Large cluster unmanned aerial vehicle management system

Abstract

The invention discloses a large-cluster unmanned aerial vehicle management system, relates to the technical field of unmanned aerial vehicle management, and solves the problem that although a large-scale unmanned aerial vehicle management system in the prior art can realize cooperative control of multiple unmanned aerial vehicles to a certain extent, the large-scale unmanned aerial vehicle management system still has obvious defects in the aspect of energy consumption management. According to the method, the charging station most suitable for the current electric quantity condition is dynamically selected by evaluating the relationship between the remaining electric quantity of the unmanned aerial vehicle and the return voyage consumption, so that the unmanned aerial vehicle can find a safe and reasonable charging point even if the electric quantity is insufficient, the electric quantity is not used up due to forced return voyage, and the charging efficiency is improved. And in the task process, the battery use condition of the unmanned aerial vehicle is dynamically allocated, the charging sequence and strategy are reasonably arranged, and the system automatically selects the optimal energy supplement scheme when the electric quantity of the unmanned aerial vehicle is insufficient, so that task failure or interruption caused by the electric quantity problem is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle management, and in particular to a large-cluster unmanned aerial vehicle management system. Background Art

[0002] As an advanced aircraft, drones have been widely used in recent years, especially in the fields of agriculture, logistics, security, geographical survey and disaster relief. Drones can perform tasks in many areas that humans cannot reach, and their advantages are obvious. However, most drones rely on batteries for power supply, and the energy density of batteries limits the endurance of drones. Generally speaking, the endurance of small drones is usually between 30 minutes and 1 hour, and even medium and large drones have a range of 2-3 hours. This limitation in endurance makes drones face severe challenges when performing long-term missions or need to cover large areas. Due to the limited battery capacity, endurance has become the main bottleneck for drones to perform tasks, especially when the battery cannot be replaced or charged during the mission, the effective operation time of the drone will be greatly limited.

[0003] Although the existing large-scale drone management system can achieve coordinated control of multiple drones to a certain extent, it still has obvious deficiencies in energy consumption management. First of all, the existing system usually manages the battery status of a single drone and monitors the power according to the preset mission plan. However, when it comes to multiple drones cooperating to perform tasks, especially in large-scale drone clusters, how to dynamically allocate the battery usage of drones during the mission and reasonably arrange the charging sequence and strategy has become a complex and important issue. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a large cluster of drone management systems, which solves the problems in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A large cluster of unmanned aerial vehicle management system, comprising:

[0006] The data collection module is used to collect the power information of each drone in the cluster and send the power information to the management center module;

[0007] The management center module is used to obtain the power information of each drone in the cluster in real time and monitor it. By comparing the power of each drone with the minimum power threshold, it determines the drones with insufficient power in the future;

[0008] The energy replenishment station determination module sequentially selects the drones whose power is insufficient as target drones, searches for and determines multiple backup energy replenishment stations according to the current position of the target drone, and obtains the distance sequence of the backup energy replenishment stations. Then, according to the charging status of the backup energy replenishment stations, the best backup energy replenishment station is determined, and the optimal backup sequence of the backup energy replenishment stations is obtained.

[0009] The energy replenishment station adjustment module is used to determine whether the current power of the target UAV can support the power consumption required to return to the best backup energy replenishment station. After the judgment result, the corresponding backup energy replenishment station is selected as the power replenishment location of the target UAV;

[0010] The emergency maintenance module is used to notify the maintenance personnel of the received alarm information, and the maintenance personnel will go to the landing site of the drone to recover and check for unexpected situations.

[0011] Preferably, in the data collection module, the method of collecting the power information of each drone is:

[0012] The drone monitors the battery power in real time through the built-in battery management system, and communicates with the management center module through the drone self-organizing network to send the drone's power to the management center module.

[0013] Preferably, in the management center module, the specific method of determining the subsequent drone with insufficient power is:

[0014] Set a minimum power threshold Vmin for each drone. Once the real-time power Vcurrent is lower than this threshold, the drone will be marked as low on power.

[0015] The management center module periodically obtains the current power Vcurrent of all drones in the cluster and compares it with the power threshold Vmin. The drones whose current power Vcurrent is lower than the power threshold Vmin are determined as the subsequent drones with insufficient power.

[0016] Preferably, in the energy replenishment station determination module, the specific method of finding and determining multiple backup energy replenishment stations is:

[0017] S1: Randomly select a drone from the drones whose subsequent power is insufficient as the target drone, and obtain the current position of the target drone:

[0018] Set the position of the target drone to (Xu, Yu, Zu), where Xu and Yu represent the projected position of the target drone on the ground: longitude and latitude, and Zu represents the flight height of the drone: altitude;

[0019] The location of a certain energy replenishment station on the ground is set to (Xs, Ys), where Xs and Ys represent the two-dimensional coordinates of the energy replenishment station on the ground: longitude and latitude;

[0020] The straight-line distance between the target UAV and the ground replenishment station is calculated using the following formula:

[0021]

[0022] Where d is the actual distance between the target UAV and the energy replenishment station;

[0023] S2: Determine whether the distance d is less than or equal to k kilometers, where k is a preset value;

[0024] For each energy replenishment station, calculate the straight-line distance d between the target UAV and it, and use the distance formula in S1 to calculate the actual distance through the current position (Xu, Yu, Zu) of the target UAV and the ground position of each energy replenishment station;

[0025] Energy replenishment stations with d less than or equal to k kilometers are selected, and those that meet the conditions are marked as backup energy replenishment stations. The backup energy replenishment stations are sorted in order from small to large according to the distance from the target UAV to determine the backup energy replenishment station distance sequence.

[0026] Preferably, in the energy replenishment station determination module, the best backup energy replenishment station is determined by:

[0027] P1: The appropriate recharge value of each standby energy replenishment station is determined by the following formula:

[0028]

[0029] Among them, S i A represents the appropriate charging value of the i-th backup energy replenishment station; i W is the number of available charging piles at the i-th backup energy replenishment station; i It is represented by the number of drones currently waiting to be charged at the i-th backup energy replenishment station; C i It is represented as the charging efficiency of the i-th backup energy supplement station; T i It is represented by the total number of charging piles at the ith backup energy replenishment station; α and β are weight coefficients, which are used to adjust the relative importance of different factors in appropriate charging. The specific values ​​are set by the staff, and: α+β=1;

[0030] P2: Through step P1, calculate the suitable charging value Si of each backup energy replenishment station, and sort them from large to small, determine the preferred backup sequence of the backup energy replenishment stations, and select the backup energy replenishment station with the largest suitable charging value as the best backup energy replenishment station.

[0031] Preferably, in the energy replenishment station adjustment module, the method for determining whether the current power can support the best standby energy replenishment station is:

[0032] AS1: Determine the current location of the target UAV (Xu, Yu, Zu) and the best backup energy replenishment station (Xc, Yc, Zc), where Zc = 0;

[0033] AS2: Calculate the horizontal displacement d between the target UAV and the optimal backup energy replenishment station using the following formula: 水平 and vertical displacement d 垂直 :

[0034]

[0035] d 垂直 =Zu

[0036] AS3: Then determine the power consumed by the target drone to reach the best backup energy replenishment station using the following formula:

[0037] G = γ(E horizontal ×d 水平 )+η(E vertical ×d 垂直 )×100%

[0038] Among them, E horizontal It is expressed as the unit power consumed by the target drone for each meter it moves, E vertical It is expressed as the unit power consumed by the target UAV for each rise or fall of one meter, and γ and η are weight coefficients;

[0039] AS4: Compare the result G with the remaining power of the target UAV. When the remaining power of the target UAV is greater than G, it means that the current power can support the best backup energy replenishment station. When the remaining power of the target UAV is less than G, it means that the current power cannot support the best backup energy replenishment station.

[0040] Preferably, in the energy replenishment station adjustment module, the method of selecting the corresponding standby energy replenishment station as the replenishment location of the target UAV is:

[0041] BS1: If the target drone has enough remaining power to return to the best backup energy refueling station, it will directly select the best backup energy refueling station to charge;

[0042] BS2: If the target UAV has insufficient remaining power to return to the best backup energy replenishment station, determine the specific position of the best backup energy replenishment station in the backup energy replenishment station distance sequence according to the backup energy replenishment station distance sequence, and select the backup energy replenishment station that is ranked before the best backup energy replenishment station;

[0043] BS3: Select the best energy replenishment station according to the order of the selected backup energy replenishment stations in the preferred backup sequence of the backup energy replenishment stations, and again go through the above steps AS1-AS4 to determine whether the current power of the target UAV can support the power consumption required to return to the best energy replenishment station;

[0044] BS4: If the target drone has enough remaining power to return to the optimal backup energy refueling station, it will directly select the optimal backup energy refueling station to charge;

[0045] If the remaining power of the target UAV is insufficient to support the return to the optimal backup energy replenishment station, repeat steps BS2-BS3 until it is determined that the remaining power of the target UAV is sufficient to support the return to the optimal backup energy replenishment station.

[0046] Preferably, the method further includes: if the energy replenishment station is not determined, indicating that an unexpected situation occurs to the target UAV, the target UAV sends a landing location and alarm information to a subsequent emergency module.

[0047] The present invention provides a large-scale cluster drone management system. Compared with the prior art, it has the following beneficial effects:

[0048] (1) The present invention uses a data acquisition module to monitor the power of all drones in the cluster in real time, and transmits the information to the management center module through the drone self-organizing network. This design ensures real-time monitoring of the power of drones, allowing the management center to promptly identify drones with insufficient power and take corresponding measures. By setting a minimum power threshold, the management center can intervene in advance when the power of a drone approaches the critical value and select an appropriate energy replenishment station for charging. This method reduces the risk of mission interruption due to power exhaustion, thereby improving the completion rate of the task.

[0049] (2) The present invention dynamically selects the charging station that best suits the current power status by evaluating the relationship between the remaining power of the drone and the consumption during return flight. This ensures that even when the power is low, the drone can find a safe and reasonable charging point and will not run out of power due to forced return flight. In addition, the battery usage of the drone is dynamically adjusted during the mission, and the charging sequence and strategy are reasonably arranged. When the drone is low on power, the system automatically selects the optimal energy replenishment plan, thereby avoiding mission failure or interruption due to power problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention will be further described below in conjunction with the accompanying drawings.

[0051] Figure 1 This is a structural framework diagram of a large cluster of drone management system of the present invention; DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Embodiment 1

[0054] See also Figure 1 ,The present invention provides a large cluster of unmanned aerial vehicle management system, including;

[0055] The data collection module is used to collect the power information of each drone in the cluster and send the power information to the management center module;

[0056] The method of collecting the power information of each drone is:

[0057] The drone monitors the battery power in real time through the built-in battery management system (BMS), and communicates with the management center module through the drone ad-hoc network to send the drone power to the management center module;

[0058] Among them, drone self-organizing network is a common way for direct communication between drones. In a large cluster of drone systems, self-organizing network can enable each drone to communicate with other nearby drones to form a dynamic, center-less network. This network can transmit information (such as power, location, etc.) to other drones and finally gather them to the management center module.

[0059] The management center module is used to obtain the power information of each drone in the cluster in real time and monitor it. By comparing the power of each drone with the minimum power threshold, it determines the drones with insufficient power in the future;

[0060] The specific method of determining the drone whose subsequent battery power is insufficient is:

[0061] Set a minimum power threshold Vmin for each drone (usually set to 15%-20%, the specific value is determined according to the mission requirements and drone model). Once the real-time power Vcurrent is lower than the threshold, the drone will be marked as low on power.

[0062] The management center module periodically obtains the current power Vcurrent of all drones in the cluster and compares it with the power threshold Vmin. The drones whose current power Vcurrent is lower than the power threshold Vmin are identified as the subsequent drones with insufficient power.

[0063] The energy replenishment station determination module sequentially selects the drones whose power is insufficient as target drones, searches for and determines multiple backup energy replenishment stations according to the current position of the target drone, and obtains the distance sequence of the backup energy replenishment stations. Then, according to the charging status of the backup energy replenishment stations, the best backup energy replenishment station is determined, and the optimal backup sequence of the backup energy replenishment stations is obtained.

[0064] The specific methods for finding and determining multiple backup energy replenishment stations are:

[0065] S1: Randomly select a drone from the drones whose subsequent power is insufficient as the target drone, and obtain the current position of the target drone:

[0066] Set the position of the target drone to (Xu, Yu, Zu), where Xu and Yu represent the projected position of the target drone on the ground: longitude and latitude, and Zu represents the flight height of the drone: altitude;

[0067] The location of a certain energy replenishment station on the ground is set to (Xs, Ys), where Xs and Ys represent the two-dimensional coordinates of the energy replenishment station on the ground: longitude and latitude;

[0068] The straight-line distance between the target UAV and the ground replenishment station is calculated using the following formula:

[0069]

[0070] Where d is the actual distance between the target UAV and the energy replenishment station;

[0071] S2: Determine whether the distance d is less than or equal to k kilometers, where k is a preset value, which is specifically set by the staff:

[0072] For each energy replenishment station, calculate the straight-line distance d between the target UAV and it, and use the distance formula in S1 to calculate the actual distance through the current position (Xu, Yu, Zu) of the target UAV and the ground position of each energy replenishment station;

[0073] Filter energy replenishment stations with distance d less than or equal to k kilometers, mark the energy replenishment stations that meet the conditions as backup energy replenishment stations, and sort the backup energy replenishment stations in order of distance from the target UAV from small to large to determine the backup energy replenishment station distance sequence;

[0074] The best backup energy replenishment station is determined by:

[0075] P1: The appropriate recharge value of each standby energy replenishment station is determined by the following formula:

[0076]

[0077] Among them, S i A represents the appropriate charging value of the i-th backup energy replenishment station; i W is the number of available charging piles at the i-th backup energy replenishment station; i It is represented by the number of drones currently waiting to be charged at the i-th backup energy replenishment station; C i It is represented as the charging efficiency of the i-th backup energy supplement station; T i It is represented by the total number of charging piles at the ith backup energy replenishment station; α and β are weight coefficients, which are used to adjust the relative importance of different factors in appropriate charging. The specific values ​​are set by the staff, and: α+β=1;

[0078] P2: Through step P1, calculate the suitable charging value Si of each backup energy replenishment station, and sort them from large to small, determine the preferred backup sequence of the backup energy replenishment stations, and select the backup energy replenishment station with the largest suitable charging value as the best backup energy replenishment station;

[0079] The data acquisition module monitors the power of all drones in the cluster in real time, and transmits the information to the management center module through the drone self-organizing network. This design ensures real-time monitoring of the drone power, allowing the management center to promptly identify drones with insufficient power and take corresponding measures. By setting the minimum power threshold, the management center can intervene in advance when the drone power is close to the critical value and select an appropriate energy replenishment station for charging. This method reduces the risk of mission interruption due to power exhaustion, thereby improving the completion rate of the mission.

[0080] The energy replenishment station adjustment module is used to determine whether the current power of the target UAV can support the power consumption required to return to the best backup energy replenishment station. After the judgment result, the corresponding backup energy replenishment station is selected as the power replenishment location of the target UAV;

[0081] The method to determine whether the current power can support the best backup energy replenishment station is:

[0082] AS1: Determine the current location of the target UAV (Xu, Yu, Zu) and the best backup energy replenishment station (Xc, Yc, Zc), where Zc = 0;

[0083] AS2: Calculate the horizontal displacement d between the target UAV and the optimal backup energy replenishment station using the following formula:水平 and vertical displacement d 垂直 :

[0084]

[0085] d 垂直 =Zu

[0086] AS3: Then determine the power consumed by the target drone to reach the best backup energy replenishment station using the following formula:

[0087] G = γ(E horizontal ×d 水平 )+η(E vertical ×d 垂直 )×100%

[0088] Among them, E horizontal It is expressed as the unit power consumed by the target drone for each meter it moves, E vertical It is expressed as the unit power consumed by the target UAV for each rise or fall of one meter. γ and η are weight coefficients, which represent the environmental impact in the horizontal and vertical directions.

[0089] AS4: Compare the result G with the remaining power of the target drone. When the remaining power of the target drone is greater than G, it means that the current power can support the best backup energy replenishment station. When the remaining power of the target drone is less than G, it means that the current power cannot support the best backup energy replenishment station.

[0090] Embodiment 2

[0091] In the specific implementation process of this embodiment, based on the first embodiment, this embodiment further specifically includes the following in the energy replenishment station adjustment module:

[0092] The method of selecting the corresponding backup energy replenishment station as the replenishment location of the target drone is:

[0093] BS1: If the target drone has enough remaining power to return to the best backup energy refueling station, it will directly select the best backup energy refueling station to charge;

[0094] BS2: If the target UAV has insufficient remaining power to return to the best backup energy replenishment station, determine the specific position of the best backup energy replenishment station in the backup energy replenishment station distance sequence according to the backup energy replenishment station distance sequence, and select the backup energy replenishment station that is ranked before the best backup energy replenishment station;

[0095] BS3: Select the best energy replenishment station according to the order of the selected backup energy replenishment stations in the backup energy replenishment station preferred backup sequence, and go through the above steps AS1-AS4 again (the difference is that the position of the best backup energy replenishment station in the steps is changed to the position of the best energy replenishment station), to determine whether the current power of the target UAV can support the power consumption required to return to the best energy replenishment station;

[0096] BS4: If the target drone has enough remaining power to return to the optimal backup energy refueling station, it will directly select the optimal backup energy refueling station to charge;

[0097] If the target UAV has insufficient remaining power to return to the optimal backup energy replenishment station, repeat steps BS2-BS3 until it is determined that the target UAV has sufficient remaining power to return to the optimal backup energy replenishment station;

[0098] If the energy replenishment station is not determined, it indicates that the target UAV has encountered an unexpected situation, and the target UAV sends the landing site location and alarm information to the subsequent emergency module;

[0099] By evaluating the relationship between the remaining power of the drone and the consumption of returning to the destination, the most suitable charging station for the current power status is dynamically selected. This method ensures that even when the power is low, the drone can find a safe and reasonable charging point, and will not run out of power due to forced return. In addition, the battery usage of the drone is dynamically allocated during the mission, and the charging sequence and strategy are reasonably arranged. When the drone is low on power, the system automatically selects the optimal energy replenishment plan, thereby avoiding mission failure or interruption due to power problems;

[0100] The emergency maintenance module is used to notify the maintenance personnel of the received alarm information, and the maintenance personnel will go to the landing site of the drone to recover and check for unexpected situations;

[0101] Embodiment 3

[0102] The specific implementation process of this embodiment includes the entire implementation process of the above two groups of embodiments.

[0103] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0104] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A large-cluster drone management system, characterized in that: include: The data collection module is used to collect the power information of each drone in the cluster and send the power information to the management center module; The management center module is used to obtain the power information of each drone in the cluster in real time and monitor it. By comparing the power of each drone with the minimum power threshold, it determines the drones with insufficient power in the future; The energy replenishment station determination module sequentially selects the drones whose power is insufficient as target drones, searches for and determines multiple backup energy replenishment stations according to the current position of the target drone, and obtains the distance sequence of the backup energy replenishment stations. Then, according to the charging status of the backup energy replenishment stations, the best backup energy replenishment station is determined, and the optimal backup sequence of the backup energy replenishment stations is obtained. The energy replenishment station adjustment module is used to determine whether the current power of the target UAV can support the power consumption required to return to the best backup energy replenishment station. After the judgment result, the corresponding backup energy replenishment station is selected as the power replenishment location of the target UAV; The emergency maintenance module is used to notify the maintenance personnel of the received alarm information, and the maintenance personnel will go to the landing site of the drone to recover and check for unexpected situations.

2. A large-cluster UAV management system according to claim 1, characterized in that: In the data collection module, the method of collecting the power information of each drone is as follows: The drone monitors the battery power in real time through the built-in battery management system, and communicates with the management center module through the drone self-organizing network to send the drone's power to the management center module.

3. A large-cluster UAV management system according to claim 2, characterized in that: In the management center module, the specific method of determining the drone with insufficient battery power is as follows: Set a minimum power threshold Vmin for each drone. Once the real-time power Vcurrent is lower than this threshold, the drone will be marked as low on power. The management center module periodically obtains the current power Vcurrent of all drones in the cluster and compares it with the power threshold Vmin. The drones whose current power Vcurrent is lower than the power threshold Vmin are determined as the subsequent drones with insufficient power.

4. A large-cluster UAV management system according to claim 1, characterized in that: In the energy replenishment station determination module, the specific method of finding and determining multiple backup energy replenishment stations is: S1: Randomly select a drone from the drones whose subsequent power is insufficient as the target drone, and obtain the current position of the target drone: Set the position of the target drone to (Xu, Yu, Zu), where Xu and Yu represent the projected position of the target drone on the ground: longitude and latitude, and Zu represents the flight height of the drone: altitude; The location of a certain energy replenishment station on the ground is set to (Xs, Ys), where Xs and Ys represent the two-dimensional coordinates of the energy replenishment station on the ground: longitude and latitude; The straight-line distance between the target UAV and the ground replenishment station is calculated using the following formula: Where d is the actual distance between the target UAV and the energy replenishment station; S2: Determine whether the distance d is less than or equal to k kilometers, where k is a preset value; For each energy replenishment station, calculate the straight-line distance d between the target UAV and it, and use the distance formula in S1 to calculate the actual distance through the current position (Xu, Yu, Zu) of the target UAV and the ground position of each energy replenishment station; Energy replenishment stations with d less than or equal to k kilometers are selected, and those that meet the conditions are marked as backup energy replenishment stations. The backup energy replenishment stations are sorted in order from small to large according to the distance from the target UAV to determine the backup energy replenishment station distance sequence.

5. A large-cluster UAV management system according to claim 4, characterized in that: In the energy replenishment station determination module, the best backup energy replenishment station is determined as follows: P1: The appropriate recharge value of each standby energy replenishment station is determined by the following formula: Among them, S i A represents the appropriate charging value of the i-th backup energy replenishment station; i W is the number of available charging piles at the ith backup energy replenishment station; i It is represented by the number of drones currently waiting to be charged at the i-th backup energy replenishment station; C i It is represented as the charging efficiency of the i-th backup energy replenishment station; T i It is represented by the total number of charging piles at the i-th backup energy replenishment station; α and β are weight coefficients, and: α+β=1; P2: Through step P1, calculate the suitable charging value Si of each backup energy replenishment station, and sort them from large to small, determine the preferred backup sequence of the backup energy replenishment stations, and select the backup energy replenishment station with the largest suitable charging value as the best backup energy replenishment station.

6. A large-cluster UAV management system according to claim 1, characterized in that: In the energy replenishment station adjustment module, the method for judging whether the current power can support the best backup energy replenishment station is: AS1: Determine the current location of the target UAV (Xu, Yu, Zu) and the best backup energy replenishment station (Xc, Yc, Zc), where Zc = 0; AS2: Calculate the horizontal displacement d between the target UAV and the optimal backup energy replenishment station using the following formula: 水平 and vertical displacement d 垂直 : the 垂直 =To AS3: Then determine the power consumed by the target drone to reach the best backup energy replenishment station using the following formula: G=γ(E horizontal ×d 水平 )+η(E vertical ×d 垂直 )×100% Among them, E horizontal It is expressed as the unit power consumed by the target drone for each meter it moves, E vertical It is expressed as the unit power consumed by the target UAV for each rise or fall of one meter, and γ and η are weight coefficients; AS4: Compare the result G with the remaining power of the target UAV. When the remaining power of the target UAV is greater than G, it means that the current power can support the best backup energy replenishment station. When the remaining power of the target UAV is less than G, it means that the current power cannot support the best backup energy replenishment station.

7. A large-cluster UAV management system according to claim 6, characterized in that: In the energy replenishment station adjustment module, the method of selecting the corresponding standby energy replenishment station as the replenishment location of the target UAV is: BS1: If the target drone has enough remaining power to return to the best backup energy refueling station, it will directly select the best backup energy refueling station to charge; BS2: If the target UAV has insufficient remaining power to return to the best backup energy replenishment station, determine the specific position of the best backup energy replenishment station in the backup energy replenishment station distance sequence according to the backup energy replenishment station distance sequence, and select the backup energy replenishment station that is ranked before the best backup energy replenishment station; BS3: Select the best energy replenishment station according to the order of the selected backup energy replenishment stations in the preferred backup sequence of the backup energy replenishment stations, and again go through the above steps AS1-AS4 to determine whether the current power of the target UAV can support the power consumption required to return to the best energy replenishment station; BS4: If the target drone has enough remaining power to return to the optimal backup energy refueling station, it will directly select the optimal backup energy refueling station to charge; If the remaining power of the target UAV is insufficient to support the return to the optimal backup energy replenishment station, repeat steps BS2-BS3 until it is determined that the remaining power of the target UAV is sufficient to support the return to the optimal backup energy replenishment station.

8. A large-cluster UAV management system according to claim 7, characterized in that: Also includes: If the energy replenishment station is not determined, it indicates that an unexpected situation has occurred in the target UAV, and the target UAV sends the landing site location and alarm information to the subsequent emergency module.

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