Cluster UAV control method based on cloud-edge architecture

Abstract

The present invention relates to the field of drone control technology, and provides a cluster drone control method based on a cloud-edge-end architecture. The overall task framework, global edge nodes and local edge nodes are determined through a cloud platform, and an operation area is divided into a plurality of sub-areas. The overall task framework is divided into sub-task modules corresponding to the sub-areas through the global edge nodes, and the sub-task modules are refined through the local edge nodes to obtain instructions to be executed, so as to realize hierarchical collaborative control of terminal drones, break the single-point bottleneck of centralized control, and avoid centralized processing of data; dynamically adjust edge computing nodes to prevent node anomalies from affecting task progress, and then dynamically locally schedule terminal drones within the sub-area to ensure the coordination of task completion, and also dynamically globally schedule terminal drone clusters to realize macro-resource allocation of terminal drones; terminal drones collect and upload task execution data to provide a reliable data basis for the scheduling of terminal drones.

Description

Technical Field

[0001] The present invention relates to the field of drone control technology, and in particular to a cluster drone control method based on a cloud-edge-end architecture. Background Art

[0002] Swarm drones have shown broad application prospects in many fields such as military, logistics, surveying and mapping, and rescue. With the characteristics of multi-machine collaborative operation, they can undertake complex and large-scale tasks such as large-area rapid monitoring in military reconnaissance, efficient transportation coverage in logistics distribution, accurate data acquisition in geographic surveying, and disaster-stricken area exploration and material delivery in emergency rescue scenarios. The core of all this lies in the effective swarm drone control method, which directly determines whether the drones can operate in an orderly and efficient manner.

[0003] At present, the traditional centralized control architecture is widely used to manage cluster drones. In this architecture, the central control station located on the ground assumes the function of the control hub of the entire cluster. First, it will comprehensively collect various initial status information of each drone in the cluster, including precise location coordinates, remaining power, flight performance parameters, etc., and integrate detailed intelligence related to the task, such as the geographical location of the mission target, the specific type of task, and the corresponding priority setting. Subsequently, based on the pre-set task allocation algorithm and flight path planning algorithm, a specific task execution plan is carefully formulated for each drone to clarify its flight route, action execution sequence, and coordination with other drones. Taking a relatively simple cluster drone performance task as an example, the central control station can accurately control the flight posture and spatial position of each drone according to the pre-designed pattern style and action choreography requirements, thereby successfully presenting a wonderful and gorgeous performance effect.

[0004] However, this traditional centralized control method has exposed a series of serious technical defects in practical applications. In terms of its own structural characteristics, since all control decisions are highly concentrated in the central control station, once the central control station encounters a hardware failure, such as a sudden server downtime, an unexpected network connection interruption, or a software problem, such as an algorithm error, a system crash, etc., the entire cluster drone system will instantly fall into a desperate situation of being out of control, which will not only directly lead to the complete failure of the mission being executed, but also very likely to cause catastrophic consequences such as drone collisions and crashes. In addition, when it is necessary to add new drone equipment to the cluster, it is often necessary to carry out large-scale hardware facility upgrades and complex and cumbersome software system modification and debugging work for the central control station, which increases the difficulty of the work and thus forms a serious shackle on the scalability of the cluster drone; and as the scale of the cluster continues to expand and the number of drones continues to increase, the amount of data that the central control station needs to process will show an exponentially rapid growth trend, thereby slowing down its data processing speed. Summary of the invention

[0005] In view of the above-mentioned existing traditional centralized control mode, once a single-point hardware failure or software failure occurs in the central control station, the entire cluster drone system will be paralyzed immediately; in addition, the process of adding new drones is cumbersome, and as the number of drones increases, the data processing pressure of the central control station will increase dramatically, reducing data processing efficiency. This application provides a cluster drone control method based on a cloud-edge-end architecture, which breaks the single-point bottleneck of traditional centralized control through the collaborative control of the cloud-edge-end architecture.

[0006] To achieve the above objectives, this application adopts the following technical solutions:

[0007] A cluster drone control method based on a cloud-edge-end architecture, wherein the cloud-edge-end architecture includes a cloud platform, an edge computing node, and a terminal drone that are sequentially communicatively connected, and wherein the control method includes the following steps:

[0008] The task processing steps of the cloud platform include: accepting a target task request, determining a general task framework based on the target task request and basic information stored in the cloud platform, and dividing the operation area of ​​the target task request into a plurality of sub-areas; determining a global edge node and a local edge node in the edge computing node based on the sub-areas;

[0009] The task processing step of the global edge node: dividing the overall task framework into a number of subtask modules with task execution priorities; and globally scheduling the terminal drones within the operating area; and adjusting the edge computing nodes according to abnormal situations;

[0010] The task processing step of the local edge node: refine the subtask module to obtain instructions to be executed; and perform task execution monitoring and local scheduling on the terminal drone within the corresponding sub-area;

[0011] The task processing steps of the terminal drone are: adjusting the flight posture based on the instructions to be executed, and performing operations according to the instructions to be executed; collecting task execution data and uploading the task execution data.

[0012] In one embodiment disclosed in the present application, the task processing steps of the cloud platform are specifically as follows:

[0013] Step S101, accepting a target task request, and storing geographic information related to the target task request and relevant parameter information of the terminal drone cluster in a distributed database of the cloud platform as basic information;

[0014] Step S102, based on the target task request and the geographic information, formulate a general task framework that meets the target task request, determine the operation area for executing the task of the target task request, and divide the operation area into a plurality of sub-areas; wherein the general task framework includes the overall task planning and the overall flight path of the terminal UAV participating in executing the target task request;

[0015] Step S103, create a global role and a local role, grant different data processing permissions to the global role and the local role; based on the geographic information corresponding to the sub-area, assign the global role and the local role to different edge computing nodes respectively, so as to obtain global edge nodes and local edge nodes, and the local edge nodes correspond one-to-one to the sub-area.

[0016] In one embodiment disclosed in the present application, in step S101: the distributed database includes a plurality of edge computing nodes, each of the edge computing nodes is provided with an independent database, and each of the independent databases stores cluster data information; the independent databases in different edge computing nodes are updated within a preset time period to ensure that the cluster data information in all the independent databases remains synchronized;

[0017] Perform data consistency check on the cluster data information in different edge computing nodes within a preset time period; if the check result of one edge computing node is inconsistent with that of other edge computing nodes, determine that data loss exists in the edge computing node, adjust the global role and / or local role of the edge computing node, or do not select the corresponding edge computing node as the global edge node or local edge node until the independent database corresponding to the edge computing node completes the next update.

[0018] In one embodiment disclosed in the present application, in the independent database corresponding to the global edge node, the data of the global edge node interacting with the cloud platform and the local edge node respectively is cached to obtain the first cluster data, and the first cluster data is synchronized to the other edge computing nodes through the data synchronization mechanism; in the independent database corresponding to the local edge node, the data of the local edge node interacting with the terminal drone is cached to obtain the second cluster data, and the second cluster data is synchronized to the other edge computing nodes through the data synchronization mechanism, so as to obtain the cluster data information composed of the first cluster data and the second cluster data.

[0019] In one embodiment disclosed in the present application, the global edge node divides the overall task framework into a number of subtask modules with task execution priorities, including the following steps:

[0020] Step S201, based on the target task request and the geographic information of the corresponding sub-area, the overall task plan is divided into a plurality of sub-task plans corresponding to the local edge nodes one by one;

[0021] Step S202, determining the workload of each sub-area, and based on the workload, sub-task planning and related parameter information, performing regional refinement on the overall flight path to obtain a flight area path of the terminal UAV within the sub-area range;

[0022] Step S203, the subtask module includes the subtask planning and the flight area path, and the task execution priority is set for the subtask module based on the task importance of the subtask module.

[0023] In one embodiment disclosed in the present application, the local edge node refines the subtask module to obtain instructions to be executed, including the following steps:

[0024] Step S301, based on the geographic information corresponding to the sub-area and the relevant parameter information of the terminal UAV, the sub-task planning and the flight area path are further refined to obtain instructions to be executed, wherein the instructions to be executed include task execution step instructions and detailed flight instructions corresponding to each of the terminal UAVs within the sub-area;

[0025] Step S302: Send the to-be-executed instructions to each of the terminal drones within the corresponding sub-area.

[0026] In one embodiment disclosed in the present application, the terminal drone carries a driving mechanism, a microprocessor, a high-precision sensor and a communication module, and has autonomous decision-making capabilities; the task processing steps of the terminal drone are specifically as follows:

[0027] Step S401, the communication module receives the instruction to be executed, and based on the instruction to be executed, the driving mechanism accurately adjusts the flight posture and flight altitude of the terminal drone, and then controls the terminal drone to perform operations according to the instruction to be executed;

[0028] Step S402: the high-precision sensor collects data to obtain task execution data, the task execution data is filtered and packaged by the microprocessor, and uploaded to the local edge node by the communication module.

[0029] In one embodiment disclosed in the present application, the local edge node performs task execution monitoring and local scheduling on the terminal drone within the corresponding sub-area, including:

[0030] Perform task execution monitoring on the terminal drones within the corresponding sub-area to obtain task execution status information, and upload the task execution status information to the global edge node; determine whether task execution abnormality occurs based on the task execution status information and the task execution data, and if so, readjust the working status of the terminal drones within the corresponding sub-area; and perform node automatic detection to obtain first node status information, and synchronize the first node status information to other edge computing nodes.

[0031] In one embodiment disclosed in the present application, the global edge node globally schedules the terminal drone cluster; and adjusting the edge computing node in response to abnormal situations includes:

[0032] Integrate the task execution status information uploaded by all the local edge nodes to obtain the overall task execution status information; based on the overall task execution status information, determine whether the workload of the sub-area matches the resources of the terminal drone, and if not, schedule the terminal drone between different sub-areas or adjust the task execution priority of the sub-task module;

[0033] The global edge node performs node automatic detection, obtains second node status information, and synchronizes the second node status information to other edge computing nodes; based on the second node status information and / or the first node status information, adjusts the global role and / or local role of the edge computing node.

[0034] In one embodiment disclosed in the present application, the cloud platform and edge computing nodes, the global edge nodes and local edge nodes, the edge computing nodes and terminal drones, and the cloud platform and terminal drones all communicate through a primary-backup bilateral communication link, and the primary-backup bilateral communication link includes a primary communication link and a backup communication link;

[0035] Acquiring communication quality parameters of the primary and standby bilateral communication links, and when the communication quality parameters are abnormal, the communication link automatically switches between the primary communication link and the standby communication link;

[0036] The communication protocol adopted by the primary and standby bilateral communication links is an optimized network protocol, which defines the data frame format, data transmission priority, and error detection and correction mechanism, and supports encrypted data transmission.

[0037] Compared with the prior art, the cluster UAV control method based on the cloud-edge-end architecture utilizes the cloud-edge-end architecture, determines the overall task framework, global edge nodes and local edge nodes through the cloud platform, divides the operation area into several sub-areas, divides the overall task framework into several sub-task modules corresponding to the sub-areas through the global edge nodes, and refines the sub-task modules through the local edge nodes to obtain instructions to be executed, thereby realizing hierarchical collaborative control of terminal UAVs, breaking the single-point bottleneck of traditional centralized control, reducing the difficulty of work when new UAVs are added, avoiding centralized processing of data, and improving data processing efficiency; and automatically detects and dynamically adjusts edge computing nodes to prevent node anomalies from affecting the progress of tasks, and then monitors and locally schedules the task execution of terminal UAVs within the sub-area to ensure the efficiency and coordination of task completion within the sub-area, and also globally schedules the terminal UAV cluster to realize macro-coordination and resource allocation of terminal UAVs; the terminal UAV collects task execution data and uploads the task execution data to provide a reliable data basis for the scheduling of the terminal UAV. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A schematic diagram of a flow chart of a cluster drone control method based on a cloud-edge-end architecture provided by the present invention;

[0040] Figure 2 A schematic diagram of the framework of the cloud-edge-end architecture provided by the present invention. DETAILED DESCRIPTION

[0041] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0042] The terms "including" and "having" and any variations thereof in this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of this application are only for describing specific embodiments and are not intended to limit this application. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0044] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0045] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0046] See also Figure 1 , which is a flow chart of the cluster drone control method based on the cloud-edge-end architecture provided by the present invention, see Figure 2 , which is a schematic diagram of the framework of the cloud-edge-end architecture provided by the present invention. In the cluster drone control method based on the cloud-edge-end architecture, the cloud-edge-end architecture includes a cloud platform, an edge computing node, and a terminal drone that are sequentially connected in communication. The control method includes the following steps:

[0047] The task processing steps of the cloud platform include: accepting a target task request, determining a general task framework based on the target task request and basic information stored in the cloud platform, and dividing the operation area of ​​the target task request into a plurality of sub-areas; determining a global edge node and a local edge node in the edge computing node based on the sub-areas;

[0048] The task processing steps of the global edge node are: dividing the overall task framework into a number of subtask modules with task execution priorities; performing global scheduling of terminal drones within the operating area; and adjusting the edge computing node according to abnormal situations;

[0049] The task processing steps of the local edge node are: refine the subtask module to obtain instructions to be executed; and perform task execution monitoring and local scheduling on the terminal drones within the sub-area;

[0050] The task processing steps of the terminal drone are: adjusting the flight posture based on the instruction to be executed, and performing the operation according to the instruction to be executed; collecting task execution data and uploading the task execution data.

[0051] The cluster UAV control method based on cloud-edge-end architecture utilizes the cloud-edge-end architecture, determines the overall task framework, global edge nodes and local edge nodes through the cloud platform, divides the operation area into several sub-areas, divides the overall task framework into several sub-task modules corresponding to the sub-areas through the global edge nodes, and refines the sub-task modules through the local edge nodes to obtain instructions to be executed, thereby realizing hierarchical collaborative control of terminal UAVs, breaking the single-point bottleneck of traditional centralized control, reducing the difficulty of work when new UAVs are added, avoiding centralized processing of data, and improving data processing efficiency; and automatically detects and dynamically adjusts edge computing nodes to prevent node anomalies from affecting the progress of tasks, and then monitors and locally schedules the terminal UAVs within the sub-area to ensure the efficiency and coordination of task completion within the sub-area, and also globally schedules the terminal UAV cluster to realize macro-coordination and resource allocation of terminal UAVs; the terminal UAV collects task execution data and uploads the task execution data to provide a reliable data basis for the scheduling of the terminal UAV.

[0052] Preferably, the task processing steps of the cloud platform are specifically as follows:

[0053] Step S101, accepting a target task request, storing geographic information related to the target task request and relevant parameter information of the terminal drone cluster in the distributed database of the cloud platform as basic information;

[0054] Step S102, based on the target task request and the geographic information, formulate a general task framework that meets the target task request, determine the operation area for executing the task of the target task request, and divide the operation area into a plurality of sub-areas; wherein the general task framework includes the overall task planning and the overall flight path of the terminal drone participating in executing the target task request;

[0055] Step S103, create a global role and a local role, and grant different data processing permissions to the global role and the local role; based on the geographic information corresponding to the sub-area, assign the global role and the local role to different edge computing nodes respectively, so as to obtain a global edge node and a local edge node, and the local edge node corresponds to the sub-area one-to-one.

[0056] In the above technical solution, a target task request is accepted, and the geographic information involved in executing the target task request and the relevant parameter information of the terminal UAV cluster are stored in the cloud platform to provide a basic data basis for the hierarchical planning of subsequent tasks, wherein the geographic information is but not limited to topography, obstacle distribution and no-fly zone distribution, and the relevant parameter information includes but is not limited to the initial data information, performance indicator information and historical task execution information of the terminal UAV, the initial data information includes but is not limited to the model, initial power and initial position of the terminal UAV, the performance indicator information includes but is not limited to the maximum endurance mileage, maximum load and flight speed range of the terminal UAV, and the historical task execution information includes but is not limited to the flight path and task completion status of the terminal UAV in executing historical tasks; based on The target task request and the geographic information are used to formulate an overall task plan, which includes the task purpose and task type, and based on the search algorithm, an overall flight path is formulated to plan the overall flight direction for the terminal UAV, and the overall flight path is the flight path from the take-off point to the operating area; a number of sub-areas are obtained by dividing the operating area, so that the sub-areas correspond to the layout edge nodes one by one, and local edge nodes and global edge nodes are deployed nearby according to the geographical location of the sub-areas, so that data can be processed in time, thereby improving data processing efficiency; task data is shared by global edge nodes and local edge nodes, which reduces the data processing pressure of the cloud platform, and global edge nodes and local edge nodes are distinguished by different data processing permissions, which facilitates the subsequent switching of global roles and / or local roles.

[0057] Preferably, in step S101:

[0058] The distributed database includes a number of edge computing nodes, each of which is provided with an independent database, and each of which stores cluster data information; the independent databases in different edge computing nodes are updated within a preset time period to ensure that the cluster data information in all the independent databases remains synchronized;

[0059] Perform data consistency check on the cluster data information in different edge computing nodes within a preset time period; if the check result of an edge computing node is inconsistent with that of other edge computing nodes, it is determined that data loss exists in the edge computing node, and the global role and / or local role of the edge computing node is adjusted, or the corresponding edge computing node is not selected as a global edge node or a local edge node until the next update of the independent database corresponding to the edge computing node is completed.

[0060] In the above technical scheme, the independent database corresponding to each edge computing node is a kv database, and the cluster data information in each kv database is kept in a synchronized state, so that any edge computing node can be used as a global edge node or a local edge node; data consistency check is performed to determine whether there is data loss in the kv database. When data loss occurs, if the terminal drone has taken off at this time, the global edge node adjusts the global role and / or local role of the edge computing node. If the terminal drone has not taken off at this time, the edge computing node is not selected as a global edge node or a local edge node until the kv database completes the next update, so that the cluster data information in the database is updated and improved.

[0061] Preferably, in the independent database corresponding to the global edge node, the data of the global edge node interacting with the cloud platform and the local edge node respectively are cached to obtain the first cluster data, and the first cluster data is synchronized to other edge computing nodes through the data synchronization mechanism; in the independent database corresponding to the local edge node, the data of the local edge node interacting with the terminal drone is cached to obtain the second cluster data, and the second cluster data is synchronized to other edge computing nodes through the data synchronization mechanism, so as to obtain the cluster data information composed of the first cluster data and the second cluster data.

[0062] In the above technical solution, the interactive data involved in executing the target task request is cached, so that any edge computing node, when used as a global edge node or a local edge node at any time, has complete cluster data information as a data source; and when the network communication is abnormal, the cached cluster data information can be used to control the terminal drone to continue working.

[0063] Preferably, the global edge node divides the overall task framework into a number of subtask modules with task execution priorities, including the following steps:

[0064] Step S201, based on the target task request and the geographic information of the corresponding sub-area, the overall task plan is divided into a plurality of sub-task plans corresponding to the local edge nodes one by one;

[0065] Step S202, determining the workload of each sub-area, and based on the workload, sub-task planning and related parameter information, performing regional refinement on the overall flight path to obtain a flight area path of the terminal UAV within the corresponding sub-area;

[0066] Step S203: The subtask module includes the subtask planning and the flight area path, and the task execution priority is set for the subtask module based on the task importance of the subtask module.

[0067] In the above technical solution, based on the target task request and the geographic information of the corresponding sub-area, the overall task plan is divided into several sub-task plans corresponding to the local edge node one by one, so that the target task request is refined so as to obtain more accurate processing; based on the workload, sub-task planning and related parameter information, the overall flight path is regionally refined to obtain the flight area path, that is, according to the workload of the sub-area and the corresponding sub-task planning, as well as the relevant parameter information of the terminal drone within the sub-area, the terminal drone cluster is regionally allocated to obtain several terminal drone sub-clusters corresponding to the sub-task planning one by one, and terminal drones with different functions can be allocated for different sub-task planning. Sub-areas with large workloads can be relatively allocated with more terminal drones, so as to achieve targeted and reasonable allocation of drones, wherein the flight area path is the flight path from the take-off point to the corresponding sub-area; the task execution priority is set for the sub-task module to ensure that all sub-task planning can be carried out in an orderly manner. When the task execution priority of the sub-task modules of each sub-area is the same, it means that the sub-task modules of each sub-area are executed simultaneously.

[0068] Preferably, the local edge node refines the subtask module to obtain instructions to be executed, including the following steps:

[0069] Step S301, based on the geographic information corresponding to the sub-area and the relevant parameter information of the terminal UAV, the sub-task planning and the flight area path are further refined to obtain instructions to be executed, which include task execution step instructions and detailed flight instructions corresponding to each terminal UAV within the sub-area;

[0070] Step S302: Send the to-be-executed instruction to each terminal drone within the corresponding sub-area.

[0071] In the above technical solution, based on the geographic information corresponding to the sub-area and the relevant parameter information of the terminal drone within the sub-area, the local edge node refines the sub-task planning and flight area path, and plans the task execution step instructions and detailed flight instructions for each terminal drone within the sub-area. The detailed flight instructions include the specific flight route, flight altitude and flight speed within the corresponding sub-area, so that each terminal drone can perform reasonable and accurate work based on the geographical environment and its own relevant parameter information.

[0072] Preferably, the terminal drone carries a driving mechanism, a microprocessor, a high-precision sensor and a communication module, and has autonomous decision-making capabilities; the task processing steps of the terminal drone are specifically as follows:

[0073] Step S401, the communication module receives the instruction to be executed, and based on the instruction to be executed, the driving mechanism accurately adjusts the flight posture and flight altitude of the terminal drone, and then controls the terminal drone to operate according to the instruction to be executed;

[0074] Step S402, the high-precision sensor performs data collection to obtain task execution data, the task execution data is filtered and packaged by the microprocessor, and uploaded to the local edge node by the communication module.

[0075] In the above technical solution, the terminal drone will make precise adjustments to the flight posture according to the instructions to be executed, and then operate according to the instructions to be executed, ensuring the accuracy of the flight direction of the terminal drone; according to the sub-task planning, the terminal drone can carry components corresponding to the task processing function, and each terminal drone carries a variety of high-precision sensors, including but not limited to inertial measurement units (IMUs) for measuring flight posture, GPS or Beidou dual-mode positioning modules to ensure location information, barometers for measuring flight altitude, and environmental sensors for collecting environmental information around the terminal drone, including but not limited to temperature and humidity sensors and wind speed sensors, and the flight environment of the corresponding sub-area can be sensed in real time through environmental sensors; the task execution data collected by the high-precision sensor is filtered to reduce interference signals, so that the task execution data is filtered and the data accuracy is improved. The autonomous decision-making ability of the terminal drone enables it to ensure its own safety and the basic integrity of the task according to preset emergency procedures, such as automatic return, hovering and waiting for communication recovery, etc., when communication with the edge computing node or cloud platform is interrupted.

[0076] Preferably, the local edge node performs task execution monitoring and local scheduling on the terminal drone within the corresponding sub-area, including:

[0077] Monitor the task execution of the terminal drones within the corresponding sub-area, obtain the task execution status information, and upload the task execution status information to the global edge node; based on the task execution status information and task execution data, determine whether there is an abnormal task execution situation. If so, readjust the working status of the terminal drones within the sub-area; and perform node automatic detection to obtain the first node status information, and synchronize the first node status information to other edge computing nodes.

[0078] In the above technical solution, the task execution status information includes but is not limited to the current power, position, load, working status and task execution progress of the terminal drone within the sub-area. The abnormal situation of task execution includes but is not limited to changes in the flight environment of the corresponding sub-area. When an abnormal situation of task execution occurs, other terminal drones within the corresponding sub-area are dispatched to take over the work of the terminal drone or suspend the task execution of the terminal drone. For example, when the flight environment is not conducive to the flight of the drone, the terminal drone is controlled to fly to a safe area and the execution of the task is suspended to reduce the possibility of damage to the terminal drone; the local edge node performs automatic node detection to obtain the first node status information, which includes the node status and task status of the local edge node, and synchronizes the first node status information to other edge computing nodes so that other edge computing nodes can know the status information of the local edge node.

[0079] Preferably, the global edge node performs global scheduling of the terminal drone cluster; adjusting the edge computing node in response to abnormal situations includes:

[0080] Integrate the task execution status information uploaded by all local edge nodes to obtain the overall task execution status information; based on the overall task execution status information, determine whether the workload of the sub-area matches the resources of the terminal drone. If not, schedule the terminal drone between different sub-areas or adjust the task execution priority of the sub-task module;

[0081] The global edge node performs node automatic detection, obtains second node status information, and synchronizes the second node status information to other edge computing nodes; based on the second node status information and / or the first node status information, adjusts the global role and / or local role of the edge computing node.

[0082] In the above technical solution, the task execution status information is integrated to obtain the overall execution status information, and the task execution progress of each subtask plan and the current working parameter information of the terminal drones within each sub-area are obtained, so as to obtain the overall task execution progress of the target task request. The working parameter information includes but is not limited to the current power, position, load, working status and task execution progress of all terminal drones within the operating area; when a new drone joins, the global edge node will automatically identify and pull the new drone into the terminal drone cluster, and assign tasks to the new terminal drone based on the overall task execution status information, thereby improving the efficiency of joining new drones and reducing the difficulty of work. Based on the working parameter information and the task execution progress of each subtask plan, determine whether the workload of the sub-area matches the resources of the terminal drone. If they do not match, such as when the terminal drone is low on power or in abnormal working condition and is unable to or finds it difficult to complete the given task, if the task execution priority of the subtask module corresponding to the sub-area is higher, terminal drones in other sub-areas that have sufficient power and / or have completed their own task instructions and / or terminal drones in sub-areas with lower task execution priorities of subtask modules can be dispatched for support; if the task execution priorities of the subtask modules corresponding to each sub-area are the same, then the task execution priority of the subtask module is lowered, and when a terminal drone that has completed its own task instructions appears in other sub-areas, the terminal drone is dispatched to the corresponding sub-area for support. The global edge node performs node automatic detection to obtain the second node status information, which includes the node status and task status of the global edge node, and synchronizes the second node status information to other edge computing nodes so that other edge computing nodes can know the status information of the global edge node. Based on the second node status information and / or the first node status information, the global role and / or local role of the edge computing node are adjusted, that is, according to the second node status information and / or the first node status information, if it is found that the node's task is not completed but the node fails, the global role and / or local role assigned to the node is withdrawn, and a new global edge node and / or local edge node is re-elected in the edge computing node. In addition, based on the overall execution status information collected in real time, the cloud platform will dynamically adjust the task allocation and flight path planning, and generate strategy adjustment instructions to adapt to various changes in the process of executing the target task request; when the edge computing node is normal, the strategy adjustment instruction is passed to the corresponding terminal drone through the global edge node and the local edge node in turn; when the edge computing node fails, the strategy adjustment instruction is directly sent to the corresponding terminal drone.

[0083] Preferably, the cloud platform and the edge computing node, the global edge node and the local edge node, the edge computing node and the terminal drone, and the cloud platform and the terminal drone all communicate through a primary and backup bilateral communication link, and the primary and backup bilateral communication link includes a primary communication link and a backup communication link;

[0084] Acquiring a communication quality parameter of the primary and standby bilateral communication link, and when the communication quality parameter is abnormal, automatically switching the communication link between the primary communication link and the standby communication link;

[0085] The communication protocol used by the active-standby bilateral communication link is an optimized network protocol, which defines the data frame format, data transmission priority, and error detection and correction mechanism, and supports encrypted data transmission.

[0086] In the above technical solution, the entire cloud-edge-end architecture adopts a multi-link redundant communication mode, and the backup communication link can be but is not limited to one, and the main communication link and the backup communication link may include a wired network, a wireless network and a dedicated drone communication frequency band; the communication quality parameters of the main and backup bilateral communication links are obtained, and the communication quality parameters include communication speed and signal strength. When the communication speed and / or signal strength are lower than the preset value, it automatically switches to other communication links according to the adaptive signal modulation technology and the intelligent routing algorithm; a special cloud-edge-end communication protocol is formulated, which is optimized and extended based on the existing mature network protocol (such as the TCP / IP protocol), and defines the data frame format, data transmission priority and error detection and correction mechanism; the task execution data uploaded by the terminal drone is transmitted with high priority, and a data check code is set to ensure the accuracy of the data. During the data transmission process, if the receiving end detects a data error, the sending end resends the erroneous data or repairs the data according to the error correction mechanism in the protocol; at the same time, the optimized network protocol also supports encrypted data transmission, and uses an encryption algorithm (such as the AES encryption algorithm) to encrypt the transmitted data to prevent the data from being stolen or tampered with, and to ensure the security of the interactive information.

[0087] It can be seen from the contents of the above embodiments that the cluster drone control method based on the cloud-edge-end architecture utilizes the cloud-edge-end architecture, determines the overall task framework, global edge nodes and local edge nodes through the cloud platform, divides the operation area into several sub-areas, and divides the overall task framework into several sub-task modules corresponding to the sub-areas through the global edge nodes. The sub-task modules are refined through the local edge nodes to obtain instructions to be executed, thereby realizing hierarchical collaborative control of terminal drones, breaking the single-point bottleneck of traditional centralized control, reducing the difficulty of work when new drones are added, avoiding centralized processing of data, and improving data processing efficiency; and automatically detecting and dynamically adjusting edge computing nodes to prevent node anomalies from affecting the progress of tasks, and then monitoring and locally scheduling the terminal drones within the sub-area to ensure the efficiency and coordination of task completion within the sub-area, and also globally scheduling the terminal drone cluster to achieve macro-coordination and resource allocation of terminal drones; the terminal drone collects task execution data and uploads the task execution data to provide a reliable data basis for the scheduling of the terminal drone.

[0088] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A cluster drone control method based on a cloud-edge-end architecture, wherein the cloud-edge-end architecture comprises a cloud platform, an edge computing node, and a terminal drone that are sequentially connected in communication, characterized in that: The control method comprises the following steps: The task processing steps of the cloud platform include: accepting a target task request, determining a general task framework based on the target task request and basic information stored in the cloud platform, and dividing the operation area of ​​the target task request into a plurality of sub-areas; determining a global edge node and a local edge node in the edge computing node based on the sub-areas; The task processing step of the global edge node: dividing the overall task framework into a number of subtask modules with task execution priorities; and globally scheduling the terminal drones within the operating area; and adjusting the edge computing nodes according to abnormal situations; The task processing step of the local edge node: refine the subtask module to obtain instructions to be executed; and perform task execution monitoring and local scheduling on the terminal drone within the corresponding sub-area; The task processing steps of the terminal drone are: adjusting the flight posture based on the instructions to be executed, and performing operations according to the instructions to be executed; collecting task execution data and uploading the task execution data.

2. The method for controlling a swarm of drones based on a cloud-edge-end architecture according to claim 1, characterized in that: The task processing steps of the cloud platform are specifically as follows: Step S101, accepting a target task request, and storing geographic information related to the target task request and relevant parameter information of the terminal drone cluster in a distributed database of the cloud platform as basic information; Step S102, based on the target task request and the geographic information, formulate a general task framework that meets the target task request, determine the operation area for executing the task of the target task request, and divide the operation area into a plurality of sub-areas; wherein the general task framework includes the overall task planning and the overall flight path of the terminal UAV participating in executing the target task request; Step S103, create a global role and a local role, grant different data processing permissions to the global role and the local role; based on the geographic information corresponding to the sub-area, assign the global role and the local role to different edge computing nodes respectively, so as to obtain global edge nodes and local edge nodes, and the local edge nodes correspond one-to-one to the sub-area.

3. The method for controlling a swarm drone based on a cloud-edge-end architecture according to claim 2 is characterized in that: In step S101: The distributed database includes a plurality of edge computing nodes, each of which is provided with an independent database, and each of which stores cluster data information; the independent databases in different edge computing nodes are updated within a preset time period to ensure that the cluster data information in all the independent databases remains synchronized; Perform data consistency check on the cluster data information in different edge computing nodes within a preset time period; if the check result of one edge computing node is inconsistent with that of other edge computing nodes, determine that data loss exists in the edge computing node, adjust the global role and / or local role of the edge computing node, or do not select the corresponding edge computing node as the global edge node or local edge node until the independent database corresponding to the edge computing node completes the next update.

4. The method for controlling a swarm of drones based on a cloud-edge-end architecture according to claim 3 is characterized in that: In the independent database corresponding to the global edge node, the data interacting with the global edge node and the cloud platform and the local edge node are cached to obtain the first cluster data, and the first cluster data is synchronized to the other edge computing nodes through the data synchronization mechanism; in the independent database corresponding to the local edge node, the data interacting with the local edge node and the terminal drone are cached to obtain the second cluster data, and the second cluster data is synchronized to the other edge computing nodes through the data synchronization mechanism, so as to obtain the cluster data information composed of the first cluster data and the second cluster data.

5. The method for controlling a swarm of drones based on a cloud-edge-end architecture according to claim 4 is characterized in that: The global edge node divides the overall task framework into a plurality of subtask modules with task execution priorities, including the following steps: Step S201, based on the target task request and the geographic information of the corresponding sub-area, the overall task plan is divided into a plurality of sub-task plans corresponding to the local edge nodes one by one; Step S202, determining the workload of each sub-area, and based on the workload, sub-task planning and related parameter information, performing regional refinement on the overall flight path to obtain a flight area path of the terminal UAV within the sub-area range; Step S203, the subtask module includes the subtask planning and the flight area path, and the task execution priority is set for the subtask module based on the task importance of the subtask module.

6. The method for controlling a swarm of drones based on a cloud-edge-end architecture according to claim 5 is characterized in that: The local edge node refines the subtask module to obtain instructions to be executed, including the following steps: Step S301, based on the geographic information corresponding to the sub-area and the relevant parameter information of the terminal UAV, the sub-task planning and the flight area path are further refined to obtain instructions to be executed, wherein the instructions to be executed include task execution step instructions and detailed flight instructions corresponding to each of the terminal UAVs within the sub-area; Step S302: Send the to-be-executed instructions to each of the terminal drones within the corresponding sub-area.

7. The method for controlling a swarm of drones based on a cloud-edge-end architecture according to claim 6, characterized in that: The terminal drone carries a driving mechanism, a microprocessor, a high-precision sensor and a communication module, and has autonomous decision-making capabilities; the task processing steps of the terminal drone are specifically as follows: Step S401, the communication module receives the instruction to be executed, and based on the instruction to be executed, the driving mechanism accurately adjusts the flight posture and flight altitude of the terminal drone, and then controls the terminal drone to perform operations according to the instruction to be executed; Step S402: the high-precision sensor collects data to obtain task execution data, the task execution data is filtered and packaged by the microprocessor, and uploaded to the local edge node by the communication module.

8. The method for controlling a swarm of drones based on a cloud-edge-end architecture according to claim 7, characterized in that: The local edge node performs task execution monitoring and local scheduling on the terminal drone within the corresponding sub-area, including: Perform task execution monitoring on the terminal drones within the corresponding sub-area to obtain task execution status information, and upload the task execution status information to the global edge node; determine whether task execution abnormality occurs based on the task execution status information and the task execution data, and if so, readjust the working status of the terminal drones within the corresponding sub-area; and perform node automatic detection to obtain first node status information, and synchronize the first node status information to other edge computing nodes.

9. The method for controlling a swarm of drones based on a cloud-edge-end architecture according to claim 8, characterized in that: The global edge node globally schedules the terminal drone cluster; and adjusting the edge computing node in response to abnormal situations includes: Integrate the task execution status information uploaded by all the local edge nodes to obtain the overall task execution status information; based on the overall task execution status information, determine whether the workload of the sub-area matches the resources of the terminal drone, and if not, schedule the terminal drone between different sub-areas or adjust the task execution priority of the sub-task module; The global edge node performs node automatic detection, obtains second node status information, and synchronizes the second node status information to other edge computing nodes; based on the second node status information and / or the first node status information, adjusts the global role and / or local role of the edge computing node.

10. The method for controlling swarm drones based on cloud-edge-end architecture according to claim 1, characterized in that: The cloud platform and edge computing nodes, global edge nodes and local edge nodes, edge computing nodes and terminal drones, and cloud platform and terminal drones all communicate through primary and backup bilateral communication links, and the primary and backup bilateral communication links include a primary communication link and a backup communication link; Acquiring communication quality parameters of the primary and standby bilateral communication links, and when the communication quality parameters are abnormal, the communication link automatically switches between the primary communication link and the standby communication link; The communication protocol adopted by the primary and standby bilateral communication links is an optimized network protocol, which defines the data frame format, data transmission priority, and error detection and correction mechanism, and supports encrypted data transmission.

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