An unmanned aerial vehicle adaptive scheduling and management system based on a nest

Abstract

The application relates to the field of unmanned planes, in particular to an unmanned plane adaptive scheduling and management system based on a machine nest, which comprises a data acquisition module, a central processing module, a scheduling module, an unmanned plane management module and a user interaction module; the data acquisition module is used for collecting operation information of each unmanned plane and working environment information where the unmanned plane is located; the central processing module is used for processing the information collected by the data acquisition module and judging whether the unmanned plane meets a recall condition by calculating relevant indexes; the scheduling module is used for controlling the unmanned plane meeting the recall condition to return to the machine nest; the unmanned plane management module is used for managing the unmanned plane returning to the machine nest; and the user interaction module is used for realizing the interaction between the system and the user. The collected information is processed by the central processing module, and environment indexes and working task indexes are set, so that whether the unmanned plane needs to be recalled can be judged, and the unmanned plane can be protected.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicles (UAVs), and more particularly to a UAV adaptive scheduling and management system based on a hive. Background Technology

[0002] To cope with ever-changing task requirements and environmental conditions, adaptive scheduling technology has emerged, which can dynamically adjust task priorities and allocation strategies based on real-time data.

[0003] For example, the prior art disclosed in CN117744994A is a method for allocating and scheduling inspection drones and their nests based on the tunic algorithm, belonging to the field of drone power line inspection technology. The steps are as follows: determine the objective function for allocating and scheduling inspection drones and their nests based on the flight cost and maintenance time of the inspection drones; determine the constraints of the objective function; establish a multi-drone-multi-nest allocation and scheduling model; construct a master-slave heterogeneous thinking evolution mechanism based on the ability division of the population leader group; and design a local optimum prevention mechanism based on a collaborative learning strategy using the principle of human brainstorming.

[0004] Another typical example is the prior art disclosed in CN117657506A, which discloses an active calibration wireless charging drone nest and charging scheduling method. It relates to the field of drone nest charging technology, including a nest, a charging platform arranged on the top surface of the nest, and an energy transmitting device installed at the center of the charging platform. The energy transmitting device can wirelessly charge the energy receiving device on the drone.

[0005] Let's look at an existing technology, such as CN117369522A, which discloses a remote control method and system for unmanned aerial vehicle (UAV) nests. This includes: a data preprocessing module for acquiring the UAV's inspection route and UAV nests; filtering out transit nests and initial nests; a real-time monitoring module for acquiring the UAV's charging completion time; and issuing a scheduling command when the remaining charge is 0; an external statistics module for calculating the UAV's estimated arrival time; an internal statistics module for acquiring the UAV's estimated arrival time and generating a UAV nest entry sequence; acquiring the time required for the UAV to complete charging and generating a UAV departure set; acquiring the minimum charge required for the UAV to reach the next UAV nest and generating a UAV demand set; and a command generation module that starts detecting from the first item in the UAV nest entry sequence, generating scheduling commands and updating the set.

[0006] Currently, existing drone scheduling and management systems have low adaptability and insufficient ability to respond to emergencies. In order to solve the common problems in this field, this invention was made. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of current systems by proposing a nest-based adaptive scheduling and management system for unmanned aerial vehicles (UAVs).

[0008] To overcome the shortcomings of the prior art, the present invention adopts the following technical solution:

[0009] A nest-based adaptive scheduling and management system for unmanned aerial vehicles (UAVs) includes a data acquisition module, a central processing module, a scheduling module, a UAV management module, a user interaction module, and a ground communication station. The data acquisition module collects relevant information from each UAV. The ground communication station includes an information acquisition unit that collects environmental information about the ground communication station. The central processing module processes the information collected by the data acquisition module and the environmental information collected by the information acquisition unit, and calculates relevant indicators based on the processed data to determine whether a UAV meets recall conditions. The scheduling module controls UAVs that meet the recall conditions to return to the nest. The UAV management module manages UAVs that have returned to the nest. The user interaction module enables interaction between the system and the user.

[0010] The relevant information collected by the data acquisition module includes electricity consumption, inspection distance, and wind force encountered by the drone; the environmental information collected by the information acquisition unit includes rainfall and wind force at the location of the ground communication station.

[0011] The calculation of relevant indicators to determine whether the drone meets the recall conditions includes: first, determining whether the environmental indicators of the work area are qualified; if not, the drone is recalled immediately; otherwise, determining whether the work task indicators representing the drone's ability to return to the nest are qualified; if not, the drone is recalled immediately; if qualified, then determining whether the work task indicators representing the drone's ability to complete the remaining work tasks are qualified; if not, the drone is recalled immediately, otherwise, it is not recalled.

[0012] Furthermore, the data acquisition module includes a communication unit and sensors installed on the UAV. The communication unit is installed on the UAV, the UAV nest, and the ground communication station. The sensors are used to collect relevant information about the UAV itself and the working environment of the UAV. The communication unit is used to send the information collected by the sensors and the information collected by the data acquisition unit to the central processing module.

[0013] Furthermore, the central processing module includes a data processing unit, a preprocessing unit, a calculation unit, and a judgment unit. The data processing unit is used to process and classify the data collected by the data acquisition module. The preprocessing unit is used to preprocess the data processed by the data processing unit, including noise reduction, etc. The calculation unit is used to calculate various indicator values ​​based on the preprocessed data. The judgment unit is used to determine whether the drone meets the recall conditions based on the various indicator values.

[0014] Furthermore, the scheduling module includes a recall signal generation unit, a route planning unit, and a management signal generation unit. The route planning unit is used to plan the return route of the UAV. The recall signal generation unit is used to generate a recall signal based on the judgment result of the judgment unit and the route planned by the route planning unit. After receiving the recall signal, the UAV will return to the nest. The management signal generation unit is used to generate a management signal for the UAV returning to the nest and send the management signal to the UAV management module.

[0015] Furthermore, the drone management module includes a parking space, a work command generation unit, a guidance unit, and a maintenance unit. The parking space is used to park drones, the maintenance unit is used to charge and maintain the drones in the parking space, the work command generation unit is used to generate work commands and send them to the drones in the parking space, and the guidance unit is used to guide drones that have entered the drone hangar to land in the designated parking space.

[0016] Furthermore, the workflow of the UAV adaptive scheduling and management system includes the following steps:

[0017] S1, the data acquisition module collects relevant information about the UAV; the ground communication station's information acquisition unit collects environmental information about the ground communication station.

[0018] S2, the central processing module determines whether the drone meets the recall conditions. If not, the drone continues to work and the judgment ends; otherwise, it proceeds to the next step.

[0019] S3, the scheduling module recalls the drone, and the drone returns to the nest;

[0020] S4, the drone management module, manages drones that return to their nests.

[0021] Furthermore, the central processing module determines whether the drone meets the recall criteria by including the following steps:

[0022] S31, The data processing unit processes and classifies the data collected by the data acquisition module and the information acquisition unit;

[0023] S32, The preprocessing unit preprocesses the data processed by the data processing unit;

[0024] S33, The calculation unit calculates environmental indicators and work task indicators based on the preprocessed data;

[0025] S34, the judgment unit judges whether both environmental indicators and work task indicators are qualified. If both are qualified, the recall conditions are not met; otherwise, the recall conditions are met.

[0026] Furthermore, the drone management module manages drones returning to the nest by including the following steps:

[0027] S41, the guidance unit guides the UAV to land at the parking position by communicating with the UAV after it enters the nest;

[0028] S42, the maintenance unit performs maintenance on the drone and sends relevant information such as photos of the drone and the remaining battery power to the user interaction module;

[0029] S43, users can learn about drone-related information through the user interaction module and input new work tasks according to their own needs;

[0030] S44, The work command generation unit generates work commands based on the work task and sends them to the corresponding UAV;

[0031] After maintenance is completed, the S45 drone will execute new tasks according to work orders.

[0032] The beneficial effects achieved by this invention are: 1. By processing the collected information through the central processing module and setting environmental and task indicators, it is helpful to determine whether the drone needs to be recalled, avoid the drone working in harsh environments or the drone having insufficient power to complete the task, protect the drone, and improve the digitalization of the system.

[0033] 2. Managing drones through a drone management module and repairing drones through a maintenance unit facilitates drone maintenance and extends drone lifespan. Attached Figure Description

[0034] The invention will be further understood from the following description taken in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but rather the emphasis is on illustrating the principles of the embodiments. In different views, the same reference numerals designate the same parts.

[0035] Figure 1 This is a schematic diagram of the structure of the present invention.

[0036] Figure 2 This is a flowchart of the process of the present invention.

[0037] Figure 3 This is a flowchart illustrating how the central processing module of the present invention determines whether a drone meets the recall conditions.

[0038] Figure 4 This is a flowchart illustrating how the drone management module of the present invention manages drones returning to their nests. Detailed Implementation

[0039] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated beforehand. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0040] Example 1: According to Figure 1 , Figure 2 , Figure 3 and Figure 4 This embodiment provides a nest-based adaptive scheduling and management system for unmanned aerial vehicles (UAVs), including a data acquisition module, a central processing module, a scheduling module, a UAV management module, and a user interaction module. The data acquisition module is used to collect relevant information of each UAV. The ground communication station includes an information acquisition unit, which is used to collect environmental information of the ground communication station. The central processing module is used to process the information collected by the data acquisition module and the environmental information collected by the information acquisition unit, and to determine whether the UAV meets the recall conditions based on the processed data and relevant indicators. The scheduling module is used to control UAVs that meet the recall conditions to return to the nest. The UAV management module is used to manage the UAVs that have returned to the UAV nest. The user interaction module is used to realize the interaction between the system and the user.

[0041] Furthermore, the data acquisition module includes a communication unit and multiple sensors mounted on the UAV. The communication unit is located at the UAV, its nest, and a ground communication station near the UAV's operating position. The sensors collect information about the UAV itself and its surrounding environment. The ground communication station includes multiple information acquisition units that collect environmental information about the station. The communication unit transmits the information collected by the sensors and the information acquisition units to the central processing module. The information acquisition units collect environmental information from the ground communication station, and the data acquisition module collects environmental information from the UAV.

[0042] Furthermore, the central processing module includes a data processing unit, a preprocessing unit, a calculation unit, and a judgment unit. The data processing unit is used to process and classify the data collected by the data acquisition module. The preprocessing unit is used to preprocess the data processed by the data processing unit, including noise reduction, etc. The calculation unit is used to calculate various indicator values ​​based on the preprocessed data. The judgment unit is used to determine whether the drone meets the recall conditions based on the various indicator values.

[0043] Furthermore, the scheduling module includes a recall signal generation unit, a route planning unit, and a management signal generation unit. The route planning unit is used to plan the return route of the UAV. The recall signal generation unit is used to generate a recall signal based on the judgment result of the judgment unit and the route planned by the route planning unit. After receiving the recall signal, the UAV will return to the nest. The management signal generation unit is used to generate a management signal for the UAV returning to the nest and send the management signal to the UAV management module.

[0044] Specifically, the return route of the drone can be the same as the departure route.

[0045] Furthermore, the drone management module includes a parking space, a work command generation unit, a guidance unit, and a maintenance unit. The parking space is used to park drones, the maintenance unit is used to charge and maintain the drones in the parking space, the work command generation unit is used to generate work commands and send them to the drones in the parking space, and the guidance unit is used to guide drones that have entered the drone hangar to land in the designated parking space.

[0046] Specifically, after receiving a work order, the drone will leave its nest and perform the corresponding task according to the order.

[0047] Furthermore, the workflow of the UAV adaptive scheduling and management system includes the following steps:

[0048] S1, the data acquisition module collects relevant information about the drone and the working environment around the drone;

[0049] S2, the central processing module determines whether the drone meets the recall conditions. If not, the drone continues to work and the judgment ends; otherwise, it proceeds to the next step.

[0050] S3, the scheduling module recalls the drone, and the drone returns to the nest;

[0051] S4, the drone management module, manages drones that return to their nests.

[0052] Specifically, the management results include information such as the drone's remaining battery power, the drone's model, and a photo of the drone.

[0053] Furthermore, the central processing module determines whether a drone meets the recall criteria by including the following steps:

[0054] S31, The data processing unit processes and classifies the data collected by the data acquisition module and the information acquisition unit;

[0055] S32, The preprocessing unit preprocesses the data processed by the data processing unit;

[0056] S33, The calculation unit calculates environmental indicators and work task indicators based on the preprocessed data;

[0057] Specifically, environmental indicators can be calculated using the following formula:

[0058]

[0059] Where ZB1 represents the current environmental indicator, A represents the number of detection cycles (the number of which is set by those skilled in the art), T represents the detection duration of each detection cycle, and rain a (t) represents the total rainfall detected by the UAV in the a-th detection cycle, and α is the rainfall threshold. a (t) represents the maximum forward wind force experienced by the UAV during the a-th detection cycle, wind2 a (t) represents the maximum rear wind force experienced by the UAV during the a-th detection cycle, wind3 a (t) represents the maximum left-side wind force experienced by the UAV during the a-th detection cycle, wind4 a (t) represents the maximum right-side wind force experienced by the UAV in the a-th detection cycle, β is the longitudinal wind force threshold, and γ is the lateral wind force threshold.

[0060] ZB2 is a future environmental indicator, Rain a (t) represents the total rainfall detected by the next ground communication station along the UAV's inspection path during the a-th detection cycle. a (t) represents the maximum wind force detected by the next ground communication station that the UAV will pass through in the inspection path during the a-th inspection cycle;

[0061] ZB3 is an environmental indicator, and D is the distance between the drone and the next ground communication station it will pass through in the drone's inspection path.

[0062] Specifically, the rainfall threshold, longitudinal wind threshold, and lateral wind threshold are set by those skilled in the art during the performance testing of the UAV by judging the rainfall and wind force when the UAV is deflected; when the environmental indicators are less than the environmental indicator thresholds, the environmental indicators are considered to be qualified.

[0063] Specifically, work task indicators can be calculated using the following formula:

[0064]

[0065]

[0066] Among them, ZB4 is the first task indicator, used to characterize the UAV's ability to complete the remaining tasks. The higher the indicator value, the stronger the capability. DIS a Let I be the distance traveled by the drone in the a-th cycle. a DIS represents the amount of electricity used by the drone in the a-th cycle. save The remaining inspection route for the UAV includes the distance required to traverse all remaining inspection targets and the return route after traversing all remaining inspection targets. save This refers to the remaining battery power of the drone.

[0067] ZB5 is the second operational performance indicator, used to characterize the drone's ability to return to its nest. A higher value indicates a stronger capability. (DIS) BACK I represents the distance the drone travels from its current location back to its nest. save B represents the remaining battery power of the drone, and B represents the warning battery power value of the drone. This warning battery power value is set by those skilled in the art based on the remaining lifespan of the drone battery. The smaller the remaining lifespan, the larger this value. The battery power of the drone should not be lower than this value during operation to avoid excessive battery wear.

[0068] Specifically, when the second task indicator is less than or equal to the task indicator threshold, the drone's remaining battery power may not be able to support the drone's return to the nest. In this case, even if the drone completes the task, it will not be able to return after completing the task. At this time, the task indicator is unqualified and the drone must be recalled immediately. When the second task indicator is greater than the task indicator threshold, it is determined whether the first task indicator is less than the task indicator threshold. If so, the task indicator is deemed unqualified and the drone's remaining battery power is insufficient to complete the remaining task. Otherwise, it is qualified.

[0069] S34, the judgment unit judges whether both environmental indicators and work task indicators are qualified. If both are qualified, the recall conditions are not met; otherwise, the recall conditions are met.

[0070] Specifically, the environmental indicator threshold can be obtained by those skilled in the art during the performance testing of the UAV, by calculating the current environmental indicators of the UAV under extreme conditions (barely maintaining normal working status), and the work task indicator threshold can be 0.

[0071] Furthermore, the drone management module manages drones returning to their nests through the following steps:

[0072] S41, the guidance unit guides the UAV to land at the parking position by communicating with the UAV after it enters the nest;

[0073] S42, the maintenance unit performs maintenance on the drone and sends relevant information such as photos of the drone and the remaining battery power to the user interaction module;

[0074] S43, users can learn about drone-related information through the user interaction module and input new work tasks according to their own needs;

[0075] S44, The work command generation unit generates work commands based on the work task and sends them to the corresponding UAV;

[0076] After maintenance is completed, the S45 drone will execute new tasks according to work orders.

[0077] The beneficial effects of this solution are: 1. By processing the collected information through the central processing module and setting environmental and task indicators, it is helpful to determine whether the drone needs to be recalled, avoid the drone working in harsh environments or when the drone's power is insufficient to complete the task, protect the drone, and improve the digitalization of the system.

[0078] 2. Managing drones through a drone management module and repairing drones through a maintenance unit facilitates drone maintenance and extends drone lifespan.

[0079] Example 2: This example should be understood as including all the features of any of the foregoing examples, and further improving upon them. It also includes a method for a route planning unit to plan the return route of a UAV, comprising the following steps:

[0080] STEP1: Generate a 2D map and mark the coordinates of the drone, the drone nest, and the ground communication stations near the drone when it returns to base on the 2D map.

[0081] Specifically, using the coordinates of the drone and its nest, the current distance between the drone and its nest can be obtained as follows:

[0082]

[0083] Where m is the distance, the coordinates of the UAV are (x1, y1), and the coordinates of the pod are (x0, y0);

[0084] Construct a circle with diameter m that passes through (x1, y1) and (x0, y0). The ground communication stations whose coordinates are within this circle (excluding the boundary) are the ground communication stations near the UAV when it returns to base. The coordinates of these ground communication stations, from closest to farthest from the UAV, are (X1, Y1) to (X0, y0). n Y n ), where n is the number of ground communication stations near the UAV when it returns to base;

[0085] STEP2: Set up restricted areas based on the coordinates of the ground communication station near the drone when it returns to base.

[0086] Specifically, the restricted area of ​​the i-th ground communication station is a circle with radius R centered on the ground communication station. i The circle, R i The initial value can be determined by the following formula:

[0087]

[0088] Among them, zb i The future environmental index, calculated for the i-th ground communication station as the next ground communication station to be passed in the UAV's inspection path, can be referenced from ZB2. YZ is the threshold value for the UAV's response to environmental changes, which can be obtained by those skilled in the art during the performance testing of the UAV, calculating the current environmental index of the UAV under extreme conditions (barely maintaining normal operation). The larger the current environmental index, the larger the threshold value, and the stronger the UAV's ability to respond to environmental changes. D i Let be the distance between the i-th ground communication station and the UAV;

[0089] Specifically, the restricted area is an area that the drone cannot enter. When the restricted areas of multiple ground communication stations overlap, the restricted area of ​​the ground communication station with the highest future environmental index is kept unrestricted. The radius of other restricted areas that overlap with this area is reduced to be tangent to this area and then its radius is reduced by d. This avoids the overlap of restricted areas and reduces the possibility of the drone taking a detour. d is the area restriction distance. The area restriction distance is set by those skilled in the art based on the index threshold of the drone's response to environmental changes. The larger the index threshold, the smaller the value. The value can be between 10m and 200m.

[0090] STEP3: Mark each restricted area as an obstacle, and obtain the drone's return route through obstacle avoidance and navigation algorithms;

[0091] STEP4: Whenever the distance between the drone and the nest is reduced by one-tenth of a meter, update the various restricted areas and update the drone's return route based on the updated restricted areas.

[0092] Specifically, the obstacle avoidance algorithm and the navigation algorithm are existing technologies and will not be elaborated on here.

[0093] The beneficial effects of this embodiment are: by setting restricted areas to mark areas with poor environmental conditions, the drone is prevented from entering such areas during its return flight, which helps protect the drone from harsh environments and extends its lifespan.

[0094] The above-disclosed content is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the description and drawings of the present invention are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops. The above units are merely examples, and those skilled in the art can adopt corresponding units according to actual needs when implementing this solution.

Claims

1. A nest-based unmanned aerial vehicle adaptive scheduling and management system, characterized in that, The system includes a data acquisition module, a central processing module, a scheduling module, a drone management module, a user interaction module, and a ground communication station. The data acquisition module collects relevant information about each drone. The ground communication station includes an information acquisition unit that collects environmental information about the ground communication station. The central processing module processes the information collected by the data acquisition module and the environmental information collected by the information acquisition unit, and calculates relevant indicators based on the processed data to determine whether a drone meets the recall criteria. The scheduling module controls drones that meet the recall criteria to return to their nest. The drone management module manages drones that have returned to their nest. The user interaction module enables interaction between the system and the user. The relevant information collected by the data acquisition module includes electricity consumption, inspection distance, and wind force encountered by the drone; the environmental information collected by the information acquisition unit includes rainfall and wind force at the location of the ground communication station. The calculation of relevant indicators to determine whether the drone meets the recall conditions includes: first, determining whether the environmental indicators of the work area are qualified; if not, the drone is recalled immediately; otherwise, determining whether the work task indicators representing the drone's ability to return to the nest are qualified; if not, the drone is recalled immediately; if qualified, then determining whether the work task indicators representing the drone's ability to complete the remaining work tasks are qualified; if not, the drone is recalled immediately, otherwise, it is not recalled. The central processing module includes a data processing unit, a preprocessing unit, a calculation unit, and a judgment unit. The data processing unit is used to process and classify the data collected by the data acquisition module. The preprocessing unit is used to preprocess the data processed by the data processing unit, including noise reduction. The calculation unit is used to calculate various indicator values ​​based on the preprocessed data. The judgment unit is used to determine whether the drone meets the recall conditions based on the various indicator values. The central processing module determines whether a drone meets the recall criteria by including the following steps: S31, The data processing unit processes and classifies the data collected by the data acquisition module and the information acquisition unit; S32, The preprocessing unit preprocesses the data processed by the data processing unit; S33, The calculation unit calculates environmental indicators and work task indicators based on the preprocessed data; S34, the judgment unit judges whether both environmental indicators and work task indicators are qualified. If both are qualified, the recall conditions are not met; otherwise, the recall conditions are met. The environmental index can be calculated according to the following formula: ; ； ； in, Here, A represents the current environmental indicators, the number of testing cycles is determined by those skilled in the art, and T represents the testing duration of each testing cycle. Let be the total rainfall detected by the drone in the a-th detection cycle. The rainfall threshold, Let be the maximum forward wind force experienced by the drone during the a-th detection cycle. Let be the maximum rear wind force experienced by the drone during the a-th detection cycle. Let be the maximum left-side wind force experienced by the drone during the a-th detection cycle. Let be the maximum right-side wind force experienced by the drone during the a-th detection cycle. For longitudinal wind force threshold, The lateral wind threshold; As future environmental indicators, This represents the total rainfall detected by the next ground communication station along the drone's inspection path during the a-th inspection cycle. The maximum wind force detected by the next ground communication station that the drone will pass through during the inspection path in the a-th inspection cycle; For the environmental index, D is the distance of the UAV from the next ground communication station that the UAV is about to pass through in the UAV inspection path. The rainfall threshold, longitudinal wind threshold, and lateral wind threshold are set by those skilled in the art during the performance testing of the UAV by judging the rainfall and wind force when the UAV is deflected; when the environmental indicators are less than the environmental indicator thresholds, the environmental indicators are considered to be qualified. Work performance indicators can be calculated using the following formula: ； ； in, This is the primary performance indicator, used to characterize the drone's ability to complete remaining tasks. The higher the indicator value, the stronger the capability. Let be the distance traveled by the drone in the a-th cycle. The amount of electricity used by the drone in the a-th cycle. The remaining inspection route for the drone includes the distance required to traverse all remaining inspection targets and the return route after traversing all remaining inspection targets. This refers to the remaining battery power of the drone. This is the second performance indicator, used to characterize the drone's ability to return to its nest; the higher the value, the stronger the capability. This represents the distance the drone travels from its current location back to its nest. B represents the remaining battery power of the drone, and B represents the warning battery power value of the drone. This warning battery power value is set by those skilled in the art based on the remaining lifespan of the drone battery. The smaller the remaining lifespan, the larger this value. The battery power of the drone should not be lower than this value during operation to avoid excessive battery depletion.

2. The nest-based drone adaptive scheduling and management system of claim 1, wherein, The data acquisition module includes a communication unit and sensors installed on the UAV. The communication unit is installed on the UAV, the UAV nest, and the ground communication station. The sensors are used to collect relevant information about the UAV itself and the working environment of the UAV. The communication unit is used to send the information collected by the sensors and the information collected by the data acquisition unit to the central processing module.

3. The nest-based adaptive scheduling and management system for UAVs of claim 2, wherein, The scheduling module includes a recall signal generation unit, a route planning unit, and a management signal generation unit. The route planning unit is used to plan the return route of the UAV. The recall signal generation unit is used to generate a recall signal based on the judgment result of the judgment unit and the route planned by the route planning unit. After receiving the recall signal, the UAV will return to the nest. The management signal generation unit is used to generate a management signal for the UAV that returns to the nest and send the management signal to the UAV management module.

4. The nest-based adaptive scheduling and management system for UAVs of claim 3, wherein, The drone management module includes a parking space, a work command generation unit, a guidance unit, and a maintenance unit. The parking space is used to park drones. The maintenance unit is used to charge and maintain the drones in the parking space. The work command generation unit is used to generate work commands and send them to the drones in the parking space. The guidance unit is used to guide drones that have entered the drone hangar to land in the designated parking space.

5. The UAV adaptive scheduling and management system based on a hive as described in claim 4, characterized in that, The workflow of the UAV adaptive scheduling and management system includes the following steps: S1, the data acquisition module collects relevant information about the UAV; the ground communication station's information acquisition unit collects environmental information about the ground communication station. S2, the central processing module determines whether the drone meets the recall conditions. If not, the drone continues to work and the judgment ends; otherwise, it proceeds to the next step. S3, the scheduling module recalls the drone, and the drone returns to the nest; S4, the drone management module, manages drones that return to their nests.

6. The nest-based drone adaptive scheduling and management system of claim 5, wherein, The drone management module manages drones returning to their nests through the following steps: S41, the guidance unit guides the UAV to land at the parking position by communicating with the UAV after it enters the nest; S42, the maintenance unit performs maintenance on the drone and sends photos of the drone and information about the drone's remaining battery power to the user interaction module; S43, users can learn about drone-related information through the user interaction module and input new work tasks according to their own needs; S44, The work command generation unit generates work commands based on the work task and sends them to the corresponding UAV; After maintenance is completed, the S45 drone will execute new tasks according to work orders.

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