Cloud collaborative unmanned aerial vehicle remote control method and system

Abstract

The invention discloses a cloud collaborative unmanned aerial vehicle remote control method and system, and particularly relates to the technical field of collaborative control. The method comprises the following steps: collecting flight control state data of a cloud control node and a local edge node, performing preprocessing and time domain alignment, and generating state synchronous input data; generating a control right competition judgment result by analyzing the control activity and the master control priority; generating control instruction conflict judgment information by evaluating the source consistency of the control instruction and the flight control execution conflict risk; performing synchronous fusion on the control right competition judgment result and the control instruction conflict judgment information to generate a control right switching coordination instruction, sensing real-time link state information, and updating state mapping information in a control right handover process; and based on the control right switching coordination instruction and the updated state mapping information, a control right uniqueness confirmation mechanism is executed, selection, confirmation and issuing of a control instruction are completed, and uniqueness, continuity and safety of an unmanned aerial vehicle control chain are ensured.

Description

Technical Field

[0001] The present invention relates to the field of collaborative control technology, and more specifically, to a cloud-coordinated drone remote control method and system. Background Art

[0002] Existing drone remote control systems, when using a cloud-based collaborative architecture, often face link failures, leading to frequent handovers of control between cloud control nodes and local edge nodes. Due to latency differences in control state perception, asynchronous data updates, and inconsistent control command generation logic between cloud and edge nodes, multiple nodes can easily issue control commands simultaneously. This can lead to flight command conflicts, control chain breaks, or abnormal flight control behavior, impacting the continuity of drone mission execution and flight safety. Summary of the Invention

[0003] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a cloud-coordinated drone remote control method and system to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions: A cloud-coordinated drone remote control method comprises the following steps: S1. Collect flight control status data from the cloud control node and local edge node in the current control chain, perform preprocessing and time domain alignment, and generate state synchronization input data; S2. Based on the state synchronization input data, the control activity and master control priority of the cloud control node and the local edge node are analyzed to generate the control right competition judgment result; S3. Evaluate the source consistency of control instructions and the flight control execution conflict risk based on the state synchronization input data, and generate control instruction conflict determination information; S4. Synchronize and integrate the control right competition judgment result and the control instruction conflict judgment information to generate a control right switching coordination instruction; S5. Based on the control handover coordination instruction, perceive the real-time link status information and update the status mapping information during the control handover process; S6. Based on the control right switching coordination instruction and the updated state mapping information, the control right uniqueness confirmation mechanism is executed to complete the selection, confirmation and issuance of the control instruction.

[0005] In a preferred embodiment, S1 is specifically: The flight control status data of the UAV is collected through the cloud control node and the local edge node respectively; The flight control status data collected by the cloud control node and the local edge node are processed in a unified data format; With the time base of the cloud control node as a reference, the flight control state data that has been uniformly processed is corrected in terms of time axis and aligned in time domain to generate state synchronization input data.

[0006] In a preferred embodiment, S2 is specifically: Parse the state synchronization input data to obtain the control instruction generation timestamp and task priority identifier of the cloud control node and local edge node respectively; The control instruction update frequency is calculated based on the control instruction generation timestamp to obtain the control activity index of the cloud control node and the local edge node; Obtain the master control priority based on the task priority identifiers of the cloud control node and the local edge node; The control activity index and the master control priority are weighted according to the preset weight coefficient to obtain the control capability score; Based on the control capability score comparison results of the cloud control node and the local edge node, the control right competition judgment result is generated.

[0007] In a preferred embodiment, S3 is specifically: Parse state synchronization input data to obtain control instructions and flight attitude data from the cloud control node and local edge node respectively; Compare the control instructions of the cloud control node and the local edge node to determine whether the sources of the control instructions are consistent; Match control commands from inconsistent sources with flight attitude data to identify control commands that cause conflicts in UAV flight control execution; Determine the flight control execution conflict risk level between the cloud control node and the local edge node based on the control instructions that cause the UAV flight control execution conflict; Based on the flight control execution conflict risk level, control instruction conflict determination information is generated.

[0008] In a preferred embodiment, S4 is specifically: Analyze the control rights competition judgment results and determine the target control node that obtains control rights between the cloud control node and the local edge node; Analyze the control command conflict determination information to obtain the flight control execution conflict risk level between the cloud control node and the local edge node; Based on the control capability score of the target control node and the flight control execution conflict risk level, the control right switching coordination instruction is generated according to the preset conflict coordination rules.

[0009] In a preferred embodiment, S5 is specifically: Real-time monitoring of the link status information between the drone and the cloud control node and the local edge node; Match the link real-time status information with the target control node information in the control right switching coordination instruction to generate a real-time status mapping relationship of the target control node; Based on the mapping relationship between the control right execution identifier and the real-time state in the control right switching coordination instruction, the state mapping information of the target control node during the control right handover process is updated.

[0010] In a preferred embodiment, S6 is specifically: Compare the target control node information in the control switching coordination instruction with the real-time control validity information and link stability information in the updated state mapping information to confirm the effective control right of the target control node; According to the effective control right of the target control node, a control instruction corresponding to the target control node is selected as an effective control instruction; Perform timing confirmation on valid control instructions and add control right execution mark to generate confirmed control instructions; The confirmed control instructions are sent to the drone execution end in real time through the target control node.

[0011] In another aspect, the present invention provides a cloud-coordinated drone remote control system, comprising: Flight control data processing module: collects flight control status data from the cloud control node and local edge node in the current control chain, performs preprocessing and time domain alignment, and generates state synchronization input data; Active Priority Analysis Module: Based on state synchronization input data, it analyzes the control activity and master control priority of cloud control nodes and local edge nodes, and generates control competition judgment results; Command conflict assessment module: This module evaluates the source consistency of control commands and the risk of flight control execution conflicts based on state synchronization input data, and generates control command conflict determination information. Control coordination fusion module: Synchronizes and integrates the control right competition judgment results with the control instruction conflict judgment information to generate the control right switching coordination instructions; Link status perception module: Based on the control handover coordination instructions, it perceives the real-time link status information and updates the status mapping information during the control handover process; Control uniqueness confirmation module: Based on the control right switching coordination instruction and the updated state mapping information, it executes the control right uniqueness confirmation mechanism to complete the selection, confirmation and issuance of the control instruction.

[0012] The technical effects and advantages of the cloud-coordinated drone remote control method and system of the present invention are as follows: By preprocessing and aligning the flight control status data of the cloud control node and the local edge node in the time domain, the basic consistency of the data is ensured; by analyzing the control activity, master control priority, source consistency of control instructions and flight control execution conflict risks, accurate judgment of control ownership and instruction conflicts is achieved; by synchronously integrating the control competition judgment results with the control instruction conflict judgment information, control authority switching coordination instructions are generated; the introduction of link real-time status perception improves the real-time response capability during control handover; through the uniqueness confirmation mechanism, the single source and security of the control instructions are guaranteed, effectively avoiding the problem of dual control instruction conflicts caused by inconsistent status during the control right alternation process, and improving the stability, consistency and security of the UAV remote control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a schematic diagram of a cloud-coordinated drone remote control method according to the present invention; Figure 2 This is a structural diagram of a cloud-coordinated drone remote control system of the present invention. DETAILED DESCRIPTION

[0014] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0015] Example 1 Figure 1 The present invention provides a cloud-coordinated drone remote control method, which includes the following steps: S1. Collect flight control status data from the cloud control node and local edge node in the current control chain, perform preprocessing and time domain alignment, and generate state synchronization input data; S2. Based on the state synchronization input data, the control activity and master control priority of the cloud control node and the local edge node are analyzed to generate the control right competition judgment result; S3. Evaluate the source consistency of control instructions and the flight control execution conflict risk based on the state synchronization input data, and generate control instruction conflict determination information; S4. Synchronize and integrate the control right competition judgment result and the control instruction conflict judgment information to generate a control right switching coordination instruction; S5. Based on the control handover coordination instruction, perceive the real-time link status information and update the status mapping information during the control handover process; S6. Based on the control right switching coordination instruction and the updated state mapping information, the control right uniqueness confirmation mechanism is executed to complete the selection, confirmation and issuance of the control instruction.

[0016] S1. Collect flight control status data from the cloud control node and local edge node in the current control chain, perform preprocessing and time domain alignment, and generate state synchronization input data, including: The flight control status data of the UAV is collected through the cloud control node and the local edge node respectively; A cloud control node refers to a server or virtual computing resource located in a cloud environment, typically deployed at a ground-based remote control center. It communicates with the drone via network, maintaining data transmission and issuing control commands. A local edge node refers to an edge computing device deployed close to the drone, equipped with local computing and processing capabilities. It can communicate with the drone at close range, collect control status data, and perform real-time control operations. Flight control status data includes parameters that characterize the flight control status of the drone during its current flight, including real-time flight attitude data, mission execution status data, control commands, and corresponding control command timestamp data. Flight attitude data includes the drone's real-time altitude, speed, flight direction, body inclination, roll, and pitch angles during flight; mission execution status data includes the type of flight mission currently being executed by the drone and its corresponding real-time progress status; control commands include the command content and corresponding command number used to control the drone's flight maneuvers; and control command timestamp data refers to the time corresponding to the generation, transmission, or reception of each control command. The cloud control node and the local edge node establish communication links with the UAV respectively, and collect flight control status data in real time through the links to obtain the flight control status data of the UAV measured and generated by the cloud control node and the local edge node respectively at the same flight time.

[0017] For example, when a drone is conducting a patrol mission, the cloud control node collects real-time flight control status data, including the current drone attitude, flight speed, flight trajectory, and mission progress, via a remote satellite link or ground network. Simultaneously, the local edge node where the drone is located also collects the same type of flight control status data in real time via short-range wireless communications (such as a local area network or dedicated communication link).

[0018] The flight control status data collected by the cloud control node and the local edge node are processed in a unified data format; The flight control status data collected by the cloud control node and local edge nodes may differ in data format due to differences in collection equipment, communication protocols, or data representation. Therefore, unified processing is necessary to ensure compatibility and comparability of the separately collected flight control status data. Data format unification involves converting and re-encoding the separately collected flight control status data using a unified data protocol or specification to eliminate format differences and ensure consistency in parameter definitions, data units, numerical ranges, and data structures for each data item. For example, if the flight attitude data collected by the cloud control node is represented in floating-point format and the flight attitude data collected by the local edge node is represented in integer format, data format unification can convert the integer data into a floating-point format consistent with that of the cloud control node. This allows for accurate data comparison and analysis between the data collected by the cloud control node and the local edge node. For control command timestamp data, the separately collected timestamp data is also converted to the same time zone and standard format to avoid analysis errors caused by time zone differences or data representation discrepancies.

[0019] For example, when the cloud control node uses the Universal Time (UTC) to represent the timestamp of the drone control status data, and the local edge node uses the local standard time (Beijing time), the timestamp data collected by the local edge node needs to be converted into the Universal Time (UTC) according to the unified processing rules to ensure that the time representation of all data is completely consistent.

[0020] Using the cloud control node's time base as a reference, the flight control state data that has been uniformly processed is time-axis corrected and time-domain aligned to generate state synchronization input data. Using the cloud control node's time base as a reference, the flight control state data of the cloud control node and local edge node, after unified processing, are mapped to a unified time axis, and any time deviations or clock differences are corrected and compensated. Time axis correction and time domain alignment, including methods such as linear interpolation, time resampling, or time axis offset compensation, ensure that the data of the cloud control node and local edge node are completely corresponding and matched at any given point in time, thereby generating state-synchronized input data. State-synchronized input data refers to the set of flight control state data that, after processing, corresponds to the same flight time and has a completely consistent data format for the cloud control node and local edge node.

[0021] For example, if the timestamp of a flight attitude data point in the flight control status data collected by the cloud control node is "11:10:01," and the timestamp of the corresponding flight attitude data point on the local edge node is displayed as "11:10:03" due to factors such as transmission delay, then time axis correction and time domain alignment technology are used to offset the timestamp of the flight attitude data point corresponding to the local edge node by 2 seconds, so that the two sets of data are precisely matched at the time of "11:10:01." The resulting state synchronization input data is a set of flight control status data with completely consistent time dimensions and a unified format that can simultaneously represent the cloud control node and the local edge node.

[0022] S2. Based on the state synchronization input data, the control activity and master control priority of the cloud control node and the local edge node are analyzed to generate the control competition judgment results, including: Parse the state synchronization input data to obtain the control instruction generation timestamp and task priority identifier of the cloud control node and local edge node respectively; State synchronization input data is a collection of flight control state data with fully aligned time dimensions and a unified data format. Parsing involves extracting and identifying specific control command generation timestamps and task priority identifiers from the state synchronization input data. The control command generation timestamp refers to the precise time information corresponding to the generation of each flight control command by the cloud control node and the local edge node. The task priority identifier refers to the urgency or importance of the UAV's current task. For example, if a UAV is simultaneously assigned a patrol mission and an obstacle avoidance mission, the obstacle avoidance mission has a higher urgency and a corresponding task priority identifier of high, while the patrol mission has a task priority identifier of medium. By parsing the state synchronization input data, the timestamps and corresponding task priority identifiers of each control command generated by the cloud control node and the local edge node at the same time are extracted. For example, if the cloud control node generates a control command for the UAV to fly east, the corresponding control command generation timestamp is "12:00:10" and the task priority identifier is medium. Simultaneously, the local edge node generates a control command for the UAV to urgently deviate north, with the corresponding control command generation timestamp being "12:00:10" and the task priority identifier being high.

[0023] The control instruction update frequency is calculated based on the control instruction generation timestamp to obtain the control activity index of the cloud control node and the local edge node; The control instruction update frequency is calculated by dividing the total number of control instructions generated within a predetermined statistical period by the corresponding statistical duration. This yields the control instruction update frequency, which reflects the level of activity of the cloud control node and local edge node in generating control instructions for the drone per unit time. The control activity index, a quantitative parameter that uses the control instruction update frequency to represent the activity level of the cloud control node and local edge node, is typically expressed as the average number of control instructions generated per second. For example, the cloud control node generated 20 flight control instructions in the past 10 seconds, with a control instruction update frequency of 20 / 10 seconds, or 2 control instructions per second. The local edge node generated 50 flight control instructions in the same 10 seconds, with a control instruction update frequency of 50 / 10 seconds, or 5 control instructions per second. Therefore, the control activity index of the local edge node is higher than that of the cloud control node.

[0024] Obtain the master control priority based on the task priority identifiers of the cloud control node and the local edge node; The master control priority is hierarchical information that describes the importance and priority of the competition between the cloud control node and the local edge node for control of the drone. There is a pre-defined mapping relationship between the task priority identifier and the master control priority, that is, the higher the task priority, the higher the corresponding master control priority. For example, the instruction generated by the cloud control node is a patrol task, and the task priority identifier is medium, so the master control priority corresponding to the cloud control node is set to secondary priority; if the obstacle avoidance instruction task priority identifier generated by the local edge node is high, the master control priority corresponding to the local edge node is set to the highest priority, thereby determining the priority between the cloud control node and the local edge node in the competition for control.

[0025] The control activity index and the master control priority are weighted according to the preset weight coefficient to obtain the control capability score; The master control priority levels (such as highest, secondary, and low) are converted into quantitative values, such as setting the highest priority to 10 points, the secondary priority to 5 points, and the low priority to 1 point. Weight coefficients are assigned to the control activity index and the master control priority score, for example, the control activity index has a weight of 0.4 and the master control priority score has a weight of 0.6. The weight coefficients are used to weight the control activity index and the master control priority score to obtain the control capability score. For example, if the cloud control node's control activity index is 2 instructions / second (converted to a score of 2 points) and the master control priority score is 5 points, the cloud control node's control capability score is 2 × 0.4 + 5 × 0.6 = 3.8 points. If the local edge node's control activity index is 5 instructions / second (score of 5 points) and the master control priority score is 10 points, the local edge node's control capability score is 5 × 0.4 + 10 × 0.6 = 8 points, thus reflecting the difference in control capability between the cloud control node and the local edge node.

[0026] Generate control rights competition judgment results based on the control capability score comparison results of cloud control nodes and local edge nodes; Based on the control capability scores of cloud control nodes and local edge nodes, a comparison is performed between the cloud control node and the local edge node. The node with the higher control capability score is determined to be the node that obtains control, and a control competition judgment result is generated, indicating the outcome of the competition. The control competition judgment result includes the node type (cloud or edge) to which control rights are assigned and the corresponding control capability score, clarifying the direction of control transfer. For example, the local edge node's control capability score is 8, which is 3.8 points higher than the cloud control node's score. Therefore, based on the control capability score comparison result, the generated control competition judgment result is: the node type to which control rights are assigned is the local edge node, and the control capability score is 8.

[0027] S3. Based on the state synchronization input data, evaluate the source consistency of the control instructions and the flight control execution conflict risk, and generate control instruction conflict determination information, including: Parse state synchronization input data to obtain control instructions and flight attitude data from the cloud control node and local edge node respectively; Flight attitude data includes the real-time altitude, speed, flight direction, body inclination, roll angle, and pitch angle of the drone during flight. Control instructions include the instruction content and corresponding instruction number used to control the drone's flight maneuvers. For example, the control instruction generated by the cloud control node at a certain moment is "Continue flying east and stabilize the flight altitude at 100 meters." The instruction number is instruction 005, and the corresponding flight attitude data is an altitude of 98 meters, a flight speed of 5 meters per second, a flight direction due east, a body inclination of 1°, a roll angle of 0°, and a pitch angle of 0°. At the same time, the control instruction generated by the local edge node is "Emergency increase the flight altitude to 150 meters and deviate to the north." The instruction number is instruction 012, and the corresponding flight attitude data is an altitude of 98 meters, a flight speed of 4.5 meters per second, a flight direction due east, a body inclination of 1.5°, a roll angle of 0°, and a pitch angle of 0.5°.

[0028] Compare the control instructions of the cloud control node and the local edge node to determine whether the sources of the control instructions are consistent; The control instruction comparison pointer compares the specific content of the parsed control instructions to determine whether there are any differences or conflicts between the control instructions issued by the cloud control node and the local edge node to the drone, and then determines whether the control instructions of the cloud control node and the local edge node are derived from the same or consistent control decisions. When the content of the control instructions of the cloud control node and the local edge node is the same, it is determined that the sources are consistent. When the content of the control instructions of the cloud control node and the local edge node is different, such as conflicts or contradictions in direction, speed, or altitude adjustments, the control instructions are determined to be inconsistent in source.

[0029] For example, the cloud control node generates a control instruction for the drone to continue flying east and maintain an altitude of 100 meters, while the local edge node generates a control instruction for the drone to deviate north and ascend to an altitude of 150 meters. Because the flight directions and altitudes of the control instructions generated by the cloud control node and the local edge node differ and conflict, the control instructions generated by the cloud control node and the local edge node are determined to have inconsistent sources.

[0030] Match control commands from inconsistent sources with flight attitude data to identify control commands that cause conflicts in UAV flight control execution; The control instructions from the cloud control node and the local edge node that are inconsistent are compared with the real-time flight attitude data of the current UAV to determine whether the control instructions will cause conflicts in the UAV's flight actions or trajectories after being executed in the current flight state.

[0031] For example, a command from the cloud control node requires the drone to maintain an eastward flight altitude of 100 meters, while a command from the local edge node requires the drone to deviate to the north and ascend to an altitude of 150 meters. By matching this with the flight attitude data (altitude 98 meters, flight direction due east), it is possible to identify and point out that the cloud control node's command and the local edge node's command conflict in flight direction and altitude, thereby identifying the control commands that may cause the drone's action conflict as the two control commands generated by the cloud control node and the local edge node.

[0032] Determine the flight control execution conflict risk level between the cloud control node and the local edge node based on the control instructions that cause the UAV flight control execution conflict; The Flight Control Execution Conflict Risk Level (FCC) indicates the severity of potential flight maneuver conflicts or safety issues when a drone executes different control commands generated by the cloud control node and local edge nodes. The risk level is categorized into three levels: high, medium, and low. A high risk level indicates that a control command conflict could cause safety issues or prevent normal flight. A medium risk level indicates that a conflict exists but poses no safety risk. A low risk level indicates that there is little or no conflict.

[0033] For example, because the cloud control node requires maintaining the flight altitude to the east, and the local edge node requires rising and turning to the north, the control instructions conflict in direction and altitude, which may cause safety problems for the drone or prevent it from flying normally. It is judged as a high-risk level to reflect the situation where the drone's flight attitude may change drastically or the safety risk may increase significantly when executing control instructions.

[0034] Generate control command conflict determination information based on the flight control execution conflict risk level; Control command conflict determination information is a comprehensive assessment of the flight control execution conflict risk level. This information includes control command source consistency information, the specific content of the conflicting control command, and the flight control execution conflict risk level. This information is used to guide control coordination and handover operations to ensure flight safety and control link continuity.

[0035] For example, the control command conflict determination information is as follows: the control command sources are inconsistent; the specific content of the conflicting control commands is the cloud control node's "continue eastward flight at an altitude of 100 meters" and the local edge node's "urgently ascend to 150 meters and fly north"; the flight control execution conflict risk level is high. This control command conflict determination information can be used to coordinate control rights.

[0036] S4. Synchronously integrate the control right competition judgment result and the control instruction conflict judgment information to generate a control right switching coordination instruction, including: Analyze the control rights competition judgment results and determine the target control node that obtains control rights between the cloud control node and the local edge node; By comparing the control capability scores of the cloud control node and the local edge node, the target control node that receives control rights is determined. For example, if the cloud control node's control capability score is 3.8 and the local edge node's control capability score is 8, the local edge node is determined as the target control node by comparing the control capability scores of the cloud control node and the local edge node, indicating that the local edge node currently has primary authority to implement flight control of the drone.

[0037] Analyze the control command conflict determination information to obtain the flight control execution conflict risk level between the cloud control node and the local edge node; The flight control execution conflict risk level is a classification of the security risks that may be caused by the control instructions generated by the cloud control node and the local edge node during the execution of the drone, including high risk, medium risk and low risk.

[0038] For example, the instructions generated by the cloud control node require the drone to maintain an altitude of 100 meters in an eastward direction, while the instructions generated by the local edge node require the drone to rise to 150 meters and deviate to the north. The flight posture conflicts of the control instructions are obvious and are judged to be at a high risk level.

[0039] Based on the control capability score of the target control node and the flight control execution conflict risk level, a control handover coordination instruction is generated according to the preset conflict coordination rules; The preset conflict coordination rules are a set of predefined judgment criteria and conditions. For example, when the flight control execution conflict risk level is high, the control right is immediately given to the node with the highest control capability score, and the control instruction execution of the conflicting node is terminated; when the flight control execution conflict risk level is medium, the control right switch is suspended or the instruction execution priority of the conflicting node is lowered; when the flight control execution conflict risk level is low, the coordinated execution is allowed to continue.

[0040] For example, if the local edge node's control capability score is 8 and the flight control execution conflict risk level is high, according to the preset conflict coordination rules, control will be immediately transferred to the node with the higher control capability score under high-risk conditions. The generated control handover coordination instruction includes the target control node information as the local edge node and the corresponding control execution identifier, which is a combination of numbers or characters used to represent the control ownership. For example, the control execution identifier is "edge control identifier 001", indicating that the drone is currently under effective control of the local edge node.

[0041] For example, if the drone is initially controlled by the cloud control node, and there is a serious conflict between commands from the cloud control node and the local edge node, resulting in a high risk level for flight control execution conflicts, then control will be transferred to the local edge node according to the conflict coordination rules, generating a control transfer coordination instruction. The control transfer coordination instruction is: "Target control node: local edge node; Control execution identifier: edge control identifier 001." This control transfer coordination instruction is used to confirm the uniqueness of control, thereby preventing safety issues caused by control confusion, double execution, or drastic changes in flight attitude.

[0042] S5. Based on the control handover coordination instruction, perceive the real-time link status information and update the status mapping information during the control handover process, including: Real-time monitoring of the link status information between the drone and the cloud control node and the local edge node; Real-time link status information refers to the real-time communication status data set of the communication links between the drone and the cloud control node, as well as between the drone and the local edge node. This includes information about communication link delays, link interruptions or stability status, and the real-time data transmission rate of the link. Real-time monitoring refers to the real-time and continuous data collection, analysis, and status tracking of the communication links between the drone and the cloud control node, as well as the local edge node, through monitoring devices to ensure the quality, stability, and reliability of communication between the drone and each node.

[0043] For example, when a drone is performing a patrol mission, the cloud control node communicates with the drone over long distances through a satellite communication link. At this time, the real-time status information of the link includes the real-time delay of the satellite link's current data transmission of 200 milliseconds, the communication link rate of 5 megabits per second, and the link status is stable and uninterrupted; the local edge node communicates with the drone at close range through local wireless communication, and real-time monitoring obtains the real-time status information of the link as a data delay of 10 milliseconds, a real-time communication rate of 20 megabits per second, and the communication link status is also stable and uninterrupted.

[0044] Match the link real-time status information with the target control node information in the control right switching coordination instruction to generate a real-time status mapping relationship of the target control node; The control handover coordination instruction includes the target control node information and the corresponding control execution identifier. The link real-time status information is used to match the target control node information, including the real-time delay, communication stability and data transmission rate of the link data of the target control node (such as the local edge node) monitored in the link real-time status information, and the corresponding verification with the target control node information indicated by the control handover coordination instruction, thereby forming a real-time status mapping relationship of the target control node. The real-time status mapping relationship represents the correspondence between the target control node information and the link real-time status information, which is used to characterize whether the target control node is currently suitable for taking over the control of the drone immediately and whether its link status can support the real-time control of the drone.

[0045] For example, if the control handover coordination instruction specifies that the target control node is a local edge node and the corresponding control execution identifier is "edge control identifier 001," the real-time monitored link status information is: the local edge node communication link real-time delay is 10 milliseconds, the link rate is 20 megabits per second, and the communication link is stable and reliable. By matching the local edge node information in the control handover coordination instruction with the real-time link status information, a real-time status mapping relationship is formed: "target control node: local edge node, communication delay: 10 milliseconds, communication rate: 20 megabits per second, link status: stable and reliable."

[0046] Based on the mapping relationship between the control right execution identifier and the real-time state in the control right handover coordination instruction, the state mapping information of the target control node during the control right handover process is updated; The state mapping information is a comprehensive information set used to reflect the current control effectiveness and link stability of the target control node during the drone control handover process. Specifically, the target control node state mapping information is updated based on the control execution identifier in the control handover coordination instruction and the real-time state mapping relationship. Real-time control effectiveness information indicates the target control node's current ability to actually control the drone's actions, as reflected in whether it can send valid control instructions immediately and whether the drone responds to valid control instructions in real time. Link stability information indicates the current quality and reliability of the target control node's communication link, such as delay indicators, data transmission rate, and whether the communication link status is continuously stable.

[0047] For example, when the target control node is a local edge node, the real-time state mapping relationship determines that the updated state mapping information includes: "Target control node: local edge node; Control validity information: real-time communication delay of 10 milliseconds, capable of issuing real-time commands; Link stability information: real-time link status is stable, communication rate is 20 megabits per second, and there are no interruptions." By updating this state mapping information in real time, the control uniqueness confirmation mechanism can determine whether the target control node can immediately take over control and effectively complete the real-time transmission and execution of drone control commands. This avoids control interruptions, command conflicts, or drone flight safety issues caused by unclear state information during control handover.

[0048] For example, the drone is initially controlled by the cloud control node. Because the conflict risk level in the flight conflict determination information is high, control is switched to the local edge node with a higher control capability score according to the coordination rules. Real-time monitoring of the link status information shows that the local edge node link has a very stable communication state. After matching the link status information with the local edge node information in the control handover coordination instruction, the real-time state mapping relationship is determined to be stable and reliable for the local edge node link, with a delay as low as 10 milliseconds. Based on the real-time state mapping relationship, the updated state mapping information includes real-time control validity information and link stability information, indicating that the local edge node is currently suitable to immediately take over the drone's flight control authority.

[0049] S6. Based on the control right handover coordination instruction and the updated state mapping information, the control right uniqueness confirmation mechanism is executed to complete the selection, confirmation, and issuance of the control instruction, including: Compare the target control node information in the control switching coordination instruction with the real-time control validity information and link stability information in the updated state mapping information to confirm the effective control right of the target control node; Specifically, the target control node's real-time control validity information is checked and verified to ensure it meets real-time control requirements, and its link stability information provides sufficient communication reliability to determine whether the target control node actually possesses the ability to effectively control the drone. Confirming the target control node's effective control means that the target control node currently possesses a stable communication link and the ability to send real-time commands, enabling it to effectively and safely control the drone.

[0050] For example, after the local edge node is designated as the target control node by the control switching coordination instruction, it is found through comparison that the real-time link state delay of the local edge node is 10 milliseconds, the rate is 20 megabits per second, and it is stable and reliable, with good real-time communication capabilities. The control effectiveness information shows that the node can send instructions in real time and the drone can respond immediately. Therefore, through comparison, it is confirmed that the local edge node actually has effective control rights and is suitable for immediate implementation of real-time and effective control of the drone.

[0051] According to the effective control right of the target control node, a control instruction corresponding to the target control node is selected as an effective control instruction; The target control node is the node that has been verified and confirmed to have the ability to effectively control the drone, for example, a local edge node. Based on the confirmed effective control rights of the target control node, the real-time and valid flight control instructions generated by the local edge node are selected and designated as the only valid control instructions actually executed by the drone, thus avoiding conflicts or duplicate execution of instructions from other nodes.

[0052] For example, if the local edge node generates a real-time command, "The drone should immediately increase its altitude to 150 meters and deviate north," while the cloud control node generates a command, "The drone should maintain an altitude of 100 meters and continue eastward," creating a conflicting control command. After confirming the local edge node's valid control authority, the command generated by the local edge node, "Immediately increase its altitude to 150 meters and fly north," is selected as the sole valid control command, disregarding the command generated by the cloud control node. This avoids potential confusion and safety risks during drone control execution.

[0053] Perform timing confirmation on valid control instructions and add control right execution mark to generate confirmed control instructions; A valid control command is a unique, valid command generated by a selected target control node and confirmed to be executed by the drone, such as the command "Immediately increase altitude to 150 meters and fly north." Timing verification verifies and confirms the generation time of the control command, ensuring that its timing information is accurate and corresponds to the current real-time state, without invalid or delayed control due to command timestamp errors or excessive latency. A control execution flag is also attached to the valid control command to confirm that the control command belongs to the target control node, thereby ensuring its reliability, validity, and traceability.

[0054] The control command generated by the local edge node is timestamped at "12:01:00." After timing verification, it is confirmed that the control command has no timestamp delays or errors. By appending the control authority execution identifier "Edge Control Authority Identifier 001" to the control command, a confirmed control command with the identifier is generated: "The drone immediately increases its flight altitude to 150 meters and deviates north; generated timestamp 12:01:00; control authority execution identifier: Edge Control Authority Identifier 001." This effectively ensures the clarity and traceability of the drone's control chain.

[0055] The confirmed control instructions are sent to the UAV execution terminal in real time through the target control node; The confirmed control instructions are sent in real time to the UAV's flight control execution terminal via a real-time communication link with the target control node. The UAV execution terminal is the control device or flight control computer responsible for actually executing the flight maneuvers. Its function is to receive real-time control instructions from the target control node and immediately implement corresponding flight maneuvers to achieve real-time flight control and maneuver execution of the UAV.

[0056] For example, a local edge node generates the command "Immediately increase altitude to 150 meters and deviate north; generate timestamp 12:01:00; control authority execution identifier: edge control authority identifier 001" and sends it in real time to the drone's flight control executor via the local edge node's local wireless communication link (e.g., low latency 10 milliseconds, communication rate 20 megabits per second). After receiving the control command, the drone's flight control executor executes the corresponding flight maneuver in real time, immediately increasing the flight altitude to 150 meters and executing a northward deviation maneuver. This avoids the risk of conflict, ensures the safe execution of the drone's flight mission, and ensures a clear and reliable control link.

[0057] Example 2 The difference between Example 2 of the present invention and Example 1 is that this example introduces a cloud-coordinated drone remote control system.

[0058] Figure 2 A schematic diagram of a cloud-coordinated UAV remote control system of the present invention is provided. The cloud-coordinated UAV remote control system includes: Flight control data processing module: collects flight control status data from the cloud control node and local edge node in the current control chain, performs preprocessing and time domain alignment, and generates state synchronization input data; Active Priority Analysis Module: Based on state synchronization input data, it analyzes the control activity and master control priority of cloud control nodes and local edge nodes, and generates control competition judgment results; Command conflict assessment module: This module evaluates the source consistency of control commands and the risk of flight control execution conflicts based on state synchronization input data, and generates control command conflict determination information. Control coordination fusion module: Synchronizes and integrates the control right competition judgment results with the control instruction conflict judgment information to generate the control right switching coordination instructions; Link status perception module: Based on the control handover coordination instructions, it perceives the real-time link status information and updates the status mapping information during the control handover process; Control uniqueness confirmation module: Based on the control right switching coordination instruction and the updated state mapping information, it executes the control right uniqueness confirmation mechanism to complete the selection, confirmation and issuance of the control instruction.

[0059] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0060] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0061] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0062] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0063] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0064] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0065] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0066] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0067] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0068] Finally: 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, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A cloud-coordinated drone remote control method, characterized in that: The steps include: S1. Collect flight control status data from the cloud control node and local edge node in the current control chain, perform preprocessing and time domain alignment, and generate state synchronization input data; S2. Based on the state synchronization input data, the control activity and master control priority of the cloud control node and the local edge node are analyzed to generate the control right competition judgment result; S3. Evaluate the source consistency of control instructions and the flight control execution conflict risk based on the state synchronization input data, and generate control instruction conflict determination information; S4. Synchronize and integrate the control right competition judgment result and the control instruction conflict judgment information to generate a control right switching coordination instruction; S5. Based on the control handover coordination instruction, perceive the real-time link status information and update the status mapping information during the control handover process; S6. Based on the control right switching coordination instruction and the updated state mapping information, the control right uniqueness confirmation mechanism is executed to complete the selection, confirmation and issuance of the control instruction.

2. The cloud-coordinated drone remote control method according to claim 1, characterized in that: S1, specifically: The flight control status data of the UAV is collected through the cloud control node and the local edge node respectively; The flight control status data collected by the cloud control node and the local edge node are processed in a unified data format; With the time base of the cloud control node as a reference, the flight control state data that has been uniformly processed is corrected in terms of time axis and aligned in time domain to generate state synchronization input data.

3. The cloud-coordinated drone remote control method according to claim 2, characterized in that: S2, specifically: Parse the state synchronization input data to obtain the control instruction generation timestamp and task priority identifier of the cloud control node and local edge node respectively; The control instruction update frequency is calculated based on the control instruction generation timestamp to obtain the control activity index of the cloud control node and the local edge node; Obtain the master control priority based on the task priority identifiers of the cloud control node and the local edge node; The control activity index and the master control priority are weighted according to the preset weight coefficient to obtain the control capability score; Based on the control capability score comparison results of the cloud control node and the local edge node, the control right competition judgment result is generated.

4. The cloud-coordinated drone remote control method according to claim 3, characterized in that: S3, specifically: Parse state synchronization input data to obtain control instructions and flight attitude data from the cloud control node and local edge node respectively; Compare the control instructions of the cloud control node and the local edge node to determine whether the sources of the control instructions are consistent; Match control commands from inconsistent sources with flight attitude data to identify control commands that cause conflicts in UAV flight control execution; Determine the flight control execution conflict risk level between the cloud control node and the local edge node based on the control instructions that cause the UAV flight control execution conflict; Based on the flight control execution conflict risk level, control instruction conflict determination information is generated.

5. The cloud-coordinated drone remote control method according to claim 4, characterized in that: S4, specifically: Analyze the control rights competition judgment results and determine the target control node that obtains control rights between the cloud control node and the local edge node; Analyze the control command conflict determination information to obtain the flight control execution conflict risk level between the cloud control node and the local edge node; Based on the control capability score of the target control node and the flight control execution conflict risk level, the control right switching coordination instruction is generated according to the preset conflict coordination rules.

6. The cloud-coordinated drone remote control method according to claim 5, characterized in that: S5, specifically: Real-time monitoring of the link status information between the drone and the cloud control node and the local edge node; Match the link real-time status information with the target control node information in the control right switching coordination instruction to generate a real-time status mapping relationship of the target control node; Based on the mapping relationship between the control right execution identifier and the real-time state in the control right switching coordination instruction, the state mapping information of the target control node during the control right handover process is updated.

7. The cloud-coordinated drone remote control method according to claim 6, characterized in that: S6, specifically: Compare the target control node information in the control switching coordination instruction with the real-time control validity information and link stability information in the updated state mapping information to confirm the effective control right of the target control node; According to the effective control right of the target control node, a control instruction corresponding to the target control node is selected as an effective control instruction; Perform timing confirmation on valid control instructions and add control right execution mark to generate confirmed control instructions; The confirmed control instructions are sent to the drone execution end in real time through the target control node.

8. A cloud-coordinated UAV remote control system, used to implement the cloud-coordinated UAV remote control method according to any one of claims 1 to 7, characterized in that: include: Flight control data processing module: collects flight control status data from the cloud control node and local edge node in the current control chain, performs preprocessing and time domain alignment, and generates state synchronization input data; Active Priority Analysis Module: Based on state synchronization input data, it analyzes the control activity and master control priority of cloud control nodes and local edge nodes, and generates control competition judgment results; Command conflict assessment module: This module evaluates the source consistency of control commands and the risk of flight control execution conflicts based on state synchronization input data, and generates control command conflict determination information. Control coordination fusion module: Synchronizes and integrates the control right competition judgment results with the control instruction conflict judgment information to generate the control right switching coordination instructions; Link status perception module: Based on the control handover coordination instructions, it perceives the real-time link status information and updates the status mapping information during the control handover process; Control uniqueness confirmation module: Based on the control right switching coordination instruction and the updated state mapping information, it executes the control right uniqueness confirmation mechanism to complete the selection, confirmation and issuance of the control instruction.

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