CN113625743A

CN113625743A - Intelligent control method for unmanned aerial vehicle, related device and storage medium

Info

Publication number: CN113625743A
Application number: CN202010379925.7A
Authority: CN
Inventors: 黄春
Original assignee: Fonair Aviation Co Ltd
Current assignee: Chongqing Fengniao Uav Research Institute Co ltd
Priority date: 2020-05-08
Filing date: 2020-05-08
Publication date: 2021-11-09

Abstract

The application discloses an unmanned aerial vehicle intelligent control method, a related device and a storage medium. The intelligent control method of the unmanned aerial vehicle comprises the following steps: acquiring load sensing data sent by an unmanned aerial vehicle in the flight process of the unmanned aerial vehicle; acquiring flight route information of other aircrafts except for the unmanned aerial vehicle in a region formed by taking the unmanned aerial vehicle as a center and presetting a distance range; determining auxiliary suggestion information for the unmanned aerial vehicle to fly based on the load sensing data and flight route information; and adjusting the flight parameters of the unmanned aerial vehicle based on the auxiliary suggestion information. This application embodiment is owing to increased supplementary advice information, consequently can be based on supplementary advice information can more the aspect and accurate automatically regulated unmanned aerial vehicle's flight parameter, reduced because of the unmanned aerial vehicle accident that the uncontrollable factor that artificial misoperation and unmanned aerial vehicle self exist leads to can effectively improve unmanned aerial vehicle operating safety coefficient and reduce unmanned aerial vehicle operation cost.

Description

Intelligent control method for unmanned aerial vehicle, related device and storage medium

Technical Field

The application relates to the technical field of unmanned aerial vehicles, in particular to an intelligent control method of an unmanned aerial vehicle, a related device and a storage medium.

Background

The subjects involved in the research and development of the unmanned aerial vehicle technology are very wide, and the unmanned aerial vehicle technology comprises various electronic communication, flight control, aerodynamic sensor technologies and the like, and has the characteristics of crossing various subjects, looking ahead in technology and the like.

The process that unmanned aerial vehicle carries out the task in flight, not pure "unmanned" control, under ground control personnel's monitoring and guide, carry out real-time control to unmanned aerial vehicle to its biography control command, adjust its flight state, accomplish predetermined task. Unmanned aerial vehicle all can not leave ground monitoring system's guarantee and support at any moment, and ground monitoring system's quality degree plays crucial effect to operating personnel control unmanned aerial vehicle's judgement, nevertheless, artificial operation and unmanned aerial vehicle system still have a lot of uncontrollable factors for unmanned aerial vehicle operation easily takes place the accident, causes the loss.

Disclosure of Invention

The embodiment of the application provides an unmanned aerial vehicle intelligent control method, a related device and a storage medium, and auxiliary suggestion information is added, so that flight parameters of an unmanned aerial vehicle can be adjusted comprehensively and accurately based on the auxiliary suggestion information, unmanned aerial vehicle accidents caused by man-made misoperation and uncontrollable factors existing in the unmanned aerial vehicle are reduced, and the unmanned aerial vehicle operation safety factor can be effectively improved and the unmanned aerial vehicle operation cost can be reduced.

The embodiment of the application provides an unmanned aerial vehicle intelligent control method, the method is applied to an unmanned aerial vehicle intelligent control system, the unmanned aerial vehicle intelligent control system comprises a ground station, an unmanned aerial vehicle and a flight auxiliary device, the flight auxiliary device is connected with the ground station and the unmanned aerial vehicle through a network, and the method comprises the following steps:

acquiring load sensing data sent by the unmanned aerial vehicle in the flight process of the unmanned aerial vehicle;

acquiring flight route information of other aircrafts except the unmanned aerial vehicle in an area formed by taking the unmanned aerial vehicle as a center and presetting a distance range;

determining auxiliary recommendation information for the unmanned aerial vehicle to fly based on the load sensing data and the flight path information;

adjusting flight parameters of the unmanned aerial vehicle based on the auxiliary suggestion information.

In some embodiments, the determining, based on the load sensing data and the flight path information, auxiliary recommendation information for the drone to fly includes:

generating flight state data and flight environment data of the unmanned aerial vehicle based on the load sensing data;

and determining auxiliary suggestion information aiming at the unmanned aerial vehicle flight according to the flight route information, the flight state data and the flight environment data.

In some embodiments, the generating flight status data and flight environment data of the drone based on the load sensing data includes:

extracting flight state data in the load sensing data to obtain the flight state data of the unmanned aerial vehicle;

and identifying the flight environment data in the load sensing data to obtain the flight environment data of the unmanned aerial vehicle.

In some embodiments, the determining, from the flight path information, the flight status data, and the flight environment data, auxiliary recommendation information for the flight of the drone includes:

detecting the flight route information to obtain flight route detection information;

detecting the flight state data to obtain flight state detection information;

detecting the flight environment data to obtain flight environment detection information;

and determining auxiliary suggestion information aiming at the unmanned aerial vehicle flight according to the flight line detection information, the flight state detection information and the flight environment detection information.

In some embodiments, the determining auxiliary recommendation information for the unmanned aerial vehicle to fly according to the flight path detection information, the flight state detection information and the flight environment detection information includes:

determining flight abnormity information of the unmanned aerial vehicle according to the flight route detection information, the flight state detection information and the flight environment detection information, wherein the flight abnormity information comprises at least one of flight route abnormity information, flight state abnormity information and the flight environment abnormity information;

and generating auxiliary suggestion information corresponding to the abnormal flight information according to the abnormal flight information.

In some embodiments, the determining auxiliary recommendation information for the unmanned aerial vehicle to fly according to the flight route information, the flight state data, and the flight environment data includes:

inputting the flight state data into a preset unmanned aerial vehicle state model to generate the state adjustment information;

inputting the flight environment data into a preset unmanned aerial vehicle obstacle avoidance prediction model to generate the emergency obstacle avoidance information;

and inputting the flight route information into a preset route state management model to generate route optimization information.

In some embodiments, the flight parameters of the drone include a state adjustment parameter, an obstacle avoidance prediction parameter, and a route planning parameter, and the adjusting the flight parameters of the drone based on the auxiliary recommendation information includes:

and sending the state adjustment information, the emergency obstacle avoidance information and the route optimization information to the unmanned aerial vehicle so that the unmanned aerial vehicle adjusts state adjustment parameters, obstacle avoidance prediction parameters and route planning parameters.

performing scene simulation on the state adjustment information, the emergency obstacle avoidance information and the route optimization information to obtain flight prediction simulation video information of the unmanned aerial vehicle;

and sending the flight prediction simulation video information of the unmanned aerial vehicle to the ground station so that the ground station adjusts the state adjustment parameters, obstacle avoidance prediction parameters and air route planning parameters of the unmanned aerial vehicle according to the flight prediction simulation video information.

On the other hand, this application provides an unmanned aerial vehicle intelligent control device, unmanned aerial vehicle intelligent control device is applied to unmanned aerial vehicle intelligent control system, unmanned aerial vehicle intelligent control system includes ground satellite station, unmanned aerial vehicle and flight auxiliary assembly, flight auxiliary assembly the ground satellite station with unmanned aerial vehicle internet access each other, unmanned aerial vehicle intelligent control device includes:

the first acquisition unit is used for acquiring load sensing data sent by the unmanned aerial vehicle in the flight process of the unmanned aerial vehicle;

the second acquisition unit is used for acquiring flight route information of other aircrafts except the unmanned aerial vehicle in an area formed by taking the unmanned aerial vehicle as a center and presetting a distance range;

a first determination unit, configured to determine auxiliary recommendation information for the unmanned aerial vehicle to fly based on the load sensing data and the flight path information;

a first adjusting unit, configured to adjust a flight parameter of the unmanned aerial vehicle based on the auxiliary suggestion information.

In some embodiments, the first determination unit comprises:

the first generation unit is used for generating flight state data and flight environment data of the unmanned aerial vehicle based on the load sensing data;

and the second determination unit is used for determining auxiliary suggestion information aiming at the unmanned aerial vehicle flight according to the flight route information, the flight state data and the flight environment data.

In some embodiments, the first generating unit is specifically configured to:

In some embodiments, the second determination unit comprises:

the first detection unit is used for detecting the flight route information to obtain flight route detection information;

the second detection unit is used for detecting the flight state data to obtain flight state detection information;

the third detection unit is used for detecting the flight environment data to obtain flight environment detection information;

and the third determining unit is used for determining auxiliary suggestion information aiming at the unmanned aerial vehicle flight according to the flight line detection information, the flight state detection information and the flight environment detection information.

In some embodiments, the third determining unit is specifically configured to:

In some embodiments, the auxiliary suggestion information includes state adjustment information corresponding to the flight state detection information, emergency obstacle avoidance information corresponding to the flight environment detection information, and route optimization information corresponding to the flight route detection information, and the third determining unit is specifically configured to:

In some embodiments, the flight parameters of the drone include a state adjustment parameter, an obstacle avoidance prediction parameter, and a route planning parameter, and the first adjustment unit is specifically configured to:

In another aspect, the present application provides a flight assistance apparatus comprising:

one or more processors;

a memory; and

one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the drone intelligent control method.

In another aspect, the present application provides a computer-readable storage medium, in which a computer program is stored, where the computer program is suitable for being loaded by a processor to execute the intelligent drone control method.

According to the control method of the unmanned aerial vehicle, the flight auxiliary equipment is added, the flight auxiliary equipment is communicated with the ground station and the unmanned aerial vehicle through network connection, the flight auxiliary equipment can acquire load sensing data sent by the unmanned aerial vehicle in the flight process and flight route information of aircrafts except the unmanned aerial vehicle in an area formed by taking the unmanned aerial vehicle as a center and within a preset distance range, and auxiliary suggestion information for the unmanned aerial vehicle to fly is determined based on the load sensing data and the flight route information; and based on this supplementary advice information, adjust unmanned aerial vehicle's flight parameter, owing to increased supplementary advice information, consequently can be based on supplementary advice information can more the aspect and accurate automatically regulated unmanned aerial vehicle's flight parameter, reduced because of the unmanned aerial vehicle accident that the uncontrollable factor that artificial misoperation and unmanned aerial vehicle self exist leads to can effectively improve unmanned aerial vehicle operation factor of safety and reduce unmanned aerial vehicle operation cost.

Drawings

The technical solution and other advantages of the present application will become apparent from the detailed description of the embodiments of the present application with reference to the accompanying drawings.

Fig. 1 is a scene schematic diagram of an intelligent control system of an unmanned aerial vehicle according to an embodiment of the present application;

fig. 2 is a schematic flow diagram of an embodiment of an intelligent control method for an unmanned aerial vehicle according to an embodiment of the present application

FIG. 3 is a flowchart illustrating an embodiment of step 203 according to the present invention;

FIG. 4 is a flowchart illustrating an embodiment of step 302 according to the present invention;

FIG. 5 is a flowchart illustrating an embodiment of step 404 according to the present invention;

fig. 6 is a schematic structural diagram of an embodiment of an intelligent control device for an unmanned aerial vehicle according to an embodiment of the present invention;

fig. 7 is a schematic structural diagram of another embodiment of an intelligent control device of an unmanned aerial vehicle provided by the embodiment of the application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

In the description of the present application, it is to be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the present application and for simplicity in description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed in a particular orientation, and be operated in a particular manner, and are not to be construed as limiting the present application. Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.

In the description of the present application, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; may be mechanically connected, may be electrically connected or may be in communication with each other; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.

In this application, unless expressly stated or limited otherwise, the first feature "on" or "under" the second feature may comprise direct contact of the first and second features, or may comprise contact of the first and second features not directly but through another feature in between. Also, the first feature being "on," "above" and "over" the second feature includes the first feature being directly on and obliquely above the second feature, or merely indicating that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature includes the first feature being directly under and obliquely below the second feature, or simply meaning that the first feature is at a lesser elevation than the second feature.

The following disclosure provides many different embodiments or examples for implementing different features of the application. In order to simplify the disclosure of the present application, specific example components and arrangements are described below. Of course, they are merely examples and are not intended to limit the present application. Moreover, the present application may repeat reference numerals and/or letters in the various examples, such repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed.

The embodiment of the invention provides an intelligent control method of an unmanned aerial vehicle, a related device and a storage medium, which are respectively described in detail below.

An application scenario of the intelligent control system of the unmanned aerial vehicle provided by the embodiment of the application is introduced firstly.

Referring to fig. 1, fig. 1 is a schematic view of a scene of an intelligent control system for an unmanned aerial vehicle according to an embodiment of the present invention, where the intelligent control system for an unmanned aerial vehicle may include an unmanned aerial vehicle 100, a flight assistance device 200, and a ground station 300, where the flight assistance device 200, the ground station 300, and the unmanned aerial vehicle 100 are connected to each other via a network, and an intelligent control apparatus for an unmanned aerial vehicle is integrated in the flight assistance device 200, such as the flight assistance device 200 in fig. 1, and the unmanned aerial vehicle 100, the flight assistance device 200, and the ground station 300 may perform data interaction with each other.

In the embodiment of the invention, the flight assisting device 200 is mainly used for acquiring load sensing data sent by the unmanned aerial vehicle in the flight process of the unmanned aerial vehicle; acquiring flight route information of other aircrafts except the unmanned aerial vehicle in an area formed by taking the unmanned aerial vehicle as a center and presetting a distance range; determining auxiliary recommendation information for the unmanned aerial vehicle to fly based on the load sensing data and the flight path information; adjusting flight parameters of the unmanned aerial vehicle based on the auxiliary suggestion information.

In an embodiment of the present invention, the flight assistance device 200 may include a server, which may be an independent server, or a server network or a server cluster composed of servers, for example, the server described in the embodiment of the present invention, which includes but is not limited to a computer, a network host, a single network server, a plurality of network server sets, or a cloud server composed of a plurality of servers. Among them, the Cloud server is constituted by a large number of computers or web servers based on Cloud Computing (Cloud Computing). In the embodiment of the present invention, the server and the User terminal may implement communication through any communication manner, including but not limited to mobile communication based on the third Generation Partnership Project (3 GPP), Long Term Evolution (LTE), Worldwide Interoperability for Microwave Access (WiMAX), or computer network communication based on the TCP/IP Protocol Suite (TCP/IP), User Datagram Protocol (UDP), and the like.

In the embodiment of the present invention, on one hand, the ground station 300 may communicate with the drone 100, so as to perform remote control and monitoring on the drone 100, and on the other hand, the ground station 300 may communicate with the flight assistance device 200, so that the flight assistance device 200 optimizes the control of the drone 100 by the ground station 300.

It should be noted that the ground station 300 may include a server, and the server in the ground station 200 and the server in the flight assistance device 200 perform network communication to remotely control and detect the drone 100 together, wherein the ground station 300 and the flight assistance device 200 perform remote control on the drone in two levels, the ground station 300 is in a first level, and the flight assistance device 200 is in a second level, and when the ground station 300 in the first level and the flight assistance device 200 in the second level give all remote control rights to the flight assistance device 200 in the second level, the flight assistance device 200 performs fully automatic remote control on the drone 100.

Those skilled in the art will understand that the application environment shown in fig. 1 is only one application scenario related to the present application, and does not constitute a limitation on the application scenario of the present application, and that other application environments may further include more or less drones than those shown in fig. 1, or a network connection relationship of flight assistance devices, for example, only 1 ground station, 1 flight assistance device, and 2 drones are shown in fig. 1, and it is understood that the drone intelligent control system may further include one or more other drones network-connected to the flight assistance devices, and is not limited herein.

In addition, as shown in fig. 1, the intelligent drone control system may further include a memory 400 for storing drone data, such as load sensing data of the drone, the latest performance state of the drone, and the like.

It should be noted that the scene schematic diagram of the intelligent control system of the unmanned aerial vehicle shown in fig. 1 is only an example, and the intelligent control system of the unmanned aerial vehicle and the scene described in the embodiment of the present invention are for more clearly illustrating the technical solution of the embodiment of the present invention, and do not form a limitation on the technical solution provided in the embodiment of the present invention.

Next, an intelligent control method for an unmanned aerial vehicle provided by the embodiment of the application is introduced.

In the embodiment of the intelligent control method for the unmanned aerial vehicle, flight auxiliary equipment in an intelligent control system of the unmanned aerial vehicle is used as an execution subject, and for the sake of simplicity and convenience in description, the execution subject is omitted in the following method embodiments, and the intelligent control method for the unmanned aerial vehicle comprises the following steps: acquiring load sensing data sent by the unmanned aerial vehicle in the flight process of the unmanned aerial vehicle; acquiring flight route information of other aircrafts except the unmanned aerial vehicle in an area formed by taking the unmanned aerial vehicle as a center and presetting a distance range; determining auxiliary recommendation information for the unmanned aerial vehicle to fly based on the load sensing data and the flight path information; adjusting flight parameters of the unmanned aerial vehicle based on the auxiliary suggestion information.

As shown in fig. 2, fig. 2 is a schematic flowchart of an embodiment of an intelligent control method for an unmanned aerial vehicle according to an embodiment of the present application. It should be noted that, although a logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in an order different than that shown or described herein. The intelligent control method of the unmanned aerial vehicle is applied to an intelligent control system of the unmanned aerial vehicle, the intelligent control system of the unmanned aerial vehicle at least comprises the unmanned aerial vehicle and flight auxiliary equipment, and the intelligent control method of the unmanned aerial vehicle comprises steps 201 to 204, wherein:

201. in the flight process of the unmanned aerial vehicle, load sensing data sent by the unmanned aerial vehicle are obtained.

Wherein, using the unmanned aerial vehicle in-process, unmanned aerial vehicle generally includes three kinds of operation processes at least, specifically take off process, flight process and descending process, and at present, the communication mode between unmanned aerial vehicle and ground station and/or the flight auxiliary assembly has following two problems:

(1) the data transmission delay between the unmanned aerial vehicle and the ground station and/or the flight auxiliary equipment in the take-off process is high in requirement.

(2) Unmanned aerial vehicle is at the flight in-process, and when the distance between unmanned aerial vehicle and ground station and/or the flight auxiliary assembly is too long, can't satisfy the data transmission distance between unmanned aerial vehicle and ground station and the flight auxiliary assembly.

In order to solve the above problems, in the embodiments of the present application, two communication modes, namely, a line-of-sight chain and an over-the-line-of-sight chain (satellite communication link) are performed between the unmanned aerial vehicle and the ground station and/or the flight assistance device, wherein the adoption of the low-delay line-of-sight is beneficial to timely acquiring the state information of the takeoff and landing aircraft of the unmanned aerial vehicle through the ground station and/or the flight assistance device; because the unmanned aerial vehicle has an inner ring autonomous system, has certain self-stability, and can send a data link switching satellite communication link signal to the unmanned aerial vehicle through the ground station and/or the flight auxiliary equipment. Through the link switching, utilize link timesharing multiplex, the utilization ratio of last stadia chain and satellite communication link can be improved to the multiplexing mechanism of link frequency division, provides the technological basis for ground station and/or flight auxiliary assembly can monitor many unmanned aerial vehicles simultaneously, improves the utilization ratio of equipment such as flight assistance simultaneously.

Furthermore, the unmanned aerial vehicle flying at a long distance is monitored, and the distance of a sight distance link is limited by the working distance, so that the unmanned aerial vehicle is required to be equipped with a plurality of ground stations and/or flight auxiliary equipment to monitor the full running state.

According to the embodiment of the application, the ground station data receiving terminals (CDT) can be deployed in a plurality of fixed places, the state information of the unmanned aerial vehicle is transmitted through the network, the ground station and/or the flight auxiliary equipment of the unmanned aerial vehicle are deployed in a centralized mode according to the region, the number of the ground stations can be effectively reduced, and the operation cost can be saved.

The load sensing data refers to sensing data which is obtained by load sensing equipment arranged on the unmanned aerial vehicle and is transmitted to the flight auxiliary equipment after being converted, specifically, the load sensing equipment can comprise sensing equipment such as a height sensor, a distance sensor, an image sensor, a radar sensor, a photoelectric/infrared sensor and the like, and the application does not limit the sensing equipment, wherein, the image shot by the image sensor and the detection signal of the radar sensor can be used for detecting whether obstacles exist around the unmanned aerial vehicle in the process of the flight state, the distance sensor can measure the distance between the unmanned aerial vehicle and surrounding obstacles, the height sensor can measure the current flying height of the unmanned aerial vehicle in real time, important information such as the posture, the direction, the airspeed, the position, the battery voltage, the instant wind speed and direction, the task time and the like of the unmanned aerial vehicle are mastered in real time by acquiring the sensing data.

202. And acquiring flight route information of other aircrafts except the unmanned aerial vehicle in an area formed by the preset distance range by taking the unmanned aerial vehicle as a center.

The term "aircraft" as used herein refers to an aircraft flying in the atmosphere. Including airplanes, airships, balloons and any other objects that can fly through the atmosphere by the reaction force of the air. Aircraft must overcome various drag in the air to be flown, and various meteorological conditions and aerodynamics in the atmosphere have various effects on aircraft flight. The aircraft is divided into two categories according to different lift force obtaining modes, one category is the aircraft lighter than air, and the aircraft floats in the air by the buoyancy of the air, such as a balloon, an airship and the like; the other type is a heavy-air aircraft, which comprises two types of non-power driving and power driving, and the aircraft can be divided into a manned aircraft (short for aircraft) and a unmanned aerial vehicle according to whether the aircraft carries people or not.

The flight route information refers to information of a preset flight route and flight time of the airplane, wherein the flight route of the airplane is called an air traffic line, and is called a route for short. The flight path of the airplane not only determines the specific flight direction, origin-destination point and transit-stop point of the airplane, but also specifies the width and flight height of the flight path according to the requirements of air traffic control.

In addition to avoiding natural factors such as mountains, birds and unidentified flying objects, the unmanned aerial vehicle also avoids the problem of overlapping air routes with other airplanes in the actual flying process.

Therefore, in some embodiments of the present invention, the flight path information of other aircraft may be obtained by associating the intelligent drone control system with an additional aircraft state system, which refers to an Air Traffic Control (ATC) system, and specifically, the air traffic control system monitors and controls the flight activities of the aircraft by using communication, navigation technology and monitoring means, so as to ensure safe and orderly flight. Different management airspaces are divided in the airspace of the flight route, including an air route, a flight information management area, an approach management area, a tower management area, a waiting airspace management area and the like, and different radar equipment is used according to different management areas. The interval division is carried out in a management airspace, and the horizontal and vertical intervals among the aircrafts form the basis of air traffic management. The air traffic management system is composed of navigation equipment, a radar system, a secondary radar, communication equipment and a ground control center, and is used for finishing monitoring, identifying and guiding the aircraft in a coverage area.

For example, the system is associated with an Automatic dependent-monitoring broadcast (ADS) system, which is composed of multiple ground stations and airborne stations and performs data bidirectional communication in a mesh, multipoint-to-multipoint manner. The ADS-B system is an information system integrating communication and monitoring, and comprises an information source, an information transmission channel and an information processing and displaying part. The integrated information display system organically combines conflict detection, conflict avoidance, conflict resolution, ATC monitoring, ATC consistency monitoring and cabin integrated information display, strengthens and expands abundant functions for a new navigation system, and has the working principle that the airborne ADS-B communication equipment sends navigation information collected by an airborne information processing unit in a broadcasting mode, receives broadcast information of other aircrafts and the ground, processes the broadcast information and sends the broadcast information to the cabin integrated information display. And the cabin comprehensive information display provides situation information and other additional information around the unmanned aerial vehicle for the unmanned aerial vehicle according to the collected ADS-B information, airborne radar information and navigation information of other aircrafts and the ground.

203. And determining auxiliary suggestion information for the unmanned aerial vehicle to fly based on the load sensing data and the flight path information.

The auxiliary suggestion information refers to auxiliary suggestion information generated by the flight auxiliary equipment aiming at the abnormal condition when the unmanned aerial vehicle is in the abnormal condition, so that the unmanned aerial vehicle can correspondingly adjust the flight parameters of the unmanned aerial vehicle according to the auxiliary suggestion information, and the abnormal condition is avoided.

The following abnormal conditions may occur during the flight of the unmanned aerial vehicle:

firstly, the unmanned aerial vehicle self performance is unusual, for example, unmanned aerial vehicle inner ring control system goes wrong for the unable manual control unmanned aerial vehicle flight direction of ground station control personnel.

Secondly, the skew appears in the unmanned aerial vehicle focus, for example, goods that transportation type unmanned aerial vehicle bore appears the goods and sideslips partial to certain side in the transportation for the skew appears in the unmanned aerial vehicle focus.

And thirdly, obstacles appear on the flight path preset by the unmanned aerial vehicle, wherein the obstacles comprise severe weather, high peaks and buildings of posters, unidentified flyers and other aircrafts, for example, a bird appears in the front 50 meters.

When the unmanned aerial vehicle encounters the abnormal condition, a flight accident of the unmanned aerial vehicle can be caused. Based on the problem, in the embodiment of the application, the auxiliary suggestion information for the flight of the unmanned aerial vehicle needs to be determined based on the load sensing data and the flight route information, so that the unmanned aerial vehicle avoids the abnormal condition.

204. Adjusting flight parameters of the unmanned aerial vehicle based on the auxiliary recommendation information.

The flight parameters of the unmanned aerial vehicle can include state adjustment parameters, obstacle avoidance prediction parameters and route planning parameters of the unmanned aerial vehicle.

According to the embodiment of the application, the newly-added flight auxiliary equipment is communicated with the ground station and the unmanned aerial vehicle through network connection, the flight auxiliary equipment can acquire load sensing data sent by the unmanned aerial vehicle in the flight process and flight route information of aircrafts except the unmanned aerial vehicle in an area formed by taking the unmanned aerial vehicle as a center and within a preset distance range, and auxiliary suggestion information for the unmanned aerial vehicle flight is determined based on the load sensing data and the flight route information; and based on this supplementary advice information, adjust unmanned aerial vehicle's flight parameter, owing to increased supplementary advice information, consequently can be based on supplementary advice information can more the aspect and accurate automatically regulated unmanned aerial vehicle's flight parameter, reduced because of the unmanned aerial vehicle accident that the uncontrollable factor that artificial misoperation and unmanned aerial vehicle self exist leads to can effectively improve unmanned aerial vehicle operation factor of safety and reduce unmanned aerial vehicle operation cost.

As shown in fig. 3, in some embodiments of the present application, the determining, in step 203, auxiliary recommendation information for the flight of the drone based on the load sensing data and the flight path information further includes:

301. and generating flight state data and flight environment data of the unmanned aerial vehicle based on the load sensing data.

Wherein, flight state data is the relevant information about the flight state of unmanned aerial vehicle, and this flight state data can include unmanned aerial vehicle flight attitude data, flight speed data, flight altitude data, flight inclination angle data, load data etc..

The flight attitude refers to the state of the three axes of the aircraft relative to a reference line or a reference plane or a fixed coordinate system in the air, and the unmanned aerial vehicle has various flight attitudes unlike vehicles moving on the ground while flying in the air. This refers to the change of the unmanned aerial vehicle's head up, head down, left tilt, right tilt, etc. The flight attitude determines the moving direction of the unmanned aerial vehicle, and affects the flight height and the flight direction. During low-speed flight, the pilot leans on observation ground, can judge unmanned aerial vehicle's flight gesture according to the position of horizon, actually acquires unmanned aerial vehicle's flight gesture data, can be in the same place through the support and the fuselage to the gyroscope, and its rotor is when high-speed rotation, and rotation axis perpendicular to ground has a horizontal guide bar and rotor shaft vertical cross to link to each other. The aircraft can change the flight attitude, but the rotor shaft will always point to the ground, and the transverse marker post will always be parallel to the horizon, which is called artificial horizon in the instrument. This instrument is called a horizon instrument, also called a gesture director.

The flight environment data is information related to the flight environment of the unmanned aerial vehicle, and the flight environment data can include radar detection data, distance sensing data, image shooting data and weather early warning data.

Specifically, based on the load sensing data, the flight state data and the flight environment data of the unmanned aerial vehicle are generated, and the following method may be adopted:

firstly, extracting flight state data in the load sensing data to obtain the flight state data of the unmanned aerial vehicle.

And secondly, identifying the flight environment data in the load sensing data to obtain the flight environment data of the unmanned aerial vehicle.

The load sensing data is acquired by various different sensing devices, the sensing data of each different sensing device has different attributes, and the specific attributes of the sensing data can include the type, format, value, expression and the like of the sensing data, for example, the voltage information of a battery of the unmanned aerial vehicle is acquired, the voltage is lower and lower along with the use of the battery, and the voltage information of the battery is an analog signal. Therefore, when a plurality of different sensing data are required to be obtained, the sensing data can be first preprocessed, such as extracting, identifying, classifying, converting, dividing or merging the plurality of different sensing data,

for example, the sensing data may be divided into "voice data", "image data", "warning data", and "usual UAV (unmanned aerial vehicle) parameters", and the like. Digitizing data acquired by the flight assistance device; converting "voice data" into numbers or some sort of identification symbol; segmenting image data, identifying or merging images, and extracting equivalent values of characteristic values and characteristic quantities of the images; the "common UAV parameters" are divided into basic UAV monitoring data and detailed UAV parameter data.

Further, along with the expansion of traffic, the demand for unmanned aerial vehicles is also bigger thereupon, and after the quantity of unmanned aerial vehicles reached certain scale, the communication demand of high frequency, big data will appear between a plurality of unmanned aerial vehicles and ground satellite station and the flight auxiliary assembly, but, every unmanned aerial vehicle's communication protocol is not necessarily the same, consequently, may not satisfy the demand of business.

For this reason, a protocol conversion device may be established between the unmanned aerial vehicle and the ground station and the flight assistance device, where the protocol conversion device is configured to convert first control data in a first communication protocol into second control data in a second communication protocol, and send the second control data to the unmanned aerial vehicle; and converting the first telemetry data under the second communication protocol to second telemetry data under the first communication protocol and transmitting the second telemetry data to the flight assistance device and/or the ground station.

The first communication protocol refers to a communication protocol supported by the flight assistance device and/or the ground station, and the second communication protocol refers to a communication protocol supported by the unmanned aerial vehicle. The first control data refers to control data to be transmitted to the drone by the flight assistance device and/or the ground station, and is control data under a first communication protocol. The second control data refers to control data to be transmitted by the flight assistance device and/or the ground station to the drone and is control data under a second communication protocol. The first telemetry data refers to telemetry data to be transmitted by the drone to the flight assistance device and/or the ground station, and is telemetry data under the second communication protocol. The second telemetry data refers to telemetry data to be transmitted by the drone to the flight assistance device and/or the ground station, and is telemetry data under the first communication protocol.

For ease of understanding, a specific example is illustrated. For example, control data is cached in the protocol conversion device, and the cached control data is used for controlling the unmanned aerial vehicle. The flight assistance device needs to send first control data to the drone (e.g., to control the drone to decelerate to 15 km/h).

First, the flight assistance device transmits first control data to the protocol conversion device based on a first communication protocol (the first communication protocol refers to a communication protocol supported by the flight assistance device).

Then, the protocol conversion device converts the first control data into second control data under a second communication protocol (the second communication protocol refers to a communication protocol supported by the drone) (the data to be transmitted still is "controlling the drone to decelerate to 15 km/h", except that a different communication protocol is adopted). On one hand, the protocol conversion equipment caches the second control data; on the other hand, the protocol conversion device cyclically transmits second control data to the drone according to a preset transmission period based on the second communication protocol.

And finally, the unmanned aerial vehicle receives second control data sent by the protocol conversion equipment, and adjusts the flight state according to the received second control data.

This application embodiment is through adopting protocol conversion equipment to realize the data communication between a plurality of unmanned aerial vehicles and ground station and/or the flight auxiliary assembly, guarantee that a plurality of unmanned aerial vehicles can be by effectual control simultaneously.

302. And determining auxiliary suggestion information aiming at the flight of the unmanned aerial vehicle according to the flight route information, the flight state data and the flight environment data.

Specifically, as shown in fig. 4, in some embodiments, the determining, in step 302, auxiliary suggestion information for the unmanned aerial vehicle to fly according to the flight route information, the flight state data, and the flight environment data may include the following steps:

401. and detecting the flight path information to obtain flight path detection information.

The flight path detection information is line result detection data generated after the obtained flight path information is detected, specifically, the flight path information of other aircrafts is mainly detected to include multiple dimension detection, the dimension detection is divided into a space dimension and a time dimension, the space dimension detection means that whether the flight paths of the other aircrafts have overlapping parts with the preset line of the unmanned aerial vehicle or not is detected, the overlapping means that three-dimensional space overlapping is conducted, namely the same longitude and latitude and the same height, and further, the time dimension detection means that whether the time points of the overlapping parts of the other aircrafts, the flight paths and the preset line of the unmanned aerial vehicle are the same or not is detected.

The detection mode has various modes, for example, the flight path information of other aircrafts and the preset flight path information parameter of the unmanned aerial vehicle can be compared and analyzed, and the detection mode is not limited herein.

402. And detecting the flight state data to obtain flight state detection information.

Wherein, flight state detection information is the state result detection data that produce after detecting the flight state data of acquireing, and is concrete, mainly detects this unmanned aerial vehicle self all kinds of flight parameters, and all kinds of flight parameters can include unmanned aerial vehicle's gesture, position, airspeed, position, battery voltage, instant wind speed wind direction, mission time isoparametric, whether through detecting these flight parameters at normal range value, judge whether the unmanned aerial vehicle that is in flight state need carry out the external interference adjustment.

403. And detecting the flight environment data to obtain flight environment detection information.

The flight environment detection information is environment result detection data generated after the acquired flight environment data is detected, specifically, mainly detects whether the collision accident factor occurs to the unmanned aerial vehicle in an area formed by a preset distance range by taking the unmanned aerial vehicle as a center, and the collision accident factor can include natural environment factors (such as mountains, high-rise buildings and the like), unknown flyer factors (birds, balloons and the like), meteorological weather factors (thunderstorm cloud layers and strong wind weather), and whether the unmanned aerial vehicle needs to perform preventive measures or not is judged by detecting the collision accident factors.

404. And determining auxiliary suggestion information aiming at the flight of the unmanned aerial vehicle according to the flight line detection information, the flight state detection information and the flight environment detection information.

In some embodiments of the application, the auxiliary suggestion information may include state adjustment information corresponding to the flight state detection information, emergency obstacle avoidance information corresponding to the flight environment detection information, and route optimization information corresponding to the flight route detection information, where the state adjustment information refers to a measure for adjusting a flight state of the unmanned aerial vehicle, which is generated according to the flight state detection information, and the measure may include a control instruction for the flight state of the unmanned aerial vehicle, so as to adjust the flight state of the unmanned aerial vehicle. And the emergency obstacle avoidance information refers to a measure for carrying out emergency obstacle avoidance on the unmanned aerial vehicle, which is generated according to the flight environment detection information. The flight route optimization information refers to measures for optimizing the flight route of the unmanned aerial vehicle, which are generated according to the flight route detection information.

Specifically, in some embodiments, since the flight speed of the unmanned aerial vehicle is fast, when an emergency situation occurs, if a corresponding solution cannot be generated quickly, a flight accident is easily caused, and therefore, the auxiliary suggestion information for the flight of the unmanned aerial vehicle is determined according to the flight route information, the flight state data, and the flight environment data, which further includes steps one, two, and three:

inputting the flight state data into a preset unmanned aerial vehicle state model, and generating the state adjustment information.

Wherein, predetermined unmanned aerial vehicle state model can be a machine learning model, can be according to flight status data generates the state adjustment information that corresponds with it, for example, will in the unmanned aerial vehicle state model that flight status data input was predetermined, this unmanned aerial vehicle state model can detect out that this unmanned aerial vehicle's electric quantity value has been less than minimum electric quantity threshold value, and this thing, unmanned aerial vehicle state model can generate various emergency measures, then matches various emergency measures and current situation, generates final emergency measure, and wherein the ground that the current situation was unmanned fuselage department is spacious unmanned scene, therefore unmanned aerial vehicle faces low electric quantity state, can suspend the task, implements emergency landing measure.

In some embodiments of the present application, before using the drone state model, the drone state model needs to be trained, specifically including steps (1) and (2):

(1) acquiring a first sample data set of the flight state of the unmanned aerial vehicle, wherein the first sample data set comprises characteristic sample data of the flight state of the unmanned aerial vehicle;

the sample data set is a set of a plurality of sample data, the first sample data can be sample data obtained in the flight process of the original unmanned aerial vehicle, the image sample data in the first sample data set can be collected through a data collection terminal or the like, or can be directly searched and obtained from an open original order database, and the specific situation is not limited here.

(2) And inputting the first sample data set into a preset network model for training to generate the unmanned aerial vehicle state model.

The preset Network model may be a Convolutional Neural Network (CNN) model, for example, an EfficientNet Network model.

On the other hand, with the development of the unmanned aerial vehicle, the flying scenes of the unmanned aerial vehicle are more and more diversified, so that the state model of the unmanned aerial vehicle needs to be iteratively upgraded, and the specific iterative upgrading mode can be used for training the network model again by acquiring new sample data to obtain the state model of the next generation of unmanned aerial vehicle.

And secondly, inputting the flight environment data into a preset unmanned aerial vehicle obstacle avoidance prediction model to generate the emergency obstacle avoidance information.

The unmanned aerial vehicle obstacle avoidance prediction model can be the same as or different from the unmanned aerial vehicle state model in type, and is not limited herein, the training process can be the same as the training mode of the unmanned aerial vehicle state model, but the obtained sample data is different, and is not repeated here, and the unmanned aerial vehicle obstacle avoidance prediction model can also be subjected to iterative upgrade.

Inputting the flight route information into a preset route state management model to generate route optimization information.

The unmanned aerial vehicle obstacle avoidance prediction model is the same as the unmanned aerial vehicle obstacle avoidance prediction model in description, and is not repeated.

In some embodiments, as shown in fig. 5, the determining, according to the flight path detection information, the flight state detection information, and the flight environment detection information, auxiliary recommendation information for the drone to fly includes:

501. determining the flight abnormity information of the unmanned aerial vehicle according to the flight route detection information, the flight state detection information and the flight environment detection information, wherein the flight abnormity information comprises at least one of the flight route abnormity information, the flight state abnormity information and the flying environment abnormity information.

The flight abnormality information may be any one of flight route abnormality information, flight state abnormality information, and flight environment abnormality information, or a combination of any two of them, or all three of them, which is not limited herein. The flight path abnormality information is an abnormality related to a detected flight path when the flight path information is detected, for example, in the detection process. And finding a detection result of the overlapping of the air routes of other aircrafts and the air route information preset by the unmanned aerial vehicle from the flight route information, and correspondingly generating corresponding flight route abnormal information according to the detection result.

502. And generating auxiliary suggestion information corresponding to the flight abnormity information according to the flight abnormity information.

The corresponding auxiliary suggestion information is corresponding to the flight abnormity information, when the flight abnormity information is the flight environment abnormity information, the corresponding auxiliary suggestion information is the emergency obstacle avoidance information, and when the flight abnormity information comprises the flight environment abnormity information and the flight route abnormity information, the corresponding auxiliary suggestion information comprises the emergency obstacle avoidance information and the route optimization information.

In some embodiments, after acquiring the auxiliary suggestion information corresponding to the flight abnormality information, the auxiliary suggestion information is further required to be utilized, specifically, the flight parameters of the unmanned aerial vehicle include a state adjustment parameter, an obstacle avoidance prediction parameter and a route planning parameter, and the flight parameters of the unmanned aerial vehicle are adjusted based on the auxiliary suggestion information, which may specifically have two implementation manners:

the first implementation mode is that the state adjustment information, the emergency obstacle avoidance information and the route optimization information are sent to the unmanned aerial vehicle, so that the unmanned aerial vehicle adjusts the state adjustment parameters, the obstacle avoidance prediction parameters and the route planning parameters. The implementation mode is an optimal implementation mode, namely the state adjustment information, the emergency obstacle avoidance information and the air route optimization information are converted into remote control signals for the unmanned aerial vehicle directly through the auxiliary equipment for the flight information, and then the remote control signals are sent to the unmanned aerial vehicle, so that the flight parameters of the unmanned aerial vehicle are adjusted.

Performing visual simulation on the state adjustment information, the emergency obstacle avoidance information and the route optimization information to obtain flight prediction simulation video information of the unmanned aerial vehicle;

In some embodiments, a corresponding operation program may be generated based on a behavior that a remote control person in the ground station performs a remote control operation on the unmanned aerial vehicle, and scene information corresponding to the operation program is recorded, and according to the operation program and the corresponding scene information, corresponding auxiliary suggestion information may also be generated.

In order to better implement the intelligent control method for the unmanned aerial vehicle in the embodiment of the present invention, on the basis of the intelligent control method for the unmanned aerial vehicle, an intelligent control device for the unmanned aerial vehicle is further provided in the embodiment of the present invention, the intelligent control device for the unmanned aerial vehicle is applied to an intelligent control system for the unmanned aerial vehicle, the intelligent control system for the unmanned aerial vehicle includes an unmanned aerial vehicle and a flight assistance device, the flight assistance device is connected to the unmanned aerial vehicle through a network, as shown in fig. 6, the intelligent control device for the unmanned aerial vehicle 600 includes:

a first obtaining unit 601, configured to obtain load sensing data sent by the unmanned aerial vehicle in a flight process of the unmanned aerial vehicle;

a second obtaining unit 602, configured to obtain flight path information of other aircraft, where the other aircraft is an aircraft except the unmanned aerial vehicle in an area formed by taking the unmanned aerial vehicle as a center and a preset distance range;

a first determining unit 603, configured to determine auxiliary recommendation information for the unmanned aerial vehicle to fly based on the load sensing data and the flight path information;

a first adjusting unit 604, configured to adjust a flight parameter of the drone based on the auxiliary suggestion information.

In some embodiments, the first determining unit 603 comprises:

In some embodiments, the first generating unit is specifically configured to:

In some embodiments, the second determination unit comprises:

In some embodiments, the third determining unit is specifically configured to:

In some embodiments, the flight parameters of the unmanned aerial vehicle include a state adjustment parameter, an obstacle avoidance prediction parameter, and a route planning parameter, and the first adjustment unit 604 is specifically configured to:

The embodiment of the present invention further provides a flight assistance device, which integrates any one of the unmanned aerial vehicle intelligent control devices provided by the embodiments of the present invention, and the flight assistance device includes:

one or more processors;

a memory; and

one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to perform the steps of the drone intelligent control method described in any of the drone intelligent control method embodiments above.

The embodiment of the invention also provides a server, which integrates any one of the unmanned aerial vehicle intelligent control devices provided by the embodiment of the invention. Fig. 7 is a schematic diagram showing a structure of a server according to an embodiment of the present invention, specifically:

the server may include components such as a processor 701 of one or more processing cores, memory 702 of one or more computer-readable storage media, a power supply 703, and an input unit 704. Those skilled in the art will appreciate that the server architecture shown in FIG. 7 is not meant to be limiting, and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.

Wherein:

the processor 701 is a control center of the server, connects various parts of the entire server using various interfaces and lines, and performs various functions of the server and processes data by running or executing software programs and/or modules stored in the memory 702 and calling data stored in the memory 702, thereby performing overall monitoring of the server. Optionally, processor 701 may include one or more processing cores; the Processor 701 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, preferably the processor 701 may integrate an application processor, which handles primarily the operating system, user interfaces, application programs, etc., and a modem processor, which handles primarily wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 701.

The memory 702 may be used to store software programs and modules, and the processor 701 executes various functional applications and data processing by operating the software programs and modules stored in the memory 702. The memory 702 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to the use of the server, and the like. Further, the memory 702 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 702 may also include a memory controller to provide the processor 701 with access to the memory 702.

The server further includes a power source 703 for supplying power to each component, and preferably, the power source 703 may be logically connected to the processor 701 through a power management system, so as to implement functions of managing charging, discharging, and power consumption through the power management system. The power supply 703 may also include any component including one or more of a dc or ac power source, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.

The server may also include an input unit 704, and the input unit 704 may be used to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

Although not shown, the server may further include a display unit and the like, which will not be described in detail herein. Specifically, in this embodiment, the processor 701 in the server loads the executable file corresponding to the process of one or more application programs into the memory 702 according to the following instructions, and the processor 701 runs the application program stored in the memory 702, thereby implementing various functions as follows:

It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.

To this end, an embodiment of the present invention provides a computer-readable storage medium, which may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like. The intelligent control method of the unmanned aerial vehicle comprises a processor, a computer program and a computer program, wherein the computer program is loaded by the processor to execute the steps of any one of the intelligent control methods of the unmanned aerial vehicle provided by the embodiment of the invention. For example, the computer program may be loaded by a processor to perform the steps of:

In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

The above detailed description is given to an intelligent control method, a related device and a storage medium for an unmanned aerial vehicle provided in the embodiment of the present application, and a specific example is applied in the detailed description to explain the principle and the implementation manner of the present application, and the description of the above embodiment is only used to help understanding the technical scheme and the core idea of the present application; those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications or substitutions do not depart from the spirit and scope of the present disclosure as defined by the appended claims.

Claims

1. An intelligent control method for an unmanned aerial vehicle is applied to an intelligent control system for the unmanned aerial vehicle, the intelligent control system for the unmanned aerial vehicle comprises a ground station, the unmanned aerial vehicle and a flight auxiliary device, and the flight auxiliary device, the ground station and the unmanned aerial vehicle are mutually connected through a network, and the method comprises the following steps:

2. The intelligent drone control method of claim 1, wherein the determining, based on the load sensing data and the flight path information, auxiliary advisory information for the drone flight includes:

3. The intelligent unmanned aerial vehicle control method of claim 2, wherein the generating flight state data and flight environment data of the unmanned aerial vehicle based on the load sensing data comprises:

4. The intelligent unmanned aerial vehicle control method of claim 2, wherein the determining auxiliary recommendation information for the unmanned aerial vehicle to fly according to the flight route information, the flight state data and the flight environment data comprises:

detecting the flight state data to obtain flight state detection information;

5. The intelligent unmanned aerial vehicle control method of claim 4, wherein the determining auxiliary recommendation information for the unmanned aerial vehicle to fly according to the flight route detection information, the flight state detection information and the flight environment detection information comprises:

determining flight abnormity information of the unmanned aerial vehicle according to the flight route detection information, the flight state detection information and the flight environment detection information, wherein the flight abnormity information comprises at least one of flight route abnormity information, flight state abnormity information and flight environment abnormity information;

6. The intelligent unmanned aerial vehicle control method of claim 4, wherein the auxiliary recommendation information includes state adjustment information corresponding to the flight state detection information, emergency obstacle avoidance information corresponding to the flight environment detection information, and route optimization information corresponding to the flight route detection information;

the determining auxiliary suggestion information for the unmanned aerial vehicle flying according to the flight route information, the flight state data and the flight environment data comprises:

7. The intelligent unmanned aerial vehicle control method of claim 6, wherein the flight parameters of the unmanned aerial vehicle include state adjustment parameters, obstacle avoidance prediction parameters, and route planning parameters, and the adjusting the flight parameters of the unmanned aerial vehicle based on the auxiliary recommendation information includes:

8. The intelligent unmanned aerial vehicle control method of claim 6, wherein the flight parameters of the unmanned aerial vehicle include state adjustment parameters, obstacle avoidance prediction parameters, and route planning parameters, and the adjusting the flight parameters of the unmanned aerial vehicle based on the auxiliary recommendation information includes:

9. The utility model provides an unmanned aerial vehicle intelligent control device, its characterized in that, unmanned aerial vehicle intelligent control device is applied to unmanned aerial vehicle intelligent control system, unmanned aerial vehicle intelligent control system includes ground station, unmanned aerial vehicle and flight auxiliary assembly, flight auxiliary assembly the ground station with unmanned aerial vehicle internet access each other, unmanned aerial vehicle intelligent control device includes:

10. A flight assistance apparatus, characterized in that the flight assistance apparatus comprises:

one or more processors;

a memory; and

one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the drone intelligence control method of any one of claims 1-8.

11. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and the computer program is adapted to be loaded by a processor to execute the intelligent drone control method according to any one of claims 1 to 8.