A method and system for automatically replacing batteries in a drone base station

Abstract

The present application relates to the technical field of battery replacement for drones, and discloses a method and system for automatic battery replacement in a drone base station. The method includes: establishing a connection between the base station and the drone, marking the battery removed from the drone as a battery to be discharged, discharging it to a set voltage, changing it to a battery to be charged and charging it. Calculate the charge capacity of the battery to be charged. If it is less than the preset capacity, charge it with a photovoltaic module; otherwise, boost the voltage of the battery to be discharged and connect it in parallel with the photovoltaic module to charge part of the battery to be charged, and the photovoltaic module charges the remaining battery. At the same time, obtain the total battery replacement distance between the drone and the base station, and adjust the number of charged batteries according to the distance. The longer the distance, the fewer the number, and vice versa. Under photovoltaic charging conditions, this method improves the charging power, shortens the charging time, and extends the battery life.

Description

Technical Field

[0001] The present application relates to the technical field of drone battery replacement, and in particular to a method and system for automatically replacing batteries in drone base stations. Background Art

[0002] The drone battery swap base station is an intelligent infrastructure that integrates battery storage, charging, and automatic replacement functions, and is mainly used to support the continuous operation of drone swarms. Its core functions include battery status monitoring, rapid replacement, intelligent scheduling, and energy management. It is widely used in agricultural plant protection, logistics distribution, security inspection, surveying and exploration, and other fields. For example, in agricultural scenarios, a battery swap base station can support the cyclic operation of multiple plant protection drones, significantly improving the efficiency of pesticide spraying; in the logistics field, it can ensure the endurance of drone express delivery in remote areas. With the large-scale development of the drone industry, the demand for the deployment of battery swap base stations is increasing, especially in areas without fixed power grid coverage, such as the wild and islands. The stability and sustainability of its energy supply have become key technical bottlenecks.

[0003] Currently, field power supply methods for drone battery swapping base stations fall into two main categories: fossil fuels and renewable energy. Fossil fuels rely on diesel or gasoline generators, which generate electricity by burning fossil fuels and are suitable for scenarios with high power demands. For example, a drone battery swapping base station equipped with a 3kW diesel generator can support simultaneous fast charging of eight batteries, but this requires regular refueling, which comes with issues such as high transportation costs and noise pollution.

[0004] Renewable energy sources are primarily solar, using photovoltaic panels to convert sunlight into electricity and store it in batteries. This can address the challenges of fossil fuel power generation, but renewable energy sources are limited in power and cannot fully charge all the batteries in the drone battery swapping base station, leading to longer charging times. Summary of the Invention

[0005] In order to shorten the time required to charge the batteries of a drone, the present application provides a method and system for automatically replacing batteries in a drone base station.

[0006] In the first aspect, the present application provides a method for automatically replacing batteries in a drone base station, which adopts the following technical solution:

[0007] A method for automatically replacing batteries in a drone base station comprises the following steps:

[0008] The base station establishes a connection with the corresponding drone;

[0009] Marking the battery removed from the drone in the base station as a battery to be discharged;

[0010] discharging the battery to be discharged, and after the voltage after discharge drops to a set first voltage value, changing the mark of the battery to be discharged to a battery to be charged, and charging the battery to be charged;

[0011] Calculating the charging capacity required for all the batteries to be charged;

[0012] If the charge amount is less than a preset first amount of charge, the photovoltaic module is used to charge the batteries to be charged; otherwise, the voltage of the batteries to be discharged is controlled to be boosted and then connected in parallel with the photovoltaic module to charge a first preset number of the batteries to be charged, and the photovoltaic module is controlled to charge the remaining batteries to be charged;

[0013] Obtain the battery exchange distance between the UAV and the base station, and calculate the sum of all the battery exchange distances as the total distance; adjust the first preset number in anti-correlation according to the total distance, the longer the total distance, the smaller the first preset number, and the shorter the total distance, the larger the first preset number.

[0014] By adopting the above technical solution, the method of charging the photovoltaic module alone or charging the battery to be discharged in parallel with the photovoltaic module after boosting the voltage can be flexibly selected according to the charge capacity of the battery to be charged, thereby effectively utilizing renewable energy, alleviating the problem of limited power supply of renewable energy to a certain extent, and improving energy utilization efficiency. The charging strategy of boosting the battery to be discharged in parallel with the photovoltaic module can increase the charging power when the charge capacity is large, thereby shortening the overall charging time, improving the turnover efficiency of the drone battery, and supporting the continuous operation of the drone group. Reasonable charge and discharge control avoids overcharging or discharging of the battery at unreasonable voltage, helps to extend the service life of the battery and reduce the cost of battery replacement. The first preset number is adaptively adjusted according to the total battery exchange distance between the drone and the base station, so that the allocation of charging resources is more reasonable, adapting to different operation scenarios, and improving the flexibility and adaptability of the system.

[0015] Optionally, the method further comprises the following steps:

[0016] Obtaining the power generated by the photovoltaic module;

[0017] According to the inverse correlation of the generated power, the total discharge power of the battery to be discharged is adjusted, the greater the generated power, the smaller the total discharge power, and the smaller the generated power, the greater the total discharge power;

[0018] The total discharge power is controlled by adjusting the output current and the number of discharges of the battery to be discharged. The greater the output current of the battery to be discharged, the greater the total discharge power, and the smaller the output current of the battery to be discharged, the smaller the total discharge power. The greater the number of discharges of the battery to be discharged, the greater the total discharge power, and the smaller the number of discharges of the battery to be discharged, the smaller the total discharge power.

[0019] Calculate the average voltage of all the batteries to be discharged, and adjust the output current in a positive correlation and the discharge quantity in an anti-correlation according to the voltage average; the larger the average value, the larger the output current and the smaller the discharge quantity; the smaller the average value, the smaller the output current and the larger the discharge quantity.

[0020] By adopting the above technical solution, through the reverse dynamic regulation of photovoltaic power generation and the discharge power of the battery to be discharged, a synergistic power supply mechanism with photovoltaic and energy storage is established, ensuring stable charging power while maximizing energy utilization efficiency. Based on the real-time photovoltaic power generation, the battery discharge intensity is automatically matched. This can not only fill the power supply gap by increasing the number of discharged batteries and the current during low light conditions, but also reduce battery loss during high light conditions. The discharge parameters are dynamically optimized according to the battery health status: when the voltage is high, the power is released in a small number, high current mode to reduce the loss of multiple batteries in parallel; when the voltage is low, a gentle discharge method with a large number and low current is adopted to avoid single cell overload.

[0021] Optionally, the method further comprises the following steps:

[0022] Obtain the number of battery swaps and the corresponding amount of battery swaps for the drone outside the base station;

[0023] Calculate the total amount of battery replacement based on the number of battery replacements and the amount of battery replacement;

[0024] The first power is adjusted in positive correlation with the total power of battery exchange. The greater the total power of battery exchange, the greater the first power, and the smaller the total power of battery exchange, the smaller the first power.

[0025] By adopting the above technical solution, the dynamic charging adjustment mechanism is driven by the battery replacement demand, and the charging strategy is adjusted in real time based on the total battery replacement power, which can adaptively match the operation intensity of the drone group; reduce the energy redundancy and battery heat loss caused by overcharging, which is conducive to reducing unnecessary charge and discharge cycles and reducing the battery aging rate.

[0026] Optionally, the method further comprises the following steps:

[0027] Marking the UAV whose battery swap distance is less than a preset reference distance value as an active point;

[0028] Counting the number of active points as the active number;

[0029] The boosted secondary voltage of the battery to be discharged is adjusted according to the active number. The more active numbers there are, the higher the secondary voltage is, and the fewer active numbers there are, the lower the secondary voltage is. The secondary voltage is not higher than the maximum charging voltage of the battery to be charged.

[0030] By adopting the above technical solution, the secondary voltage is dynamically adjusted according to the number of active points to achieve precise adaptation of charging power and operation density; the voltage is increased when short-range drones gather to accelerate the charging efficiency in high-demand scenarios, and the voltage is reduced during low-activity periods to reduce ineffective energy consumption.

[0031] Optionally, the method further comprises the following steps:

[0032] If the active number is less than the second preset number, the sum of the exchange amounts of all the active points is calculated as the local power;

[0033] If the local power level is less than the preset reference area power level, screening out the to-be-discharged batteries having a voltage higher than a preset second voltage value from the base station;

[0034] From the screened batteries to be discharged, a third preset number of batteries are screened according to their voltages and their labels are modified to be the batteries to be charged.

[0035] By adopting this technical solution, high-voltage batteries waiting to be charged are selected for discharge based on the number of active points and local power levels, dynamically optimizing the battery charging and discharging queue. When the number of active points and local power levels are low, high-voltage batteries waiting to be discharged are prioritized for charging, avoiding overall waste of charging resources and improving energy efficiency. Furthermore, this precisely matches actual charging needs, reducing unnecessary battery charge and discharge cycles and extending battery life. Furthermore, the ability to dynamically adjust charging strategies based on real-time operational status enhances the adaptability and flexibility of the drone battery swap base station system in complex scenarios.

[0036] Optionally, the method further comprises the following steps:

[0037] The third preset number is adjusted inversely according to the number of active users, wherein the greater the number of active users, the smaller the third preset number, and the smaller the number of active users, the larger the third preset number;

[0038] The second voltage value is adjusted according to the anti-correlation of the local electric quantity. The larger the local electric quantity is, the lower the second voltage value is; and the smaller the local electric quantity is, the higher the second voltage value is.

[0039] By employing this technical solution, the number of active points and local power levels dynamically matches operational demand with regional energy consumption, adaptively adjusting the battery conversion threshold and number. This optimizes resource allocation efficiency while precisely maintaining battery health, achieving a balance between rapid response to high loads and low-redundancy energy supply. This bidirectional dynamic adjustment further enhances the overall efficiency of the drone battery swapping base station. First, the third preset number is adjusted inversely based on the number of active points. When the number of active points is high, the number of high-voltage discharge batteries converted to charging batteries is reduced, avoiding over-allocation of resources, prioritizing the battery swapping needs of active drones and improving charging efficiency. When the number of active points is low, the number is increased to optimize the use of base station battery resources. Second, the second voltage value is adjusted inversely based on the local power level. When the local power level is high, the second voltage value is lowered to expand the selection range and meet larger charging demands. When the local power level is low, the second voltage value is increased to ensure that the selected batteries can effectively meet charging needs. This not only significantly improves resource utilization efficiency but also allows the system to flexibly respond to battery swapping needs in different scenarios, reducing battery loss and extending battery life.

[0040] Optionally, the method further comprises the following steps:

[0041] Acquiring an environmental image based on a camera module on the drone connected to the outside of the base station;

[0042] identifying the surrounding environment of the base station from the environmental image;

[0043] Extracting a first ambient light intensity of the surrounding environment;

[0044] Calculating a first simulated power of the photovoltaic module according to the first ambient light intensity;

[0045] Calculating a first integrated power according to the first simulated power and the generated power;

[0046] The generated power is updated using the first integrated power.

[0047] By adopting the above technical solution, the changes in illumination can be perceived in real time based on the drone's environmental images, and the photovoltaic power prediction data can be dynamically corrected to improve the accuracy of power generation estimation. By combining the fusion calculation of measured and simulated data, adaptive calibration of photovoltaic output power can be achieved, charging and discharging strategies can be optimized, and the base station's ability to adapt to weather changes can be enhanced.

[0048] Optionally, the method further comprises the following steps:

[0049] Acquiring photosensitive point data based on photosensitive modules on a plurality of drones connected to the base station;

[0050] Establishing a photosensitive image based on the coordinates of the drone to which the photosensitive module belongs and the photosensitive point data;

[0051] identifying the surrounding environment of the base station from the photosensitive image;

[0052] Extracting a second ambient light intensity of the surrounding environment;

[0053] Calculating a second simulated power of the photovoltaic module according to the second ambient light intensity;

[0054] Calculating a second integrated power according to the second simulated power and the generated power;

[0055] The generated power is updated using the second integrated power.

[0056] By adopting the above technical solution, lighting information is collected from multiple locations, making the acquired ambient light intensity data more comprehensive and accurate, and making the assessment of the photovoltaic module's power generation capacity more precise; based on more accurate power generation power updates, the charging strategy can be more reasonably and dynamically adjusted, optimizing the allocation of battery charging resources and improving charging efficiency; the system's adaptability to complex and changeable ambient lighting conditions is enhanced, and it can quickly respond to changes in different lighting scenarios, ensuring the stable and reliable operation of the drone battery swap base station; it helps to avoid irrational use of energy, achieve efficient energy utilization, and reduce unnecessary energy loss and waste.

[0057] In a second aspect, the present application provides a system for automatically replacing batteries in a drone base station, which adopts the following technical solutions:

[0058] A system for automatically replacing batteries in a drone base station includes a processor, wherein the processor executes the steps of any of the above-mentioned methods for automatically replacing batteries in a drone base station.

[0059] A storage medium having a program stored therein, wherein the program, when executed by a processor, implements the steps of any of the above-mentioned methods and systems for automatically replacing batteries in a drone base station.

[0060] In summary, this application includes at least one of the following beneficial technical effects:

[0061] The remaining power of the battery to be discharged is reused in parallel with photovoltaic energy, and the power supply mode is dynamically switched according to the real-time charging demand: when the charging amount exceeds the preset threshold, the boosted battery to be discharged and the photovoltaic module form a hybrid power supply system, and the output power of the two is superimposed, which significantly improves the parallel charging speed of multiple batteries; at the same time, by setting the discharge termination voltage threshold, the capacity attenuation caused by deep discharge of the battery is avoided. Combined with the parallel battery quantity adjustment mechanism that is adaptive to the battery replacement distance, it not only guarantees the priority and rapid power replenishment needs of long-distance operating drones, but also optimizes the overall charging efficiency in near-field scenarios; introduces cyclic load balancing technology, dynamically allocates charging and discharging tasks based on battery status data, reduces the cycle stress difference of single cells, and extends the overall service life of the battery pack; in addition, the battery to be discharged serves as a buffer energy storage unit, which can smooth out the fluctuations of photovoltaic power generation and enhance the stability of base station energy supply; under the condition of pure renewable energy power supply, the charging power is improved and the battery life is extended.

[0062] Based on real-time perception of ambient light intensity and integrated calibration of photovoltaic power, accurate prediction of power generation capacity and dynamic optimization of charging and discharging strategies are achieved; combined with multi-parameter feedback on drone operation density, battery replacement needs and battery health status, the discharge power and charging voltage thresholds of the battery to be discharged are intelligently adjusted.

[0063] By utilizing the number of active points and local power analysis, the boost voltage and battery conversion threshold are adaptively matched to achieve rapid response in high-load scenarios and intensive resource management during low-energy consumption periods. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A step-by-step diagram of a method for automatically replacing batteries in a drone base station.

[0065] Figure 2 It is a step diagram for adjusting the first power according to the positive correlation of the total power of battery replacement.

[0066] Figure 3 This is a step diagram for adjusting the secondary voltage of the battery after boosting to be discharged according to the number of active batteries. DETAILED DESCRIPTION

[0067] Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings.

[0068] Throughout this specification, reference to the terms "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0069] The present application embodiment discloses a method for automatically replacing batteries in a drone base station, referring to Figure 1 , including the following steps:

[0070] The base station establishes a connection with the corresponding drone; this can be achieved through wireless communication technologies such as Bluetooth, Wi-Fi, ZigBee, etc. By establishing a connection, the base station can obtain relevant information about the drone, such as battery status, remaining power, flight mission, etc.

[0071] The base station marks the batteries removed from the drone as batteries to be discharged; when the drone returns to the base station for battery replacement, the base station will identify and mark the removed batteries for subsequent discharge and charging management.

[0072] After the battery to be discharged is discharged and the voltage after discharge drops to the set first voltage value, the mark of the battery to be discharged is changed to a battery to be charged, and the battery to be charged is charged. The discharge process can be achieved through a specific discharge circuit to ensure the safe and stable discharge of the battery; or the discharge can also be a temporary power source to charge other batteries. The set first voltage value can be adjusted according to the type, performance and usage requirements of the battery to ensure the rational use of the battery. Taking the common lithium battery as an example, the first voltage value is set to 3.2V. When the voltage of the battery to be discharged drops to 3.2V, the base station control system changes its mark to a battery to be charged and starts the charging program.

[0073] Calculate the required charge capacity for all rechargeable batteries. The base station uses the battery management system to obtain the current and rated capacity of each rechargeable battery and calculate the total required charge capacity. For example, in a base station with 10 rechargeable batteries, each with a rated capacity of 5000mAh and a different current capacity, the battery management system calculates that these 10 batteries require a total charge capacity of 30,000mAh.

[0074] If the charge capacity is less than a preset first capacity, the photovoltaic modules are used to charge the batteries to be charged. Otherwise, the batteries to be discharged are boosted and connected in parallel with the photovoltaic modules to charge a first preset number of batteries to be charged, and the photovoltaic modules are controlled to charge the remaining batteries to be charged. As a key source of renewable energy, prioritizing photovoltaic modules for charging effectively utilizes solar energy and reduces reliance on traditional energy sources. When the charge capacity is high, boosting the charge capacity of the batteries to be discharged and connecting them in parallel with the photovoltaic modules can increase charging power and improve charging efficiency. For example, during sunny days, the photovoltaic modules have a strong power generation capacity. A preset first capacity is 20,000 mAh. When the calculated charge capacity is 15,000 mAh, which is less than the first capacity, the base station uses only the photovoltaic modules to charge the batteries to be charged, fully utilizing renewable energy. When the charge capacity reaches 25,000 mAh, which is greater than the first capacity, the base station boosts the charge capacity of the batteries to be discharged and connects them in parallel with the photovoltaic modules. Assuming the first preset number is five, five batteries to be charged are charged in parallel while the photovoltaic modules simultaneously charge the remaining five batteries to be charged, increasing charging power.

[0075] Obtain the battery swap distance between the drone and the base station, and calculate the sum of all battery swap distances as the total distance; adjust the first preset number in anti-correlation with the total distance. The longer the total distance, the smaller the first preset number, and the shorter the total distance, the larger the first preset number. The length of the battery swap distance reflects the time and frequency of the drone's return to the base station. By adjusting the first preset number according to the total distance, the allocation of charging resources can be made more reasonable, giving priority to meeting the charging needs of drones with shorter distances and frequent returns. In a security inspection scenario, multiple drones perform inspection tasks in different areas. The base station obtains the battery swap distance between each drone and the base station through the drone's positioning information. When the total distance is long, it means that the drone takes a relatively long time to return to the base station. In order to reasonably allocate charging resources, the first preset number will be reduced accordingly. Conversely, if the total distance is short, the first preset number will be increased, and parallel charging will be prioritized for more batteries to be charged to shorten the charging time.

[0076] Assume there are three drones, named drone A, drone B, and drone C, whose battery swap distances from the base station are d1, d2, and d3 respectively. The total distance D = d1 + d2 + d3.

[0077] For example, the battery swap distance between drone A and the base station is d1 = 5 kilometers, the battery swap distance between drone B and the base station is d2 = 3 kilometers, and the battery swap distance between drone C and the base station is d3 = 7 kilometers, then the total distance D = 5 + 3 + 7 = 15 kilometers. A simple linear relationship between the total distance D and the first preset number n. Assume that the linear relationship is n = kD + b, where k < 0, because the longer the total distance, the smaller the first preset number. Assume that k = -1 and b = 20. Here, the values ​​of k and b need to be adjusted according to specific circumstances in actual applications. When D = 15 kilometers, the first preset number n = -1 × 15 + 20 = 5.

[0078] If a new drone D joins the fleet, its battery swap distance from the base station is d4 = 2 km, and the new total distance D' = 5 + 3 + 7 + 2 = 17 km. Therefore, the new first preset number n' = -1 × 17 + 20 = 3.

[0079] As the total distance increases from 15 kilometers to 17 kilometers, the first preset number decreases from 5 to 3, that is, the longer the total distance is, the smaller the first preset number is.

[0080] Parameter settings, such as a first voltage value, a first charge level, and a first preset quantity, can be adjusted based on the battery type, performance, operating environment, and operational requirements. For example, different battery types may have different charging characteristics and safe voltage ranges, requiring a suitable first voltage value to be set based on actual conditions. Different operational scenarios, such as agricultural plant protection and logistics distribution, also have different charging requirements and drone distribution conditions, requiring a suitable first charge level and first preset quantity to be set based on actual conditions.

[0081] Based on the charge level of the battery to be charged, the system flexibly chooses to charge the photovoltaic module independently or charge the battery to be discharged in parallel with the photovoltaic module. This effectively utilizes renewable energy and, to a certain extent, alleviates the problem of limited renewable energy power supply. For example, during daytime hours with ample sunlight, photovoltaic modules are prioritized for charging; when the charge level is high, charging is combined with the battery to be discharged, fully leveraging the advantages of renewable energy and battery energy storage, improving energy efficiency. The charging strategy of charging the battery to be discharged in parallel with the photovoltaic module after boosting the battery to be discharged increases charging power when the charge level is high, thereby shortening the overall charging time. For example, in agricultural plant protection scenarios, where multiple drones operate in a cyclical manner, fast charging can reduce drone waiting time and improve pesticide spraying efficiency. Appropriate charge and discharge control prevents overcharging or discharging of the battery at unreasonable voltages, helping to extend battery life. By setting the first voltage value and controlling the charging process, the battery operates within a safe and reasonable range, reducing battery loss and lowering battery replacement costs. The first preset number is adaptively adjusted based on the total battery exchange distance between the drone and the base station, ensuring more efficient allocation of charging resources to suit different operational scenarios. Whether in vast farmland, remote mountainous areas or busy urban logistics distribution areas, the system can flexibly adjust the charging strategy according to actual conditions, improving the flexibility and adaptability of the system.

[0082] The method further comprises the steps of:

[0083] The base station is equipped with specialized power monitoring equipment that can obtain real-time information about the power generated by the photovoltaic modules. For example, high-precision power sensors can accurately measure the power conversion efficiency of the photovoltaic modules under different lighting conditions and transmit the measured power data to the base station's control system in real time.

[0084] Based on the acquired photovoltaic module power generation, the total discharge power of the battery to be discharged is inversely adjusted. That is, when the generated power is higher, the total discharge power of the battery to be discharged decreases; when the generated power is lower, the total discharge power of the battery to be discharged increases. This regulation method achieves complementary benefits between photovoltaic energy and battery energy storage, ensuring a stable and efficient charging process.

[0085] Assume that the power generation of the photovoltaic module is Pgeneration, and the total discharge power of the battery to be discharged is Pdischarge. According to the inverse correlation, the simple linear relationship between them is:

[0086] Pdischarge = k1 × Pgeneration + b1, where k1 < 0, unit: watt.

[0087] For example, assuming k1=-0.5 and b1=1000, the values ​​of k1 and b1 here need to be determined appropriately through experiments or analysis based on specific devices and scenarios in actual applications.

[0088] When the power generation power Pgenerated by the photovoltaic module = 2000 watts, the total discharge power Pdischarged of the battery to be discharged = -0.5×2000+1000=0 watts.

[0089] When the power generation power Pgenerated by the photovoltaic module = 1000 watts, the total discharge power Pdischarge of the battery to be discharged = -0.5×1000+1000=500 watts.

[0090] As the power generation power decreases from 2000 watts to 1000 watts, the total discharge power of the battery to be discharged increases from 0 watts to 500 watts. The greater the power generation power, the smaller the total discharge power of the battery to be discharged.

[0091] Regulating the output current through a current regulation circuit can control the output current of the battery in the process of discharge. A higher output current in the battery in the process of discharge increases the total discharge power; a lower output current in the battery in the process of discharge decreases the total discharge power. For example, using pulse-width modulation (PWM) technology to regulate the output current can achieve current control accurate to the milliampere level. PWM technology is currently available and will not be discussed in detail here.

[0092] The base station can flexibly select the number of batteries to be discharged based on actual needs by adjusting the number of batteries to be discharged. The greater the number of batteries discharged, the greater the total discharge power; the fewer batteries discharged, the lower the total discharge power. For example, in a base station with 10 batteries to be discharged, the control system can select 2, 5, or more batteries to be discharged as needed.

[0093] Calculate the average voltage of all batteries to be discharged. Use this average to adjust the output current in a positive correlation and the number of batteries to be discharged in an inverse correlation. A higher average indicates a higher overall battery charge. In this case, increase the output current while reducing the number of batteries to be discharged. A lower average indicates a lower overall battery charge. In this case, reduce the output current while increasing the number of batteries to be discharged.

[0094] Assume that there are 10 uncharged batteries in the base station. The current voltage values ​​(unit: V) are as follows: 3.8, 3.7, 3.6, 3.5, 3.4, 3.3, 3.2, 3.1, 3.0, 2.9. The calculated average voltage is 3.35.

[0095] Dynamic adjustment based on the average voltage V:

[0096] Output current I=k5×V; for example, k5=2(A / V);

[0097] In this example: I=2×3.35=6.7A.

[0098] The number of discharged batteries N = total number - kN × V, for example, kN = 2 (blocks / V);

[0099] In this example: N=10-2×3.35, rounded up to 3 pieces.

[0100] By dynamically adjusting the power generated by photovoltaics and the discharge power of the batteries to be discharged, a synergistic power supply mechanism combining photovoltaics and energy storage is established, ensuring stable charging power while maximizing energy efficiency. Automatically matching battery discharge intensity based on real-time photovoltaic power generation can not only fill power supply gaps by increasing the number of discharged batteries and current during low light conditions, but also reduce battery loss during high light conditions. Discharge parameters are dynamically optimized based on battery health: when voltage is high, energy is released in a low-volume, high-current mode to reduce losses in multiple batteries connected in parallel; when voltage is low, a gentle discharge method with a high-volume, low-current mode is adopted to avoid single-cell overload.

[0101] Reference Figure 2 , the method further comprises the steps of:

[0102] Obtain the number of battery swaps and the corresponding amount of battery swaps for drones outside the base station.

[0103] The total battery replacement capacity is calculated based on the number of battery replacements and the battery replacement capacity; for example, if there are 3 drones that need battery replacement, and the battery replacement capacities are 2000mAh, 3000mAh, and 1500mAh respectively, then the total battery replacement capacity is 6500mAh.

[0104] The first charge is adjusted based on the positive correlation between the total amount of battery swapped and the total amount of battery swapped. The greater the total amount of battery swapped, the greater the first charge, and the smaller the total amount of battery swapped, the smaller the first charge. For example, when the total amount of battery swapped is small, the first charge is also reduced accordingly, and the photovoltaic module charging method may be used more frequently. When the total amount of battery swapped is large, the first charge is increased, and the method of boosting the voltage of the uncharged battery and charging it in parallel with the photovoltaic module may be used more frequently.

[0105] Assume that the total amount of battery swapped is Qtotal (unit: mAh), the first amount of battery is Q1 (unit: mAh), and based on the positive correlation, the simple linear relationship between them is:

[0106] Q1=k2×Qtotal+b2, where k2>0.

[0107] For example, assuming k2=1, b2=5000, the values ​​of k2 and b2 in actual applications need to be determined through experiments or data analysis based on specific device performance, charging requirements and other factors.

[0108] When the total battery capacity Qtotal = 6000mAh, the first battery capacity Q1 = 1×6000+5000=11000mAh.

[0109] When the total battery capacity Qtotal = 30000mAh, the first battery capacity Q1 = 1×30000+5000=35000mAh.

[0110] As the total battery swap capacity increases from 6000mAh to 30000mAh, the first capacity increases from 11000mAh to 35000mAh. The greater the total battery swap capacity, the greater the first capacity.

[0111] Because the total swapped battery capacity exceeds the adjusted primary capacity, the base station boosts the voltage of the uncharged battery and connects it in parallel with the photovoltaic module, allowing for rapid simultaneous charging of multiple drones and ensuring smooth crop protection operations. During the off-season, only two to three drones require battery swaps each day, resulting in a total swapped capacity of 6,000 to 9,000 mAh. The primary capacity is correspondingly reduced to 10,000 mAh, allowing for more independent charging from the photovoltaic module. This not only meets charging needs but also reduces energy consumption and battery loss.

[0112] Through a dynamic charging regulation mechanism driven by battery swapping demand, the charging strategy is adjusted in real time based on the total battery swapping capacity, adaptively matching the drone swarm's operating intensity. The operating intensity of a drone swarm can vary significantly across different application scenarios. For example, in agricultural plant protection, during the critical growth period of crops, extensive pesticide spraying operations are required. This increases the frequency and duration of drone flights, and correspondingly, the demand for battery swapping. During this period, the total battery swapping capacity captured by the base station will be high. By increasing the primary capacity and utilizing more pre-discharged batteries for parallel charging with photovoltaic modules, the charging needs of a large number of drones can be quickly met, ensuring the swarm's continued efficient operation. During the off-season, the demand for drone battery swapping decreases, the total battery swapping capacity decreases, and the primary capacity also decreases. Relying more on independent charging from photovoltaic modules, fully utilizing renewable energy, and reducing energy consumption.

[0113] Reference Figure 3 , the method further comprises the steps of:

[0114] The drones whose battery swap distance is less than the preset reference distance value are marked as active points, and the number of active points is counted as the active number.

[0115] The secondary voltage of the battery to be discharged after boosting is adjusted according to the number of active cells. The more active cells there are, the higher the secondary voltage is, and the fewer active cells there are, the lower the secondary voltage is. The secondary voltage is not higher than the maximum charging voltage of the battery to be charged.

[0116] Assume that the number of active cells is N and the secondary voltage is U (unit: V). Based on the positive correlation between the number of active cells and the secondary voltage, and that the secondary voltage is not higher than the maximum charging voltage of the battery to be charged, and the maximum charging voltage is Umax, the relationship between them is as follows:

[0117] U=k3×N+b3, where k3>0, and satisfies 0<U<Umax.

[0118] For example, given the maximum charging voltage Umax = 48V for a battery to be charged, assume k3 = 4 and b3 = 0. In practice, appropriate values ​​for k3 and b3 need to be determined through experimentation or data analysis based on factors such as the specific battery system performance and charging requirements. When the number of active cells N = 10, the secondary voltage U = 4 × 10 + 0 = 40V. When the number of active cells N = 3, the secondary voltage U = 4 × 3 + 0 = 12V.

[0119] Dynamically adjusting the secondary voltage based on the number of active points enables precise adaptation of charging power to operation density. Drone operation density varies significantly across different application scenarios. For example, near distribution centers, large numbers of drones frequently travel back and forth to base stations for battery swaps, resulting in a high operation density. In these situations, a large number of drones are marked as active points, and with a high number of active points, the base station increases the secondary voltage and charging power to quickly recharge the drones and meet the charging needs of this high operation density. In remote areas, however, drones are sparsely distributed, with low operation density and a small number of active points. Therefore, the base station reduces the secondary voltage to avoid energy waste caused by excessively high voltages.

[0120] The method further comprises the steps of:

[0121] If the number of active points is less than the second preset number, the local power consumption is calculated by summing the power consumption of all active points. For example, if the second preset number is set to 5 and the base station counts 3 active points, the power consumption required by each of these three active drones is summed to obtain the local power consumption. Assuming the power consumption of these three drones is 1500mAh, 2000mAh, and 1200mAh respectively, the local power consumption is 4700mAh.

[0122] If the calculated local power is less than the preset reference area power, it means that the overall demand for battery replacement at the current active point is low. At this time, the base station will screen out batteries with a voltage higher than the preset second voltage value from its own batteries to be discharged. For example, the preset reference area power is 6000mAh, and the local power of 4700mAh calculated above is less than this value, the base station will start screening batteries to be discharged. Assuming that the second voltage value is set to 3.6V, the base station will check the voltage of all batteries to be discharged and select batteries with a voltage higher than 3.6V.

[0123] The drones perform the same tasks, such as patrolling; the drones' batteries are power batteries with the characteristics of fast charging and discharging.

[0124] From the screened batteries to be discharged, a third preset number of batteries are selected in descending order of voltage and their labels are changed to batteries to be charged. For example, if the third preset number is set to 2, the base station selects the two batteries with the highest voltage from the screened high-voltage batteries to be discharged, changes their labels from batteries to be discharged to batteries to be charged, and places them in the charging queue.

[0125] When the number of active drones and local battery levels are low, the need for battery replacement is not urgent. If all unused batteries are prepared for charging, overall charging resources would be wasted. By prioritizing high-voltage unused batteries for charging, only those with high battery levels and the potential for immediate use are charged, avoiding unnecessary energy consumption and improving energy efficiency. For example, in a logistics and delivery scenario, during late night hours, when the number of active drones is low and local battery levels are low, charging only high-voltage unused batteries, rather than charging all unused batteries, saves significant energy. This dynamic optimization of battery charging and discharging queues precisely matches actual charging needs. It avoids unnecessary charge and discharge cycles for batteries that don't need immediate charging, reducing battery wear and aging. Because the number of charge and discharge cycles is a key factor affecting a battery's lifespan, reducing unnecessary cycles can effectively extend its lifespan. For example, in agricultural plant protection scenarios, during breaks between operations, when the number of active drones and local battery levels are low, prioritizing high-voltage unused batteries for charging reduces the charge and discharge frequency of other batteries, extending the overall battery lifespan.

[0126] The method further comprises the steps of:

[0127] The value of the third preset number calculated next time is adjusted inversely according to the number of active units. The more active units there are, the smaller the third preset number is, and the fewer active units there are, the larger the third preset number is. When the number of active units is large, it means that there are more drones in urgent need of battery replacement. At this time, it is necessary to prioritize the battery replacement needs of these active drones. In order to avoid over-allocation of resources, the number of high-voltage batteries to be discharged that are converted to batteries to be charged is reduced, that is, the third preset number will become smaller. On the contrary, when the number of active units is small, the demand for battery replacement is relatively low. In order to rationally utilize the battery resources of the base station, the number of high-voltage batteries to be discharged that are converted to batteries to be charged is increased, and the third preset number will become larger.

[0128] Assume that the number of active users is N and the third preset number is M. Since the greater the number of active users, the smaller the third preset number is, and the two are inversely correlated, a linear relationship is established: M = a1 × N + a2, where a1 < 0.

[0129] For example, by statistically analyzing data from a specific base station over a period of time, it is found that when the active number N=5, the third preset number M=10; when the active number N=10, the third preset number M=5. Substituting these two sets of data into the above equation yields:

[0130] 10=5a1+a2; 5=10a1+a2;

[0131] The solution is a1=-1; a2=15;

[0132] Therefore, the linear relationship between the two is M=-N+15.

[0133] The preset number in this embodiment is a fixed value set in advance when used for the first time, and the value will be adaptively adjusted according to the actual situation during operation.

[0134] The second voltage value is adjusted inversely based on the local charge level. The larger the local charge level, the lower the second voltage value, and the smaller the local charge level, the higher the second voltage value. The local charge level refers to the sum of the charge levels of all active points. The system adjusts the second voltage value inversely based on the size of the local charge level. The second voltage value is a voltage threshold used to screen batteries to be discharged. Only batteries with a voltage higher than this value will be considered for conversion to batteries to be charged. When the local charge level is high, it indicates that the total battery replacement demand of the currently active drones is high. To meet this high charging demand, the system will lower the second voltage value, thereby expanding the range for screening high-voltage batteries to be discharged, giving more batteries to be discharged the opportunity to be converted to batteries to be charged. When the local charge level is low, to ensure that the screened batteries can effectively meet the charging demand, the system will increase the second voltage value, selecting only batteries with higher voltages for conversion.

[0135] Assume that the local charge is Q and the second voltage is V. Since the larger the local charge, the lower the second voltage, the two are inversely correlated, and a linear relationship is established: V = a3Q + a4, where a3 < 0.

[0136] Taking the agricultural plant protection scenario in this article as an example, the default second voltage value is 3.7V. When the local power Q = 2000mAh, the second voltage value V = 3.8V; when the local power Q = 8000mAh, the second voltage value V = 3.5V. Substituting these two sets of data into the equation, we get a3 = -0.00005; a4 = 3.9;

[0137] Therefore, the linear relationship between the two is V=-0.00005Q+3.9.

[0138] By dynamically matching the number of active points with local power levels to operational demands and regional energy consumption, the battery conversion threshold and number are adaptively adjusted. This optimizes resource allocation efficiency while precisely maintaining battery health, achieving a balance between rapid response to high loads and low-redundancy energy supply. This bidirectional dynamic adjustment further enhances the overall efficiency of the drone battery swapping base station. First, the third preset number is inversely adjusted based on the number of active points. When the number of active points is high, the number of high-voltage discharge batteries converted to charging batteries is reduced, avoiding over-allocation of resources, prioritizing the battery swapping needs of active drones and improving charging efficiency. When the number of active points is low, the number is increased to optimize the utilization of base station battery resources. Second, the second voltage value is inversely adjusted based on the local power level. When the local power level is high, the second voltage value is lowered to expand the selection range and meet larger charging demands. When the local power level is low, the second voltage value is increased to ensure that the selected batteries can effectively meet charging needs. This not only significantly improves resource utilization efficiency but also allows the system to flexibly respond to battery swapping needs in different scenarios, reducing battery loss and extending battery life.

[0139] In order to further optimize the discharge strategy, the method further includes the following steps:

[0140] The system captures environmental images using a camera module on a drone connected to the base station, analyzing and processing them using advanced image recognition technology. Using a trained deep learning model, it accurately identifies various elements in the image, such as the sky, clouds, trees, and buildings, and determines the base station's surrounding environment type, such as whether it is sunny, overcast, or partly cloudy, or located in a forest, city, or other geographical environment. For example, if the image mostly shows a blue sky with only a few thin clouds, the model will identify the current environment as sunny; if the sky is covered by thick clouds and the light is dim, the image is considered overcast.

[0141] After identifying the surrounding environment, the base station extracts the first ambient light intensity from the ambient image. This process utilizes the image's brightness information and color characteristics. By statistically analyzing the brightness values ​​of each pixel in the image and combining color space conversion with an illumination model algorithm, the base station calculates the light intensity reflected in the ambient image. To improve the accuracy of light intensity extraction, factors such as the time, location, and weather when the image was captured are also considered.

[0142] The base station calculates the first simulated power of the PV module based on the extracted first ambient light intensity, combined with the module's characteristic parameters and power generation model. Different PV module models have different power generation characteristic curves. The system uses a dedicated power generation model to calculate the simulated power generated by the PV module under the current first ambient light intensity, based on the module's maximum power point power, short-circuit current, open-circuit voltage, temperature coefficient, and other parameters.

[0143] To more accurately determine the actual power generated by the PV modules, the base station combines the first simulated power with the generated power acquired in real time by power monitoring equipment to calculate the first composite power. The system uses a weighted average method to assign different weights to the first simulated power and generated power based on the stability of the actual lighting conditions. In stable lighting conditions, the weight of generated power is appropriately increased; in conditions with drastic lighting fluctuations, the weight of the first simulated power is increased. Finally, the base station uses the calculated first composite power to update the generated power, providing an accurate basis for subsequent adjustments to the charging and discharging strategies.

[0144] By using drone environmental images to perceive changes in illumination in real time, the system can promptly capture subtle changes in ambient light intensity, dynamically correct photovoltaic power prediction data, and effectively improve the accuracy of power generation estimation. Compared with traditional single-sensor monitoring methods, this solution obtains more comprehensive and accurate illumination information, avoiding errors in power generation estimation caused by localized illumination anomalies. By combining the fusion calculation of measured and simulated data, adaptive calibration of photovoltaic output power is achieved. When the weather changes suddenly, such as when clouds move rapidly to block sunlight, or when the sky changes from sunny to cloudy, the first simulated power can quickly reflect this change and be integrated with the measured power generation power to adjust the first comprehensive power. Based on the updated power generation power, the base station dynamically adjusts the charging and discharging strategy to ensure a more reasonable battery charging and discharging process.

[0145] In other embodiments, in order to further optimize the discharge strategy, the method further includes the following steps:

[0146] The base station establishes a stable and efficient data transmission channel with multiple drones connected outside the base station. Each drone is equipped with a high-precision photosensitive module that can sense the light intensity of the surrounding environment in real time and convert it into corresponding electrical signals to generate photosensitive point data. Since multiple drones are distributed in different locations, they can collect light information from multiple angles and areas, avoiding the limitations of light data in a single location. For example, in a drone battery replacement scenario in a large industrial park, there are 5 drones performing tasks in different corners of the park. The photosensitive modules on each drone continuously collect light intensity data at their location and transmit this data back to the base station in a timely manner.

[0147] After receiving photosensitivity data from multiple drones, the base station combines it with the coordinate information of the drones to which each photosensitive module belongs, and uses advanced image processing and data analysis techniques to create a photosensitivity image. The drone's coordinate information can be obtained using a high-precision positioning system (such as GPS), ensuring that each photosensitivity data point accurately corresponds to its position in real space. By integrating and interpolating these discrete photosensitivity data points, a photosensitivity image is generated that intuitively reflects the light distribution around the base station. For example, in a drone-based crop protection operation in a field, the base station constructs a detailed image of the field's light intensity distribution based on the photosensitivity data and coordinate information provided by eight drones. Different colors in the image represent different light intensity levels.

[0148] Leveraging advanced image recognition technology and machine learning algorithms, the base station conducts in-depth analysis and processing of the established photosensitive image. The trained model can identify various elements and features within the image, thereby determining the base station's surrounding environment. For example, if the light intensity in most areas of the photosensitive image is high and evenly distributed, the current environment may be identified as sunny and open. However, if the image contains significant shadows and low light intensity, it may be determined to be obstructed by buildings or in a forested environment. In urban logistics and distribution scenarios, the base station can analyze the photosensitive image to identify the surrounding area as a densely populated urban area, where obstruction by different buildings can lead to complex light distribution.

[0149] From the identified surrounding environment information, the base station further extracts the second environment light intensity. This process is not just a simple numerical extraction, but also takes into account factors such as the overall light distribution of the environment and the changing trend of light intensity. By statistically analyzing the light intensity values ​​of each pixel in the photosensitive image and combining the characteristics of the environment type, a second environment light intensity that can represent the overall light conditions around the base station is calculated. For example, in a drone inspection scenario in a mountainous area, although the light intensity at different locations varies greatly, by analyzing the photosensitive image, the base station can comprehensively consider factors such as the orientation of the hillside and the obstruction of vegetation to accurately extract the second environment light intensity.

[0150] Based on the extracted second ambient light intensity, the base station calculates the second simulated power of the photovoltaic module by combining the characteristic parameters of the photovoltaic module and the power generation model. The power generation power of the photovoltaic module is closely related to the light intensity, and different photovoltaic modules have different power generation characteristic curves. The base station will use the corresponding mathematical model to calculate the simulated power generation power of the photovoltaic module under the current second ambient light intensity based on the specific parameters of the photovoltaic module used, such as maximum power point power, conversion efficiency, temperature coefficient, etc. For example, if the maximum power point power of a photovoltaic module under standard light intensity is known to be 400W, when the extracted second ambient light intensity is 70% of the standard light intensity, the second simulated power of the photovoltaic module calculated by the power generation model is 280W.

[0151] To more accurately estimate the actual generated power of the PV modules, the base station combines the second simulated power with the generated power acquired in real time by the power monitoring equipment to calculate the second integrated power. A weighted average method is typically used, assigning different weights to the second simulated power and generated power based on the actual situation. In relatively stable lighting conditions, the weight of generated power can be appropriately increased; in conditions with significant lighting fluctuations, the weight of the second simulated power can be increased. For example, the weight of the second simulated power can be set to 0.3 and the weight of generated power to 0.7. If the second simulated power is 280W and the generated power is 290W, the second integrated power is 280 × 0.3 + 290 × 0.7 = 287W.

[0152] The base station uses the calculated second integrated power to update the generated power. This updated generated power serves as an important basis for subsequent operations such as adjusting the discharge power of the battery to be discharged and formulating charging and discharging strategies, enabling the base station to more accurately manage energy based on actual photovoltaic power generation conditions.

[0153] Collecting lighting information from multiple locations provides more comprehensive and accurate data on ambient light intensity. Compared to traditional single-location lighting monitoring methods, the deployment of multiple drone photosensitive modules can cover a larger area, capturing differences and variations in lighting across different locations. For example, at a large event, surrounding buildings, trees, and other structures can create complex light obstructions and reflections. By using multiple drones distributed across different locations to collect lighting information, a more comprehensive understanding of the lighting conditions at the entire event site can be achieved, avoiding errors in power generation estimation caused by localized lighting anomalies.

[0154] Collecting light information from multiple locations provides more comprehensive and accurate ambient light intensity data. Compared to traditional single-location light monitoring methods, the deployment of multiple drone photosensitive modules can cover a larger area, capturing differences and variations in light intensity across different locations. For example, at a large event, surrounding buildings, trees, and other structures can create complex light obstructions and reflections. By distributing multiple drones to collect light information at different locations, a more comprehensive understanding of the entire event site's lighting conditions can be achieved, avoiding errors in power generation estimation caused by localized light anomalies. The second simulated power calculated based on the more accurate second ambient light intensity, combined with the second integrated power calculated from the generated power, enables a more accurate assessment of the photovoltaic module's power generation capacity. This allows the base station to more clearly understand the actual power generation level of the photovoltaic module, providing a reliable basis for subsequent charging strategy adjustments. For example, in a drone monitoring scenario on an island, light intensity fluctuates frequently and complexly due to the influence of marine climate. Light information collected by multiple drone photosensitive modules can more accurately assess the power generation capacity of the photovoltaic module under different lighting conditions, avoiding battery overcharging or over-discharging problems caused by inaccurate power generation capacity assessments.

[0155] An embodiment of the present application also discloses a system for automatically replacing batteries in a drone base station, including a processor that executes the steps of any of the above-described methods for automatically replacing batteries in a drone base station.

[0156] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for automatically replacing batteries in a drone base station, characterized in that: The steps include: The base station establishes a connection with the corresponding drone; Marking the battery removed from the drone in the base station as a battery to be discharged; discharging the battery to be discharged, and after the voltage after discharge drops to a set first voltage value, changing the mark of the battery to be discharged to a battery to be charged, and charging the battery to be charged; Calculating the charging capacity required for all the batteries to be charged; If the charge amount is less than a preset first amount of charge, the photovoltaic module is used to charge the batteries to be charged; otherwise, the voltage of the batteries to be discharged is controlled to be boosted and then connected in parallel with the photovoltaic module to charge a first preset number of the batteries to be charged, and the photovoltaic module is controlled to charge the remaining batteries to be charged; Obtain the battery exchange distance between the UAV and the base station, and calculate the sum of all the battery exchange distances as the total distance; adjust the first preset number in anti-correlation according to the total distance, the longer the total distance, the smaller the first preset number, and the shorter the total distance, the larger the first preset number.

2. The method for automatically replacing batteries in a drone base station according to claim 1, characterized in that: The method further comprises the steps of: Obtaining the power generated by the photovoltaic module; According to the inverse correlation of the generated power, the total discharge power of the battery to be discharged is adjusted, the greater the generated power, the smaller the total discharge power, and the smaller the generated power, the greater the total discharge power; The total discharge power is controlled by adjusting the output current and the number of discharges of the battery to be discharged. The greater the output current of the battery to be discharged, the greater the total discharge power, and the smaller the output current of the battery to be discharged, the smaller the total discharge power. The greater the number of discharges of the battery to be discharged, the greater the total discharge power, and the smaller the number of discharges of the battery to be discharged, the smaller the total discharge power. Calculate the average voltage of all the batteries to be discharged, and adjust the output current in a positive correlation and the discharge quantity in an anti-correlation according to the voltage average; the larger the average value, the larger the output current and the smaller the discharge quantity; the smaller the average value, the smaller the output current and the larger the discharge quantity.

3. The method for automatically replacing batteries in a drone base station according to claim 1, characterized in that: The method further comprises the steps of: Obtain the number of battery swaps and the corresponding amount of battery swaps for the drone outside the base station; Calculate the total amount of battery replacement based on the number of battery replacements and the amount of battery replacement; The first power is adjusted in positive correlation with the total power of battery exchange. The greater the total power of battery exchange, the greater the first power, and the smaller the total power of battery exchange, the smaller the first power.

4. The method for automatically replacing batteries in a drone base station according to claim 1, characterized in that: The method further comprises the steps of: Marking the UAV whose battery swap distance is less than a preset reference distance value as an active point; Counting the number of active points as the active number; The boosted secondary voltage of the battery to be discharged is adjusted according to the active number. The more active numbers there are, the higher the secondary voltage is, and the fewer active numbers there are, the lower the secondary voltage is. The secondary voltage is not higher than the maximum charging voltage of the battery to be charged.

5. The method for automatically replacing batteries in a drone base station according to claim 4, characterized in that: The method further comprises the steps of: If the active number is less than the second preset number, the sum of the exchange amounts of all the active points is calculated as the local power; If the local power level is less than the preset reference area power level, screening out the to-be-discharged batteries having a voltage higher than a preset second voltage value from the base station; From the screened batteries to be discharged, a third preset number of batteries are screened according to their voltages and their labels are modified to be the batteries to be charged.

6. The method for automatically replacing batteries in a drone base station according to claim 5, characterized in that: The method further comprises the steps of: The third preset number is adjusted inversely according to the number of active users, wherein the greater the number of active users, the smaller the third preset number, and the smaller the number of active users, the larger the third preset number; The second voltage value is adjusted according to the anti-correlation of the local electric quantity. The larger the local electric quantity is, the lower the second voltage value is; and the smaller the local electric quantity is, the higher the second voltage value is.

7. The method for automatically replacing batteries in a drone base station according to claim 2, characterized in that: The method further comprises the steps of: Acquiring an environmental image based on a camera module on the drone connected to the outside of the base station; identifying the surrounding environment of the base station from the environmental image; Extracting a first ambient light intensity of the surrounding environment; Calculating a first simulated power of the photovoltaic module according to the first ambient light intensity; Calculating a first integrated power according to the first simulated power and the generated power; The generated power is updated using the first integrated power.

8. The method for automatically replacing batteries in a drone base station according to claim 2, characterized in that: The method further comprises the steps of: Acquiring photosensitive point data based on photosensitive modules on a plurality of drones connected to the base station; Establishing a photosensitive image based on the coordinates of the drone to which the photosensitive module belongs and the photosensitive point data; identifying the surrounding environment of the base station from the photosensitive image; Extracting a second ambient light intensity of the surrounding environment; Calculating a second simulated power of the photovoltaic module according to the second ambient light intensity; Calculating a second integrated power according to the second simulated power and the generated power; The generated power is updated using the second integrated power.

9. A system for automatically replacing batteries in a drone base station, characterized in that: The method comprises a processor, wherein the processor executes the steps of the method for automatically replacing the battery of the drone base station as described in any one of claims 1 to 8.

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