Cooperative control device and method for unmanned aerial vehicle and unmanned airfield

Abstract

The application provides a kind of unmanned plane and unmanned airport collaborative control device and method, the device includes cloud collaborative management module, unmanned airport end control module and at least one unmanned plane end control module.Cloud collaborative management module issues job task instruction, receives and stores relevant data;Unmanned airport end control module is deployed in unmanned airport, after receiving task instruction, in combination with airport resource state, environmental data and unmanned plane state information, generate collaborative scheduling instruction containing operation timing, take-off and landing sequence and energy supply strategy, get rid of existing passive response mode;Unmanned plane end control module is deployed in unmanned plane, execute scheduling instruction and feedback own state and task progress, form closed loop interaction;Data interaction is realized between each module through multi-link redundant communication network, avoid single link interference problem, guarantee collaborative reliability, improve automated operation efficiency.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) control and intelligent operation and maintenance technology, and in particular to a collaborative control device and method for UAVs and unmanned aerial vehicle (UAV) airports. Background Technology

[0002] With the rapid development of drone technology, drones have been widely used in wind power inspection, power line inspection, environmental monitoring, logistics and transportation, and other fields. Unmanned aerial vehicle (UAV) airports, serving as "ground support bases" for UAVs, enable automatic take-off and landing, charging / battery swapping, data exchange, and maintenance storage, significantly improving the continuity and autonomy of UAV operations. The collaborative control of UAVs and UAV airports is the core of achieving fully automated operations; its collaborative efficiency and stability directly affect the quality and safety of the operation.

[0003] Existing collaborative control technologies for drones and drone airports still have several shortcomings: First, the collaborative control logic is simple, mostly adopting a mode of autonomous drone flight + passive response from the drone airport, lacking an active collaborative scheduling mechanism. For example, after a drone completes its mission and returns autonomously, the drone airport only passively receives and executes charging commands, without considering scenarios such as scheduling optimization for multiple drones operating at different times, or emergency collaborative handling of sudden failures, resulting in low operational efficiency. Second, the reliability of communication interaction is poor. Existing collaborations mostly rely on a single wireless communication link, which is susceptible to signal interference in complex environments, leading to delays and loss of mission command transmission, affecting the accuracy of collaborative control. Third, the status perception is incomplete. Drone airports' status monitoring of drones is mostly limited to basic information such as battery level and location, lacking comprehensive perception of the drone's equipment health status and operating environment, making it impossible to predict collaborative risks in advance. Fourth, the collaborative adaptability is poor. The communication protocols and control interfaces of different drone models and drone airports are not unified, resulting in low universality of collaborative control schemes and difficulty in achieving multi-model compatible scheduling. Summary of the Invention

[0004] This invention provides a device and method for collaborative control of unmanned aerial vehicles (UAVs) and unmanned aerial vehicle (UAV) airports, which solves the problems of simple collaborative control logic and poor communication reliability in the prior art.

[0005] On one hand, the present invention provides a collaborative control device for unmanned aerial vehicles (UAVs) and unmanned aerial vehicle (UAV) airports, comprising: The system includes a cloud-based collaborative management module, an unmanned aerial vehicle (UAV) airport control module, and at least one UAV terminal control module. The cloud-based collaborative management module is used to issue operation task instructions to the UAV field control module, and to receive and store operation data and running data from the UAV field control module. The unmanned aerial vehicle (UAV) airport control module is deployed at the UAV airport and is used to receive the operation task instructions and generate collaborative scheduling instructions based on the UAV airport resource status, environmental data, and the status information of at least one UAV. The collaborative scheduling instructions include operation sequence, take-off and landing sequence, and energy supply strategy. The UAV terminal control module is deployed on the UAV and is used to receive and execute the collaborative scheduling instructions, and to feed back the collected UAV status information and task progress information to the UAV field terminal control module. The UAV field control module and the UAV terminal control module, as well as the UAV field control module and the cloud collaborative management module, exchange data through a multi-link redundant communication network.

[0006] Optionally, the unmanned aerial vehicle (UAV) field control module generates collaborative scheduling instructions specifically including: Based on the unmanned airport resource status, environmental data, status information of each unmanned aerial vehicle (UAV), and the operation task instructions, a pre-set multi-factor collaborative optimization model is used to calculate and generate collaborative scheduling instructions that include operation timing, take-off and landing sequence, and energy replenishment strategy. The multi-factor collaborative optimization model aims to maximize global operational efficiency and optimize energy consumption, while comprehensively constraining factors such as UAV endurance, airport charging facility capacity, airspace safety intervals, and task priority.

[0007] Optionally, the multi-factor collaborative optimization model is used for: Based on the input human-machine state information and the task instructions, a set of decision variables is generated, wherein each decision variable is used to characterize assigning an atomic operation to a specific UAV to be executed in a specific time interval and spatial location; Based on the decision variables, the UAV energy consumption model in the UAV status information, and the risk information in the environmental data, an objective function is constructed. The value of the objective function is negatively correlated with the overall task time, total energy consumption of the UAV fleet, and comprehensive risk level estimated by the scheme. Based on the UAV physical performance data in the human-machine status information, the task logic in the operation task instruction, the airport resource topology in the UAV airport resource status, and the environmental data, a set of constraints that must be satisfied are constructed.

[0008] Optionally, the constraints include: Capability constraints are generated from the physical performance data of the UAV; timing and path constraints are generated from the task logic; spatial and temporal exclusivity constraints are generated from the airport resource topology; and dynamic feasible domain constraints are generated from the environmental data.

[0009] Optionally, generating dynamic feasible domain constraints from the environmental data includes: Based on the wind speed and direction information in the environmental data, calculate and output the maximum allowable airspeed and energy consumption correction coefficient per unit distance for each UAV in different airspace grids. Based on the obstacle information in the environmental data and the pre-stored geographic information system data, a three-dimensional dynamic no-fly zone layer with time validity is generated and output. The maximum permissible airspeed, the energy consumption correction coefficient per unit distance, and the three-dimensional dynamic no-fly zone layer are injected as time-varying parameters into the constraint conditions to generate dynamic feasible domain constraints.

[0010] Optionally, the cloud-based collaborative management module is also used for: The task instruction is broken down into multiple sub-tasks; According to the preset global task priority rules, the execution priority is assigned to the multiple subtasks; The task instructions, including the sub-tasks and their corresponding priorities, are sent to the UAV field control module.

[0011] Optionally, the multi-link redundant communication network includes a primary wireless data link and at least one backup wireless data link; The UAV field control module is also used to: monitor the communication quality of each link between the UAV field control module and the UAV terminal control module, and switch to the backup link for data interaction when the communication quality of the primary link is lower than a first preset threshold.

[0012] Optionally, the UAV terminal control module is further configured to: When a communication interruption with the UAV field control module is detected or an abnormal command is received, the task is executed according to a preset local emergency strategy; the local emergency strategy includes at least autonomous return, hovering and waiting, or continuing to execute locally cached task commands.

[0013] Optionally, the energy replenishment strategy specifically includes: Based on the remaining battery power of each drone, the number of available charging facilities, and the operation sequence, a battery charging / replacement queue and a charging scheduling plan are generated. The UAV field control module is used to control the automatic charging device or robotic arm in the UAV field to perform energy replenishment operations for designated UAVs according to the charging scheduling plan.

[0014] On the other hand, the present invention also provides a method for coordinated control of unmanned aerial vehicles (UAVs) and unmanned aerial vehicle (UAV) airports, comprising: The cloud-based collaborative management module sends operation task instructions to the unmanned aerial vehicle (UAV) field control module. The operation task instruction is received by the UAV field control module; The unmanned aerial vehicle (UAV) airport control module generates a collaborative scheduling instruction based on the UAV airport resource status, environmental data, and the status information of at least one UAV. The collaborative scheduling instruction includes the operation sequence, take-off and landing sequence, and energy replenishment strategy. The coordinated scheduling command is sent to the target UAV terminal control module through the multi-link redundant communication network. The UAV terminal control module receives and executes the collaborative scheduling command, and feeds back the collected UAV status information and task progress information to the UAV field terminal control module through the multi-link redundant communication network. The unmanned aerial vehicle (UAV) field control module uploads the operation data and running data to the cloud-based collaborative management module for storage.

[0015] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the collaborative control method for drones and drone airports as described above.

[0016] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the cooperative control method for UAVs and UAV airports as described above.

[0017] On the other hand, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the collaborative control method for unmanned aerial vehicles and unmanned airfields as described above.

[0018] This invention provides a collaborative control device and method for unmanned aerial vehicles (UAVs) and unmanned airfields. The device includes a cloud-based collaborative management module, an unmanned airfield-based control module, and at least one UAV-based control module. The cloud-based collaborative management module issues task instructions and receives and stores relevant data. The unmanned airfield-based control module is deployed at the unmanned airfield. After receiving the task instructions, it combines airport resource status, environmental data, and UAV status information to generate collaborative scheduling instructions containing operation sequence, take-off and landing order, and energy replenishment strategy, thus breaking away from the existing passive response mode. The UAV-based control module is deployed on the UAV, executes the scheduling instructions, and reports its own status and task progress, forming a closed-loop interaction. Data interaction between the modules is achieved through a multi-link redundant communication network, avoiding single-link interference problems, ensuring reliable collaboration, and improving the efficiency of automated operations. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the collaborative control device for drones and drone airports provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a multi-link redundant communication network structure provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0021] Figure label: 110. Cloud-based collaborative management module; 120. Unmanned aerial vehicle (UAV) airport control module; 130. UAV terminal control module; 140. Multi-link redundant communication network; 1401. Primary wireless data link; 1402. Backup wireless data link; 310. Processor; 320. Communication interface; 330. Memory; 340. Communication bus. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0023] Figure 1 This is a schematic diagram of the collaborative control device for drones and drone airports provided in an embodiment of the present invention.

[0024] like Figure 1 As shown, the collaborative control device for unmanned aerial vehicles (UAVs) and unmanned aerial vehicle (UAV) airports provided in this embodiment of the invention includes: The cloud-based collaborative management module 110, the UAV field control module 120, and at least one UAV field control module 130; The cloud-based collaborative management module 110 is used to issue operation task instructions to the UAV airport control module 120, and to receive and store operation data and running data from the UAV airport control module 120. The unmanned aerial vehicle (UAV) airport control module 120 is deployed at the UAV airport to receive operation task instructions and generate collaborative scheduling instructions based on the UAV airport resource status, environmental data, and the status information of at least one UAV. The collaborative scheduling instructions include operation sequence, take-off and landing sequence, and energy replenishment strategy. The UAV field control module 130 is deployed on the UAV and is used to receive and execute collaborative scheduling instructions, and to feed back the collected UAV status information and task progress information to the UAV field control module 120. Data exchange is conducted between the UAV field control module 120 and the UAV field control module 130, and between the UAV field control module 120 and the cloud collaborative management module 110, through a multi-link redundant communication network 140.

[0025] Specifically, the cloud-based collaborative management module 110 is deployed on a remote cloud platform. This module sends task instructions, including task type, scope, and time requirements, to the unmanned aerial vehicle (UAV) control module 120, while simultaneously monitoring the progress of task execution in real time. Upon receiving anomaly alarms from the UAV, it generates targeted emergency response plans; and synchronously receives and stores operational data, running data, and collaborative decision-making data from both the UAV and the UAV.

[0026] The unmanned aerial vehicle (UAV) airport control module 120 is deployed inside the UAV airport and is responsible for receiving operation task instructions issued by the cloud-based collaborative management module 110. By collecting the UAV airport resource status, environmental data, and status information of multiple UAVs, it generates collaborative scheduling instructions including operation sequence, take-off and landing sequence, and energy replenishment strategy. At the same time, it collects the UAV airport's own operating status and UAV status data in real time to achieve full-dimensional status perception; based on the remaining battery power of the UAVs, it provides energy replenishment services such as fast / slow charging adaptive charging or automatic battery swapping; it controls the opening and closing of the cabin door, the lifting and lowering of the take-off and landing platform, and positioning calibration to ensure the safe take-off, landing, and docking of UAVs; and by integrating multiple communication modules, it constructs a multi-link redundant communication channel to achieve stable data interaction with the UAV airport control module 130 and the cloud-based collaborative management module 110.

[0027] The UAV field control module 130 is deployed on the UAV to receive collaborative scheduling commands issued by the UAV field control module 120. It adjusts the flight attitude and trajectory based on real-time environmental data to complete takeoff, landing, and preset operational tasks. It collects real-time information on UAV flight status, equipment health status, and task progress, and synchronously feeds this information back to the UAV field control module 120 via multi-link communication. This ensures that the UAV field control module 120 can promptly grasp the UAV's operational status and task progress, providing a basis for optimizing subsequent scheduling commands and guaranteeing the efficient and orderly advancement of operational tasks.

[0028] In some embodiments, the unmanned aerial vehicle (UAV) field control module 120 generates cooperative scheduling instructions specifically including: Based on the unmanned airport resource status, environmental data, status information of each UAV, and operational task instructions, a pre-set multi-factor collaborative optimization model is used to calculate and generate collaborative scheduling instructions, including operational timing, take-off and landing sequence, and energy replenishment strategy. Among them, the multi-factor collaborative optimization model aims to maximize global operational efficiency and optimize energy consumption, while comprehensively constraining the drone's endurance, airport charging facility capacity, airspace safety intervals, and task priority.

[0029] Specifically, the multi-factor collaborative optimization model integrates and correlates multi-dimensional input data, including the status of unmanned airport resources, real-time environmental data, status information of each drone, and operational task instructions issued from the cloud, ensuring that the input data includes all factors affecting collaborative scheduling.

[0030] Secondly, the multi-factor collaborative optimization model takes the highest global operational efficiency and the optimal energy consumption as its dual core objectives. The highest global operational efficiency is achieved by optimizing the operation sequence and take-off and landing order to shorten the overall task completion time. The optimal energy consumption is achieved by matching energy replenishment strategies, combining the remaining power of the drone with the operation time requirements, and selecting fast charging / slow charging or battery swapping modes to reduce energy waste and extend battery life.

[0031] Meanwhile, the multi-factor collaborative optimization model follows four major constraints: First, the drone's endurance constraint requires the drone's endurance matching degree to be ≥80% to avoid insufficient power during operation; second, the airport charging facility capacity constraint controls the charging station occupancy rate to ≤70% to ensure balanced resource allocation; third, the airspace safety interval constraint, combined with obstacle and wind speed information in environmental data, reserves a safe flight distance to avoid airspace conflicts; and fourth, the task priority constraint, prioritizes the allocation of the best-performing drones and supply resources to high-priority tasks to ensure that core tasks are completed first.

[0032] Finally, the multi-factor collaborative optimization model performs iterative calculations using preset parameters, such as population size of 50, number of iterations of 100, crossover probability of 0.7, and mutation probability of 0.1. The final output is a collaborative scheduling instruction that includes operation sequence, take-off and landing sequence, and energy supply strategy, achieving optimal collaborative decision-making under the balance of multiple factors.

[0033] Among them, the multi-factor collaborative optimization model is used for: Based on the input human-machine status information and task instructions, a set of decision variables is generated, where each decision variable is used to characterize assigning an atomic operation to a specific UAV to be executed in a specific time interval and spatial location. Based on decision variables, UAV energy consumption model in UAV status information, and risk information in environmental data, an objective function is constructed. The value of the objective function is negatively correlated with the overall task time, total energy consumption of the UAV fleet, and comprehensive risk level predicted by the scheme. Based on the UAV physical performance data in the human-machine status information, the task logic in the operation task instructions, the airport resource topology in the UAV airport resource status, and environmental data, a set of constraints that must be satisfied are constructed.

[0034] Specifically, after the UAV status information and task instructions are input into the multi-factor collaborative optimization model, a set of structured decision variables are generated based on the input UAV status information and task instructions. Each decision variable clearly represents the assignment of an atomic operation, including task execution operations such as wind power inspection and data acquisition, energy replenishment operations such as fast / slow charging and battery swapping, and take-off and landing operations, to a specific UAV, and limits the execution time interval and spatial location of the UAV.

[0035] Then, with overall operational efficiency, energy utilization efficiency, and operational safety as objectives, based on the aforementioned decision variables and combined with the energy consumption model in the UAV status information, and incorporating remaining battery power, flight attitude, operational load, and risk information from environmental data, an objective function is constructed. The value of the objective function is negatively correlated with the estimated overall mission time, total fleet energy consumption, and comprehensive risk level; that is, the larger the objective function value, the shorter the mission time, the lower the energy consumption, and the lower the risk.

[0036] Based on the UAV physical performance data in the human-machine status information, the task logic in the operation task instructions, the airport resource topology in the UAV airport resource status, and environmental data, a set of constraints that must be met are constructed. These include: task completion time ≤ 2 hours, UAV endurance matching degree ≥ 80%, UAV airport charging station occupancy rate ≤ 70%; UAV flight speed not exceeding the physical rated speed, and operation trajectory not exceeding the preset operation range; only one charging station / power exchange station can serve one UAV at a time, and the safe flight interval between UAVs in the airspace is not less than the preset distance; wind speed not exceeding the UAV safe flight threshold, and visibility meeting operational requirements, etc., ensuring the feasibility and safety of the scheduling plan.

[0037] During operation, the multi-factor collaborative optimization model continuously adjusts dynamically based on real-time feedback data. For example, if a drone encounters sudden strong winds while performing a mission, leading to increased energy consumption and reduced flight speed, the drone field control module 130 will promptly feed the data back to the drone field control module 120. After receiving the data, the drone field control module 120 will input it into the multi-factor collaborative optimization model.

[0038] The multi-factor collaborative optimization model will reassess the current operational situation. Considering that the drone's endurance may be affected, it will adjust the operational sequence. For example, it will control the drone to return early for energy replenishment, and at the same time, it will re-plan the take-off and landing sequence and operational tasks of other drones to ensure that the overall operational efficiency is not greatly affected.

[0039] In addition, if the charging facilities at the unmanned airport malfunction, reducing the number of available charging facilities, the unmanned airport control module 120 will immediately update the unmanned airport resource status information and input it into the multi-factor collaborative optimization model. The multi-factor collaborative optimization model will recalculate the energy replenishment strategy based on the new resource status. It may prioritize battery swapping services for drones with lower battery levels and higher task priority, while for drones with relatively sufficient battery power, it may switch to slow charging mode to balance energy replenishment needs and the use of charging facilities.

[0040] Throughout the collaborative control process, data interaction between modules is frequent. The UAV farm-side control module 130 continuously feeds back the collected UAV status and task progress information to the UAV farm-side control module 120. The UAV farm-side control module 120 then uploads this information, along with its own collected UAV farm operation status data, to the cloud-based collaborative management module 110. The cloud-based collaborative management module 110 then performs comprehensive analysis and storage based on this data, providing data support for subsequent task planning and decision-making. For example, by analyzing a large amount of historical operational data, the parameters of the multi-factor collaborative optimization model can be further optimized, improving the accuracy and efficiency of collaborative scheduling.

[0041] Meanwhile, to ensure the reliability and stability of the entire collaborative control device and method, corresponding fault diagnosis and fault tolerance mechanisms are also set up. When a module malfunctions, the system can quickly detect it and take corresponding measures to handle it. For example, if the UAV airport control module 130 malfunctions and cannot receive and execute collaborative scheduling commands normally, the UAV airport control module 120 will, according to the preset emergency strategy, command the UAV to autonomously return to its home position or hover in standby mode to avoid serious impact on the entire operation. If the communication link between the UAV airport control module 120 and the cloud collaborative management module 110 fails, the UAV airport control module 120 will temporarily store the relevant data and upload the data to the cloud after communication is restored to ensure the integrity and continuity of the data.

[0042] In some embodiments, the constraints generated by the multi-factor collaborative optimization model include: Capability constraints are generated from UAV physical performance data; timing and path constraints are generated from mission logic; spatial and temporal exclusivity constraints are generated from airport resource topology; and dynamic feasible domain constraints are generated from environmental data.

[0043] Among them, the dynamic feasible domain constraints generated from environmental data include: Based on the wind speed and direction information in the environmental data, calculate and output the maximum allowable airspeed and energy consumption correction coefficient per unit distance for each UAV in different airspace grids. Based on obstacle information in environmental data and pre-stored geographic information system data, a time-valid 3D dynamic no-fly zone layer is generated and output. The maximum permissible airspeed, energy consumption correction factor per unit distance, and three-dimensional dynamic no-fly zone layer are injected into the constraint conditions as time-varying parameters to generate dynamic feasible domain constraints.

[0044] Specifically, when calculating the maximum permissible airspeed and energy consumption correction coefficient, the calculation is based on real-time wind speed and direction information collected from environmental data, combined with the physical performance parameters of each UAV, and refined according to the airspace grid. For the differences in wind speed and direction within different airspace grids, the maximum permissible airspeed of the UAV adapted to the airspace grid is output. Simultaneously, based on the impact of wind speed on flight drag, a unit distance energy consumption correction coefficient is generated. The unit distance energy consumption correction coefficient is adjusted upwards in headwind scenarios and downwards in tailwind scenarios, providing dynamic input for the energy consumption model and ensuring the accuracy of endurance assessment.

[0045] When generating a 3D dynamic no-fly zone layer, it is necessary to integrate obstacle information from environmental data with pre-stored geographic information system data to construct a time-validated 3D dynamic no-fly zone layer. This layer clearly marks the 3D coordinates, extent, and duration of each obstacle, and updates in real time as environmental data changes. For example, when a sudden obstacle appears, the no-fly zone is immediately updated to prevent drone flight conflicts and provide visual and precise support for airspace safety separation constraints.

[0046] The maximum permissible airspeed, energy consumption correction coefficient per unit distance, and the three-dimensional dynamic no-fly zone layer calculated above are used as time-varying parameters and injected into the constraints of the multi-factor collaborative optimization model. Specifically, the maximum permissible airspeed constrains the UAV's flight speed to not exceed limits, the energy consumption correction coefficient per unit distance optimizes the calculation accuracy of the endurance matching degree, and the three-dimensional dynamic no-fly zone layer limits the flight space range. These three factors combine to form a dynamic feasible domain constraint, ensuring that the scheduling scheme always adapts to real-time environmental changes, avoids operational risks caused by environmental interference, and meets the needs of full-dimensional state perception and dynamic collaborative adjustment.

[0047] Understandably, when generating capability constraints, all aspects of the drone's physical performance, such as flight speed, endurance, and payload capacity, are comprehensively considered. This determines the scope and intensity of tasks that each drone can perform, avoiding assigning tasks beyond its capabilities and ensuring that the drone operates in a safe and reliable state.

[0048] When generating timing and path constraints based on task logic, the system analyzes the sequence, relationships, and geospatial information of the tasks. It calculates the optimal time sequence and flight path for each drone to execute its tasks, ensuring tight and efficient task coordination among multiple drones, reducing unnecessary waiting and overlapping flight paths, and improving overall operational efficiency.

[0049] When considering the spatial and temporal exclusivity constraints of airport resource topology generation, a detailed analysis of resource conditions such as the layout of unmanned airports, the location of charging facilities, and the number of take-off and landing platforms is conducted. The usage rights and occupancy rules for each resource at different times are clearly defined to ensure that only one drone can use the same resource at a time, avoiding resource competition and conflicts, and achieving rational utilization and efficient allocation of resources.

[0050] The aforementioned constraints interact and influence each other, forming the constraint system of the multi-factor collaborative optimization model. In actual operation, as the UAV's status, operational tasks, and environmental data continuously change, the constraints will also be dynamically adjusted to ensure that the entire collaborative control process remains in an optimal state, achieving efficient collaborative operation between the UAV and the UAV airport.

[0051] In some embodiments, the cloud-based collaborative management module 110 is further configured to: Break down the task instructions into multiple subtasks; Based on the preset global task priority rules, assign execution priorities to multiple subtasks; The task instructions, including subtasks and their corresponding priorities, are sent to the UAV field control module 120.

[0052] Specifically, after receiving the task instruction, the task instruction is broken down into multiple independent and executable sub-tasks based on the task scope, task type, and UAV operational capability threshold. For example, the task of inspecting 10 wind turbines can be broken down into sub-tasks of inspecting a single wind turbine. Each sub-task clearly defines the boundaries of the operational area, data collection requirements, and completion deadlines, ensuring that the sub-tasks match the UAV operational capabilities and airport resource supply.

[0053] Based on the preset global task priority rules, execution priorities are assigned to each subtask. When assigning execution priorities, the urgency, time constraints, and importance of the task are taken into account, while also considering the logical relationships between subtasks to avoid priority conflicts.

[0054] After obtaining the subtasks and their corresponding priorities, the integrated task instructions, including details of each subtask and its corresponding priority, are sent to the UAV field control module 120 through the multi-link redundant communication network 140. This provides a priority basis for the UAV field scheduling decision unit to generate a collaborative scheduling scheme, ensuring that high-priority subtasks are given priority in allocating high-quality UAV resources and energy supply guarantees, with the goal of achieving optimal overall operational efficiency.

[0055] In some embodiments, the multi-link redundant communication network 140 includes a primary wireless data link 1401 and at least one backup wireless data link 1402. The UAV field control module 120 is also used to: monitor the communication quality of each link between the UAV field control module 130 and the UAV field control module 130, and switch to the backup link for data interaction when the communication quality of the primary link is lower than a first preset threshold.

[0056] Specifically, the multi-link redundant communication network 140 includes a primary wireless data link 1401 and at least one backup wireless data link 1402. The primary link preferentially selects a 5G communication link with a high transmission rate, while the backup link can be configured with LoRa, WiFi 6, or private network communication links. Each link supports data transmission and status interaction functions.

[0057] The UAV farm control module 120 monitors the communication quality of each link between itself and the UAV farm control module 130. The communication quality indicators include signal strength, transmission success rate, and latency. The first preset threshold is set to a primary link signal strength ≤ -80dBm or a transmission success rate < 95%. When the communication quality of the primary link is detected to be lower than the first preset threshold, the UAV farm control module 120 will automatically trigger a link switching mechanism to switch to a backup link to continue data interaction, ensuring that data such as command transmission and status feedback are not lost or delayed.

[0058] For example, in an offshore wind farm inspection scenario, after initialization, the unmanned aerial vehicle (UAV) and the UAV establish a 5G primary link and a LoRa backup link connection. When the UAV is performing a wind turbine inspection task, a sudden gust of wind causes interference to the 5G signal in the work area. The UAV monitoring station detects that the 5G primary link signal strength drops to -85dBm, which is lower than the first preset threshold of -80dBm, and the transmission success rate drops to 93%, which is lower than 95%. At this time, the UAV control module 120 immediately initiates link switching, completing the switch from the 5G primary link to the LoRa backup link within 90ms. The UAV's flight status data, inspection progress feedback, and UAV scheduling commands can still be transmitted stably without any command loss or work interruption, ensuring the continuous progress of the inspection task.

[0059] In some embodiments, the UAV field control module 130 is further configured to: When a communication interruption with the UAV field control module 120 is detected or an abnormal command is received, the task is executed according to the preset local emergency strategy; the local emergency strategy includes at least autonomous return, hovering and waiting, or continuing to execute locally cached task commands.

[0060] Specifically, when the drone airport control module 130 detects a communication interruption, it triggers the invocation of a preset local emergency strategy. If the autonomous return-to-home strategy is selected, the drone will automatically adjust its flight attitude and direction based on its built-in positioning system and pre-planned route to return to the drone airport via the fastest and safest path. During the return-to-home process, the drone will monitor its battery level, flight altitude, and speed in real time to ensure a successful arrival at the drone airport.

[0061] If the hovering standby strategy is implemented, the drone will maintain a stable hovering state at its current location. During this time, the drone's sensors will continue to operate, monitoring the surrounding environment in real time. Once communication is restored or a valid command is received, it will immediately execute the new command. Furthermore, the hovering standby state also prevents the drone from flying blindly during communication failures, reducing the risk of collisions and other accidents.

[0062] When choosing to continue executing locally cached task instructions, the drone will continue its operation based on the task information previously stored locally. These cached task instructions contain key information such as flight paths and operational targets, enabling the drone to complete the corresponding tasks according to a predetermined process.

[0063] In some embodiments, the energy replenishment strategy specifically includes: Based on the remaining battery power of each drone, the number of available charging facilities, and the operation sequence, a battery charging / replacement queue and a charging scheduling plan are generated. The UAV field control module 120 is used to control the automatic charging device or robotic arm in the UAV field to perform energy replenishment operations for designated UAVs according to the charging scheduling plan.

[0064] The generation of the battery charging / replacement queue takes into account multiple factors. Drones with low remaining battery power and about to perform important tasks will be given priority in being placed in the charging / replacement queue. At the same time, the charging time for each drone will be rationally planned according to the operation sequence to avoid delays in operation due to charging.

[0065] The charging schedule will be dynamically adjusted based on the availability of charging facilities. If there are sufficient charging facilities, multiple drones will be charged or have their batteries swapped simultaneously to improve energy replenishment efficiency; if charging facilities are limited, priority will be given to ensuring energy replenishment for drones with high-priority missions.

[0066] When performing energy replenishment operations, the UAV field control module 120 controls the movement of the automatic charging device or robotic arm. For charging operations, it selects the appropriate charging mode, such as fast charging or slow charging, based on the characteristics of the UAV battery to ensure safe and efficient charging. For battery swapping operations, the robotic arm precisely removes the old battery and installs the new battery; the entire process is fast and stable.

[0067] During energy replenishment, the UAV field control module 120 also monitors the UAV's battery status in real time. If a battery abnormality is detected during charging, such as overheating or overcharging, the charging operation will be stopped immediately, and corresponding safety measures will be taken, such as issuing an alarm and isolating the faulty battery. At the same time, the battery abnormality information will be fed back to the cloud-based collaborative management module 110 for further analysis and processing.

[0068] Based on the same general inventive concept, this invention also protects a collaborative control method for drones and drone airports. The collaborative control method for drones and drone airports provided by this invention will be described below. The collaborative control method for drones and drone airports described below can be referred to in correspondence with the collaborative control device for drones and drone airports described above.

[0069] In some embodiments, the present invention also provides a method for coordinated control of a drone and a drone airport, comprising: The cloud-based collaborative management module 110 sends operation task instructions to the unmanned aerial vehicle (UAV) airport control module 120. The unmanned aerial vehicle (UAV) field control module 120 receives the operation task instructions. The unmanned aerial vehicle (UAV) airport control module 120 generates a collaborative scheduling instruction based on the UAV airport resource status, environmental data, and the status information of at least one UAV. The collaborative scheduling instruction includes the operation sequence, take-off and landing sequence, and energy replenishment strategy. The collaborative scheduling command is sent to the target UAV airport control module 130 through the multi-link redundant communication network 140. The UAV field control module 130 receives and executes the collaborative scheduling command, and feeds back the collected UAV status information and task progress information to the UAV field control module 120 through the multi-link redundant communication network 140. The unmanned aerial vehicle (UAV) field control module 120 uploads the operation data and running data to the cloud collaborative management module 110 for storage.

[0070] Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0071] like Figure 3As shown, the electronic device may include a processor 310, a communications interface 320, a memory 330, and a communication bus 340. The processor 310, communications interface 320, and memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions from the memory 330 to execute a collaborative control method between the UAV and the UAV airport.

[0072] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0073] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the collaborative control method for UAVs and UAV airports provided by the above methods.

[0074] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the cooperative control method for UAVs and UAV airports provided by the above methods.

[0075] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0076] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A collaborative control device for unmanned aerial vehicles (UAVs) and unmanned aerial vehicle (UAV) airports, characterized in that, include: The system includes a cloud-based collaborative management module, an unmanned aerial vehicle (UAV) airport control module, and at least one UAV terminal control module. The cloud-based collaborative management module is used to issue operation task instructions to the UAV field control module, and to receive and store operation data and running data from the UAV field control module. The unmanned aerial vehicle (UAV) airport control module is deployed at the UAV airport and is used to receive the operation task instructions and generate collaborative scheduling instructions based on the UAV airport resource status, environmental data, and the status information of at least one UAV. The collaborative scheduling instructions include operation sequence, take-off and landing sequence, and energy supply strategy. The UAV terminal control module is deployed on the UAV and is used to receive and execute the collaborative scheduling instructions, and to feed back the collected UAV status information and task progress information to the UAV field terminal control module. The UAV field control module and the UAV terminal control module, as well as the UAV field control module and the cloud collaborative management module, exchange data through a multi-link redundant communication network.

2. The collaborative control device for unmanned aerial vehicles and unmanned aerial vehicle airports according to claim 1, characterized in that, The unmanned aerial vehicle (UAV) field control module generates coordinated scheduling instructions, specifically including: Based on the unmanned airport resource status, environmental data, status information of each unmanned aerial vehicle (UAV), and the operation task instructions, a pre-set multi-factor collaborative optimization model is used to calculate and generate collaborative scheduling instructions that include operation timing, take-off and landing sequence, and energy replenishment strategy. The multi-factor collaborative optimization model aims to maximize global operational efficiency and optimize energy consumption, while comprehensively constraining factors such as UAV endurance, airport charging facility capacity, airspace safety intervals, and task priority.

3. The collaborative control device for unmanned aerial vehicles and unmanned aerial vehicle airports according to claim 2, characterized in that, Multi-factor collaborative optimization models are used for: Based on the input human-machine state information and the task instructions, a set of decision variables is generated, wherein each decision variable is used to characterize assigning an atomic operation to a specific UAV to be executed in a specific time interval and spatial location; Based on the decision variables, the UAV energy consumption model in the UAV status information, and the risk information in the environmental data, an objective function is constructed. The value of the objective function is negatively correlated with the overall task time, total energy consumption of the UAV fleet, and comprehensive risk level estimated by the scheme. Based on the UAV physical performance data in the human-machine status information, the task logic in the operation task instruction, the airport resource topology in the UAV airport resource status, and the environmental data, a set of constraints that must be satisfied are constructed.

4. The collaborative control device for unmanned aerial vehicles and unmanned aerial vehicle airports according to claim 3, characterized in that, The constraints include: Capability constraints are generated from the physical performance data of the UAV; timing and path constraints are generated from the task logic; spatial and temporal exclusivity constraints are generated from the airport resource topology; and dynamic feasible domain constraints are generated from the environmental data.

5. The collaborative control device for unmanned aerial vehicles and unmanned aerial vehicle airports according to claim 4, characterized in that, The generation of dynamic feasible domain constraints from the environmental data includes: Based on the wind speed and direction information in the environmental data, calculate and output the maximum allowable airspeed and energy consumption correction coefficient per unit distance for each UAV in different airspace grids. Based on the obstacle information in the environmental data and the pre-stored geographic information system data, a three-dimensional dynamic no-fly zone layer with time validity is generated and output. The maximum permissible airspeed, the energy consumption correction coefficient per unit distance, and the three-dimensional dynamic no-fly zone layer are injected as time-varying parameters into the constraint conditions to generate dynamic feasible domain constraints.

6. The collaborative control device for unmanned aerial vehicles and unmanned aerial vehicle airports according to claim 1, characterized in that, The cloud-based collaborative management module is also used for: The task instruction is broken down into multiple sub-tasks; According to the preset global task priority rules, the execution priority is assigned to the multiple subtasks; The task instructions, including the sub-tasks and their corresponding priorities, are sent to the UAV field control module.

7. The collaborative control device for unmanned aerial vehicles and unmanned aerial vehicle airports according to claim 1, characterized in that, The multi-link redundant communication network includes a primary wireless data link and at least one backup wireless data link. The UAV field control module is also used to: monitor the communication quality of each link between the UAV field control module and the UAV terminal control module, and switch to the backup link for data interaction when the communication quality of the primary link is lower than a first preset threshold.

8. The collaborative control device for unmanned aerial vehicles and unmanned aerial vehicle airports according to claim 1, characterized in that, The UAV terminal control module is also used for: When a communication interruption with the UAV field control module is detected or an abnormal command is received, the task is executed according to a preset local emergency strategy; the local emergency strategy includes at least autonomous return, hovering and waiting, or continuing to execute locally cached task commands.

9. The collaborative control device for unmanned aerial vehicles and unmanned aerial vehicle airports according to claim 1, characterized in that, The energy replenishment strategy specifically includes: Based on the remaining battery power of each drone, the number of available charging facilities, and the operation sequence, a battery charging / replacement queue and a charging scheduling plan are generated. The UAV field control module is used to control the automatic charging device or robotic arm in the UAV field to perform energy replenishment operations for designated UAVs according to the charging scheduling plan.

10. A method for coordinated control of unmanned aerial vehicles (UAVs) and unmanned airfields, characterized in that, include: The cloud-based collaborative management module sends operation task instructions to the unmanned aerial vehicle (UAV) airport control module. The operation task instruction is received by the UAV field control module; The unmanned aerial vehicle (UAV) airport control module generates a collaborative scheduling instruction based on the UAV airport resource status, environmental data, and the status information of at least one UAV. The collaborative scheduling instruction includes the operation sequence, take-off and landing sequence, and energy replenishment strategy. The collaborative scheduling command is sent to the target UAV terminal control module through a multi-link redundant communication network; The UAV terminal control module receives and executes the collaborative scheduling command, and feeds back the collected UAV status information and task progress information to the UAV field terminal control module through the multi-link redundant communication network. The unmanned aerial vehicle (UAV) field control module uploads the operation data and running data to the cloud-based collaborative management module for storage.

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