CN113625743B

CN113625743B - Unmanned aerial vehicle intelligent control method, related device and storage medium

Info

Publication number: CN113625743B
Application number: CN202010379925.7A
Authority: CN
Inventors: 黄春
Original assignee: Chongqing Fengniao Uav Research Institute Co ltd
Current assignee: Chongqing Fengniao Uav Research Institute Co ltd
Priority date: 2020-05-08
Filing date: 2020-05-08
Publication date: 2024-11-29
Anticipated expiration: 2040-05-08
Also published as: CN113625743A

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 steps of obtaining load sensing data sent by the unmanned aerial vehicle in the flight process of the unmanned aerial vehicle, obtaining flight route information of other aircrafts which are aircrafts except the unmanned aerial vehicle in a region formed by taking the unmanned aerial vehicle as a center and a preset distance range, determining auxiliary suggestion information for the unmanned aerial vehicle to fly based on the load sensing data and the flight route information, and adjusting flight parameters of the unmanned aerial vehicle based on the auxiliary suggestion information. According to the embodiment of the application, the auxiliary suggestion information is added, so that the flight parameters of the unmanned aerial vehicle can be automatically adjusted more comprehensively and accurately based on the auxiliary suggestion information, unmanned aerial vehicle accidents caused by human misoperation and uncontrollable factors existing in the unmanned aerial vehicle are reduced, and the operation safety coefficient of the unmanned aerial vehicle can be effectively improved, and the operation cost of the unmanned aerial vehicle is reduced.

Description

Unmanned aerial vehicle intelligent control method, related device and storage medium

Technical Field

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

Background

The research and development of unmanned aerial vehicle technology relates to the subjects very widely, including various electronic communication, flight control, air force sensor technology and the like, and has the characteristics of various subject intersections, technical foresight and the like.

The unmanned aerial vehicle performs the task in flight, is not purely unmanned, and actually monitors the unmanned aerial vehicle in real time under the monitoring and guidance of ground control personnel, uploads control instructions to the unmanned aerial vehicle, and adjusts the flight state of the unmanned aerial vehicle to complete the preset task. The unmanned aerial vehicle is independent of the ground monitoring system at any moment, the quality degree of the ground monitoring system plays a vital role in judging that an operator controls the unmanned aerial vehicle, but many uncontrollable factors exist in manual operation and the unmanned aerial vehicle system, so that accidents are easy to occur in unmanned aerial vehicle operation, and loss is caused.

Disclosure of Invention

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

The embodiment of the application provides an unmanned aerial vehicle intelligent control method, which is applied to an unmanned aerial vehicle intelligent control system, wherein the unmanned aerial vehicle intelligent control system comprises a ground station, an unmanned aerial vehicle and flight auxiliary equipment, the ground station and the unmanned aerial vehicle are mutually connected 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, wherein the other aircrafts are aircrafts except for the unmanned aerial vehicle in a region formed by taking the unmanned aerial vehicle as a center and a preset distance range;

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

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

In some embodiments, the determining assistance advice information for the unmanned aerial vehicle flight based on the load sensing data and the flight path information comprises:

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

And determining auxiliary advice information for the unmanned aerial vehicle to fly 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 unmanned aerial vehicle based on the load sensing data includes:

extracting flight state data in the load sensing data to obtain 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 auxiliary advice information for the unmanned aerial vehicle to fly according to the flight path information, the flight status data, and the flight environment data comprises:

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 advice 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.

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

Determining flight anomaly 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 anomaly information comprises at least one of flight route anomaly information, flight state anomaly information and flight environment anomaly information;

And generating auxiliary suggestion information corresponding to the flight abnormality information according to the flight abnormality information.

In some embodiments, the auxiliary advice information includes status adjustment information corresponding to the flight status 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 advice information for the unmanned aerial vehicle to fly according to the flight route information, the flight status 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, and generating the emergency obstacle avoidance information;

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

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 adjusting the flight parameters of the unmanned aerial vehicle based on the auxiliary advice 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 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;

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 route planning parameters of the unmanned aerial vehicle according to the flight prediction simulation video information.

In another aspect, the present application provides an unmanned aerial vehicle intelligent control device, the unmanned aerial vehicle intelligent control device is applied to an unmanned aerial vehicle intelligent control system, the unmanned aerial vehicle intelligent control system includes a ground station, an unmanned aerial vehicle and a flight auxiliary device, the ground station and the unmanned aerial vehicle are connected to each other through a network, the 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, wherein the other aircrafts are aircrafts except for the unmanned aerial vehicle in a region formed by taking the unmanned aerial vehicle as a center and a preset distance range;

A first determination unit configured to determine auxiliary advice information for the unmanned aerial vehicle to fly based on the load sensing data and the flight route information;

and the first adjusting unit is used for adjusting the flight parameters of the unmanned aerial vehicle based on the auxiliary suggestion information.

In some embodiments, the first determining 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 determining unit is used for determining 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.

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

In some embodiments, the second determining unit includes:

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 flying environment data to obtain flying environment detection information;

And the third determining unit is used for determining auxiliary suggestion 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.

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

In some embodiments, the auxiliary advice information includes status adjustment information corresponding to the flight status 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 unmanned aerial vehicle 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;

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 having a computer program stored therein, the computer program being adapted to be loaded by a processor to perform the unmanned aerial vehicle intelligent control method.

According to the unmanned aerial vehicle control method, the flight auxiliary equipment is additionally arranged and is in communication with the ground station and the unmanned aerial vehicle through network connection, the flight auxiliary equipment can acquire load sensing data transmitted by the unmanned aerial vehicle in the flight process and flight route information of an aircraft except the unmanned aerial vehicle in a region formed by a preset distance range by taking the unmanned aerial vehicle as a center, 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 the auxiliary suggestion information, flight parameters of the unmanned aerial vehicle are adjusted.

Drawings

The technical solution and other advantageous effects of the present application will be made apparent by the following detailed description of the specific embodiments of the present application with reference to the accompanying drawings.

Fig. 1 is a schematic view of a scenario of a smart control system for a unmanned aerial vehicle according to an embodiment of the present application;

Fig. 2 is a schematic flow chart of an embodiment of a method for controlling a smart unmanned aerial vehicle according to an embodiment of the present application

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

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

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

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

fig. 7 is a schematic structural diagram of another embodiment of the intelligent control device for a unmanned aerial vehicle according to the embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to fall within the scope of the application.

In the description of the present application, it should 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", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.

In the description of the present application, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected, mechanically connected, electrically connected, or communicable with each other, directly connected, indirectly connected via an intermediary, or in communication between two elements or in an interaction relationship between two elements. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art according to the specific circumstances.

In the present application, unless expressly stated or limited otherwise, a first feature "above" or "below" a second feature may include both the first and second features being in direct contact, as well as the first and second features not being in direct contact but being in contact with each other through additional features therebetween. Moreover, a first feature being "above," "over" and "on" a second feature includes the first feature being directly above and obliquely above the second feature, or simply indicating that the first feature is higher in level than the second feature. The first feature being "under", "below" and "beneath" the second feature includes the first feature being directly under and obliquely below the second feature, or simply means that the first feature is less level 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 present disclosure, components and arrangements of specific examples are described below. They are, of course, merely examples and are not intended to limit the application. Furthermore, the present application may repeat reference numerals and/or letters in the various examples, which are for the purpose of brevity and clarity, and which do not themselves indicate the relationship between the various embodiments and/or arrangements discussed.

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

First, an application scenario of the unmanned aerial vehicle intelligent control system provided by the embodiment of the application is introduced.

Referring to fig. 1, fig. 1 is a schematic view of a scenario of an unmanned aerial vehicle intelligent control system provided by an embodiment of the present invention, where the unmanned aerial vehicle intelligent control system 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 through a network, and an unmanned aerial vehicle intelligent control device 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.

The flight auxiliary equipment 200 in the embodiment of the invention 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 which are aircrafts except 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 the flight route information, and adjusting flight parameters of the unmanned aerial vehicle based on the auxiliary suggestion information.

In the embodiment of the present invention, the flight assistance device 200 may include a server, which may be a stand-alone server, or may be a server network or a server cluster formed by servers, for example, a 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 formed by a plurality of servers. Wherein the Cloud server is composed of a large number of computers or web servers based on Cloud Computing (Cloud Computing). In embodiments of the present invention, communication between the server and the user terminal may be achieved by any communication means, including, but not limited to, mobile communication based on the third generation partnership project (3rd Generation Partnership Project,3GPP), long term evolution (Long Term Evolution, LTE), worldwide interoperability for microwave access (Worldwide Interoperability for Microwave Access, wiMAX), or computer network communication based on the TCP/IP protocol family (TCP/IP Protocol Suite, TCP/IP), user datagram protocol (User Datagram Protocol, UDP), etc.

In the embodiment of the present invention, the ground station 300 may communicate with the unmanned aerial vehicle 100 on one hand, so that the unmanned aerial vehicle 100 may be remotely controlled and monitored, and the ground station 300 may communicate with the flight assistance device 200 on the other hand, so that the flight assistance device 200 optimizes the control of the unmanned aerial vehicle 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, so as to perform remote control and detection on the unmanned aerial vehicle 100 together, where the remote control of the unmanned aerial vehicle by the ground station 300 and the flight assistance device 200 is divided into two levels, the ground station 300 is a first level, the flight assistance device 200 is a second level, and when the ground station 300 in the first level and the ground station 300 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 unmanned aerial vehicle 100.

It will be appreciated by those skilled in the art that the application environment shown in fig. 1 is merely an application scenario of the present application, and is not limited to the application scenario of the present application, and other application environments may also include more or fewer unmanned aerial vehicles 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 unmanned aerial vehicles are shown in fig. 1, and it will be appreciated that the unmanned aerial vehicle intelligent control system may also include one or more other unmanned aerial vehicles connected to a network of flight assistance devices, which is not limited herein.

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

It should be noted that, the schematic view of the scenario of the unmanned aerial vehicle intelligent control system shown in fig. 1 is only an example, and the unmanned aerial vehicle intelligent control system and the scenario described in the embodiments of the present invention are for more clearly describing the technical solution of the embodiments of the present invention, and do not constitute a limitation to the technical solution provided by the embodiments of the present invention, and as a person of ordinary skill in the art can know that the technical solution provided by the embodiments of the present invention is equally applicable to similar technical problems with evolution of the unmanned aerial vehicle intelligent control system and occurrence of new service scenarios.

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

In the embodiment of the unmanned aerial vehicle intelligent control method, flight auxiliary equipment in an unmanned aerial vehicle intelligent control system is taken as an execution main body, and in order to simplify and facilitate description, the execution main body is omitted in the subsequent method embodiment; the method comprises the steps of acquiring flight route information of other aircrafts, wherein the other aircrafts are aircrafts except for the unmanned aerial vehicle in a region which is 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 the flight route information, and adjusting flight parameters of the unmanned aerial vehicle based on the auxiliary suggestion information.

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

201. and acquiring load sensing data sent by the unmanned aerial vehicle in the flight process of the unmanned aerial vehicle.

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

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

(2) In the flight process of the unmanned aerial vehicle, when the distance between the unmanned aerial vehicle and the ground station and/or the flight auxiliary equipment is too long, the data transmission distance between the unmanned aerial vehicle and the ground station and/or the flight auxiliary equipment cannot be met.

In order to solve the problems, the embodiment of the application carries out two communication modes of a sight line chain and a beyond sight line chain (guard link) between the unmanned aerial vehicle and the ground station and/or the flight auxiliary equipment, wherein the lower-delay sight line chain is favorable for timely acquiring the state information of the take-off and landing aircraft of the unmanned aerial vehicle through the ground station and/or the flight auxiliary equipment, and the unmanned aerial vehicle has certain self-stability due to the inner-loop automatic control system, and can send a guard link signal for switching a data link to the unmanned aerial vehicle through the ground station and/or the flight auxiliary equipment. Through link switching, the utilization rate of an up-line-of-sight link and a satellite link can be improved by utilizing a link time division multiplexing mechanism, a technical basis is provided for ground stations and/or flight auxiliary equipment to monitor a plurality of unmanned aerial vehicles simultaneously, and the utilization rate of flight auxiliary equipment and the like is improved.

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

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

The load sensing data refers to sensing information obtained by a load sensing device arranged on the unmanned aerial vehicle, and the sensing information is converted and then sent to the flight auxiliary device, and specifically, the load sensing device may include sensing devices such as a height sensor, a distance sensor, an image sensor, a radar sensor, a photoelectric/infrared sensor and the like, which is not limited in the application, the image shot by the image sensor and the detection signal of the radar sensor can be used for detecting whether an obstacle exists around the unmanned aerial vehicle in the flight state process, the distance between the unmanned aerial vehicle and the surrounding obstacle can be measured by the distance sensor, the height of the unmanned aerial vehicle in the current flight can be measured in real time by the height sensor, and important information such as the gesture, the azimuth, the airspeed, the position, the battery voltage, the instant wind speed and the wind direction, the task time and the like of the unmanned aerial vehicle can be mastered in real time by acquiring the sensing data.

202. And acquiring flight route information of other aircrafts, wherein the other aircrafts are aircrafts except for the unmanned aerial vehicle in a region formed by taking the unmanned aerial vehicle as a center and a preset distance range.

Wherein an aircraft refers to an aircraft flying in the atmosphere. Including aircraft, airships, balloons, and any other device that can fly through the atmosphere by the reactive force of air. Aircraft must overcome various drag in the air to fly, while various meteorological conditions and aerodynamic forces in the atmosphere have a variety of effects on aircraft flight. Aircraft are classified into two main types according to the way of obtaining lift, one type is lighter than air aircraft, which floats in the air by means of buoyancy of air, such as a balloon, an airship, etc., and the other type is heavier than air aircraft, which comprises two types of non-power driving and power driving, and the aircraft can be classified into a manned aircraft (abbreviated as an aircraft) and an unmanned aircraft according to whether or not the aircraft is manned.

The flight route information refers to information of a flight route and flight time of an airplane according to preset flight, wherein the flight route of the airplane is called an air traffic route, and is called a route for short. The course of the aircraft not only determines the specific direction, origin-destination and destination-destination of the aircraft, but also defines the width and altitude of the course according to the air traffic control requirements.

In the actual flight process, the unmanned aerial vehicle avoids natural factors such as mountain peaks, birds and unidentified flying objects, and the like, and also avoids the problem of overlapping with other airplanes.

Thus, in some embodiments of the present invention, flight path information for other aircraft may be obtained by associating an unmanned aircraft intelligent control system with an additional aircraft state system, referred to as an Air Traffic Control (ATC) system, which in particular monitors and controls aircraft flight activities using communication, navigation techniques and monitoring means, ensuring safe and orderly flight. Different management airspace is divided in the airspace of a flight route, including a route, a flight information management area, a approach management area, a tower management area, a waiting airspace management area and the like, and different radar devices are used according to different management areas. The space division is carried out in the management space domain, and the horizontal and vertical space between the aircrafts forms the basis of air traffic management. The air traffic management system is composed of navigation equipment, a radar system, secondary radars, communication equipment and a ground control center, and is used for monitoring, identifying and guiding the aircraft in the coverage area.

For example, the system is associated with an ADS-B system (Automatic dependent surveillance-broadcast automatic correlation monitoring system), wherein the ADS-B system is composed of a plurality of ground stations and airborne stations, and data bidirectional communication is realized in a mesh and multipoint-to-multipoint mode. The ADS-B system is an information system integrating communication and monitoring, and consists of an information source, an information transmission channel and an information processing and displaying part. The method organically combines conflict detection, conflict avoidance, conflict resolution, ATC monitoring, ATC consistency monitoring and cabin comprehensive information display, enhances and expands very rich functions for a new navigation system, and the working principle is that an onboard ADS-B communication device broadcasts navigation information collected by an onboard information processing unit, receives broadcast information of other aircrafts and the ground, and then processes the broadcast information to a cabin comprehensive 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. Based on the load sensing data and the flight path information, auxiliary advice information for the unmanned aerial vehicle to fly is determined.

The auxiliary suggestion information refers to auxiliary suggestion information generated by the flight auxiliary equipment aiming at an 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 in the unmanned aerial vehicle during the flight:

1. The unmanned aerial vehicle self performance is unusual, for example, unmanned aerial vehicle inner loop control system goes wrong for ground station control personnel can't manual control unmanned aerial vehicle flight direction.

2. The unmanned aerial vehicle focus appears shifting, for example, the cargo that transportation formula unmanned aerial vehicle born appears the cargo sideslip and inclines to one side in the transportation for unmanned aerial vehicle focus appears shifting.

3. The unmanned aerial vehicle is expected to have obstacles on a preset flight path, wherein the obstacles comprise severe weather, peaks and buildings with high altitude, unidentified flying objects and other aircrafts, and for example, a bird appears in front of 50 meters.

When the unmanned aerial vehicle encounters the abnormal conditions, the unmanned aerial vehicle may have a flight accident. Based on the problem, the auxiliary suggestion information for the unmanned aerial vehicle to fly is determined based on the load sensing data and the flight route information, so that the unmanned aerial vehicle avoids abnormal conditions.

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

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

According to the embodiment of the application, 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 transmitted by the unmanned aerial vehicle in the flight process and flight route information of an aircraft except the unmanned aerial vehicle in a region formed by a preset distance range by taking the unmanned aerial vehicle as a center, auxiliary suggestion information for the unmanned aerial vehicle to fly is determined based on the load sensing data and the flight route information, and the flight parameters of the unmanned aerial vehicle are adjusted based on the auxiliary suggestion information.

As shown in fig. 3, in some embodiments of the present application, the determining auxiliary advice information for the unmanned aerial vehicle to fly in step 203 based on the load sensing data and the flight path information further includes:

301. Based on the load sensing data, flight state data and flight environment data of the unmanned aerial vehicle are generated.

The flight state data is information related to the flight state of the unmanned aerial vehicle, and the flight state data can comprise flight attitude data, flight speed data, flight height data, flight inclination angle data, load data and the like of the unmanned aerial vehicle.

The flying attitude refers to a state of three axes of the aircraft in the air relative to a certain reference line or a certain reference plane or a certain fixed coordinate system, and the unmanned aerial vehicle flies in the air and has various flying attitudes unlike the vehicles moving on the ground. This means that the unmanned plane changes in pitch, yaw, pitch left, pitch right, etc. The flight attitude determines the direction of the unmanned aerial vehicle, and affects not only the flight altitude, but also the direction of flight. When flying at a low speed, a driver can judge the flying attitude of the unmanned aerial vehicle according to the position of the horizon by observing the ground, and the flying attitude data of the unmanned aerial vehicle can be obtained actually. The aircraft can change its attitude, but the rotor axis will always be pointing towards the ground, and the transverse indicator bar will always be parallel to the horizon, which is called the artificial horizon in the instrument. This instrument is called a horizon finder, also called a posture director.

The flight environment data is information related to the flight environment of the unmanned aerial vehicle, and the flight environment data can comprise 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:

1. And extracting flight state data in the load sensing data to obtain the flight state data of the unmanned aerial vehicle.

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

Because the load sensing data is obtained by a plurality of different sensing devices, the attribute of the sensing data of each different sensing device is different, and the specific attribute of the sensing data can comprise 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 obtained, the voltage can be lower and lower along with the use of the battery, and the voltage information of the battery is an analog signal, on the other hand, when the unmanned aerial vehicle realizes the on-off of one device, the digital signal is adopted for transmission. Thus, when a plurality of different sensing data needs to be acquired, the sensing data may be first subjected to a preprocessing, which may be a process of extracting, identifying, classifying, converting, dividing or merging the plurality of different sensing data,

For example, the sensory data may be divided into "voice data", "image data", "warning data", and "common UAV (unmanned AERIAL VEHICLE ) parameters", etc. The method comprises the steps of digitizing data acquired by flight auxiliary equipment, converting voice data into numbers or a certain identification sign, dividing image data, identifying or merging images, extracting characteristic values and characteristic quantity equivalent values of the images, and dividing common UAV parameters into basic UAV monitoring data and detailed UAV parameter data.

Further, along with the expansion of the traffic, the demand for the unmanned aerial vehicle will be larger, when the number of unmanned aerial vehicles reaches a certain scale, the communication demands of high frequency and large data will appear between a plurality of unmanned aerial vehicles and the ground station and the flight auxiliary equipment, but the communication protocol of each unmanned aerial vehicle is not necessarily the same, so that the demand of the traffic may not be satisfied.

To this end, the embodiment of the application can establish a protocol conversion device between the unmanned aerial vehicle and the ground station and the flight auxiliary device, wherein the protocol conversion device is used for converting first control data under a first communication protocol into second control data under a second communication protocol and transmitting the second control data to the unmanned aerial vehicle, converting first telemetry data under the second communication protocol into second telemetry data under the first communication protocol and transmitting the second telemetry data to the flight auxiliary device and/or the ground station.

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

For ease of understanding, a specific example is described. 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 slow down 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 (the data to be transmitted is still "control the drone to slow down to 15km/h" with the difference that a different communication protocol is adopted) under a second communication protocol (the second communication protocol refers to a communication protocol supported by the drone). On the one hand, the protocol conversion device caches the second control data, and on the other hand, the protocol conversion device circularly transmits the second control data to the unmanned aerial vehicle 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.

According to the embodiment of the application, the protocol conversion equipment is adopted, so that data communication between a plurality of unmanned aerial vehicles and the ground station and/or the flight auxiliary equipment is realized, and the plurality of unmanned aerial vehicles can be effectively controlled at the same time.

302. And determining auxiliary advice information for the unmanned aerial vehicle to fly according to the flight route information, the flight state data and the flight environment data.

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

401. And detecting the flight route information to obtain flight route detection information.

The flight route detection information is route result detection data generated after the acquired flight route information is detected, specifically, the flight route information of other aircrafts is mainly detected to comprise multiple dimensional detections, the dimensional detections are divided into a space dimension and a time dimension, the space dimension detection refers to detecting whether the flight route of the other aircrafts is overlapped with a preset route of the unmanned aerial vehicle, the overlapping refers to three-dimensional space overlapping, namely, the same longitude and latitude and the same height, and further, the time dimension detection refers to whether the time point of the overlapping part of the flight route of the other aircrafts and the preset route of the unmanned aerial vehicle is the same.

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

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

The flight state detection information is state result detection data generated after the acquired flight state data are detected, specifically, various flight parameters of the unmanned aerial vehicle are mainly detected, the various flight parameters can comprise parameters such as the attitude, the azimuth, the airspeed, the position, the battery voltage, the instant wind speed and the wind direction, the task time and the like of the unmanned aerial vehicle, and whether the unmanned aerial vehicle in the flight state needs external interference adjustment is judged by detecting whether the flight parameters are in a normal range value or not.

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 are detected, specifically, mainly, whether the unmanned aerial vehicle is used as a center, in an area formed by a preset distance range, collision accident factors which cause the unmanned aerial vehicle exist or not are detected, the collision accident factors can include natural environment factors (such as mountains, high-rise buildings and the like), unidentified flying object factors (such as birds and balloons), weather factors (thunderstorm cloud layer and strong wind weather), and whether the unmanned aerial vehicle needs preventive measures or not is judged by detecting the collision accident factors.

404. And determining auxiliary advice 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.

In some embodiments of the present application, the auxiliary advice information may include status adjustment information corresponding to the flight status 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 status adjustment information refers to a measure for adjusting the flight status of the unmanned aerial vehicle, which is generated according to the flight status detection information, and the measure may include a control instruction for adjusting the flight status of the unmanned aerial vehicle. The emergency obstacle avoidance information refers to measures for carrying out emergency obstacle avoidance on the unmanned aerial vehicle, which are generated according to the flight environment detection information. The route optimization information refers to measures for optimizing the 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 is encountered, if the corresponding solution cannot be generated quickly, a flight accident is easy to be caused, so that the auxiliary advice information for the unmanned aerial vehicle to fly is determined according to the flight route information, the flight state data and the flight environment data, and further the method may include steps one, two and three:

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

The unmanned aerial vehicle state model can be a machine learning model, state adjustment information corresponding to the unmanned aerial vehicle state model can be generated according to the flight state data, for example, the flight state data are input into the unmanned aerial vehicle state model, the unmanned aerial vehicle state model can detect that the electric quantity value of the unmanned aerial vehicle is lower than the minimum electric quantity threshold value, various emergency measures can be generated by the unmanned aerial vehicle state model, then various emergency measures are matched with the current situation, final emergency measures are generated, the current situation is a scene of an empty unmanned aerial vehicle, and therefore the unmanned aerial vehicle faces the low electric quantity state, tasks can be suspended, and emergency landing measures are implemented.

In some embodiments of the present application, before using the unmanned plane state model, training the unmanned plane state model is further required, and specifically includes steps (1) and (2):

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

The sample data set is a set of a plurality of sample data, the first sample data can be sample data acquired in the flight process of the original unmanned aerial vehicle, the image sample data in the first sample data set can be acquired through a data acquisition terminal or the like, or can be directly searched and acquired from an open original order database, and the method is not limited in the specific point.

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

The predetermined network model may be a convolutional neural network model (Convolutional Neural Network, CNN), in particular, for example, a EFFICIENTNET network model.

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

2. And inputting the flight environment data into a preset unmanned aerial vehicle obstacle avoidance prediction model, and generating the emergency obstacle avoidance information.

The unmanned aerial vehicle obstacle avoidance prediction model may be the same as or different from the unmanned aerial vehicle state model in type, and is not limited herein, the training process may be the same as the training mode of the unmanned aerial vehicle state model, but the obtained sample data is different, and is not described herein, and the unmanned aerial vehicle obstacle avoidance prediction model may also be iteratively updated.

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

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

In some embodiments, as shown in fig. 5, the determining auxiliary advice 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:

501. and determining flight abnormality 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 abnormality information comprises at least one of the flight route abnormality information, the flight state abnormality information and the flight environment abnormality information.

The flight anomaly information may be any one of flight route anomaly information, flight state anomaly information and flight environment anomaly information, or may be any combination of two or three, and is not limited thereto. The flight course anomaly information is an anomaly associated with a detected course when the flight course information is detected, for example, during the detection. And finding out detection results of overlapping of the routes of other aircrafts and the preset route information of the unmanned aerial vehicle from the flight route information, and correspondingly, generating corresponding flight route abnormal information according to the detection results.

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

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

In some embodiments, after the auxiliary advice information corresponding to the flight anomaly information is obtained, the auxiliary advice information is further required to be used, and 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 advice information, and specifically, two embodiments may be adopted:

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

The second embodiment is that the state adjustment information, the emergency obstacle avoidance information and the route optimization information are subjected to visual simulation to obtain flight prediction simulation video information of the unmanned aerial vehicle;

In some embodiments, the method may further include converting the behavior of the remote control personnel in the ground station to perform remote control operation on the unmanned aerial vehicle, generating a corresponding operation program, recording scene information corresponding to the operation program, and generating corresponding auxiliary suggestion information according to the operation program and the corresponding scene information.

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

the first acquiring unit 601 is configured to acquire load sensing data sent by the unmanned aerial vehicle during a flight process of the unmanned aerial vehicle;

A second acquiring unit 602, configured to acquire flight line information of other aircraft, where the other aircraft is an aircraft except the unmanned aerial vehicle in a region formed by a preset distance range with the unmanned aerial vehicle as a center;

A first determining unit 603 for determining auxiliary advice information for the unmanned aerial vehicle to fly based on the load sensing data and the flight path information;

The first adjusting unit 604 is configured to adjust a flight parameter of the unmanned aerial vehicle based on the auxiliary suggestion information.

In some embodiments, the first determining unit 603 includes:

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

In some embodiments, the second determining unit includes:

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

In some embodiments, the flight parameters of the unmanned aerial vehicle include a status 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 invention also provides flight auxiliary equipment, which integrates any one of the unmanned aerial vehicle intelligent control devices provided by the embodiment of the invention, wherein the flight auxiliary equipment comprises:

one or more processors;

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 intelligence control method described in any of the drone intelligence control method embodiments described 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. As shown in fig. 7, a schematic diagram of a server according to an embodiment of the present invention is shown, specifically:

The server may include one or more processors 701 of a processing core, memory 702 of one or more computer readable storage media, power supply 703, and input unit 704, among other components. Those skilled in the art will appreciate that the server architecture shown in fig. 7 is not limiting of the server and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.

Wherein:

The processor 701 is a control center of the server, connects respective portions 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. Alternatively, the Processor 701 may include one or more processing cores, and the Processor 701 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA), or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, and preferably, the processor 701 may integrate an application processor primarily handling operating systems, user interfaces, application programs, and the like, with a modem processor primarily handling 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 executing the software programs and modules stored in the memory 702. The memory 702 may mainly include a storage program area that may store an operating system, an application program required for at least one function (such as a sound playing function, an image playing function, etc.), etc., and a storage data area that may store data created according to the use of a server, etc. In addition, 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 access to the memory 702 by the processor 701.

The server also includes a power supply 703 for powering the various components, preferably, the power supply 703 is logically connected to the processor 701 via a power management system, such that functions such as charge, discharge, and power consumption management are performed by the power management system. The power supply 703 may also include one or more of any component, such as a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, etc.

The server may further comprise an input unit 704, which input unit 704 may be used for receiving input digital or character information and generating keyboard, mouse, joystick, optical or trackball signal inputs in connection with user settings and function control.

Although not shown, the server may further include a display unit or the like, which is not described herein. In this embodiment, the processor 701 in the server loads executable files corresponding to the processes of one or more application programs into the memory 702 according to the following instructions, and the processor 701 executes the application programs stored in the memory 702, so as to implement various functions as follows:

Those of ordinary skill in the art will appreciate that all or a portion of the steps of the various methods of the above embodiments may be performed by instructions, or by instructions controlling associated hardware, which may be stored in a computer-readable storage medium and loaded and executed by a processor.

To this end, embodiments of the present invention provide a computer-readable storage medium that may include a Read Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or optical disk, and the like. On which a computer program is stored, which computer program is loaded by a processor for executing the steps of any one of the unmanned aerial vehicle intelligent control methods provided by the embodiments of the present invention. For example, the loading of the computer program by the processor may perform the steps of:

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.

The foregoing describes in detail a method for controlling an intelligent unmanned aerial vehicle, a related apparatus and a storage medium according to embodiments of the present application, and specific examples are used herein to describe the principles and implementations of the present application, and the foregoing examples are only for aiding in understanding of the technical solutions and core ideas of the present application, and those skilled in the art should understand that they may still modify the technical solutions described in the foregoing embodiments or substitute some of the technical features of the foregoing embodiments, and these modifications or substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for intelligent control of a drone, characterized in that the method is applied to a drone intelligent control system, the drone intelligent control system includes a ground station, a drone and a flight assistance device, the flight assistance device, the ground station and the drone are connected to each other through a network, the method is applied to the flight assistance device, and the method includes:

During the flight of the UAV, acquiring load sensing data sent by the UAV;

Acquire flight route information of other aircraft, where the other aircraft are aircraft other than the drone in an area formed by a preset distance range with the drone as the center;

Based on the load sensing data and the flight route information, determine auxiliary suggestion information for the flight of the UAV, wherein the auxiliary suggestion information is auxiliary suggestion information generated by the flight assistance device for the abnormal condition when the UAV has an abnormal condition, and the auxiliary suggestion information is used for the UAV to make corresponding adjustments to its own flight parameters according to the auxiliary suggestion information, so as to avoid the abnormal condition, wherein the abnormal condition includes an obstacle appearing on a preset flight path of the UAV;

Adjusting the flight parameters of the UAV based on the auxiliary suggestion information;

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;

Before adjusting the flight parameters of the UAV based on the auxiliary suggestion information, the method further includes:

Inputting the flight status data into a preset drone status model to generate the status adjustment information;

Inputting the flight environment data into a preset UAV obstacle avoidance prediction model to generate the emergency obstacle avoidance information;

Inputting the flight route information into a preset route status management model to generate the route optimization information;

The flight parameters of the UAV include state adjustment parameters, obstacle avoidance prediction parameters and route planning parameters;

The adjusting the flight parameters of the UAV based on the auxiliary suggestion information includes:

Perform visual simulation on the state adjustment information, emergency obstacle avoidance information and route optimization information to obtain flight prediction simulation video information of the UAV;

The flight prediction simulation video information of the UAV is sent to the ground station, so that the ground station adjusts the state adjustment parameters, obstacle avoidance prediction parameters and route planning parameters of the UAV according to the flight prediction simulation video information.

2. The intelligent control method for a drone according to claim 1, characterized in that the determining of auxiliary suggestion information for the drone flight based on the load sensing data and the flight route information comprises:

Based on the load sensing data, generating flight status data and flight environment data of the UAV;

Auxiliary suggestion information for the UAV flight is determined according to the flight route information, the flight status data and the flight environment data.

3. The intelligent control method for a drone according to claim 2, characterized in that the generating of the flight status data and the flight environment data of the drone based on the load sensing data comprises:

Extracting flight status data from the load sensing data to obtain the flight status data of the UAV;

The flight environment data in the load sensing data is identified to obtain the flight environment data of the UAV.

4. The intelligent control method for a drone according to claim 2, characterized in that the determining of auxiliary suggestion information for the drone flight according to the flight route information, the flight status data and the flight environment data comprises:

Detecting the flight status data to obtain flight status detection information;

Auxiliary suggestion information for the UAV flight is determined according to the flight route detection information, the flight status detection information and the flight environment detection information.

5. The intelligent control method for a drone according to claim 4, characterized in that the determining of auxiliary suggestion information for the drone flight according to the flight route detection information, the flight state detection information and the flight environment detection information comprises:

Determine flight abnormality information of the UAV according to the flight route detection information, the flight state detection information and the flight environment detection information, wherein the flight abnormality information includes at least one of flight route abnormality information, flight state abnormality information and flight environment abnormality information;

According to the flight abnormality information, auxiliary suggestion information corresponding to the flight abnormality information is generated.

6. The intelligent control method for a UAV according to claim 4, characterized in that the flight parameters of the UAV include state adjustment parameters, obstacle avoidance prediction parameters and route planning parameters, and adjusting the flight parameters of the UAV based on the auxiliary suggestion information comprises:

The state adjustment information, emergency obstacle avoidance information and route optimization information are sent to the drone, so that the drone adjusts the state adjustment parameters, obstacle avoidance prediction parameters and route planning parameters.

7. An intelligent control device for a drone, characterized in that the intelligent control device for a drone is applied to an intelligent control system for a drone, the intelligent control system for a drone comprises a ground station, a drone and a flight assistance device, the flight assistance device, the ground station and the drone are connected to each other through a network, the flight assistance device is integrated with the intelligent control device for a drone, and the intelligent control device for a drone comprises:

A first acquisition unit, used for acquiring load sensing data sent by the UAV during the flight of the UAV;

A second acquisition unit is used to acquire flight route information of other aircraft, where the other aircraft are aircraft other than the drone in an area formed by a preset distance range with the drone as the center;

A first determining unit is configured to determine auxiliary suggestion information for the flight of the UAV based on the load sensing data and the flight route information, wherein the auxiliary suggestion information is auxiliary suggestion information generated by the flight assistance device for an abnormal condition when the UAV has an abnormal condition, and the auxiliary suggestion information is used for the UAV to make corresponding adjustments to its own flight parameters according to the auxiliary suggestion information, so as to avoid the abnormal condition, wherein the abnormal condition includes an obstacle appearing on a preset flight path of the UAV;

A first adjustment unit, configured to adjust a flight parameter of the UAV based on the auxiliary suggestion information;

Before adjusting the flight parameters of the UAV based on the auxiliary suggestion information, the device is further used to:

8. A flight assistance device, characterized in that the flight assistance device comprises:

one or more processors;

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 according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, and the computer program is suitable for being loaded by a processor to execute the unmanned aerial vehicle intelligent control method according to any one of claims 1 to 6.