0011] According to the charging control instruction, the charging current of the target UAV is adjusted, and the real-time charging data and real-time battery data of the target UAV at the next moment are recollected, until the battery voltage of the target UAV reaches a preset voltage, the fast charging of the target UAV is completed.

[0012] In a possible design, the fuzzy control parameters include a preset fuzzy strategy table, and based on the fuzzy control parameters and the feedback input variables, a fuzzy logic control algorithm is used to perform data processing on the feedback input variables to obtain the charging current value of the target UAV at the next moment, including:

[0013] Calculate the fuzzy control input quantity according to the feedback input variable;

[0014] Performing fuzzy processing on the fuzzy control input to obtain a fuzzy quantized value, and performing fuzzy reasoning on the fuzzy quantized value according to the preset fuzzy strategy table to obtain a corresponding fuzzy output signal;

[0015] The fuzzy output signal is defuzzified to obtain the charging current value of the target UAV at the next moment.

[0016] In a possible design, the fuzzy control input quantity calculated according to the feedback input variable includes:

[0017] According to the real-time battery data and the maximum battery voltage of the target drone, the voltage difference at the current moment is obtained;

[0018] Based on the real-time charging data, determining the voltage change rate of the target UAV at the current moment;

[0019] The fuzzy control input is obtained according to the voltage change rate and the voltage difference.

[0020] In a possible design, the fuzzy control input is fuzzified to obtain a fuzzy quantized value, and the fuzzy quantized value is fuzzy inferred according to the preset fuzzy strategy table to obtain a corresponding fuzzy output signal:

[0021] Obtain a first quantization factor and a second quantization factor;

[0022] Calculating a first fuzzy quantized value based on the voltage change rate and the first quantization factor, and calculating a second fuzzy quantized value based on the voltage difference and the second quantization factor;

[0023] According to the first fuzzy quantization value and the second fuzzy quantization value, a current regulation level is determined in the preset fuzzy strategy table to obtain a fuzzy output signal according to the current regulation level.

[0024] In a possible design, the fuzzy output signal is defuzzified to obtain the charging current value of the target UAV at the next moment, including:

[0025] Get the anti-blur scaling factor;

[0026] The product of the current regulation level and the anti-fuzzy proportional factor is taken as the current change value;

[0027] The charging current value at the current moment is obtained, and the charging current value of the target UAV at the next moment is obtained based on the charging current value and the current change value.

[0028] In a possible design, before collecting the real-time charging data and real-time battery data of the target UAV during the charging process at the current moment, it also includes:

[0029] Obtaining the position of the target drone, and determining whether the target drone is in a charging position according to the position of the target drone;

[0030] If yes, then the initial input variables are obtained, and according to the initial input variables, it is determined whether the target UAV needs to be charged; otherwise, the position of the target UAV is obtained again, and according to the position of the target UAV, it is determined whether the target UAV is in the charging position;

[0031] If so, the target UAV is charged to collect real-time charging data and real-time battery data of the target UAV during the charging process.

[0032] In a second aspect, the present invention provides a fast-charging drone nest charging system, which is used to implement any fast-charging drone nest charging method described in the first aspect, including:

[0033] A feedback detection module is used to collect the real-time charging data and real-time battery data of the target UAV during the charging process at the current moment, and send the real-time charging data and real-time battery data as feedback input variables to the micro-control processing module;

[0034] The micro-control processing module is used to obtain the fuzzy control parameters and the feedback input variables sent by the feedback detection module, and based on the fuzzy control parameters and the feedback input variables, adopt the fuzzy logic control algorithm to perform data processing on the feedback input variables to obtain the charging current value of the target UAV at the next moment;

[0035] The microcontrol processing module is further used to generate a charging control instruction based on the charging current value and send the charging control instruction to the charging output module;

[0036] A charging output module, used to respond to the charging control instruction and adjust the charging current of the target drone until the battery voltage of the target drone reaches a preset voltage, thereby completing the fast charging of the target drone;

[0037] Among them, the sampling input end of the feedback detection module is electrically connected to the sampling output end of the charging output module, the output end of the feedback detection module is electrically connected to the variable input end of the micro-control processing module, and the output end of the micro-control processing module is electrically connected to the controlled end of the charging output module.

[0038] In one possible design, the micro-control processing module includes:

[0039] An input variable acquisition unit, used for calculating a fuzzy control input quantity according to the feedback input variable, and sending the fuzzy control input quantity to a fuzzy processing unit;

[0040] A fuzzy inference unit, configured to perform fuzzification processing on the fuzzy control input to obtain a fuzzy quantized value, and perform fuzzy inference on the fuzzy quantized value according to the preset fuzzy strategy table to obtain a corresponding fuzzy output signal, and send the fuzzy output signal to a defuzzification unit;

[0041] an anti-fuzzy output unit, configured to perform anti-fuzzification processing on the fuzzy output signal to obtain a charging current value of the target UAV at the next moment, and generate a charging control instruction according to the charging current value of the target UAV at the next moment;

[0042] The output end of the input variable acquisition unit is electrically connected to the input end of the fuzzy processing unit, the output end of the fuzzy processing unit is electrically connected to the input end of the fuzzy decision unit, and the output end of the fuzzy decision unit is electrically connected to the input end of the anti-fuzzy output unit.

[0043] In a third aspect, the present invention provides a fast-charging drone nest, comprising a memory, a processor and a transceiver that are communicatively connected in sequence, wherein the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer program to execute the charging method of the fast-charging drone nest as described in the second aspect or any possible design of the second aspect.

[0044] In a fourth aspect, the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed on a computer, executes the charging method for a fast-charging drone nest as described in the second aspect or any possible design of the second aspect.

[0045] Beneficial effect: The present invention provides a fast-charging drone nest charging method and a charging system thereof, which realizes the function of fast charging of the drone nest through fuzzy logic control method, and the charging system includes a micro-control processing module, a charging output module and a feedback detection module; wherein the micro-control processing module is used to take preset conditions and input variables, and performs data processing on the input variables based on the fuzzy logic control method, so as to issue a charging control instruction to the charging output module and make real-time adjustments, and is the execution subject of the charging method; the charging output module is the execution subject of the charging action, which is mainly used to respond to the charging control instruction, and correspondingly output the charging to the target drone in the drone nest through its discharge output terminal to complete the charging; the feedback detection module constructs a negative feedback regulation path for the real-time adjustment of the charging control instruction, obtains the real-time charging data of the charging output module and the real-time battery data of the target drone, and outputs it as a feedback input variable to the micro-control processing module, providing basic data support for the real-time adjustment of the charging control instruction. This fast-charging drone nest charging system and charging method thereof can avoid the disadvantages brought by constant current charging, accurately control the nonlinear charging process of the drone, and realize fast charging. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A flow chart of a charging method for a fast-charging drone nest;

[0047] Figure 2 Functional block diagram of the fast-charging drone nest charging system. DETAILED DESCRIPTION

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in combination with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0049] It should be understood that although the terms first, second, etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another unit. For example, a first unit can be referred to as a second unit, and similarly, a second unit can be referred to as a first unit without departing from the scope of the exemplary embodiments of the present invention.

[0050] It should be understood that the term "and / or" that may appear in this article is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B can represent three situations: A exists alone, B exists alone, and A and B exist at the same time. The term " / and" that may appear in this article describes another type of association object relationship, indicating that two relationships may exist. For example, A / and B can represent two situations: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this article generally indicates that the previous and next associated objects are in an "or" relationship.

[0051] Embodiment 1:

[0052] like Figure 1 As shown, this embodiment provides a charging method for a fast-charging drone nest, which includes the following steps:

[0053] S100. Collecting real-time charging data and real-time battery data of the target drone during the current charging process, and generating feedback input variables based on the real-time charging data and real-time battery data;

[0054] S200. Obtain fuzzy control parameters, and based on the fuzzy control parameters and feedback input variables, use fuzzy logic control algorithm to perform data processing on the feedback input variables to obtain the charging current value of the target UAV at the next moment;

[0055] S300. Generate a charging control instruction based on the charging current value;

[0056] S400. According to the charging control instruction, adjust the charging current of the target UAV, and re-collect the real-time charging data and real-time battery data of the target UAV at the next moment, until the battery voltage of the target UAV reaches the preset voltage, and the fast charging of the target UAV is completed.

[0057] By collecting and feeding back the real-time charging data and real-time battery data during the charging process to build a feedback control path, and performing data processing on the feedback input variables based on the fuzzy logic control algorithm, the nonlinear charging process can be simplified, and the complex charging control can be simplified to the charging current increment adjustment, and the charging current value at the next moment can be controlled in real time, which not only avoids the damage to the battery caused by large current, but also avoids the slow charging speed caused by small current charging, which affects the normal operation of the target UAV.

[0058] As a possible design, the fuzzy control parameters include a preset fuzzy strategy table, and in step S200, the fuzzy control parameters are obtained, and based on the fuzzy control parameters and the feedback input variables, the fuzzy logic control algorithm is used to perform data processing on the feedback input variables to obtain the charging current value of the target UAV at the next moment, including:

[0059] S2001. Calculate the fuzzy control input according to the feedback input variable;

[0060] S2002. Fuzzy processing is performed on the fuzzy control input to obtain a fuzzy quantized value, and fuzzy reasoning is performed on the fuzzy quantized value according to a preset fuzzy strategy table to obtain a corresponding fuzzy output signal;

[0061] S2003. Defuzzify the fuzzy output signal to obtain the charging current value of the target UAV at the next moment, and generate a charging control instruction according to the charging current value of the target UAV at the next moment.

[0062] As a possible design, in step S2001, the fuzzy control input quantity calculated according to the feedback input variable includes:

[0063] S20011. Calculate the voltage difference at the current moment based on the real-time battery data and the maximum battery voltage of the target drone;

[0064] S20012. Based on the real-time charging data, determine the voltage change rate of the target UAV at the current moment;

[0065] S20013. Obtain fuzzy control input according to the voltage change rate and voltage difference.

[0066] As a possible design, in step S2002, the fuzzy control input is fuzzified to obtain a fuzzy quantized value, and the fuzzy quantized value is fuzzy inferred according to a preset fuzzy strategy table to obtain a corresponding fuzzy output signal:

[0067] S20021. Obtain a first quantization factor and a second quantization factor;

[0068] S20022. Calculate a first fuzzy quantization value based on the voltage change rate and the first quantization factor, and calculate a second fuzzy quantization value based on the voltage difference and the second quantization factor;

[0069] S20023. Determine a current regulation level in a preset fuzzy strategy table according to the first fuzzy quantization value and the second fuzzy quantization value, so as to obtain a fuzzy output signal according to the current regulation level.

[0070] As a possible design, in step S2003, the fuzzy output signal is defuzzified to obtain the charging current value of the target UAV at the next moment, including:

[0071] S20031. Obtain anti-blurring scaling factor;

[0072] S20032. The product of the current adjustment level and the anti-fuzzy proportional factor is used as the current change value;

[0073] S20033. Obtain the charging current value at the current moment, and obtain the charging current value of the target UAV at the next moment based on the charging current value and the current change value.

[0074] This design based on fuzzy logic control algorithm can design and select the preset fuzzy strategy table according to actual needs. The specific design and implementation process can be as follows:

[0075] First of all, the selection of fuzzy control input has a great impact on the charging system. Input variables such as power supply temperature, power supply terminal voltage and charging current can all be used as fuzzy control inputs, but these methods are difficult to implement in engineering. Therefore, more easily controllable input variables can be reselected as fuzzy control inputs. This requires a preliminary study of the charging response curve of the target UAV's battery. The voltage change rate △U / △t of the most drastic voltage change in the charging response curve of the target UAV's battery is used as the fuzzy control input, and then supplemented by the difference △E between the real-time output voltage value and the maximum chargeable voltage value of the battery, to achieve more perfect control, where △t is the duty cycle increment of the PWM wave, and △U is the voltage change.

[0076] Since different power sources or the same power source have different specific charging performance under different usage conditions, if only △E is used as the main criterion, it will inevitably bring a large deviation to the control result. Based on this understanding, it is necessary to increase the △U / △t weight to improve it. The analytical formula is:

[0077] U=<αE+(1-α)E C >, (1)

[0078] Where: α∈[0,1] is called the correction factor; <...> indicates rounding; E and Ec are the fuzzy quantized values ​​of △E and △U / △t respectively. Considering the differences of batteries, the weight is appropriately increased, and α=0.6 is set. After the above operation and the maximum membership calculation, the preset fuzzy strategy table can be obtained.

[0079] Among them, E and Ec are the fuzzy quantized values ​​of △E and △U / △t respectively, which can be calculated as follows:

[0080] Determine the domain (-4, -3, -2, -1, 0, +1, +2, +3, +4), and define 8 language variable values ​​for it: NB (negative big), NM (negative medium), NS (negative small), NO (zero negative), PO (zero positive), PS (positive small), PM (positive medium) and PB (positive big), using the normal function model:

[0081]

[0082] Construct the membership function μ A ; Among them: parameter a takes an appropriate value to map the set {-4, -3, -2, -1, 0, +1, +2, +3, +4} to the set {NB, NM, NS, NO, PO, PS, PM, PB}; parameter b determines the shape of the membership function, and the appropriate control sensitivity and stability can be obtained according to the adjustment of the system error. Here, the single-point fuzzy method is used when the exact value is mapped to the domain of the corresponding fuzzy output through fuzzification.

[0083] Take (-4, -3, -2, -1, 0, +1, +2, +3, +4) as the domain. When the basic domain of the input △E is [0, 18], the first quantization factor is:

[0084] K E =8 / 18; (3)

[0085] E can be obtained by △E and K E The product of is:

[0086] Correspondingly, the maximum value of △U / △t can be obtained from the experiment to be 5.2360×10 -4 V / s, the second quantization factor is:

[0087] K C =8 / 5.2360; (4)

[0088] Ec can be calculated by △U / △t and K C The product of is:

[0089] In the preset fuzzy strategy table, E and Ec can be used to look up the table to quickly determine the output quantity, which is the current regulation level mentioned in the above design.

[0090] The current regulation level is characterized by the fuzzy output signal. This instruction is used to control the size of the charging current. The division method of the domain segment is the same as above, and the anti-fuzzy proportional factor is determined as:

[0091] K U =0.05; (5)

[0092] The current change value can be adjusted by the current adjustment level and K U The product of the two is obtained, and the charging current value of the target UAV at the next moment can be obtained by adding the charging current value at the current moment to the current change value.

[0093] As a possible design, before collecting the real-time charging data and real-time battery data of the target UAV during the charging process at the current moment, it also includes;

[0094] Obtain the location of the target drone, and determine whether the target drone is in a charging position according to the location of the target drone;

[0095] If not, repeat the previous step until the judgment result is yes;

[0096] If so, the initial input variables are obtained, and based on the initial input variables, it is determined whether the target UAV needs to be charged.

[0097] This is to avoid repeated charging that affects the battery life of the target drone and to optimize overall work efficiency.

[0098] Embodiment 2:

[0099] like Figure 2As shown, this embodiment provides a fast-charging drone nest charging system, which is used to implement one or more drone nest charging methods in Example 1, solves the problem of nonlinear control of drone charging, and realizes fast charging of drones; this system includes a micro-control processing module, a charging output module and a feedback detection module; wherein the micro-control processing module is the control execution center of the system, the micro-control processing module can collect external input variables and use them as initial data to issue charging control instructions to the target drone, and the content of the charging control instructions needs to be obtained through the micro-control processing module based on fuzzy logic control processing; and the charging output module serves as a charging execution unit for the target drone, responding to the charging control instructions issued by the micro-control processing module; the feedback detection module is a key module for realizing fast charging, and constructs a negative feedback regulation path for this charging system, inputs the detected charging data into the micro-control processing module, and helps the micro-control processing module to realize real-time adjustment of the charging control instructions, so as to achieve the purpose of fast charging.

[0100] Specifically, the micro-control processing module is used to obtain the fuzzy control parameters and the feedback input variables sent by the feedback detection module, and based on the fuzzy control parameters and the feedback input variables, the fuzzy logic control algorithm is used to perform data processing on the feedback input variables to obtain the charging current value of the target UAV at the next moment; it is also used to generate a charging control instruction based on the charging current value, and send the charging control instruction to the charging output module; it should be noted that the preset fuzzy strategy table mentioned here is based on the fuzzy logic control setting, which can be calculated by the domain and membership function of the fuzzy control, and for the battery of the target UAV that needs to be charged, it is necessary to study its charging response curve in advance, and determine the specific domain size and membership function expression formula according to the charging response curve, and establish a preset fuzzy strategy table on this basis. When determining the content of the charging control instruction, it only needs to be compared in the preset fuzzy strategy table, which greatly reduces the response time; that is, the preset conditions are input into the micro-control processing module in order to respond more simply and quickly by setting the initial conditions, and calculate, compare and finally obtain the charging control instruction.

[0101] A charging output module, used to respond to the charging control command and adjust the charging current of the target UAV until the battery voltage of the target UAV reaches a preset voltage, thereby completing the rapid charging of the target UAV;

[0102] A feedback detection module is used to collect the real-time charging data and real-time battery data of the target UAV during the charging process at the current moment, and send the real-time charging data and real-time battery data as feedback input variables to the micro-control processing module;

[0103] Among them, the sampling input end of the feedback detection module is electrically connected to the sampling output end of the charging output module, the output end of the feedback detection module is electrically connected to the variable input end of the micro-control processing module, and the output end of the micro-control processing module is electrically connected to the controlled end of the charging output module.

[0104] Through the coordinated work of various modules, the nonlinear charging problem of the target UAV is solved during charging. There will be no excessive current that damages the target UAV battery, nor will there be a problem of slow charging caused by too small current. Instead, the real-time charging current can be output at a current size that always meets the charging response curve of the target UAV battery, achieving a fast charging effect.

[0105] As a possible implementation, the micro-control processing module includes:

[0106] An input variable acquisition unit, used for calculating a fuzzy control input quantity according to the feedback input variable, and sending the fuzzy control input quantity to the fuzzy processing unit;

[0107] A fuzzy inference unit is used to perform fuzzification processing on the fuzzy control input to obtain a fuzzy quantized value, and perform fuzzy inference on the fuzzy quantized value according to a preset fuzzy strategy table to obtain a corresponding fuzzy output signal, and send the fuzzy output signal to the defuzzification unit;

[0108] An anti-fuzzy output unit, used for performing anti-fuzzification processing on the fuzzy output signal to obtain a charging current value of the target UAV at the next moment, and generating a charging control instruction according to the charging current value of the target UAV at the next moment;

[0109] The output end of the input variable acquisition unit is electrically connected to the input end of the fuzzy processing unit, the output end of the fuzzy processing unit is electrically connected to the input end of the fuzzy decision unit, and the output end of the fuzzy decision unit is electrically connected to the input end of the anti-fuzzy output unit.

[0110] It should be noted that these calculations and data processing are all implemented in the micro-control processing module, and the micro-control processing module can preferably be implemented by using Motorola's MC68HC05SR3 chip and its peripheral circuits. This is because the chip has rich internal resources, a large ROM and RAM space, and is easier to store preset conditions and implement various complex calculations under fuzzy logic control. In addition, it also has 4 A / D converters, which is very convenient for detecting analog quantities. The chip and the corresponding peripheral circuits cooperate to form the control core of the charging system in this embodiment.

[0111] As a possible implementation, the input variable acquisition unit includes:

[0112] The ambient temperature detection subunit is used to detect the ambient temperature and input it as ambient temperature data to the input variable acquisition unit;

[0113] The battery power detection subunit is used to detect the battery power of the target drone and input it as battery power data to the input variable acquisition unit.

[0114] As a possible implementation, the charging output module includes:

[0115] The charging current adjustment unit, as the controlled end of the charging output module, is used to respond to the charging control instruction sent by the micro-control processing module and adjust the charging current output of the charging power supply;

[0116] The charging power supply, as the discharge end of the charging output module, is used to provide charging output to the target drone in the drone nest.

[0117] It should be noted that, after receiving the charging control signal, the charging current adjustment unit here will adjust the output voltage of the charging power supply in real time to achieve the purpose of controlling the charging current and obtaining the optimal charging current.

[0118] As a possible implementation, the feedback detection module includes:

[0119] The current and voltage feedback detection unit, as a sampling input terminal of the feedback detection module, is used to detect the real-time output current value and the real-time output voltage value of the charging output module, and output them as real-time charging data to the input variable acquisition unit;

[0120] The target UAV battery terminal detection unit is used to detect the real-time battery temperature value and the maximum chargeable battery voltage value of the target UAV, and output them as real-time battery data to the input variable acquisition unit.

[0121] It should be noted that the current and voltage feedback detection unit can detect the real-time output current value of the charging output module through the A / D converter in the negative feedback path as the sampling input terminal, and output it as real-time charging data to the input variable acquisition unit, so as to serve as the data basis for real-time adjustment of the charging control instructions; the target UAV battery-end detection unit can also detect the real-time battery temperature value and the maximum chargeable voltage value of the battery through the thermistor and sampling circuit in the negative feedback path, and output it as real-time battery data to the input variable acquisition unit, so as to serve as the data basis for real-time adjustment of the charging control instructions.

[0122] In the present system, a position detection subunit may also be included, which is used to monitor the position of the target UAV and input it as the target UAV position data to the input variable acquisition unit;

[0123] An ambient temperature detection unit, used to obtain a safe charging current value according to ambient temperature data;

[0124] A battery power data detection unit, used to determine whether a charging control instruction needs to be issued to the target UAV based on the battery power data;

[0125] If the battery power data of the target drone indicates that it does not need to be charged, then there is no need to perform subsequent charging operations.

[0126] Embodiment 3:

[0127] This embodiment provides a fast-charging drone nest, which includes a memory, a processor and a transceiver that are communicatively connected in sequence, wherein the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer program to execute a charging method for the fast-charging drone nest.

[0128] For specific examples, the memory here may include but is not limited to random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out memory (FIFO) and / or first-in-last-out memory (FILO), etc.; the processor may be but is not limited to a microprocessor of the MC68HC05SR3 series, ARM (Advanced RISC-Machines), X86 and other architecture processors, or a processor with integrated NPU (neural-network processing units); the transceiver may be but is not limited to a WiFi (Wireless Fidelity) wireless transceiver, a Bluetooth wireless transceiver, a General Packet Radio Service (GPRS) wireless transceiver, a ZigBee protocol (a low-power local area network protocol based on the IEEE802.15.4 standard, ZigBee) wireless transceiver, a 3G transceiver, a 4G transceiver and / or a 5G transceiver, etc.

[0129] Embodiment 4:

[0130] This embodiment provides a computer program product, including a computer program or instruction, wherein the computer program or instruction implements a charging method for a fast-charging drone nest when executed by a computer. The computer here may refer to a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0131] It should be understood that specific details are provided in the above description to facilitate a complete understanding of the example embodiments. However, it should be understood by those of ordinary skill in the art that the example embodiments can be implemented without these specific details. For example, the system can be shown in a block diagram to avoid making the example unclear with unnecessary details. In other examples, well-known processes, structures, and techniques may not be shown in unnecessary detail to avoid making the example embodiments unclear.

[0132] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A fast charging method for drone nest charging, characterized in that: include: Collect the real-time charging data and real-time battery data of the target UAV during the charging process at the current moment, and use the real-time charging data and real-time battery data as feedback input variables; Obtaining fuzzy control parameters, and based on the fuzzy control parameters and feedback input variables, using a fuzzy logic control algorithm to perform data processing on the feedback input variables to obtain a charging current value of the target UAV at the next moment; generating a charging control instruction based on the charging current value; According to the charging control instruction, the charging current of the target UAV is adjusted, and the real-time charging data and real-time battery data of the target UAV at the next moment are recollected, until the battery voltage of the target UAV reaches a preset voltage, the fast charging of the target UAV is completed.

2. The fast charging drone nest charging method according to claim 1 is characterized in that: The fuzzy control parameters include a preset fuzzy strategy table, and based on the fuzzy control parameters and the feedback input variables, a fuzzy logic control algorithm is used to perform data processing on the feedback input variables to obtain the charging current value of the target UAV at the next moment, including: Calculate the fuzzy control input quantity according to the feedback input variable; Performing fuzzy processing on the fuzzy control input to obtain a fuzzy quantized value, and performing fuzzy reasoning on the fuzzy quantized value according to the preset fuzzy strategy table to obtain a corresponding fuzzy output signal; The fuzzy output signal is defuzzified to obtain the charging current value of the target UAV at the next moment.

3. The fast charging drone nest charging method according to claim 2 is characterized in that: The fuzzy control input quantity calculated according to the feedback input variable includes: According to the real-time battery data and the maximum battery voltage of the target drone, the voltage difference at the current moment is obtained; Based on the real-time charging data, determining the voltage change rate of the target UAV at the current moment; The fuzzy control input is obtained according to the voltage change rate and the voltage difference.

4. The fast charging drone nest charging method according to claim 2 is characterized in that: The fuzzy control input is fuzzified to obtain a fuzzy quantized value, and the fuzzy quantized value is fuzzy inferred according to the preset fuzzy strategy table to obtain a corresponding fuzzy output signal: Obtain a first quantization factor and a second quantization factor; Calculating a first fuzzy quantized value based on the voltage change rate and the first quantization factor, and calculating a second fuzzy quantized value based on the voltage difference and the second quantization factor; According to the first fuzzy quantization value and the second fuzzy quantization value, a current regulation level is determined in the preset fuzzy strategy table to obtain a fuzzy output signal according to the current regulation level.

5. The fast charging drone nest charging method according to claim 2, characterized in that: Defuzzification is performed on the fuzzy output signal to obtain the charging current value of the target UAV at the next moment, including: Get the anti-blur scaling factor; The product of the current regulation level and the anti-fuzzy proportional factor is taken as the current change value; The charging current value at the current moment is obtained, and the charging current value of the target UAV at the next moment is obtained based on the charging current value and the current change value.

6. The fast charging drone nest charging method according to claim 1, characterized in that: Before collecting the real-time charging data and real-time battery data of the target UAV during the charging process at the current moment, it also includes; Obtaining the position of the target drone, and determining whether the target drone is in a charging position according to the position of the target drone; If yes, the initial input variables are obtained, and based on the initial input variables, it is determined whether the target UAV needs to be charged; Otherwise, the position of the target UAV is reacquired, and whether the target UAV is in the charging position is determined according to the position of the target UAV; If so, the target UAV is charged to collect real-time charging data and real-time battery data of the target UAV during the charging process.

7. A fast-charging drone nest charging system, used to implement the fast-charging drone nest charging method described in any one of claims 1-5, characterized in that: include: A feedback detection module is used to collect the real-time charging data and real-time battery data of the target UAV during the charging process at the current moment, and send the real-time charging data and real-time battery data as feedback input variables to the micro-control processing module; The micro-control processing module is used to obtain the fuzzy control parameters and the feedback input variables sent by the feedback detection module, and based on the fuzzy control parameters and the feedback input variables, adopt the fuzzy logic control algorithm to perform data processing on the feedback input variables to obtain the charging current value of the target UAV at the next moment; The microcontrol processing module is further used to generate a charging control instruction based on the charging current value and send the charging control instruction to the charging output module; A charging output module, used to respond to the charging control instruction and adjust the charging current of the target drone until the battery voltage of the target drone reaches a preset voltage, thereby completing the fast charging of the target drone; Among them, the sampling input end of the feedback detection module is electrically connected to the sampling output end of the charging output module, the output end of the feedback detection module is electrically connected to the variable input end of the micro-control processing module, and the output end of the micro-control processing module is electrically connected to the controlled end of the charging output module.

8. The fast-charging drone nest charging system according to claim 7, characterized in that: The micro-control processing module comprises: An input variable acquisition unit, used for calculating a fuzzy control input quantity according to the feedback input variable, and sending the fuzzy control input quantity to a fuzzy processing unit; A fuzzy inference unit, configured to perform fuzzification processing on the fuzzy control input to obtain a fuzzy quantized value, and perform fuzzy inference on the fuzzy quantized value according to the preset fuzzy strategy table to obtain a corresponding fuzzy output signal, and send the fuzzy output signal to a defuzzification unit; an anti-fuzzy output unit, configured to perform anti-fuzzification processing on the fuzzy output signal to obtain a charging current value of the target UAV at the next moment, and generate a charging control instruction according to the charging current value of the target UAV at the next moment; The output end of the input variable acquisition unit is electrically connected to the input end of the fuzzy processing unit, the output end of the fuzzy processing unit is electrically connected to the input end of the fuzzy decision unit, and the output end of the fuzzy decision unit is electrically connected to the input end of the anti-fuzzy output unit.

9. A fast-charging drone nest, characterized in that: It includes a memory, a processor and a transceiver which are communicatively connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program to execute the charging method of the fast-charging drone nest as described in any one of claims 6 to 8.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or the instruction is executed by a computer, the charging method of the fast-charging drone nest as described in any one of claims 6 to 8 is implemented.

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