Unmanned aerial vehicle control method and system based on relay end

Abstract

The invention is suitable for the technical field of unmanned aerial vehicle control, and particularly relates to an unmanned aerial vehicle control method and system based on a relay end, and the method comprises the steps: obtaining a task of an unmanned aerial vehicle, and determining a data transmission link between the unmanned aerial vehicle and an edge device; the method comprises the following steps: establishing an edge layer by using data in an unmanned aerial vehicle, establishing a fog layer by using data in edge equipment, establishing a distributed computing architecture, generating separators by using the edge layer, segmenting a task, performing containerization processing on slices obtained after segmentation, generating a container snapshot, and caching the container snapshot into the fog layer; and opening the control authority of the unmanned aerial vehicle, and determining the control relationship between the distributed computing architecture and the unmanned aerial vehicle. According to the method, the container snapshot is generated and recovered when the unmanned aerial vehicle breaks down, the response time of the unmanned aerial vehicle can be greatly shortened, meanwhile, the backup unmanned aerial vehicle can be rapidly started to continue to execute the task, seamless replacement is achieved, data loss is avoided, and normal proceeding of the task is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle control, and in particular to a relay-based unmanned aerial vehicle control method and system. Background Art

[0002] Usually, the transmission distance of wireless network signals is limited. When used over a large area, a large number of base stations need to be set up along the transmission path of the wireless network as nodes in the network transmission process, so as to achieve wireless network signal coverage to a wider range; however, if the drone needs to temporarily access the wireless network in an area outside the coverage of the wireless network, the method of setting up base stations in the existing technology is obviously not applicable, and this requires the use of a relay terminal. The relay terminal plays a key role in drone systems, wireless communications and other remote sensing networks, especially in the process of performing complex tasks and extending the operating range. The main function of the relay terminal is to enhance signal transmission, extend the communication distance, and improve the flexibility and reliability of the system.

[0003] However, when the drone is performing its mission, it needs to work in coordination with the cloud server, which requires a large amount of data transmission, which can easily lead to transmission delays. In addition, this will also increase the difficulty of data processing. When a drone fails, a longer response time is required, affecting the normal execution of the mission. Therefore, "how to respond quickly when a drone fails" is a technical problem that the present invention needs to solve. Summary of the invention

[0004] The purpose of the present invention is to provide a drone control method and system based on a relay terminal to solve the problem of "how to respond quickly when a drone fails" raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for controlling a drone based on a relay terminal, the method comprising:

[0007] Obtain the mission of the drone and determine the data transmission link between the drone and the edge device;

[0008] The edge layer is built using the data in the drone, the fog layer is built using the data in the edge device, a distributed computing architecture is constructed, the delimiter is generated using the edge layer, the task is segmented, the segmented slices are containerized, a container snapshot is generated, and the container snapshot is cached in the fog layer;

[0009] Open the control authority of the drone and establish the control relationship between the distributed computing architecture and the drone. After receiving the fault data sent by the drone, extract the container snapshot in the fog layer and restore the snapshot in the pre-configured backup drone. Use the restored data to redeploy the backup drone, use the backup drone as the controlled object, and migrate the control relationship.

[0010] Furthermore, the step of obtaining the mission of the drone and determining the data transmission link between the drone and the edge device includes:

[0011] Collecting location data of the area where the task is located, and clustering the task into a flight part and an execution part;

[0012] Establish the corresponding relationship between the flight part, the execution part and the position data respectively;

[0013] Define a data transmission protocol, debug the data transmission link, and determine an optimization method based on the debugging result.

[0014] Furthermore, the steps of using the data in the drone to build the edge layer and the data in the edge device to build the fog layer to build a distributed computing architecture include:

[0015] Perform data preprocessing in the edge layer to obtain valid data, and superimpose the created local decision mechanism into the edge layer;

[0016] The number of edge devices in the fog layer is determined, the number being at least one, and a division of labor is set for all edge devices.

[0017] Furthermore, the steps of using the edge layer to generate delimiters, segmenting the tasks, containerizing the segments obtained after segmentation, generating container snapshots, and caching the container snapshots in the fog layer include:

[0018] Generate a separator according to the progress of the task, and divide the task into unfinished task slices and completed task slices;

[0019] Containerize the unfinished task slices, obtain container snapshots, and cache the container snapshots in the fog layer;

[0020] Using the progress, an incremental snapshot is generated, and the container snapshot is amended.

[0021] Furthermore, the control authority of the drone is opened, and the control relationship between the distributed computing architecture and the drone is established. After receiving the fault data sent by the drone, the container snapshot in the fog layer is extracted, and the snapshot is restored in the pre-configured backup drone. The backup drone is redeployed using the restored data, and the backup drone is used as the controlled object. The steps of migrating the control relationship include:

[0022] Load the container snapshot and restore it, and redeploy the restored data to the backup drone through the control relationship;

[0023] Formulate and execute flight plans, and transmit data acquired by the backup drone during flight to the edge layer.

[0024] Furthermore, the method further comprises:

[0025] Initialize the properties of the edge device and configure the corresponding relationship between the task and the edge device;

[0026] By traversing the created localized decision set, the data to be uploaded is selected from the edge layer;

[0027] By utilizing the upload channel between the edge device and the cloud, the data to be uploaded is transferred to the processing model in the cloud;

[0028] Establishing a federated architecture of edge devices, backing up the processing model to the edge devices, and training the processing model using the federated architecture;

[0029] The parameters obtained during training are uploaded to the cloud and aggregated, and the processing model is updated using the aggregated data.

[0030] Furthermore, the method further comprises:

[0031] Inserting a time scheduling window into the federated architecture and developing an asynchronous update strategy;

[0032] Insert version tags into the processing models of the edge device and the cloud, and determine the rollback steps of the processing model based on a preset rollback mechanism.

[0033] Furthermore, the system comprises:

[0034] A determination module is used to obtain the mission of the drone and determine the data transmission link between the drone and the edge device;

[0035] A slicing module is used to build an edge layer using data from drones and a fog layer using data from edge devices, to build a distributed computing architecture, to generate delimiters using the edge layer, to slice the tasks, to containerize the slices obtained after the slicing, to generate container snapshots, and to cache the container snapshots in the fog layer;

[0036] The recovery module is used to open the control authority of the drone and establish the control relationship between the distributed computing architecture and the drone. After receiving the fault data sent by the drone, it extracts the container snapshot in the fog layer and performs snapshot recovery in the pre-configured backup drone. The backup drone is redeployed using the restored data, and the control relationship is migrated with the backup drone as the controlled object.

[0037] Furthermore, the determination module includes:

[0038] A clustering unit, used for collecting location data of the area where the task is located, and clustering the task into a flight part and an execution part;

[0039] A corresponding unit, used to respectively establish corresponding relationships between the flight part, the execution part and the position data;

[0040] The debugging unit is used to define the data transmission protocol, debug the data transmission link, and determine the optimization method according to the debugging result.

[0041] Furthermore, the segmentation module includes:

[0042] An acquisition unit, configured to perform data preprocessing in the edge layer, acquire valid data, and superimpose the created local decision mechanism into the edge layer;

[0043] The statistical unit is used to determine the number of edge devices in the fog layer, the number is at least one, and set a division of labor for all edge devices.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. By determining the data transmission link, the data in the drone can be transmitted to the edge device for processing, shortening the data transmission distance, reducing latency, and greatly improving the interactivity of the drone. By building a distributed computing architecture, the bandwidth requirements of the drone are reduced, greatly improving the data processing efficiency. By splitting the tasks, the workload of data transmission is further reduced. By generating container snapshots and restoring them when the drone fails, the response time of the drone can be greatly shortened. At the same time, when a drone fails, the backup drone can be quickly started to continue the mission, thereby achieving seamless replacement, avoiding data loss, and ensuring the normal progress of the mission.

[0046] 2. By traversing the localized decision set, decisions can be made in edge devices, greatly improving the decision-making efficiency of drones. At the same time, by building a federal architecture, the processing model in the cloud can be trained locally, which protects data privacy while greatly reducing data transmission costs. By determining the rollback steps, the processing model can be restored to the most stable version, thereby improving the accuracy of the processing model. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0048] Figure 1 A flowchart of a method for controlling a drone based on a relay terminal provided in an embodiment of the present invention;

[0049] Figure 2 A first sub-flow chart of a relay-based drone control method according to an embodiment of the present invention;

[0050] Figure 3 A second sub-flow chart of the relay-based drone control method provided in an embodiment of the present invention;

[0051] Figure 4 A third sub-flow chart of the relay-based drone control method provided in an embodiment of the present invention;

[0052] Figure 5 A block diagram of a relay-based drone control system according to an embodiment of the present invention;

[0053] Figure 6 A block diagram of the composition of a determination module in a relay-based drone control system provided by an embodiment of the present invention;

[0054] Figure 7 A block diagram of the composition of a segmentation module in a relay-based drone control system provided by an embodiment of the present invention;

[0055] Figure 8 A block diagram of the composition of a recovery module in a relay-based drone control system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] In Example 1, Figure 1The implementation process of the drone control method based on the relay end provided by the embodiment of the present invention is shown, and is described in detail below, as follows:

[0058] S100: Obtain the mission of the drone and determine the data transmission link between the drone and the edge device.

[0059] Obtain the tasks that the drone needs to perform and determine the data transmission link between the drone and the edge device. The edge device here refers to a device that can process drone data. It can be a ground device or a drone with integrated edge computing capabilities. When the edge device needs to be used in conjunction with an edge gateway, control device, etc., after determining the data transmission link, testing is required to ensure the stability and reliability of the data transmission link.

[0060] S200: Use the data in the drone to build an edge layer, use the data in the edge device to build a fog layer, build a distributed computing architecture, use the edge layer to generate delimiters, and split the task, containerize the slices obtained after the split, generate container snapshots, and cache the container snapshots in the fog layer.

[0061] The edge layer is built using the data in the drone. The data in the drone should be divided into two parts. One is the flight data needed by the drone during the flight, and the other is the data generated when the drone collects or performs tasks. These data are transferred to the edge layer, and a fog layer is built in the edge device to build a distributed computing structure. The edge layer is located at the outermost layer, and the fog layer is located between the edge layer and the cloud. The difference between the two is that the edge layer and the fog layer are used to process different data. For some data with high latency requirements, they should be processed in the edge layer, while for some data with low latency requirements and large computational complexity, they need to be processed in the fog layer or the cloud.

[0062] According to the data generated during the flight of the UAV, a delimiter is generated, and the task is divided into a completed part and an unfinished part using the delimiter. The unfinished part and the corresponding data are containerized, and the generated container snapshot is cached in the fog layer.

[0063] S300: Open the control authority of the drone and establish the control relationship between the distributed computing architecture and the drone. After receiving the fault data sent by the drone, extract the container snapshot in the fog layer and restore the snapshot in the pre-configured backup drone. Use the data obtained after recovery to redeploy the backup drone, use the backup drone as the controlled object, and migrate the control relationship.

[0064] Open the control rights of drones to edge devices in the distributed computing architecture. When the drones are performing tasks, they will send data to the edge devices. When the edge devices receive the fault data, they will extract the container snapshots in the edge devices and restore the snapshots. It should be noted that the backup drones need to be pre-configured to ensure that the drones can take off quickly and perform tasks. Use the recovered data to redeploy the backup drones, and use the backup drones as controlled objects to migrate and establish a control relationship between the backup drones and edge devices.

[0065] In Example 2, Figure 2 The implementation process of the drone control method based on the relay end provided by the embodiment of the present invention is shown. The steps of obtaining the task of the drone and determining the data transmission link between the drone and the edge device are described in detail as follows:

[0066] S101: Collecting location data of the area where the task is located, and clustering the task into a flight part and an execution part.

[0067] Delineate the area where the drone performs the mission according to the mission, determine the location data of the area, and divide the mission into the flight part and the execution part. The flight part is the task of controlling the drone, and the execution part is the actions required to perform the mission.

[0068] For example, a UAV flight mission is to reach point A and use the multispectral camera on the UAV to capture high-resolution images of farmland. Here, reaching point A and adjusting the specific flight attitude during image acquisition are the tasks of the flight part, and how to start and adjust the multispectral camera for image acquisition is the task of the execution part.

[0069] S102: Establish corresponding relationships between the flight part, the execution part and the position data respectively.

[0070] Establish the correspondence between the flight part and the position data, and the execution part and the position data, so as to determine the actions that the drone needs to take at each position.

[0071] S103: define a data transmission protocol, debug the data transmission link, and determine an optimization method according to the debugging result.

[0072] Determine the data transmission protocols between drones and edge devices, and between edge devices and the cloud, debug the data transmission link, and optimize the data transmission link based on the debugging results to ensure the stability and reliability of the data transmission link. Specific optimization methods can be data compression, batch transmission, etc.

[0073] In Example 3, Figure 3The implementation process of the drone control method based on the relay end provided by the embodiment of the present invention is shown. The steps of using the data in the drone to build the edge layer, the data in the edge device to build the fog layer, and the distributed computing architecture are described in detail as follows:

[0074] S201: Perform data preprocessing in the edge layer to obtain valid data, and superimpose the created local decision mechanism into the edge layer.

[0075] Preprocess the data in the edge layer to improve the accuracy of data processing and determine the useful data. Some of the data in the edge layer needs to be processed in the drone, while some needs to be sent to the edge device for processing. Here, the data that needs to be sent to the edge device for processing is defined as valid data, and the valid data is classified into the fog layer, and then the local decision-making mechanism is superimposed on the edge layer. When the data is processed in the edge layer, a decision can be made by integrating the local decision-making mechanism. The advantage of this is that it can improve the decision-making efficiency of the edge layer and reduce data transmission.

[0076] S202: Determine the number of edge devices in the fog layer, which number is at least one, and set division of labor for all edge devices.

[0077] Determine the number of edge devices in the fog layer and set up division of labor for all edge devices. Different edge devices are responsible for processing different valid data. The advantage of this is that valid data can be calculated in parallel, thereby improving the processing efficiency of valid data.

[0078] In Example 4, Figure 3 The implementation process of the drone control method based on the relay end provided by the embodiment of the present invention is shown. The steps of generating delimiters by using the edge layer, segmenting the tasks, containerizing the slices obtained after segmentation, generating container snapshots, and caching the container snapshots in the fog layer are described in detail as follows:

[0079] S203: Generate a separator according to the progress of the task, and divide the task into unfinished task slices and completed task slices.

[0080] The progress of the task is determined according to the data in the edge layer, and a separator is generated. The separator is mainly used to split the task into unfinished task slices and completed task slices. After the task is split, it can be processed in different edge devices to improve data processing efficiency.

[0081] S204: Containerize the unfinished task slices, obtain a container snapshot, and cache the container snapshot in the fog layer.

[0082] The unfinished task slices are containerized. The advantage of containerization is that they can be quickly deployed in different application environments. At the same time, they can also update container snapshots while using fewer transmission resources to ensure the accuracy of container snapshots.

[0083] S205: Generate an incremental snapshot using the progress, and modify the container snapshot.

[0084] The data in the progress and edge layers are used to generate incremental snapshots, and the incremental snapshots are used to correct the container snapshots. By using incremental snapshots, the consumption of transmission resources can be further reduced.

[0085] In Example 5, Figure 4 The implementation process of the drone control method based on the relay end provided by the embodiment of the present invention is shown. The control authority of the open drone is established, and the control relationship between the distributed computing architecture and the drone is established. After receiving the fault data sent by the drone, the container snapshot in the fog layer is extracted, and the snapshot is restored in the pre-configured backup drone. The backup drone is redeployed using the restored data, and the backup drone is used as the controlled object. The steps of migrating the control relationship are described in detail as follows:

[0086] S301: Load the container snapshot and restore it, and redeploy the restored data to the backup drone through the control relationship.

[0087] Determine the control relationship between the distributed computing architecture and the drone, extract the container snapshot from the fog layer, and restore the snapshot. Use the control relationship to redeploy the restored data to the backup drone, and migrate the control relationship originally established with the drone to the backup drone.

[0088] S302: Formulate and execute a flight plan, and transmit the data acquired by the backup UAV during the flight to the edge layer.

[0089] A flight plan for the backup drone is formulated in the distributed computing architecture, the flight plan is sent to the backup drone, and the data generated by the backup drone during the flight is transmitted to the edge layer.

[0090] In Example 6, different from Example 1, in this embodiment of the present invention, the method further includes:

[0091] Initialize the properties of the edge device and configure the corresponding relationship between the task and the edge device;

[0092] By traversing the created localized decision set, the data to be uploaded is selected from the edge layer;

[0093] By utilizing the upload channel between the edge device and the cloud, the data to be uploaded is transferred to the processing model in the cloud;

[0094] Establishing a federated architecture of edge devices, backing up the processing model to the edge devices, and training the processing model using the federated architecture;

[0095] The parameters obtained during training are uploaded to the cloud and aggregated, and the processing model is updated using the aggregated data.

[0096] Determine the attributes of edge devices, that is, the data processing capabilities and types of edge devices, so as to establish the corresponding relationship between unfinished task slices, completed task slices and edge devices. Use edge devices to process unfinished task slices and completed task slices at the same time. According to the processing results, traverse the localized decision set, select the corresponding decision, and complete the processing of data in the fog layer. In reality, the completed task slices also need to be processed in order to mine useful data.

[0097] The data that needs to be uploaded to the cloud for processing, that is, the data to be uploaded, is selected from the fog layer, and the data to be uploaded is transferred to the processing model on the cloud through the upload channel between the edge device and the cloud, and the uploaded data is analyzed and processed; a federal architecture is established through edge devices, the processing model is backed up to all edge devices, the processing model is trained using the data in the fog layer, the variables of the parameters in the training processing model are uploaded to the cloud, and in the cloud, the parameters of the variables and the processing model in the cloud are aggregated, and the processing model in the cloud is updated according to the aggregation results.

[0098] In Example 7, different from Example 1, in this embodiment of the present invention, the method further includes:

[0099] Inserting a time scheduling window into the federated architecture and developing an asynchronous update strategy;

[0100] Insert version tags into the processing models of the edge device and the cloud, and determine the rollback steps of the processing model based on the preset rollback mechanism

[0101] Insert a time scheduling window into the federated architecture. The time scheduling window is mainly used to determine the training time of the processing model. The asynchronous update strategy allows different edge devices to train the processing model in different time periods. At the same time, insert a version tag into the trained processing model. That is to say, after each training of the processing model, insert a version tag and set a rollback mechanism to roll back the processing model, so as to facilitate the restoration of the processing model and find the most stable processing model. The preset rollback mechanism is the method for restoring the processing model.

[0102] Figure 5 The structure block diagram of the drone control system based on the relay terminal provided by the embodiment of the present invention is shown. The drone control system based on the relay terminal 1 includes:

[0103] A determination module 11 is used to obtain the mission of the drone and determine the data transmission link between the drone and the edge device;

[0104] The segmentation module 12 is used to build an edge layer using data in the drone and a fog layer using data in the edge device, to construct a distributed computing architecture, to generate a delimiter using the edge layer, to segment the task, to containerize the slices obtained after segmentation, to generate a container snapshot, and to cache the container snapshot in the fog layer;

[0105] The recovery module 13 is used to open the control authority of the drone and establish the control relationship between the distributed computing architecture and the drone. After receiving the fault data sent by the drone, it extracts the container snapshot in the fog layer and performs snapshot recovery in the pre-configured backup drone. The backup drone is redeployed using the data obtained after recovery, and the control relationship is migrated with the backup drone as the controlled object.

[0106] Figure 6 The structure block diagram of the UAV control system based on the relay terminal provided by the embodiment of the present invention is shown, and the determination module 11 includes:

[0107] A clustering unit 111 is used to collect location data of the area where the task is located, and cluster the task into a flight part and an execution part;

[0108] A corresponding unit 112, used to establish corresponding relationships between the flight part, the execution part and the position data respectively;

[0109] The debugging unit 113 is used to define a data transmission protocol, debug the data transmission link, and determine an optimization method according to the debugging result.

[0110] Figure 7 The structure block diagram of the UAV control system based on the relay terminal provided by the embodiment of the present invention is shown, and the segmentation module 12 includes:

[0111] An acquisition unit 121 is used to perform data preprocessing in the edge layer, acquire valid data, and superimpose the created local decision mechanism into the edge layer;

[0112] The statistical unit 122 is used to determine the number of edge devices in the fog layer, which number is at least one, and set a division of labor for all edge devices.

[0113] A separation unit 123, used to generate a separator according to the progress of the task, and divide the task into unfinished task slices and completed task slices;

[0114] The cache unit 124 is used to containerize the unfinished task slices, obtain container snapshots, and cache the container snapshots in the fog layer;

[0115] The correction unit 125 is used to generate an incremental snapshot using the progress and to correct the container snapshot.

[0116] Figure 8 The structure block diagram of the UAV control system based on the relay terminal provided by the embodiment of the present invention is shown, and the recovery module 13 includes:

[0117] A deployment unit 131 is used to load the container snapshot and restore it, and redeploy the restored data to the backup drone through the control relationship;

[0118] The backup unit 132 is used to formulate and execute a flight plan and transmit the data of the backup UAV during flight to the edge layer.

[0119] The determination module 11 is used to complete step S100, the segmentation module 12 is used to complete step S200, and the recovery module 13 is used to complete step S300;

[0120] The clustering unit 111 is used to complete step S101, the corresponding unit 112 is used to complete step S102, and the debugging unit 113 is used to complete step S103;

[0121] The acquisition unit 121 is used to complete step S201, the statistical unit 122 is used to complete step S202, the separation unit 123 is used to complete step S203, the cache unit 124 is used to complete step S204, and the correction unit 125 is used to complete step S205;

[0122] The deployment unit 131 is used to complete step S301, and the backup unit 132 is used to complete step S302.

[0123] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0124] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0125] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A drone control method based on a relay terminal, characterized in that: The method comprises: Obtain the mission of the drone and determine the data transmission link between the drone and the edge device; The edge layer is built using the data in the drone, the fog layer is built using the data in the edge device, a distributed computing architecture is constructed, the delimiter is generated using the edge layer, the task is segmented, the segmented slices are containerized, a container snapshot is generated, and the container snapshot is cached in the fog layer; Open the control authority of the drone and establish the control relationship between the distributed computing architecture and the drone. After receiving the fault data sent by the drone, extract the container snapshot in the fog layer and restore the snapshot in the pre-configured backup drone. Use the restored data to redeploy the backup drone, use the backup drone as the controlled object, and migrate the control relationship.

2. The method for controlling a drone based on a relay terminal according to claim 1, characterized in that: The steps of obtaining the mission of the drone and determining the data transmission link between the drone and the edge device include: Collecting location data of the area where the task is located, and clustering the task into a flight part and an execution part; Establish the corresponding relationship between the flight part, the execution part and the position data respectively; Define a data transmission protocol, debug the data transmission link, and determine an optimization method based on the debugging result.

3. The method for controlling a drone based on a relay terminal according to claim 1, characterized in that: The steps of using the data in the drone to build the edge layer and the data in the edge device to build the fog layer to build a distributed computing architecture include: Perform data preprocessing in the edge layer to obtain valid data, and superimpose the created local decision mechanism into the edge layer; The number of edge devices in the fog layer is determined, the number being at least one, and a division of labor is set for all edge devices.

4. The method for controlling a drone based on a relay terminal according to claim 3, characterized in that: The steps of using the edge layer to generate delimiters, segmenting the tasks, containerizing the segments obtained after segmentation, generating container snapshots, and caching the container snapshots in the fog layer include: Generate a separator according to the progress of the task, and divide the task into unfinished task slices and completed task slices; Containerizing the unfinished task slices, obtaining container snapshots, and caching the container snapshots in the fog layer; Using the progress, an incremental snapshot is generated, and the container snapshot is amended.

5. The method for controlling a drone based on a relay terminal according to claim 4, characterized in that: The control authority of the drone is opened, and the control relationship between the distributed computing architecture and the drone is established. After receiving the fault data sent by the drone, the container snapshot in the fog layer is extracted, and the snapshot is restored in the pre-configured backup drone. The backup drone is redeployed using the restored data, and the backup drone is used as the controlled object. The steps of migrating the control relationship include: Load the container snapshot and restore it, and redeploy the restored data to the backup drone through the control relationship; Formulate and execute flight plans, and transmit data acquired by the backup drone during flight to the edge layer.

6. The method for controlling a drone based on a relay terminal according to claim 5, characterized in that: The method further comprises: Initialize the properties of the edge device and configure the corresponding relationship between the task and the edge device; By traversing the created localization decision set, the data to be uploaded is selected from the edge layer; By utilizing the upload channel between the edge device and the cloud, the data to be uploaded is transferred to the processing model in the cloud; Establishing a federated architecture of edge devices, backing up the processing model to the edge devices, and training the processing model using the federated architecture; The parameters obtained during training are uploaded to the cloud and aggregated, and the processing model is updated using the aggregated data.

7. The method for controlling a drone based on a relay terminal according to claim 6, characterized in that: The method further comprises: Inserting a time scheduling window into the federated architecture and developing an asynchronous update strategy; Insert version tags into the processing models of the edge device and the cloud, and determine the rollback steps of the processing model based on a preset rollback mechanism.

8. A UAV control system based on a relay terminal, characterized in that: The system comprises: A determination module is used to obtain the mission of the drone and determine the data transmission link between the drone and the edge device; A slicing module is used to build an edge layer using data from drones and a fog layer using data from edge devices, to build a distributed computing architecture, to generate delimiters using the edge layer, to slice the tasks, to containerize the slices obtained after the slicing, to generate container snapshots, and to cache the container snapshots in the fog layer; The recovery module is used to open the control authority of the drone and establish the control relationship between the distributed computing architecture and the drone. After receiving the fault data sent by the drone, it extracts the container snapshot in the fog layer and performs snapshot recovery in the pre-configured backup drone. The backup drone is redeployed using the restored data, and the control relationship is migrated with the backup drone as the controlled object.

9. The relay-based drone control system according to claim 8, characterized in that: The determination module comprises: A clustering unit, used for collecting location data of the area where the task is located, and clustering the task into a flight part and an execution part; A corresponding unit, used to respectively establish corresponding relationships between the flight part, the execution part and the position data; The debugging unit is used to define the data transmission protocol, debug the data transmission link, and determine the optimization method according to the debugging result.

10. The relay-based drone control system according to claim 8, characterized in that: The segmentation module includes: An acquisition unit, configured to perform data preprocessing in the edge layer, acquire valid data, and superimpose the created local decision mechanism into the edge layer; The statistical unit is used to determine the number of edge devices in the fog layer, the number is at least one, and set a division of labor for all edge devices.

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