CN113853596A

CN113853596A - Method, apparatus, removable platform and computer storage medium for updating restricted area data

Info

Publication number: CN113853596A
Application number: CN202080031236.5A
Authority: CN
Inventors: 邸健; 李子健
Original assignee: SZ DJI Technology Co Ltd
Current assignee: SZ DJI Technology Co Ltd
Priority date: 2020-09-03
Filing date: 2020-09-03
Publication date: 2021-12-28
Also published as: WO2022047709A1

Abstract

A method, an apparatus, a movable platform and a computer storage medium for updating restricted area data are provided, the method for updating restricted area data is applied to the movable platform, and comprises the following steps: based on current state information of the movable platform, obtaining an update threshold of current restricted area data (S201), the current state information including one or more of the following information: the current moving speed of the movable platform, the current processor load of the movable platform, the current used query radius of the limited area of the movable platform, the current country of the movable platform, the current density of the limited area of the movable platform or the current airborne computing unit temperature of the movable platform; determining a movement distance of the movable platform based on the position information of the movable platform and the current position information of the movable platform when the update of the restricted area data was started last time (S202); based on the movement distance and the update threshold, it is determined whether to update the limited area data (S203).

Description

Method, apparatus, removable platform and computer storage medium for updating restricted area data

Technical Field

The present invention relates generally to the field of data processing technology, and more particularly, to a method, apparatus, removable platform, and computer storage medium for updating restricted area data.

Background

On some unmanned aerial vehicles, because of the limitations of computational power and memory, all flight-limiting data are not read into the memory at one time, but the flight-limiting data around the unmanned aerial vehicle are read into the memory according to the current position of the unmanned aerial vehicle in the flight process. Because the data that only flies to limit around the unmanned aerial vehicle that load into the memory, so in the process of carrying out the judgement that flies to limit, along with the expansion of flight distance, after unmanned aerial vehicle flies to a distance from last update position, need read the data that flies to limit around the unmanned aerial vehicle again and update it in the memory.

In the conventional technology, the update threshold of the flight-restricted database is selected as a fixed value set by experience, and is not flexible, so how to optimize the update threshold of the flight-restricted database is a technical problem to be solved at present.

Disclosure of Invention

The present application has been made to solve at least one of the above problems. Specifically, one aspect of the present application provides a method for updating restricted area data, the method being applied to a movable platform, the method including:

based on the current state information of the movable platform, obtaining an update threshold value of the current limited area data, wherein the current state information comprises one or more of the following information: the current moving speed of the movable platform, the current processor load of the movable platform, the current used query radius of the limited area of the movable platform, the current country of the movable platform, the current area density of the limited area of the movable platform or the current onboard computing unit temperature of the movable platform;

determining a movement distance of the movable platform based on the position information of the movable platform and the current position information of the movable platform when the update of the restricted area data was started last time;

determining whether to update the restricted area data based on the movement distance and the update threshold.

Yet another aspect of the present application provides a movable platform, comprising:

the power mechanism is used for enabling the movable platform to move;

a memory for storing executable program instructions;

one or more processors configured to execute the program instructions stored in the memory, causing the processors to perform the following acts:

based on the current state information of the movable platform, obtaining an update threshold value of the current limited area data, wherein the current state information comprises at least one of the following information: the current moving speed of the movable platform, the current processor load of the movable platform, the current used query radius of the limited area of the movable platform, the current country of the movable platform, the current area density of the limited area of the movable platform or the current onboard computing unit temperature of the movable platform;

Another aspect of the present application further provides a method for updating restricted area data, including:

adjusting an update threshold of restricted area data of a movable platform when state information of the movable platform changes, the state information including one or more of the following information: the current moving speed of the movable platform, the current processor load of the movable platform, the current used query radius of the limited area of the movable platform, the current country of the movable platform, the current area density of the limited area of the movable platform or the current onboard computing unit temperature of the movable platform;

and updating the limited area data of the movable platform according to the adjusted updating threshold value.

Yet another aspect of the present application also provides an apparatus for updating restricted area data, the apparatus comprising:

a memory for storing executable program instructions;

a processor for executing the program instructions stored in the memory, causing the processor to perform the acts of:

Yet another aspect of the present application provides a computer storage medium having stored thereon a computer program which, when executed by a processor, implements the aforementioned method of updating restricted area data.

According to the method for updating the data of the limited area and the movable platform, the updating threshold value of the data of the current limited area is obtained based on the current state information of the movable platform, so that the updating threshold value of the data of the limited area is adjusted in real time, the function of the limited area is guaranteed, the calculation force of a machine is saved, and the problem that the movable platform mistakenly breaks through the limited moving area (such as a flight limiting area) is effectively avoided.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.

FIG. 1 illustrates a schematic view of an aircraft in one embodiment of the present application;

FIG. 2 illustrates a flow diagram of a method of updating restricted area data in one embodiment of the present application;

FIG. 3 shows a flow chart of a method of updating restricted area data in another embodiment of the present application;

FIG. 4 illustrates a schematic view of an aircraft seeking a flight-restricted zone in another embodiment of the present application;

FIG. 5 is a diagram illustrating obtaining an update threshold based on a trained neural network in one embodiment of the present application;

FIG. 6 is a block diagram of a recursive RBF neural network in one embodiment of the present application;

FIG. 7 is a flow chart illustrating a method of updating restricted area data in yet another embodiment of the present application;

fig. 8 shows a schematic block diagram of an apparatus for updating restricted area data in an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, exemplary embodiments according to the present application will be described in detail below with reference to the accompanying drawings. It should be understood that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and that the present application is not limited by the example embodiments described herein.

In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present application. It will be apparent, however, to one skilled in the art, that the present application may be practiced without one or more of these specific details. In other instances, well-known features of the art have not been described in order to avoid obscuring the present application.

It is to be understood that the present application is capable of implementation in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the application to those skilled in the art.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of the associated listed items.

In order to provide a thorough understanding of the present application, detailed structures and methods will be provided in the following description in order to explain the technical solutions proposed in the present application. Alternative embodiments of the present application are described in detail below, however, the present application may have other implementations in addition to these detailed descriptions.

The method for updating a restricted area (e.g., flight zone) database and the movable platform of the present application will be described in detail below with reference to the accompanying drawings. The features of the following examples and embodiments may be combined with each other without conflict.

It should be noted that the method for updating a database of a restricted area (e.g., a flight-restricted area, a height-restricted area, etc.) provided in this embodiment of the present application may be applied to a movable platform and any other application scenario involving the restricted area (e.g., a flight-restricted area), where for example, the movable platform may include an aircraft (e.g., an unmanned aerial vehicle), a robot, an unmanned vehicle, and an unmanned ship, and this embodiment of the present application does not limit a specific application scenario. Correspondingly, when the movable platform is an aircraft, the restricted area can be a flight-limiting area, a no-flight area, a height-limiting area, or the like. When the movable platform is a robot, the restricted area may be an area for restricting the activity, an area for prohibiting the activity, an area for restricting the activity height, or the like. When the movable platform is an unmanned vehicle, the restricted area can be a restricted area, a forbidden area, or an area for restricting the driving height and the like. When the movable platform is an unmanned ship, the restricted area can be a restricted area, a forbidden area and the like.

It should be noted that, on the premise that the network bandwidth is higher than the threshold bandwidth, the method for updating the restricted area (e.g., flight-restricted zone) database according to the embodiment of the present application may also be applied to update the restricted area (e.g., flight-restricted zone) database between two devices, including updating the restricted area data by another device such as a terminal or a server, and the movable platform acquires the updated restricted area data and stores the data, for example, the terminal updates the restricted area (e.g., flight limit) data through the server, and the movable platform acquires the updated restricted area (such as the flight-limiting area) data through the terminal, and stores the data in the memory, alternatively, the aircraft updates the restricted area (e.g., flight zone) data via the terminal, or the movable platform updates the restricted area (e.g., flight zone) data via the server, and the movable platform directly accesses the server to obtain the updated restricted area data.

It is noted that a restricted area (e.g., a flight-limiting zone) herein can include at least one of: (1) large airports for civil aviation; (2) navigating a fixed-wing airport; (3) helicopter airports; (4) special area restricted areas (e.g., flight-restricted areas), such as washington, beijing, commercial areas, military areas, office areas, etc.; (5) other restricted areas (e.g., flight limits), such as temporary restricted areas (e.g., flight limits).

In the embodiment of the present application, the solution of the present application is mainly described by taking a case where the movable platform is an aircraft as an example, but it is to be understood that this is not intended to limit the application scenario of the present application.

In one example, FIG. 1 illustrates a schematic view of an aircraft 100 in one embodiment of the present application. The aircraft 100 includes a carrier (i.e., airframe) 102 and a load 104. It should be understood by those skilled in the art that any of the embodiments described herein with respect to an aircraft are applicable to any aircraft (e.g., unmanned aerial vehicles, also referred to as drones). In some embodiments, load 104 may be located directly on aircraft 100 without carrier 102. Aircraft 100 may include a processor 101, a memory 102, a power mechanism 106, a sensing system 108, and a communication system 110. These components are interconnected by a bus system and/or other form of connection mechanism (not shown).

The power mechanism 106 may include one or more of a rotator, propeller, blade, engine, motor, wheel, bearing, magnet, nozzle. For example, the rotator of the power mechanism may be a self-fastening rotator, a rotator assembly, or other rotator power unit. The aircraft may have one or more powered mechanisms. All power mechanisms may be of the same type. Alternatively, one or more of the power mechanisms may be of a different type. The power mechanism 106 may be mounted on the aircraft by any suitable means, such as by a support member (e.g., a drive shaft). The power mechanism 106 may be mounted at any suitable location on the aircraft 100, such as the top, bottom, front, back, sides, or any combination thereof.

In some embodiments, the powered mechanism 106 enables the aircraft to take off from a surface vertically, or land on a surface vertically, without requiring any horizontal movement of the aircraft 100 (e.g., without requiring taxiing on a runway). Alternatively, the power mechanism 106 may allow the aircraft 100 to hover at a preset position and/or direction in the air. One or more of the power mechanisms 106 may be controlled independently of the other power mechanisms. Alternatively, one or more power mechanisms 106 may be controlled simultaneously. For example, the aircraft 100 may have multiple horizontally oriented rotating bodies to track the lift and/or thrust of the target. The horizontally oriented rotating bodies may be actuated to provide the capability for the aircraft 100 to take off vertically, land vertically, hover. In some embodiments, one or more of the horizontally oriented rotator may rotate in a clockwise direction while the other one or more of the horizontally oriented rotator may rotate in a counter-clockwise direction. For example, the number of the rotating bodies rotating clockwise is the same as that of the rotating bodies rotating counterclockwise. The rate of rotation of each horizontally oriented rotating body may be independently varied to effect the lifting and/or pushing action caused by each rotating body to adjust the spatial orientation, velocity, and/or acceleration (e.g., rotation and translation with respect to up to three degrees of freedom) of the aircraft 100.

Sensing system 108 may include one or more sensors to sense the spatial orientation, velocity, and/or acceleration (e.g., rotation and translation with respect to up to three degrees of freedom) of aircraft 100. The one or more sensors include any of the sensors described above, including a GPS sensor, a motion sensor, an inertial sensor, a proximity sensor, or an image sensor. The sensing data provided by the sensing system 108 may be used to track the spatial orientation, velocity and/or acceleration of the target 100 (using a suitable processing unit and/or control unit, as described below). Alternatively, the sensing system 108 may be used to gather data of the environment of the aircraft, such as weather conditions, potential obstacles to approach, location of geographical features, location of man-made structures, and the like.

The communication system 110 is capable of communicating with a terminal 112 having a communication system 114 via wireless signals 116. The

communication systems

110, 114 may include any number of transmitters, receivers, and/or transceivers for wireless communication. The communication may be a one-way communication such that data may be transmitted from one direction. For example, one-way communication may include only aircraft 100 transmitting data to terminal 112, or vice versa. One or more transmitters of communication system 110 may transmit data to one or more receivers of communication system 112 and vice versa. Alternatively, the communication may be a two-way communication, such that data may be transmitted in both directions between the aircraft 100 and the terminal 112. Two-way communication includes one or more transmitters of communication system 110 that may transmit data to one or more receivers of communication system 114, and vice versa.

In some embodiments, terminal 112 may provide control data to one or more of aircraft 100, carrier 102, and load 104 and receive information (e.g., position and/or motion information of the aircraft, carrier, or load, load-sensed data, such as image data captured by a camera) from one or more of aircraft 100, carrier 102, and load 104. In some embodiments, the control data of the terminal may include instructions regarding position, motion, actuation, or control of the aircraft, carrier, and/or load. For example, the control data may cause a change in the position and/or orientation of the aircraft (e.g., by controlling the power mechanism 106), or cause movement of the carrier body relative to the aircraft (e.g., by controlling the carrier body 102). The control data of the terminal may result in load control such as controlling the operation of a camera or other image capture device (capturing still or moving images, zooming, turning on or off, switching imaging modes, changing image resolution, changing focal length, changing depth of field, changing exposure time, changing angle of view or field of view). In some embodiments, the communication of the aircraft, carrier, and/or load may include information from one or more sensors (e.g., sensing system 108 or load 104). The communication may include sensed information transmitted from one or more different types of sensors, such as a GPS sensor, a motion sensor, an inertial sensor, a proximity sensor, or an image sensor. The sensed information is related to the position (e.g., direction, position), motion, or acceleration of the aircraft, carrier, and/or load. The sensed information transmitted from the load includes data captured by the load or a state of the load. The terminal 112 transmits the provided control data that may be used to track the status of one or more of the aircraft 100, the carrier 102, or the load 104. Alternatively or simultaneously, carrier 102 and load 104 may each include a communication module for communicating with terminals 112 so that the terminals may communicate or track aircraft 100, carrier 102 and load 104 individually.

In certain embodiments, aircraft 100 may communicate with other remote devices other than terminal 112, and terminal 112 may also communicate with other remote devices other than aircraft 100. For example, the aircraft and/or the terminal 112 may communicate with another aircraft or a carrier or load of another aircraft. The additional remote device may be a second terminal or other computing device (such as a computer, desktop, tablet, smartphone, or other mobile device) when desired. The remote device may transmit data to aircraft 100, receive data from aircraft 100, transmit data to terminal 112, and/or receive data from terminal 112. Alternatively, the remote device may be connected to the internet or other telecommunications network to enable data received from the aircraft 100 and/or the terminal 112 to be uploaded to a website or server.

In some embodiments, the movement of the aerial vehicle, the movement of the carrier, and the movement of the load relative to a fixed reference (e.g., the external environment), and/or relative to each other, may be controlled by the terminal. The terminal may be a remote control terminal located remotely from the aircraft, carrier and/or load. The terminal may be located on or affixed to the support platform. Alternatively, the terminal may be hand-held or wearable. For example, the terminal may include a smartphone, a tablet, a desktop, a computer, glasses, gloves, a helmet, a microphone, or any combination thereof. The terminal may comprise a user interface such as a keyboard, mouse, joystick, touch screen or display. Any suitable user input may interact with the terminal, such as manual input commands, voice control, gesture control, or position control (e.g., through motion, position, or tilt of the terminal).

The aircraft 100 may include one or more memories 102, the memory 102 having stored thereon a computer program for execution by the processor, e.g., for storing corresponding steps and program instructions for implementing a method of updating a restricted area (e.g., flight restriction zone) database according to embodiments of the present application. May include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc.

The aircraft 100 may include one or more processors 101, and the processor 101 may be a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the aircraft 100 to perform desired functions. The processor is capable of executing program instructions stored in the memory to perform the associated steps in the method of updating a restricted area (e.g., flight restriction) database of the embodiments of the application described below. For example, a processor can include one or more embedded processors, processor cores, microprocessors, logic circuits, hardware Finite State Machines (FSMs), Digital Signal Processors (DSPs), or a combination thereof. In this embodiment, the processor comprises a Field Programmable Gate Array (FPGA), or one or more ARM processors.

According to regulations on airspace regulation and regulations on the management of drones, a drone must fly in a specified airspace, a drone geofence system is a way to limit the flight of drones, which presents two forms on some present drones: one type of unmanned aerial vehicle (without advanced functions such as front view obstacle avoidance) is an unmanned aerial vehicle without a high-performance processor, only the simplest circular restricted area (such as a flight-limiting area) can be realized on the unmanned aerial vehicle at present, the data storage format of the restricted area (such as the flight-limiting area) is very simple, and the number of databases is small; the other type is an unmanned aerial vehicle (with advanced functions such as visual obstacle avoidance and tracking) with a high-performance processor, a complex polygonal restricted area (such as a flight restriction area) is currently realized on the unmanned aerial vehicle, and the number of the restricted areas (such as the flight restriction area) is also large.

The restricted area (e.g., Flight control area) function of the drone without the high-performance processor can only be implemented on a Micro Control Unit (MCU) where a Flight control system (FC) is located, while the restricted area (e.g., Flight control area) function of the drone with the high-performance processor is mainly implemented on an AP (Application processor), and is only executed on the FC.

In the conventional unmanned aerial vehicle, an airport restricted area (e.g., a flight control area) is usually an extended area (also referred to as a restricted area (e.g., a flight control area)) having a fixed height restriction around a no-fly area where no take-off is permitted, in which the unmanned aerial vehicle is considered in the horizontal direction and the vertical direction, respectively. During flight, a drone may enter a restricted area (e.g., a flight control area) or a no-flight area (hereinafter, referred to as a restricted area (e.g., a flight control area)), and if the drone flies out of the restricted area, the flight safety of the drone and other aircraft may be affected, so that the drone needs to be restricted in the restricted area (e.g., the flight control area).

On some aircrafts such as unmanned aerial vehicles, all data of a limited area (such as a flight limiting area) is not read into the memory at one time because of the limitations of calculation power and memory, but the data of the limited area (such as the flight limiting area) around the unmanned aerial vehicle is read into the memory according to the current position of the unmanned aerial vehicle during the flight process. Because only the data of the restricted area (e.g., the flight restriction area) around the drone is loaded into the memory, in the process of determining the restricted area (e.g., the flight restriction area), along with the expansion of the flight distance, after the drone flies a distance away from the last updated position, the data of the restricted area (e.g., the flight restriction area) around the drone needs to be read again and updated into the memory.

In the conventional technology, the update threshold of the restricted area (e.g., flight-restricted area) database is selected as a fixed value set by experience, real-time optimization adjustment is not performed in combination with actual flight dynamics of the unmanned aerial vehicle, if the update threshold is set too small, frequent reading of the restricted area (e.g., flight-restricted area) database may be caused, vehicle computational power is wasted, and if the update threshold is set too large, the unmanned aerial vehicle may approach a previous data boundary, and new restricted area (e.g., flight-restricted area) data is not updated yet, or update is not started yet, so that the unmanned aerial vehicle mistakenly enters the no-fly zone, and unnecessary risks are caused.

In view of the above problems, an embodiment of the present application provides a method for updating restricted area data, the method being applied to a movable platform, and the method including: based on the current state information of the movable platform, obtaining an update threshold value of the current limited area data, wherein the current state information comprises at least one of the following information: a current movement speed of the movable platform, a current processor load of the movable platform, a current used restricted area query radius of the movable platform, a current country of the movable platform, a restricted area density of a current area of the movable platform, or a current onboard computing unit temperature of the movable platform (e.g., an aircraft); determining a movement distance of the movable platform based on the position information of the movable platform and the current position information of the movable platform when the update of the restricted area data was started last time; determining whether to update the restricted area data based on the movement distance and the update threshold.

According to the method for updating the data of the limited area, the updating threshold of the data of the current limited area is obtained based on the current state information of the movable platform, so that the updating threshold of the data of the limited area is adjusted in real time, the limiting function of the limited area on the movement of the movable platform is guaranteed, the vehicle computing power is saved, and the problem that the movable platform mistakenly breaks through the forbidden activity area is effectively avoided.

It should be noted that, according to the method for updating a restricted area (e.g., a flight-restricted area) database provided in the embodiment of the present application, an execution subject may be a movable platform (e.g., an aircraft), and may also be part or all of a computer device that implements the method for updating a restricted area (e.g., a flight-restricted area) database through software, hardware, or a combination of software and hardware.

Next, a method of updating the restricted area data provided in the embodiment of the present application is described with reference to fig. 2 to 6.

As an example, as shown in fig. 2, a method 200 for updating restricted area data provided in an embodiment of the present application includes the following steps S201 to S203.

First, in step S201, an update threshold value of the current limited area data is acquired based on the current state information of the movable platform. Based on the current state information of the movable platform, the updating threshold value of the data of the restricted area is adjusted in real time, the function of the restricted area is guaranteed, the machine-mounted computing power is saved, and the problem that the movable platform mistakenly runs into the prohibited active area is effectively avoided.

The current state information includes at least one of the following information: a current movement speed (e.g., airspeed) of the movable platform (e.g., aircraft), a current processor load of the movable platform (e.g., aircraft), a restricted area (e.g., flight limit zone) query radius currently used by the movable platform (e.g., aircraft), a restricted area (e.g., flight limit zone) density of a region where the movable platform (e.g., aircraft) is currently located, or a current on-board computing unit temperature of the movable platform (e.g., aircraft). Alternatively, other information may be included that affects updates to a restricted area (e.g., flight limit zone) database of a movable platform (e.g., aircraft).

The update threshold for obtaining the database of the current restricted area (e.g., the flight restriction zone) may be obtained by any suitable calculation method based on the current state information of the movable platform (e.g., the aircraft), for example, the update threshold for the database of the current restricted area (e.g., the flight restriction zone) may be obtained by indicating a mapping relationship between a plurality of information in the current state information and the update threshold by, for example, a table. As another example, various machine learning algorithms may be utilized to learn a mapping relationship that identifies current state information and an update threshold of a movable platform (e.g., an aircraft), once trained, the trained machine learning algorithms may be stored by the movable platform (e.g., the aircraft) for obtaining an update threshold of a database of current restricted areas (e.g., restricted flight areas) based on the current state information of the movable platform (e.g., the aircraft), some examples of which may include supervised or unsupervised machine learning algorithms, including regression algorithms (e.g., ordinary least squares regression), example-based algorithms (e.g., learning vector quantization), decision tree algorithms (e.g., classification and regression trees), bayesian algorithms (e.g., na iotave bayes), clustering algorithms (e.g., k-means clustering), association rule learning algorithms (e.g., a priori algorithm), an artificial neural network algorithm (e.g., a perceptron), a deep learning algorithm (e.g., a deep boltzmann machine, or a deep neural network), a dimensionality reduction algorithm (e.g., principal component analysis), an integration algorithm (e.g., stacked generalization), and/or other machine learning algorithms.

In one example, the obtaining an updated threshold value of a current restricted area (e.g., flight limit zone) database based on current state information of the movable platform (e.g., aircraft) includes: inputting the current state information of the movable platform (such as an aircraft) into a trained neural network for processing, and acquiring an update threshold value of a database of a current restricted area (such as a flight-limiting area). For example, as shown in fig. 5, the current moving speed (e.g., flying speed) of the movable platform (e.g., aircraft), the current processor load of the movable platform (e.g., aircraft), and the query radius of the restricted area (e.g., flight restriction area) currently used by the movable platform (e.g., aircraft) are input into the trained neural network for processing, so as to obtain the updated threshold X of the database of the current restricted area (e.g., flight restriction area). Or one or more of the aforementioned current state information may be used as an input quantity, and input into a trained neural network for processing, so as to obtain an update threshold of a current restricted area (e.g., a flight-limiting area) database. The trained neural network can acquire the updated threshold value of the database of the current restricted area (such as the flight-limiting area) more quickly, accurately and effectively.

Where the airspeed of the aircraft may be the airspeed of the aircraft, the wind speed may generally affect the airspeed of the aircraft, and thus, in some examples, the airspeed of the aircraft may be the sum of the airspeed of the aircraft and the wind speed.

The trained neural network may be any suitable type of neural network, wherein preferably the trained neural network comprises a trained Recurrent (RBF) neural network. The RBF neural network can directly approximate a complex nonlinear relation by using input and output data through a simple topological structure.

In one example, the trained neural network includes a neural network having a feedback structure, such as a neural network having a feedback structure, and more such as a recursive RBF neural network having a feedback structure, wherein, as shown in fig. 6, the recursive RBF neural network having a feedback structure includes an input side, an hidden layer, an output layer, and a feedback connection, and the output layer feeds back output information to the hidden layer through the feedback connection to adjust input information of the hidden layer. The dynamic selection of the updated radius has high requirements on the dynamic characteristics of the system, the recursive RBF neural network with a feedback structure is adopted, the RBF neural network can directly approximate a complex nonlinear relation by using input and output data through a simple topological structure, and output information is fed back to the hidden layer through a feedback channel to adjust the input of the hidden layer, so that the dynamic characteristics of the system can be improved.

The neural network may be trained based on any suitable training method to obtain a trained neural network. Hereinafter, a training process of the RBF neural network for outputting the update threshold of the database of the restricted area (e.g., the flight-restricted area) will be described, which is only an example, and other suitable training methods may be applied to the present application.

FIG. 6 shows a block diagram of a recursive RBF neural network that includes an input layer, a hidden layer, and an output layer. The input layer contains n neurons in total, and the output of each neuron is:

o_i(t)＝x_i(t)，i＝(1，2，...，n)

wherein x is [ x ]₁(t)，x₂(t)，...x_n(t)]^TIs the input to a recursive RBF neural network, o_i(t) corresponds to the output of the ith neuron at time t. In the present application, n is three, and the three inputs are the current moving speed (e.g. flying speed) of the movable platform, for example, an aircraft, the current CPU load condition, and the set search radius of the restricted area (e.g. flight limit area). Or other suitable movement data information (e.g., flight data information for an aircraft) may also be entered.

Each neuron of the hidden layer is connected not only to neurons of the input layer, but also to each neuron of the output, each neuron of the hidden layer outputting:

wherein, c_j(t) is the coordinate vector of the center point of the j-th neuron Gaussian function of the hidden layer at the time t, b_j(t) is the width of Gaussian function of j-th neuron of the hidden layer at the time t, | |, represents Euclidean distance, σ_j(t) represents:

σ_j(t)＝[o₁(t)，o₂(t)，...o_n(t)，p_j(t)y(t-1)]^T

wherein p is_jAnd (t) is the feedback connection weight between the output neuron at the time t and the jth neuron of the hidden layer, and y (t-1) is the output of the neuron of the t-1 output layer.

The output layer has only one neuron, which can be expressed as:

wherein, ω is_j(t) is the connection weight between the output neuron and the jth hidden layer neuron.

The error index of the network approximation is as follows:

the weight of the network is adjusted by adopting a gradient descent method, and the specific optimization updating method comprises the following steps:

where η ∈ (0, 1) is the learning rate, η ═ 0.5 in the present application, or other suitable values, and the initial weight of the network takes a random value from 0 to 1. The defined error indicator is minimized by training.

The trained neural network is trained based on movement data information (such as flight data information of an aircraft) of the movable platform (such as the aircraft) in at least one historical movement process (such as a historical flight process of the aircraft), namely, the movement data information (such as flight data information of the aircraft) of the movable platform (such as the aircraft) in at least one historical movement process is used as a training data set, and the movement data information (such as flight data information of the aircraft) in the historical movement process can reflect movement (such as flight) state information of the movable platform (such as the aircraft), so that the trained neural network is trained based on the data, and output values of the trained neural network in use can be more reliable, accurate and effective.

The movement data information (such as flight data information of the aircraft) of the movable platform (such as the aircraft) in at least one historical movement process can be obtained through a log (log) in the actual historical movement process, namely, the movement data information (such as flight data information of the aircraft) is recorded in the actual historical movement process and is stored in a memory, and only the movement data information needs to be called in the memory if necessary.

In one example, the movement data information (e.g., flight data information of the aircraft) includes first movement data information (e.g., first flight data information of the aircraft) and second movement data information (e.g., second flight data information of the aircraft), the first movement data information (e.g., flight data information of the aircraft) including at least one of: the moving speed (such as flying speed) when triggering the update of the database of the restricted area (such as a flight-restricted area) in the historical moving process of the movable platform (such as an aircraft), the CPU load when triggering the update of the database of the restricted area (such as a flight-restricted area) and the search radius of the restricted area (such as a flight-restricted area) when triggering the update of the database of the restricted area (such as a flight-restricted area), the country where the update of the database of the restricted area (such as a flight-restricted area) is located, the density of the restricted area (such as a flight-restricted area) in the area where the update of the database of the restricted area (such as a flight-restricted area) is currently located, and the temperature of the on-board computing unit when triggering the update of the database of the restricted area (such as a flight-restricted area) are determined, the second movement data information (e.g., flight data information of the aircraft) includes a flight distance of the movable platform (e.g., aircraft) from the start of updating the restricted area (e.g., flight restriction zone) database to the completion of updating. In the training process of the trained neural network, the first movement data information (for example, first flight data information of an aircraft) is used as input information of a neural network input layer, for example, a movement speed (for example, a flight speed) when the database of the limited area (for example, a flight limit zone) is triggered to be updated in the historical movement process of the movable platform (for example, the aircraft) in the first movement data information (for example, flight data information of the aircraft), a CPU load when the database of the limited area (for example, the flight limit zone) is triggered to be updated, and a search radius of the area of the limited area (for example, the flight limit zone) when the database of the limited area (for example, the flight limit zone) is triggered to be updated are used as input information of the neural network input layer. Or at least one or more of the first mobile data information (e.g. first flight data information of the aircraft) may be used as input information for the input layer, which may be specific to the condition of the actual mobile platform (e.g. the aircraft).

During the historical movement of the movable platform (e.g., an aircraft), the second movement data information (e.g., flight data information of the aircraft) is recorded after the update of the restricted area (e.g., flight-restricted area) database is completed, for example, the movement distance of the movable platform (e.g., a drone) from the start of the update to the completion of the update, such as the flight distance (i.e., the distance flown) of the drone. The second movement data information (e.g. flight data information of the aircraft) is used for correcting the output result of the neural network output layer, that is, the output result of the neural network output layer is corrected based on the movement distance of the movable platform (e.g. the aircraft), such as the flight distance of the unmanned aerial vehicle, from the update of the restricted area (e.g. the flight-restricted area) database to the update completion, and the expected update threshold value is corrected by using the movement distance, such as the flight distance of the unmanned aerial vehicle. For example, as shown in fig. 4, the desired update threshold should be the boundary of the limited-flight data search circle with the database update position B as a circular point and the limited-flight area search radius R as a radius when the update of the limited-flight database at the database update position B is completed, if the aircraft is located within the limited-flight data search circle at the database update position a when the update of the limited-flight database at the database update position B is completed, it indicates that the output update threshold is smaller than the desired value, which may result in an increase in the number of updates and affect the recording computation power, and if the aircraft is located outside the limited-flight data search circle at the database update position a, which indicates that the output update threshold is greater than the desired value, it may occur that the drone has approached the previous data boundary (e.g., the boundary of the limited-flight data search circle located at the database update position a), the new flight-limit data is not updated or is not updated, so that the unmanned aerial vehicle mistakenly enters the no-fly zone, and unnecessary risks are caused, and therefore, the flight-limit database of the position B needs to be updated by the database until the flight distance of the aircraft is updated, the output value is corrected, and the expected update threshold value is obtained.

The training of the neural network is completed by utilizing data stored offline after the movable platform (such as an aircraft) moves for a plurality of times, such as after flying, and the offline trained neural network (namely, the trained neural network) is integrated on the movable platform (such as the aircraft) for acquiring the update threshold value in real time according to the current state information of the movable platform (such as the aircraft).

Next, as shown in fig. 2, in step S202, the movement distance of the movable platform is determined based on the position information of the movable platform and the current position information of the movable platform when the update of the limited area data was started last time.

The location information (e.g., GPS location) of a movable platform (e.g., aircraft) at the time the update of the restricted area (e.g., flight limit zone) database was last started and the current location information (e.g., GPS location) of the movable platform (e.g., aircraft) may be obtained based on sensors, such as GPS sensors, on the movable platform (e.g., aircraft). For example, taking an aircraft as an example, the moving distance D of the aircraft is obtained by comparing longitude and latitude coordinates of the aircraft and the aircraft.

Next, continuing as shown in fig. 2, in step S203, it is determined whether to update the limited region data based on the movement distance and the update threshold.

The current update threshold X is obtained by the method in the foregoing step S201, as shown in fig. 3, when the movement distance D is greater than or equal to the update threshold X, the database of the restricted area (e.g., flight-limiting area) is triggered to be updated, that is, a new database of the restricted area (e.g., flight-limiting area) is triggered to be updated, so as to update the data of the restricted area (e.g., flight-limiting area) within the radius range R searched by the restricted area (e.g., flight-limiting area) from the distance between the movable platform (e.g., aircraft) and the restricted area (e.g., flight-limiting area) into the database of the restricted area (e.g., flight-limiting area) within R meters around the aircraft, and read the data of the flight-limiting area into the memory for standby.

As shown in fig. 3, when the movement distance D is smaller than the update threshold X, the update is not triggered, but the movement, such as flying, of the movable platform (e.g., aircraft) is continued, and the database is continuously obtained in real time according to the method in the foregoing step S201 during the movement of the movable platform (e.g., aircraft) to update the threshold X.

For example, as shown in fig. 4, the position information of the movable platform (e.g., aircraft) when the update of the restricted area (e.g., flight-restricted area) database is started last time is database update position a, the current position of the movable platform (e.g., aircraft) is database update position B in fig. 4, the moving distance D between the database update position a and the database update position B, at the database update position B, the update threshold value X of the database is obtained based on the current state information of the movable platform (e.g., aircraft), at which time, the moving distance D is greater than the update threshold value X, so that the update of the restricted area (e.g., flight-restricted area) database is triggered once at the database update position B, that is, the restricted area (e.g., flight-restricted area) data of the restricted area (e.g., flight-restricted area) within R meters around the database update position B is searched for and updated into the restricted area (e.g., flight-restricted area) database, for example, into a restricted area (e.g., flight-restricted zone) database in memory for use.

Optionally, the movable platform (e.g. an aircraft) includes a first storage and a second storage, for example, the first storage includes an internal memory, and the second storage includes a hard disk, where the restricted area (e.g. flight-restricted area) database is stored in the first storage, and the restricted area (e.g. flight-restricted area) data is data read from the second storage, for example, data read from a structured database stored in the second storage, or data read from a database organized by geohash, or data read from another database with restricted area (e.g. flight-restricted area) data. In the scheme of the application, the data of the limited area (such as the flight-limiting area) in a certain range around the movable platform (such as an aircraft) is read from the physical storage medium to the memory, the data of the limited area (such as the flight-limiting area) around the movable platform can be conveniently and quickly acquired from the memory, and then the relative position relationship between the unmanned aerial vehicle and the limited area (such as the flight-limiting area) is timely judged, so that the moving track, the speed, the height and the like of the movable platform (such as the aircraft) are controlled.

The restricted area (e.g., flight restriction) search radius range R may be a predetermined restricted area (e.g., flight restriction) search radius, which may be set reasonably empirically.

It should be noted that the restricted area (e.g., flight restriction zone) data of the restricted area (e.g., flight restriction zone) zone in the present application may include data information such as length, width, height, center position, shape, and type of the restricted area (e.g., flight restriction zone) zone.

Further, the method of the present application further comprises the steps of:

firstly, according to the current position information of the movable platform (such as an aircraft) and the updated restricted area (such as a flight-limiting area) database, the position relationship of the movable platform (such as the aircraft) and at least one restricted area (such as a flight-limiting area) around the movable platform is obtained. For example, the geometric relationship of a movable platform (e.g., an aircraft) and a plurality of surrounding restricted areas (e.g., flight limits) is obtained.

The judgment method of the geometric relationship between the movable platform (such as an aircraft) and the restricted area (such as a flight restriction area) is various, and the judgment method can be calculated by adopting a plane geometric method, can also be finished by adopting a solid geometric method, a potential energy function method and the like, or can also be calculated by adopting other suitable methods.

Then, according to the geometric relationship, a target restricted area (e.g., a flight-limiting area) is determined, wherein the target restricted area (e.g., a flight-limiting area) is a restricted area (e.g., a flight-limiting area) closest to the movable platform (e.g., an aircraft) in the horizontal movement direction (e.g., a flight direction) in the at least one restricted area (e.g., a flight-limiting area), and for example, a target restricted area (e.g., a flight-limiting area) is determined from a plurality of restricted area (e.g., flight-limiting areas) which may affect the movement of the movable platform (e.g., the aircraft) first.

Finally, movement parameters (e.g., flight parameters) of the movable platform (e.g., aircraft) are controlled based on the positional relationship of the target restricted area (e.g., flight restriction area) zone and the movable platform (e.g., aircraft), the movement parameters (e.g., flight parameters) including at least one of a movement speed (e.g., flight speed), a movement altitude (e.g., flight altitude), and a movement direction (e.g., flight direction). After determining the target restricted area (e.g., flight-restricted zone) zone, relevant restrictions on the movable platform (e.g., aircraft) are performed, such as controlling the aircraft to slow down logic, changing flight direction, lowering flight altitude, etc., based on restricted area (e.g., flight-restricted zone) data of the target restricted area (e.g., flight-restricted zone) zone. Thereby avoiding that the movable platform (e.g. an aircraft) violates the regulations of the restricted area (e.g. the flight-limit zone) and causes unnecessary risks.

The relative position relationship between the target restricted area (e.g., flight-restricted area) and the movable platform (e.g., aircraft) is determined based on the coordinates, positions, etc. of the target restricted area (e.g., flight-restricted area), and then the movement of the movable platform (e.g., aircraft) is controlled, for example, if the aircraft is located in the flight-restricted area, if the flying height of the aircraft is higher than the flight-restricted height of the flight-restricted area, the aircraft is controlled to reduce the flying height of the aircraft to be below the flight-restricted height, and for example, if the aircraft is located in the flight-restricted area, the heading (i.e., the flying direction) of the aircraft is adjusted to control the unmanned aerial vehicle to fly outside the flight-restricted area. As another example, if the aircraft is outside of the flight restriction zone and within a predetermined distance from the flight restriction zone, the aircraft is prevented from takeoff or is prohibited from flying in the direction of the flight restriction zone. For example, if the aircraft is in a no-fly zone, the aircraft is controlled to land on the ground. For example, the aircraft is located in a flight-limiting zone, and the aircraft is controlled to decelerate, or the heading is changed after deceleration, so as to prevent mistaken intrusion into the no-fly zone.

In summary, the update threshold is obtained again in real time according to the foregoing method during the whole moving process (flight process) of the movable platform (e.g., an aircraft), and when the foregoing conditions are met, the update of the database of the restricted area (e.g., a flight-restricted zone) is triggered, and the geometric relationship with the surrounding restricted area (e.g., a flight-restricted zone) is checked in real time according to the database of the restricted area (e.g., a flight-restricted zone), so as to implement a reasonable moving strategy (e.g., a flight strategy) and avoid the problem that the movable platform (e.g., an aircraft) enters the restricted area (e.g., a flight-restricted zone) by mistake. Meanwhile, the updating threshold of the database is updated in real time, so that the problem that the updating threshold is too small and the updating times are too many, which causes influence on the vehicle computing power, can be avoided, and unnecessary risks caused by the fact that a movable platform (such as an aircraft) mistakenly breaks into a prohibited activity area due to the fact that the updating threshold is too large can be avoided. That is, the method of the application can save the machine-borne computing power while guaranteeing the normal function of the limited area (such as the flight-limiting area).

The embodiment of the application also provides a movable platform, which can comprise an aircraft (such as an unmanned aerial vehicle), a robot, an unmanned vehicle and an unmanned ship, and the movable platform can comprise a power mechanism, wherein the power mechanism is used for enabling the movable platform to move; a memory for storing executable program instructions; one or more processors configured to execute the program instructions stored in the memory, causing the processors to perform the steps associated with the method 200 of updating restricted area data described above.

Continuing with reference to FIG. 1, taking the case where the movable platform is an aircraft 100 as an example, the aircraft 100 includes one or more processors 101 for executing the program instructions stored in the memory, so that the processors perform the relevant steps of the method 200 of updating restricted area data in the foregoing embodiment.

By way of example, the movable platform (e.g., aircraft) 100 includes one or more processors 101 for executing the program instructions stored in the memory, causing the processors to perform the following acts: obtaining an updated threshold value of a database of a current restricted area (e.g., a flight-restricted area) based on current state information of a movable platform (e.g., an aircraft), the current state information including at least one of: a current movement speed (e.g., flight speed) of the movable platform (e.g., aircraft), a current processor load of the movable platform (e.g., aircraft), a restricted area (e.g., flight limit zone) query radius currently used by the movable platform (e.g., aircraft), a country in which the movable platform (e.g., aircraft) is currently located, a restricted area (e.g., flight limit zone) zone density in which the movable platform (e.g., aircraft) is currently located, or a current on-board computing unit temperature of the movable platform (e.g., aircraft); determining a movement distance of the movable platform (e.g., aircraft) based on the position information of the movable platform (e.g., aircraft) and the current position information of the movable platform (e.g., aircraft) at the time of the previous start of updating the restricted area (e.g., flight restriction) database; based on the movement distance and the update threshold, it is determined whether to update the restricted area (e.g., flight restriction zone) database.

In one example, the processor 101 is configured to obtain an updated threshold value of current restricted area (e.g., flight limit zone) data based on current status information of the movable platform (e.g., aircraft), including:

inputting the current state information of the movable platform (such as an aircraft) into a trained neural network for processing, and acquiring the update threshold of the data of the current restricted area (such as a flight-limiting area).

In one example, the trained neural network comprises a trained RBF neural network.

In one example, the trained neural network comprises a neural network having a feedback structure, wherein the neural network comprises an input side, a hidden layer, an output layer, and a feedback connection, the output layer feeding back output information to the hidden layer through the feedback connection to adjust input information of the hidden layer.

In one example, the trained neural network is trained based on movement data information (e.g., flight data information of an aircraft) of the movable platform (e.g., aircraft) during at least one historical movement.

In one example, the movement data information (e.g., flight data information of the aircraft) includes first movement data information (e.g., flight data information of the aircraft) and second movement data information (e.g., flight data information of the aircraft), the first movement data information (e.g., flight data information of the aircraft) including at least one of: the moving speed (such as flying speed) when triggering data updating of a restricted area (such as a flight-restricted area) in the historical flying process of the movable platform (such as an aircraft), the CPU load when triggering data updating of the restricted area (such as the flight-restricted area) and the searching radius of the restricted area (such as the flight-restricted area) when triggering data updating of the restricted area (such as the flight-restricted area), the country where the restricted area (such as the flight-restricted area) is located when triggering data updating of the restricted area (such as the flight-restricted area), the density of the restricted area (such as the flight-restricted area) in the area where the restricted area (such as the flight-restricted area) is currently located when triggering data updating of the restricted area (such as the flight-restricted area), and the temperature of an on-board computing unit when triggering data updating of the restricted area (such as the flight-restricted area), the second movement data information (e.g., flight data information of the aircraft) includes a movement distance from the start of updating the restricted area (e.g., flight restriction zone) data to the completion of updating the movable platform (e.g., aircraft).

In one example, in the training process of the trained neural network, the first movement data information (e.g., flight data information of an aircraft) is used as input information of an input layer of the neural network, and the second movement data information (e.g., flight data information of the aircraft) is used for correcting an output result of an output layer of the neural network.

In one example, processor 101 is further configured to determine whether to update the restricted area (e.g., flight limit zone) data based on the movement distance and the update threshold, including:

when the movement distance is larger than or equal to the updating threshold value, triggering to update the restricted area (e.g. flight restriction zone) data so as to update the restricted area (e.g. flight restriction zone) data of the restricted area (e.g. flight restriction zone) zone of which the distance from the movable platform (e.g. aircraft) is within the search radius range of the restricted area (e.g. flight restriction zone) into the restricted area (e.g. flight restriction zone) database.

In one example, the movable platform (e.g., an aircraft) includes a first storage and a second storage, for example, the first storage includes an internal memory, and the second storage includes a hard disk, wherein the restricted area (e.g., a flight-restricted area) database is stored in the first storage, and the restricted area (e.g., a flight-restricted area) data is data read from the second storage, for example, data read from a structured database stored in the second storage is queried, or data read from a database organized by geohash is queried, or data read from another database having restricted area (e.g., a flight-restricted area) data is also queried.

In one example, the processor is further configured to:

acquiring the geometric relationship between the movable platform (such as an aircraft) and at least one limit area (such as a flight-limiting area) around the movable platform according to the current position information of the movable platform (such as the aircraft) and the updated limit area (such as the flight-limiting area) data;

determining a target restricted area (e.g. flight-limiting area) zone according to the geometric relationship, wherein the target restricted area (e.g. flight-limiting area) zone is the restricted area (e.g. flight-limiting area) zone which is closest to the movable platform (e.g. aircraft) in the horizontal flight direction in the at least one restricted area (e.g. flight-limiting area) zone;

controlling flight parameters of the movable platform (e.g., aircraft) according to a geometric relationship between the target restricted area (e.g., flight restriction area) zone and the movable platform (e.g., aircraft), the flight parameters including at least one of a moving speed (e.g., flying speed), a moving altitude, and a flying direction.

The movable platform (e.g., an aircraft) of the embodiments of the present application also has the aforementioned advantages because it can be used to perform the aforementioned method of updating restricted area (e.g., flight-restricted zone) data. The movable platform (such as an aircraft) can save the computational power on board while ensuring the function of a restricted area (such as a flight-limiting area).

Next, a method for updating the data of the restricted area in another embodiment of the present application is explained and illustrated with reference to fig. 7, wherein some steps of the method may refer to the description in the foregoing, which is not repeated herein, and the technical features in the foregoing may be incorporated into the present application on the premise of no conflict.

As an example, as shown in fig. 7, the method for updating the restricted area data of the present application includes the following steps:

in step S701, when state information of a movable platform changes, the update threshold of the limited area data of the movable platform is adjusted, and the state information includes one or more of the following information: the current moving speed of the movable platform, the current processor load of the movable platform, the current used query radius of the limited area of the movable platform, the current country of the movable platform, the current area density of the limited area of the movable platform or the current onboard computing unit temperature of the movable platform.

In one example, the adjusting the update threshold of the restricted area data of the movable platform when the state information of the movable platform changes includes: when the sensor detects that at least one state information of the movable platform changes, outputting prompt information, wherein the prompt information at least comprises data information of the changed state information; and the display device of the movable platform acquires the prompt information and displays the prompt information on a display interface. Optionally, the prompt message may be presented on the display interface in the form of a text symbol, and may also be displayed by highlighting, flashing, additional symbol display, differentiated shading color display, or differentiated font color display. The reminder information may also be presented simultaneously, for example acoustically. Through the prompt message, the user can be reminded of the type of the changed state information and the data information of the changed state information, such as specific numerical values and the like, so that the user can really adjust and update the threshold value according to the change.

In one example, the adjusting the update threshold of the restricted area data of the movable platform when the state information of the movable platform changes includes: acquiring an operation instruction input by a user, wherein the operation instruction is used for displaying a setting window on a display interface of a display device, for example, a first key is arranged on the display interface of the display device, and the user can input a corresponding operation instruction through an input device comprising one or more of a keyboard, a trackball, a mouse, a microphone, a touch screen and the like, or other control buttons; based on the operation instruction, controlling the display device to display a setting window on a display interface, and enabling a user to input corresponding changed state information through the setting window; acquiring a setting instruction input by a user, wherein the setting instruction is used for setting changed state information as data information of the changed state information; adjusting an update threshold of the restricted area data of the movable platform based on the changed data information of the state information.

In one example, adjusting an update threshold of restricted area data of a movable platform when state information of the movable platform changes includes: and inputting the changed data information of the state information of the movable platform into a trained neural network for processing, and acquiring an update threshold of the limited area data of the movable platform. Optionally, the trained neural network comprises a trained RBF neural network.

The trained neural network may be any suitable network, and the trained neural network comprises a neural network with a feedback structure, wherein the neural network comprises an input side, a hidden layer, an output layer and a feedback connection, and the output layer feeds back output information to the hidden layer through the feedback connection to adjust the input information of the hidden layer.

The neural network may be trained based on any suitable method, for example, the trained neural network is trained based on movement data information of the movable platform during at least one historical movement.

The mobile data information includes first mobile data information and second mobile data information, the first mobile data information includes at least one of the following information: the mobile platform comprises a mobile speed when the data of the limited area is triggered to be updated in the historical mobile process of the mobile platform, a CPU load when the data of the limited area is triggered to be updated, a search radius of the limited area when the data of the limited area is triggered to be updated, a country where the data of the limited area is triggered to be updated, the density of the limited area of the current area where the data of the limited area is triggered to be updated, and the temperature of an onboard computing unit when the data of the limited area is triggered to be updated, wherein the second mobile data information comprises the mobile distance from the beginning of updating the data of the limited area to the completion of updating the mobile platform. In the training process of the trained neural network, the first mobile data information is used as input information of a neural network input layer, and the second mobile data information is used for correcting an output result of the neural network output layer.

In step S701, the limited area data of the movable platform is updated according to the adjusted update threshold.

In one example, updating the restricted area data of the movable platform according to the adjusted update threshold comprises: determining a movement distance of the movable platform based on the position information of the movable platform and the current position information of the movable platform when the update of the restricted area data was started last time; updating the restricted area data based on the movement distance and the adjusted update threshold, for example, when the movement distance is greater than or equal to the adjusted update threshold, triggering updating the restricted area data to update the restricted area data of the restricted area having the distance to the movable platform within the restricted area search radius into a restricted area database of the movable platform.

The execution body of the method can be a movable platform such as an aircraft, or the movable platform such as the aircraft can directly acquire updated data of the limited area from other external equipment and update the updated data of the limited area into the memory of the movable platform, and the method for updating the data of the limited area is realized by other external equipment and is used for real-time iterative update through the external equipment. Or, the update threshold may be iteratively adjusted in real time by an external device, and the movable platform acquires the adjusted update threshold from the external device, and then updates the restricted area data, and reads the updated restricted area data from the memory of the physical hard disk, for example, into the memory.

According to the method for updating the data of the limited area, when the state information of the movable platform changes, the updating threshold value of the data of the limited area of the movable platform is adjusted, so that the updating threshold value of the data of the limited area is adjusted in real time, the limiting function of the limited area on the movement of the movable platform is guaranteed, the calculation force of a machine is saved, and the problem that the movable platform mistakenly runs into the activity forbidden area is effectively avoided.

In this context, the onboard computational power is the computational power of the hardware, such as a processor, carried by the movable platform itself.

Next, the apparatus for updating the restricted area data of the present application, which may be the aforementioned movable platform, or may also be another external device, is described with reference to fig. 8.

As shown in FIG. 8, the means for updating 800 the restricted area data includes one or more memories 802, one or more processors 801, a display device 803, etc., which are interconnected via a bus system and/or other form of connection mechanism (not shown). It should be noted that the components and configuration of the apparatus 800 shown in fig. 8 are exemplary only, and not limiting, and that the apparatus 800 may have other components and configurations as desired.

The memory 802 is used for storing various data and executable program instructions generated during movement of the associated movable platform, such as algorithms for storing various application programs or implementing various specific functions. May include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc.

The processor 801 may be a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the apparatus 800 to perform desired functions. For example, a processor can include one or more embedded processors, processor cores, microprocessors, logic circuits, hardware Finite State Machines (FSMs), Digital Signal Processors (DSPs), image processing units (GPUs), or a combination thereof.

The processor 801 is configured to execute the program instructions stored in the memory, causing the processor to perform the following acts:

In one example, the processor 801 is further configured to: when the sensor detects that at least one state information of the movable platform changes, outputting prompt information, wherein the prompt information at least comprises data information of the changed state information; the sensor may be a plurality of sensors on the movable platform that may obtain status information of the movable platform from the sensors of the movable platform when the apparatus is an external device independent of the movable platform.

In one example, the device 800 further includes an output device that may output various information (e.g., images or sounds) to an external (e.g., user), and may include one or more of a display device, a speaker, and the like.

In one example, the apparatus 800 further includes a display unit 803, configured to obtain the prompt information and display the prompt information on a display interface.

In one example, the apparatus 800 further comprises a communication interface (not shown) for performing communication between various components of the apparatus 800 and other apparatuses outside the system, for example, when the apparatus is an external device, the communication interface may be used for performing communication with the movable platform, so as to enable information interaction between the two.

The communication interface may be any interface of any presently known communication protocol, such as a wired interface or a wireless interface, wherein the communication interface may include one or more serial ports, USB interfaces, ethernet ports, WiFi, wired network, DVI interfaces, device integrated interconnect modules, or other suitable various ports, interfaces, or connections. The apparatus 800 may also access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G, 5G, or a combination thereof. In one exemplary embodiment, the communication interface receives a broadcast signal or broadcast associated information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication interface further comprises a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In one example, the device 800 further includes an input device (not shown) which may be a device used by a user to input instructions, and which may include one or more of a keyboard, a trackball, a mouse, a microphone, a touch screen, and the like, or other input devices of control buttons.

Further, the processor 801 is further configured to: acquiring an operation instruction input by a user, wherein the operation instruction is used for displaying a setting window on a display interface of a display device; controlling the display device to display a setting window on a display interface based on the operation instruction; acquiring a setting instruction input by a user, wherein the setting instruction is used for setting changed state information as data information of the changed state information; adjusting an update threshold of the restricted area data of the movable platform based on the changed data information of the state information.

In one example, the processor 801 is configured to update the restricted area data of the movable platform according to the adjusted update threshold, including:

updating the restricted area data based on the movement distance and the adjusted update threshold.

In one example, the processor 801 is configured to adjust the update threshold of the restricted area data of the movable platform when the state information of the movable platform changes, and includes:

and inputting the changed data information of the state information of the movable platform into a trained neural network for processing, and acquiring an update threshold of the limited area data of the movable platform. Optionally, the trained neural network comprises a trained RBF neural network.

In one example, the trained neural network is trained based on movement data information of the movable platform during at least one historical movement. For example, the mobile data information includes first mobile data information and second mobile data information, and the first mobile data information includes at least one of the following information: the mobile platform comprises a mobile speed when the data of the limited area is triggered to be updated in the historical mobile process of the mobile platform, a CPU load when the data of the limited area is triggered to be updated, a search radius of the limited area when the data of the limited area is triggered to be updated, a country where the data of the limited area is triggered to be updated, the density of the limited area of the current area where the data of the limited area is triggered to be updated, and the temperature of an onboard computing unit when the data of the limited area is triggered to be updated, wherein the second mobile data information comprises the mobile distance from the beginning of updating the data of the limited area to the completion of updating the mobile platform. Further, in the training process of the trained neural network, the first mobile data information is used as input information of an input layer of the neural network, and the second mobile data information is used for correcting an output result of an output layer of the neural network.

In one example, the processor is configured to update the restricted area data based on the movement distance and the adjusted update threshold, including: when the movement distance is larger than or equal to the updating threshold, triggering to update the limited area data so as to update the limited area data of the limited area with the distance between the limited area data and the movable platform within the limited area searching radius range into the limited area database of the movable platform.

The device in the embodiment of the application is used for realizing the method, so that the method has the advantages that when the state information of the movable platform changes, the updating threshold value of the limited area data of the movable platform is adjusted, the updating threshold value of the limited area data is adjusted in real time, the limiting function of the limited area on the movement of the movable platform is guaranteed, the vehicle computing power is saved, and the problem that the movable platform mistakenly breaks through the forbidden activity area is effectively avoided.

In addition, the embodiment of the present application also provides a computer storage medium, such as a computer readable storage medium, on which a computer program is stored. One or more computer program instructions may be stored on the computer storage medium, the processor may execute the program instructions stored by the memory to implement the functions of the embodiments of the present application (implemented by the processor) described herein and/or other desired functions, such as to perform the corresponding steps of the

methods

200 and 700 of updating restricted area data according to the embodiments of the present application, and various applications and various data, such as various data used and/or generated by the applications, etc., may also be stored in the computer-readable storage medium.

For example, the computer-readable storage medium may include, for example, a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM), a portable compact disc read only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic Gate circuit for implementing a logic function on a data signal, an asic having a suitable combinational logic Gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), and the like.

Although the example embodiments have been described herein with reference to the accompanying drawings, it is to be understood that the above-described example embodiments are merely illustrative and are not intended to limit the scope of the present application thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the scope or spirit of the present application. All such changes and modifications are intended to be included within the scope of the present application as claimed in the appended claims.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another device, or some features may be omitted, or not executed.

In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it should be appreciated that in the description of exemplary embodiments of the present application, various features of the present application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various inventive aspects. However, the method of the present application should not be construed to reflect the intent: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.

It will be understood by those skilled in the art that all of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where such features are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

The various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some of the modules according to embodiments of the present application. The present application may also be embodied as apparatus programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present application may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.

Claims

1. A method for updating restricted area data, the method being applied to a movable platform, the method comprising:

2. The method of claim 1, wherein obtaining the updated threshold for the current restricted area data based on the current state information of the movable platform comprises:

and inputting the current state information of the movable platform into a trained neural network for processing, and acquiring the update threshold of the current limited area data.

3. The method of claim 2, in which the trained neural network comprises a trained RBF neural network.

4. The method of claim 2 or 3, wherein the trained neural network comprises a neural network having a feedback structure, wherein the neural network comprises an input side, a hidden layer, an output layer, and a feedback connection, wherein the output layer feeds back output information to the hidden layer through the feedback connection to adjust input information of the hidden layer.

5. The method of any of claims 2 to 4, wherein the trained neural network is trained based on movement data information of the movable platform during at least one historical movement.

6. The method of claim 5, wherein the mobile data information comprises first mobile data information and second mobile data information, the first mobile data information comprising at least one of: the mobile platform comprises a mobile speed when the data of the limited area is triggered to be updated in the historical mobile process of the mobile platform, a CPU load when the data of the limited area is triggered to be updated, a search radius of the limited area when the data of the limited area is triggered to be updated, a country where the data of the limited area is triggered to be updated, the density of the limited area of the current area where the data of the limited area is triggered to be updated, and the temperature of an airborne computing unit when the data of the limited area is triggered to be updated, wherein the second mobile data information comprises the flight distance from the time when the data of the limited area is updated to the time when the data of the limited area is updated.

7. The method of claim 6, wherein the first mobile data information is used as input information of an input layer of the neural network during the training process of the trained neural network, and the second mobile data information is used for modifying an output result of an output layer of the neural network.

8. The method of claim 1, wherein determining whether to update the restricted area data based on the movement distance and the update threshold comprises:

when the movement distance is larger than or equal to the updating threshold value, triggering to update the limited area data so as to update the limited area data of the limited area with the distance between the movable platform and the limited area within the limited area searching radius range into the limited area data.

9. The method of claim 8, wherein the movable platform comprises a first memory and a second memory, wherein the restricted area data is stored in the first memory and the restricted area data is read from the second memory.

10. The method of claim 1, wherein the method further comprises:

acquiring the geometric relationship between the movable platform and at least one surrounding limited area of the movable platform according to the current position information of the movable platform and the updated limited area data;

determining a target limiting area according to the geometric relationship, wherein the target limiting area is the limiting area which is closest to the movable platform in the horizontal moving direction in the at least one limiting area;

and controlling the movement parameters of the movable platform according to the geometrical relation between the target limit area and the movable platform, wherein the flight parameters comprise at least one of moving speed, moving height and moving direction.

11. A movable platform, comprising:

the power mechanism is used for enabling the movable platform to move;

a memory for storing executable program instructions;

12. The movable platform of claim 11, wherein the processor is configured to obtain the updated threshold for the current restricted area data based on current state information of the movable platform, comprising:

13. The movable platform of claim 12, wherein the trained neural network comprises a trained RBF neural network.

14. The movable platform of claim 12 or 13, wherein the trained neural network comprises a neural network having a feedback structure, wherein the neural network comprises an input side, a hidden layer, an output layer, and a feedback connection, the output layer feeding back output information to the hidden layer through the feedback connection to adjust input information of the hidden layer.

15. The movable platform of any one of claims 12-14, wherein the trained neural network is trained based on movement data information of the movable platform during at least one historical flight.

16. The movable platform of claim 15, wherein the movement data information comprises first movement data information and second movement data information, the first movement data information comprising at least one of: the mobile platform comprises a mobile speed when the data of the limited area is triggered to be updated in the historical mobile process of the mobile platform, a CPU load when the data of the limited area is triggered to be updated, a search radius of the limited area when the data of the limited area is triggered to be updated, a country where the data of the limited area is triggered to be updated, the density of the limited area of the current area where the data of the limited area is triggered to be updated, and the temperature of an onboard computing unit when the data of the limited area is triggered to be updated, wherein the second mobile data information comprises the mobile distance from the beginning of updating the data of the limited area to the completion of updating the mobile platform.

17. The movable platform of claim 16, wherein the first movement data information is used as input information for an input layer of the neural network during training of the trained neural network, and the second movement data information is used to modify an output result of an output layer of the neural network.

18. The movable platform of claim 11, wherein determining whether to update the restricted area data based on the movement distance and the update threshold comprises:

when the movement distance is larger than or equal to the updating threshold, triggering to update the limited area data so as to update the limited area data of the limited area with the distance between the movable platform and the limited area within the limited area searching radius range into a limited area database.

19. The movable platform of claim 18, comprising a first memory and a second memory, wherein the restricted area database is stored in the first memory and the restricted area data is data read from the second memory.

20. The movable platform of claim 11, wherein the processor is further to:

and controlling the movement parameters of the movable platform according to the geometrical relation between the target limit area and the movable platform, wherein the movement parameters at least comprise at least one of movement speed, movement height and movement direction.

21. A method of updating restricted area data, the method comprising:

22. The method of claim 21, wherein adjusting the update threshold for the restricted area data of the movable platform when the state information of the movable platform changes comprises:

when the sensor detects that at least one state information of the movable platform changes, outputting prompt information, wherein the prompt information at least comprises data information of the changed state information;

and the display device of the movable platform acquires the prompt information and displays the prompt information on a display interface.

23. The method of claim 22, wherein adjusting the update threshold for the moveable platform's restricted area data when a change in the moveable platform's state information occurs comprises:

acquiring an operation instruction input by a user, wherein the operation instruction is used for displaying a setting window on a display interface of a display device;

controlling the display device to display a setting window on a display interface based on the operation instruction;

acquiring a setting instruction input by a user, wherein the setting instruction is used for setting changed state information as data information of the changed state information;

adjusting an update threshold of the restricted area data of the movable platform based on the changed data information of the state information.

24. The method of claim 21, wherein updating the restricted area data of the movable platform based on the adjusted update threshold comprises:

25. The method of claim 21 or 22, wherein adjusting the update threshold of the moveable platform's restricted area data when a change in the moveable platform's state information occurs comprises:

and inputting the changed data information of the state information of the movable platform into a trained neural network for processing, and acquiring an update threshold of the limited area data of the movable platform.

26. The method of claim 25, wherein the trained neural network comprises a trained RBF neural network.

27. The method of claim 25, wherein the trained neural network comprises a neural network having a feedback structure, wherein the neural network comprises an input side, a hidden layer, an output layer, and a feedback connection, the output layer feeding output information back to the hidden layer through the feedback connection to adjust input information of the hidden layer.

28. The method of claim 25, wherein the trained neural network is trained based on movement data information of the movable platform during at least one historical movement.

29. The method of claim 28, wherein the mobile data information comprises first mobile data information and second mobile data information, the first mobile data information comprising at least one of: the mobile platform comprises a mobile speed when the data of the limited area is triggered to be updated in the historical mobile process of the mobile platform, a CPU load when the data of the limited area is triggered to be updated, a search radius of the limited area when the data of the limited area is triggered to be updated, a country where the data of the limited area is triggered to be updated, the density of the limited area of the current area where the data of the limited area is triggered to be updated, and the temperature of an onboard computing unit when the data of the limited area is triggered to be updated, wherein the second mobile data information comprises the mobile distance from the beginning of updating the data of the limited area to the completion of updating the mobile platform.

30. The method of claim 29, wherein the first mobile data information is used as input information of an input layer of the neural network during the training process of the trained neural network, and the second mobile data information is used for modifying an output result of an output layer of the neural network.

31. The method of claim 24, wherein updating the restricted area data based on the movement distance and the adjusted update threshold comprises:

when the movement distance is larger than or equal to the updating threshold, triggering to update the limited area data so as to update the limited area data of the limited area with the distance between the limited area data and the movable platform within the limited area searching radius range into the limited area database of the movable platform.

32. An apparatus for updating restricted area data, the apparatus comprising:

a memory for storing executable program instructions;

33. The apparatus of claim 31, wherein the processor is further configured to: when the sensor detects that at least one state information of the movable platform changes, outputting prompt information, wherein the prompt information at least comprises data information of the changed state information;

the device also comprises a display device which is used for acquiring the prompt information and displaying the prompt information on a display interface.

34. The apparatus of claim 33, wherein the processor is further configured to:

35. The apparatus of claim 32, wherein the processor is configured to update the restricted area data of the movable platform based on the adjusted update threshold, comprising:

36. The apparatus of claim 34 or 35, wherein the processor is configured to adjust the update threshold of the moveable platform's restricted area data when a change in the moveable platform's state information occurs, comprising:

37. The apparatus of claim 36, in which the trained neural network comprises a trained RBF neural network.

38. The apparatus of claim 36, wherein the trained neural network comprises a neural network having a feedback structure, wherein the neural network comprises an input side, a hidden layer, an output layer, and a feedback connection, the output layer feeding output information back to the hidden layer through the feedback connection to adjust input information of the hidden layer.

39. The apparatus of claim 36, wherein the trained neural network is trained based on movement data information of the movable platform during at least one historical movement.

40. The apparatus of claim 39, wherein the mobile data information comprises first mobile data information and second mobile data information, the first mobile data information comprising at least one of: the mobile platform comprises a mobile speed when the data of the limited area is triggered to be updated in the historical mobile process of the mobile platform, a CPU load when the data of the limited area is triggered to be updated, a search radius of the limited area when the data of the limited area is triggered to be updated, a country where the data of the limited area is triggered to be updated, the density of the limited area of the current area where the data of the limited area is triggered to be updated, and the temperature of an onboard computing unit when the data of the limited area is triggered to be updated, wherein the second mobile data information comprises the mobile distance from the beginning of updating the data of the limited area to the completion of updating the mobile platform.

41. The apparatus of claim 40, wherein the first movement data information is used as input information of an input layer of the neural network during the training process of the trained neural network, and the second movement data information is used for modifying an output result of an output layer of the neural network.

42. The apparatus of claim 35, wherein the processor is configured to update the restricted area data based on the movement distance and the adjusted update threshold, comprising:

43. A computer readable storage medium having stored thereon computer program instructions, wherein the computer program instructions, when executed by a processor, implement the method of updating restricted area data of any one of claims 1 to 10, or wherein the computer program instructions, when executed by a processor, implement the method of updating restricted area data of any one of claims 21 to 31.