CN110703790A - Unmanned aerial vehicle flight safety protection method and protection system based on cloud big data - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle flight safety protection method and system based on cloud big data, which is implemented based on the cloud big data, and comprises the following steps of acquiring the weather data of a takeoff place, a destination and a route and analyzing and providing the optimal takeoff time; acquiring state data of the unmanned aerial vehicle and analyzing the operation capacity of the unmanned aerial vehicle; acquiring an airport operation state and analyzing whether landing conditions are met; acquiring states of an unmanned aerial vehicle, an airport, a airline and a task in real time and allocating resources; and receiving the optimal takeoff time, the operation capacity and the landing condition, namely the resource allocation information, and correspondingly sending a control instruction to the unmanned aerial vehicle or the airport. The invention relates to a method for protecting an unmanned aerial vehicle during taking off and landing from an airport parking apron and during flight, which mainly applies cloud big data analysis to ensure the taking off and landing stability and safety of the unmanned aerial vehicle and the safety of the flight process. So that the whole set of unattended goods receiving and dispatching system reaches the commercial standard.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle control, and particularly relates to an unmanned aerial vehicle flight safety protection method and system based on cloud big data.
Background
At present, methods for planning a dispatching route of an unmanned aerial vehicle comprise methods of using a shortest path, linear dispatching, manual dispatching and the like. The dispatching route planning methods basically do not consider environmental factors, but directly give a route, and the unmanned aerial vehicle is easy to deviate from the air route and even crash during flight due to various extreme weathers. The manual scheduling method also has a large amount of labor cost, and cannot acquire meteorological data in real time, so that the problem exists in long-distance flight.
And the prior dispatching control can not ensure the observability and controllability of the whole system, thus increasing the difficulty of deployment and transportation.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an unmanned aerial vehicle flight safety protection method based on cloud big data.
Another objective of the present invention is to overcome the deficiencies of the prior art, and provide a system for protecting flight safety of an unmanned aerial vehicle based on cloud big data.
The invention is realized by the following technical scheme:
an unmanned aerial vehicle flight safety protection method based on cloud big data comprises the following steps,
acquiring the departure place, the destination and the meteorological data on the airway, and analyzing and providing the optimal departure time;
acquiring state data of the unmanned aerial vehicle and analyzing the operation capacity of the unmanned aerial vehicle;
acquiring an airport operation state and analyzing whether landing conditions are met;
acquiring states of an unmanned aerial vehicle, an airport, a airline and a task in real time and allocating resources;
receiving the optimal takeoff time, the operation capacity and the landing condition, namely the resource allocation information, and correspondingly sending a control instruction to the unmanned aerial vehicle or the airport;
unmanned aerial vehicle's control command including take off, hover, landing airport, descending to reserve airport, return instructions such as navigate, the control command at airport including opening airport skylight, accomodate unmanned aerial vehicle, suspend and accomodate unmanned aerial vehicle, open light guide etc..
In the technical scheme, the method further comprises the step of receiving the alarm information in the optimal take-off time, the operation capacity, the landing condition and the resource allocation information and pushing the alarm information to the client.
In the technical scheme, the method further comprises the step of obtaining the state of a destination airport and giving a landing instruction, a hovering instruction or a standby landing instruction before the unmanned aerial vehicle arrives at the destination.
In the technical scheme, the unmanned aerial vehicle control system comprises the steps of obtaining the posture of the unmanned aerial vehicle after landing and sending the posture to the instruction management module to carry out storage instructions or alarm operation.
In the technical scheme, the method further comprises the step of collecting illumination data of the place where the target airport is located and determining whether to turn on the visual auxiliary guide lamp of the parking apron according to the illumination value when the unmanned aerial vehicle lands so as to accurately assist the unmanned aerial vehicle to land.
Unmanned aerial vehicle flight safety protection system based on high in clouds big data includes
The meteorological data analysis module is used for acquiring meteorological data on a takeoff place, a destination and an airway and analyzing and providing an optimal takeoff time point;
the airplane telemetering data analysis module is used for acquiring unmanned aerial vehicle state data and analyzing the operation capacity of the unmanned aerial vehicle;
the airport data analysis module is used for acquiring an airport operation state and analyzing whether landing conditions are met;
the resource scheduling module acquires states of the unmanned aerial vehicle, the airport, the airline and the task in real time and allocates resources;
and the instruction management module is used for receiving the output information of the meteorological data analysis module, the airplane telemetering data analysis module, the airport data analysis module and the resource scheduling module and correspondingly sending a control instruction to the unmanned aerial vehicle or the airport.
In the technical scheme, the comprehensive alarm module is used for receiving alarm information output by the meteorological data analysis module, the airplane telemetering data analysis module, the airport data analysis module or the resource scheduling module and pushing the alarm information to the client.
In the above technical solution, the instruction management module obtains the destination airport state and gives a landing instruction, a hovering instruction or a standby landing instruction from the airport data analysis module before the unmanned aerial vehicle arrives at the destination.
In the technical scheme, the airplane telemetry data analysis module acquires the posture of the unmanned aerial vehicle after landing and sends the posture to the instruction management module to carry out storage instruction or alarm operation.
In the technical scheme, the landing auxiliary guide module is used for acquiring illumination data of the place where the target airport is located and determining whether to start the visual auxiliary guide lamp of the parking apron to land with the more accurate auxiliary unmanned aerial vehicle according to the illumination value when the unmanned aerial vehicle lands.
The invention has the advantages and beneficial effects that:
the invention relates to a method for protecting an unmanned aerial vehicle during taking off and landing from an airport parking apron and during flight, which mainly applies cloud big data analysis to ensure the taking off and landing stability and safety of the unmanned aerial vehicle and the safety of the flight process. So that the whole set of unattended goods receiving and dispatching system reaches the commercial standard.
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Fig. 1 is a schematic structural diagram of the unmanned aerial vehicle flight safety protection system based on cloud big data.
For a person skilled in the art, other relevant figures can be obtained from the above figures without inventive effort.
Detailed Description
In order to make the technical solution of the present invention better understood, the technical solution of the present invention is further described below with reference to specific examples.
Example one
The invention relates to a cloud big data-based unmanned aerial vehicle flight safety protection method, which comprises the following steps,
and acquiring and analyzing meteorological data on a takeoff place, a destination and an airway to provide the optimal takeoff time, wherein the meteorological conditions comprise but are not limited to wind speed, wind direction, rain and snow, haze index, air pressure and other factors. Through the meteorological data processing module at high in the clouds, the meteorological condition stability of unmanned aerial vehicle place of departure and the airline location in the recent time quantum is analyzed, combines unmanned aerial vehicle's environmental suitability, assesss out when unmanned aerial vehicle takes off. The meteorological data can be acquired through a data interface such as a meteorological bureau, a prediction model or prediction data can be acquired directly by using a meteorological bureau API (application programming interface), the prediction model or the prediction data can be directly used as a judgment standard of the subsequent optimal take-off time, model training can also be carried out after the data is acquired, then the optimal take-off time is generated according to the model, the optimal take-off time refers to the conditions such as illumination, wind speed, precipitation, temperature and the like to carry out weighting judgment, and the optimal take-off time can be set according to different pert;
acquiring state data of the unmanned aerial vehicle and analyzing the operation capacity of the unmanned aerial vehicle to determine whether the aircraft has a take-off condition, when the aircraft has the take-off condition, whether the aircraft has a storage condition, whether the aircraft needs to be charged subsequently and the like; the unmanned aerial vehicle state data comprises but is not limited to an electric regulation state, an airplane attitude, a position precision factor, a triaxial speed, satellite data, abnormal information, intelligent battery data and the like. Still including analysis unmanned aerial vehicle's after the decline gesture, intelligent electricity are transferred, data such as descending precision, intelligent battery state, judge whether will accomodate and subsequent charging flow to unmanned aerial vehicle.
The method comprises the steps of obtaining an airport operation state and analyzing whether landing conditions are met, wherein the airport operation state comprises whether landing vacancy is met when an unmanned aerial vehicle flies to reach and whether the airport can work normally, and whether a skylight is opened in place or not, and the landing precision and the post-landing posture of the airplane can be analyzed by means of a vision system and the like;
the method comprises the steps of acquiring states of the unmanned aerial vehicle, the airport, the air route and tasks in real time and allocating resources, specifically, comprehensively considering the overall operation state of the system, analyzing idle time periods and states of the unmanned aerial vehicle, the airport and air route resources, ensuring that the air routes flown by the unmanned aerial vehicle do not conflict, and ensuring that the target airport is in a storable state after the unmanned aerial vehicle reaches the target airport. Especially, the target airport has the time quantum of accomodating the aircraft condition, confirms that suitable dispatch unmanned aerial vehicle takes off from the airport of starting point, ensures that the whole efficiency of system is the highest.
And receiving the optimal takeoff time, the operation capacity and the landing condition, namely the resource allocation information, correspondingly sending a control instruction to the unmanned aerial vehicle or the airport, and determining whether the storage and charging process is executed according to the state of the airport.
Unmanned aerial vehicle's control command including take off, hover and wait, descend the airport, descend to reserve airport, return instructions such as navigate, the control command at airport including opening airport skylight, accomodate unmanned aerial vehicle, suspend and accomodate unmanned aerial vehicle, open light guide etc.. Specifically, before the unmanned aerial vehicle lands, meteorological conditions near a target airport are comprehensively analyzed, and whether the unmanned aerial vehicle is in fixed-point hovering waiting, landing or standby landing is evaluated. The unmanned airport parking apron has limited space and high requirement on the landing precision of the unmanned aerial vehicle, the landing of the unmanned aerial vehicle is greatly influenced by environmental factors, the cloud meteorological data processing module gives a meteorological evaluation conclusion, the aircraft agent module interacts with the aircraft in real time, and the unmanned aerial vehicle hovers for waiting if the current meteorological conditions are not met; if the landing condition can not be met within the appointed time, the vehicle lands on a ground standby landing point; if the landing condition is met, the vehicle lands.
And (3) the unmanned aerial vehicle requests the state of the unmanned airport in real time before landing, and whether the airport has airplane accepting conditions is evaluated. The cloud acquires the opening ratio of the airport skylight and the airport parking apron state, and interacts with the unmanned aerial vehicle in real time through the airplane agent module when the unmanned aerial vehicle lands. When the skylight is not completely opened, the unmanned aerial vehicle hovers for waiting; the skylight is not opened or the apron is abnormal within a period of time, and the unmanned aerial vehicle lands to a standby landing point. The skylight is completely opened and the apron is not abnormal, the unmanned aerial vehicle lands, and whether the storage and charging process should be executed or not is determined by combining the state of the unmanned aerial vehicle and the state of the airport after landing.
And further, the method also comprises the step of receiving the alarm information in the optimal take-off time, the operation capacity, the landing condition and the resource allocation information and pushing the alarm information to the client. When the information such as takeoff time or operation capacity, landing conditions, resource allocation and the like is processed, alarm information is generated, if the electric quantity is insufficient, the airplane body is damaged, meteorological conditions are not met or the airline can not be allocated in preset time, fault information is generated, and the client side is used for alarming in time to realize information interaction in the first time. Specifically, after meteorological data abnormity, unmanned aerial vehicle telemetering data abnormity and unattended airport state abnormity are detected, unmanned aerial vehicle landing data abnormity can be achieved, abnormal information can be pushed to channels such as mobile phone apps, ground stations and web ground stations in real time through the abnormity processing module, and therefore relevant personnel can find and process abnormity in time.
Specifically, the method further comprises the steps of acquiring the state of a destination airport before the unmanned aerial vehicle arrives at the destination, and giving a landing instruction, a hovering instruction or a standby landing instruction by combining the current state of the unmanned aerial vehicle. When the target unmanned aerial vehicle airport does not meet the landing condition for the moment, different instructions can be made, and meanwhile, the weather condition and the like can be referred.
The method comprises the steps of obtaining the posture of the unmanned aerial vehicle after landing and sending the posture to an instruction management module for carrying out storage instructions or alarm operation. After unmanned aerial vehicle falls, data such as analysis unmanned aerial vehicle's gesture, paddle state, descending precision, whether mainly used follow-up convenient judgement will be accomodate unmanned aerial vehicle. Prevent that unmanned aerial vehicle landing position is inaccurate and the paddle from not taking in the normal position to the aircraft under the normal position condition, protection aircraft and unmanned on duty airport mechanism. The unmanned aerial vehicle gesture is too big or the paddle does not rightly position to certain angle or the descending deviation is great, then does not accomodate and charge the action to unmanned aerial vehicle. The positive position of paddle means to many rotor unmanned aerial vehicle, through the low-speed rotation of the rotor after control falls, make the paddle draw in a circle, whether positive position can be adjusted the feedback by intelligent electricity, also can judge according to the visual system in the airport, effectively improves state analysis's precision.
And simultaneously, the airport further comprises a step of acquiring illumination data, and the visual auxiliary guide lamp of the parking apron is judged whether to be turned on or not according to the illumination data when the unmanned aerial vehicle lands. When unmanned aerial vehicle descends, whether the supplementary guide lamp of vision of air park, more accurate supplementary unmanned aerial vehicle descends is determined according to the illumination numerical value.
Example two
The application relates to an unmanned aerial vehicle flight safety protection system based on cloud big data, include
The meteorological data analysis module is used for acquiring meteorological data on a takeoff place, a destination and an airway and analyzing and providing an optimal takeoff time point;
the airplane telemetering data analysis module is used for acquiring unmanned aerial vehicle state data and analyzing the operation capacity of the unmanned aerial vehicle;
the airport data analysis module is used for acquiring an airport operation state and analyzing whether landing conditions are met;
the resource scheduling module acquires states of the unmanned aerial vehicle, the airport, the airline and the task in real time and allocates resources;
and the instruction management module is used for receiving the output information of the meteorological data analysis module, the airplane telemetering data analysis module, the airport data analysis module and the resource scheduling module and correspondingly sending a control instruction to the unmanned aerial vehicle or the airport.
And the comprehensive alarm module is used for receiving alarm information output by the meteorological data analysis module, the airplane telemetering data analysis module, the airport data analysis module or the resource scheduling module and pushing the alarm information to the client.
Specifically, the meteorological data analysis module 1 includes three parts, one part is data collected by a takeoff airport, one part is meteorological data collected on an air route, and the other part is data collected by a destination airport. Before the unmanned aerial vehicle takes off from an unattended airport, the meteorological data analysis module 1 comprehensively separates out an optimal taking-off time point or time period according to three meteorological data and meteorological change trends, the optimal taking-off time point or time period is provided for the instruction management module 6, and the instruction management module 6 generates a timing task to manage the timed taking-off of the aircraft. Guarantee that unmanned aerial vehicle can not receive the influence of weather factor at its air route in-process of flying. When unmanned aerial vehicle will take off and land, meteorological data analysis module 1 and instruction management module 6 can be real-time interaction, wait that the weather condition is stable just can carry out and take off, when descending, consider that unmanned on duty airport space is limited, it is higher to unmanned aerial vehicle descending required precision, if meteorological condition does not satisfy, the aircraft can hover and wait, if still do not satisfy in the time of regulation, then descend at the point of preparing for landing. Thereby protecting the unmanned aerial vehicle and the airport.
The aircraft telemetering data analysis module 2 acquires and analyzes the unmanned aerial vehicle telemetering data in real time, after capturing the abnormality of the unmanned aerial vehicle, pushes the abnormality data to the comprehensive warning module 5, and the comprehensive warning module 5 pushes the abnormality information to the client in real time so that operation and maintenance personnel can handle the problem in time. During the flight process of the airplane, the system can determine whether to immediately execute the standby landing, the standby landing at a destination airport or the return standby landing according to the abnormal level. And on the other hand, the aircraft telemetry data analysis module 2 analyzes the telemetry data in the recent period of time and evaluates whether the unmanned aerial vehicle has the take-off condition or not according to the variation trend of the telemetry data.
Unmanned aerial vehicle is descending the success back, and intelligent electricity is transferred can reach shared space minimum according to the angle of the angular adjustment unmanned aerial vehicle oar that sets up in advance. The airplane telemetering data analysis module 2 can detect intelligent electric tuning data, if the propeller is abnormal in normal position, the intelligent electric tuning data is pushed to the comprehensive warning module 5, meanwhile, the intelligent electric tuning data is pushed to the instruction management module 6, and the instruction management module 6 sends a pause storage instruction to an airport.
The telemetering data monitored by the aircraft telemetering data analysis module 2 comprises but is not limited to an electric regulation state, an aircraft attitude, a position accuracy factor, a three-axis speed, satellite data, abnormal information, an intelligent battery state and the like.
Airport data analysis module 3, obtain airport mechanism's state in real time, when unmanned aerial vehicle arrived the penultimate waypoint, airport data analysis module 3 reported the airport state to instruction management module 6, and instruction management module 6 judges when to send the landing instruction for the aircraft according to the airport state, if airport mechanism is unusual not normally open the skylight, or the parking apron trouble, instruction management module 6 notifies the aircraft to land to the point of reselling, synthesizes simultaneously and reports an emergency and asks for help or increased vigilance module 5 propelling movement abnormity and reports an emergency and asks for help or increased vigilance to the client.
Meanwhile, the airport data analysis module 3 can detect airport sensor data in real time, and if the airport sensor data is found to be abnormal, abnormal information is pushed to the comprehensive alarm module 5. Sensor data includes, but is not limited to, temperature, humidity, light, smoke, and the like.
Resource scheduling module 4 be the resource scheduling module of system, system's resource includes unmanned aerial vehicle, unmanned on duty airport, airline, resource scheduling module 4 updates the state of unmanned aerial vehicle, airport, airline, task in real time, the state includes: whether occupied, available time period, etc. Through reasonable dispatch, when guaranteeing that unmanned aerial vehicle reachs the purpose airport, the purpose airport can accomodate unmanned aerial vehicle, guarantees that the air route that unmanned aerial vehicle flies does not conflict, guarantees the high-efficient operation of whole receiving and dispatching goods system.
The instruction management module 6 can determine whether the aircraft needs to hover for waiting, land or prepare for landing after arriving at a target airport according to the outputs of the meteorological data analysis module 1, the aircraft telemetering data analysis module 2, the airport data analysis module 3 and the resource scheduling module 4. If the skylight is not completely opened, hovering for waiting; if the wind speed is unstable, hovering for waiting; if the airport mechanism is abnormal, the airplane is reserved; if the positioning accuracy of the airplane is not enough, the airplane is landed; and only when all the conditions meet the landing requirements, the airplane can land in the airport. Thereby guarantee the safety of unmanned aerial vehicle take off and land.
The system also comprises a landing auxiliary guide module for acquiring illumination data of the place where a target airport is located and determining whether to start the visual auxiliary guide lamp of the parking apron to land with a more accurate auxiliary unmanned aerial vehicle according to the illumination value when the unmanned aerial vehicle lands, wherein the illumination value of the visual angle of the unmanned aerial vehicle and the visual angle of the airport can be combined to judge when the judgment is made.
Spatially relative terms, such as "upper," "lower," "left," "right," and the like, may be used in the embodiments for ease of description to describe one element or feature's relationship to another element or feature as illustrated in the figures. It will be understood that the spatial terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "below" other elements or features would then be oriented "above" the other elements or features. Thus, the exemplary term "lower" can encompass both an upper and a lower orientation. The device may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
Moreover, relational terms such as "first" and "second," and the like, may be used solely to distinguish one element from another element having the same name, without necessarily requiring or implying any actual such relationship or order between such elements.
The invention has been described in an illustrative manner, and it is to be understood that any simple variations, modifications or other equivalent changes which can be made by one skilled in the art without departing from the spirit of the invention fall within the scope of the invention.
Claims (10)
1. A unmanned aerial vehicle flight safety protection method based on cloud big data is characterized in that: comprises the following steps of (a) carrying out,
acquiring the departure place, the destination and the meteorological data on the airway, and analyzing and providing the optimal departure time;
acquiring state data of the unmanned aerial vehicle and analyzing the operation capacity of the unmanned aerial vehicle;
acquiring an airport operation state and analyzing whether landing conditions are met;
acquiring states of an unmanned aerial vehicle, an airport, a airline and a task in real time and allocating resources;
receiving the optimal takeoff time, the operation capacity and the landing condition, namely the resource allocation information, and correspondingly sending a control instruction to the unmanned aerial vehicle or the airport;
unmanned aerial vehicle's control command including take off, hover, landing airport, descending to reserve airport, return instructions such as navigate, the control command at airport including opening airport skylight, accomodate unmanned aerial vehicle, suspend and accomodate unmanned aerial vehicle and open light guide.
2. The unmanned aerial vehicle flight safety protection method based on cloud big data as claimed in claim 1, wherein: and receiving the alarm information in the optimal take-off time, the operation capacity, the landing condition and the resource allocation information and pushing the alarm information to the client.
3. The unmanned aerial vehicle flight safety protection method based on cloud big data as claimed in claim 1, wherein: the method also comprises the steps of acquiring the state of a destination airport before the unmanned aerial vehicle arrives at the destination and giving a landing instruction, a hovering instruction or a standby landing instruction.
4. The unmanned aerial vehicle flight safety protection method based on cloud big data as claimed in claim 1, wherein: the method comprises the steps of obtaining the posture of the unmanned aerial vehicle after landing and sending the posture to an instruction management module for carrying out storage instructions or alarm operation.
5. The unmanned aerial vehicle flight safety protection method based on cloud big data as claimed in claim 1, wherein: the method also comprises the steps of acquiring illumination data of the place where the target airport is located and determining whether to turn on a visual auxiliary guide lamp of the parking apron according to the illumination value when the unmanned aerial vehicle lands so as to accurately assist the unmanned aerial vehicle to land.
6. The utility model provides an unmanned aerial vehicle flight safety protection system based on high in clouds big data which characterized in that: comprises that
The meteorological data analysis module is used for acquiring meteorological data on a takeoff place, a destination and an airway and analyzing and providing an optimal takeoff time point;
the airplane telemetering data analysis module is used for acquiring unmanned aerial vehicle state data and analyzing the operation capacity of the unmanned aerial vehicle;
the airport data analysis module is used for acquiring an airport operation state and analyzing whether landing conditions are met;
the resource scheduling module acquires states of the unmanned aerial vehicle, the airport, the airline and the task in real time and allocates resources;
and the instruction management module is used for receiving the output information of the meteorological data analysis module, the airplane telemetering data analysis module, the airport data analysis module and the resource scheduling module and correspondingly sending a control instruction to the unmanned aerial vehicle or the airport.
7. The unmanned aerial vehicle flight safety protection system based on big data in cloud according to claim 6, characterized in that: and the comprehensive alarm module is used for receiving alarm information output by the meteorological data analysis module, the airplane telemetering data analysis module, the airport data analysis module or the resource scheduling module and pushing the alarm information to the client.
8. The unmanned aerial vehicle flight safety protection system based on big data in cloud according to claim 6, characterized in that: the command management module acquires the state of a destination airport and gives a landing command, a hovering command or a standby landing command before the unmanned aerial vehicle arrives at the destination airport data analysis module.
9. The unmanned aerial vehicle flight safety protection system based on big data in cloud according to claim 6, characterized in that: the aircraft telemetry data analysis module acquires the posture of the unmanned aerial vehicle after landing and sends the posture to the instruction management module to carry out storage instruction or alarm operation.
10. The unmanned aerial vehicle flight safety protection system based on big data in cloud according to claim 6, characterized in that: the landing auxiliary guide module is used for acquiring illumination data of the place where the target airport is located and determining whether to start the visual auxiliary guide lamp of the parking apron to land with the more accurate auxiliary unmanned aerial vehicle according to the illumination value when the unmanned aerial vehicle lands.
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| CN113190048B (en) * | 2021-03-16 | 2023-09-26 | 西北工业大学 | An automatic control system and method for vertical take-off and landing drone airports |
| CN113190048A (en) * | 2021-03-16 | 2021-07-30 | 西北工业大学 | Automatic control system and method for vertical take-off and landing unmanned aerial vehicle airport |
| CN112731950A (en) * | 2021-04-02 | 2021-04-30 | 北京云圣智能科技有限责任公司 | Unmanned aerial vehicle landing control method and device and server |
| CN116931593A (en) * | 2022-04-07 | 2023-10-24 | 广东汇天航空航天科技有限公司 | Flight control method and device, aircraft and storage medium |
| CN114815889A (en) * | 2022-04-28 | 2022-07-29 | 江苏省环境科学研究院 | Unmanned aerial vehicle airport monitoring control system based on big data |
| CN115202396A (en) * | 2022-07-25 | 2022-10-18 | 上海市格致中学 | A UAV flight control system and method |
| CN115456486A (en) * | 2022-11-10 | 2022-12-09 | 深圳市道通智能航空技术股份有限公司 | Task planning method and device of cluster system and electronic equipment thereof |
| CN115793706A (en) * | 2022-12-02 | 2023-03-14 | 安徽送变电工程有限公司 | Coordinated operation method and system for off-site take-off and landing of aircraft fleet |
| CN116448189A (en) * | 2023-06-13 | 2023-07-18 | 北京神导科技股份有限公司 | Test equipment of supporting facility of flight command system |
| CN116448189B (en) * | 2023-06-13 | 2023-09-08 | 北京神导科技股份有限公司 | A test equipment for flight command system supporting facilities |
| CN116620596A (en) * | 2023-07-21 | 2023-08-22 | 国网四川省电力公司成都供电公司 | Intelligent Airport Control Method for Unmanned Aerial Vehicles |
| CN118192623A (en) * | 2024-03-04 | 2024-06-14 | 智慧尘埃(上海)通信科技有限公司 | Unmanned aerial vehicle take-off and landing control method, parking apron, unmanned aerial vehicle and system |
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