Wind condition prediction method, wind condition prompting method, unmanned aerial vehicle and control terminal

Abstract

The present application relates to the technical field of unmanned aerial vehicles, and provides a wind condition prediction method, a wind condition prompt method, an unmanned aerial vehicle and a control terminal. The wind condition prediction method includes: determining the target flight speed of the unmanned aerial vehicle under the target control command based on the mapping relationship between the control command and the flight speed; detecting the target attitude parameters of the unmanned aerial vehicle if the flight speed of the unmanned aerial vehicle reaches the target flight speed under the condition of meeting the preset environmental conditions; calculating the theoretical thrust of the unmanned aerial vehicle in the horizontal direction based on the target attitude parameters; obtaining the aerodynamic resistance and actual thrust of the unmanned aerial vehicle in the horizontal direction during the flight process when flying under the target control command; predicting wind condition information based on the theoretical thrust, aerodynamic resistance and actual thrust, and sending the wind condition information to the control terminal. The present method can improve the prediction accuracy of wind condition information, help to make timely flight adjustments to the unmanned aerial vehicle, and reduce the risk of damage to the unmanned aerial vehicle.

Description

Technical Field

[0001] The present application relates to the technical field of unmanned aerial vehicles, and in particular to a wind condition prediction method, a wind condition prompt method, an unmanned aerial vehicle, and a control terminal. Background Art

[0002] The flight attitude of the UAV (such as the direction and tilt of the UAV) is an important parameter that affects the flight safety of the UAV. Usually, the factors that affect the flight attitude of the UAV include control instructions, external environmental factors, etc. For example, when the UAV receives a control instruction, it will adjust its flight attitude accordingly; or, when it is disturbed by the outside world, such as the force of the ambient wind, the UAV will change its flight attitude to generate a reaction force to resist the ambient wind in order to maintain its position.

[0003] In the related art, the pilot estimates the wind conditions (such as wind direction and wind speed) of the UAV's flight environment by observing the UAV's flight attitude, and makes corresponding adjustments to the UAV's flight based on the estimated results. However, this method relies on the pilot's experience, and the pilot needs to be able to distinguish the impact of control instructions and ambient wind on the UAV's flight attitude. In addition, in strong wind weather, it is usually difficult to effectively estimate the wind conditions of the UAV's flight environment through the flight attitude displayed on the control terminal, which may lead to misjudgment of the wind conditions. In this case, if the ambient wind causes the UAV to fly abnormally, it may delay the timing of flight adjustments to the UAV, increasing the risk of damage to the UAV. Summary of the invention

[0004] In view of this, the present application provides a wind condition prediction method, a wind condition prompt method, an unmanned aerial vehicle and a control terminal to solve the problem in the related technology that the wind conditions of the unmanned aerial vehicle's flight environment are estimated by relying on the pilot's experience, which may delay the timing of flight adjustments to the unmanned aerial vehicle and increase the risk of damage to the unmanned aerial vehicle.

[0005] A first aspect of an embodiment of the present application provides a wind condition prediction method, which is applied to an unmanned aerial vehicle, wherein the unmanned aerial vehicle is communicatively connected to a control terminal, and the wind condition prediction method includes: determining the target flight speed of the unmanned aerial vehicle under the target control instruction based on a mapping relationship between a control instruction and a flight speed; if the flight speed of the unmanned aerial vehicle reaches the target flight speed under the condition that preset environmental conditions are met, detecting the target attitude parameters of the unmanned aerial vehicle; based on the target attitude parameters, calculating the theoretical thrust of the unmanned aerial vehicle in the horizontal direction; when flying under the target control instruction, obtaining the aerodynamic resistance and actual thrust of the unmanned aerial vehicle in the horizontal direction during the flight; based on the theoretical thrust, the aerodynamic resistance and the actual thrust, predicting wind condition information, and sending the wind condition information to the control terminal.

[0006] In some embodiments, obtaining the aerodynamic drag of the unmanned aerial vehicle in the horizontal direction during flight includes: predicting the aerodynamic drag parameters of the unmanned aerial vehicle using a preset model based on the flight speed of the unmanned aerial vehicle during flight; and calculating the aerodynamic drag based on the flight speed and the aerodynamic drag parameters.

[0007] In some embodiments, the method also includes: collecting sample flight data of the unmanned aerial vehicle under the condition of meeting preset environmental conditions, the sample flight data including at least the flight speed of the unmanned aerial vehicle in different flight directions and the corresponding flight resistance; calling the initial model to predict the corresponding sample resistance parameters based on the sample flight data; training the initial model using the sample flight data to predict the sample resistance parameters corresponding to the sample flight data, and calculating the prediction error; if the prediction error is less than a preset error threshold, obtaining the preset model based on the trained initial model.

[0008] In some embodiments, obtaining the actual thrust of the UAV in the horizontal direction during flight includes: obtaining the thrust acceleration of the UAV in the horizontal direction; and calculating the actual thrust according to the thrust acceleration.

[0009] In some embodiments, predicting the wind condition information based on the theoretical thrust, the aerodynamic drag and the actual thrust includes: calculating a first difference between the theoretical thrust and the aerodynamic drag, and calculating a second difference between the first difference and the actual thrust; predicting wind speed and wind direction based on the second difference and the aerodynamic drag parameter; and determining the wind condition information according to the wind speed and the wind direction.

[0010] A second aspect of an embodiment of the present application provides a wind condition prompt method, which is applied to a control terminal, receives wind condition information sent by the unmanned aerial vehicle; and displays the wind condition information in a posture ball displayed on a display screen of the control terminal.

[0011] In some embodiments, the wind condition information includes wind speed, and the method further includes: if the wind speed is greater than or equal to a preset wind speed threshold, displaying the wind condition information in the attitude ball; if the wind speed is less than the preset wind speed threshold, not displaying the wind condition information in the attitude ball.

[0012] In some embodiments, the method also includes: determining the predicted risk level of the unmanned aerial vehicle based on the wind condition information and / or the flight attitude of the unmanned aerial vehicle; determining the color parameters corresponding to the predicted risk level based on the corresponding relationship between the preset risk level and the color parameters; and updating the color of the attitude mark corresponding to the flight attitude displayed on the display screen based on the determined color parameters.

[0013] A third aspect of an embodiment of the present application provides an unmanned aerial vehicle, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor implements the above-mentioned wind condition prompt method when executing the computer-readable instructions.

[0014] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions implement the above-mentioned wind condition prompt method when executed by a processor.

[0015] In a wind condition prediction method provided in an embodiment of the present application, the theoretical thrust in the horizontal direction corresponding to the attitude change generated by the unmanned aerial vehicle in response to the target control command when the preset environmental conditions are met is obtained, and the aerodynamic drag and actual thrust in the horizontal direction of the unmanned aerial vehicle during the flight under the target control command are obtained. Based on the theoretical thrust, aerodynamic drag and actual thrust, the wind condition information is predicted and sent to the control terminal. The method does not need to rely on the driver's experience, and in strong wind weather, it can effectively improve the prediction accuracy of wind condition information, thereby helping to make timely flight adjustments to the unmanned aerial vehicle and reduce the risk of damage to the unmanned aerial vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0017] Figure 1 This is an application scenario diagram of the wind condition prediction method and wind condition prompt method provided in the embodiments of the present application.

[0018] Figure 2 It is a flow chart for implementing the wind condition prediction method provided in the embodiment of the present application.

[0019] Figure 3 It is a flow chart for implementing the wind condition prompt method provided in an embodiment of the present application.

[0020] Figure 4 This is an example diagram of the interface of the gesture ball provided in an embodiment of the present application.

[0021] Figure 5 It is a structural schematic diagram of the wind condition prediction device provided in an embodiment of the present application.

[0022] Figure 6 It is a structural schematic diagram of the wind condition prompting device provided in an embodiment of the present application.

[0023] Figure 7 It is a schematic diagram of the structure of the unmanned aerial vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field in this application. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. It should be understood that, unless otherwise specified in this application, " / " means or. For example, A / B can represent A or B. "And / or" in this application is only a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. "At least one" means one or more. "Multiple" means two or more than two. For example, at least one of a, b or c can represent: a, b, c, a and b, a and c, b and c, a, b and c seven situations.

[0027] See also Figure 1 As shown, it is an application scenario diagram of the wind condition prediction method and the wind condition prompt method provided in the embodiment of the present application, such as Figure 1 As shown, the UAV 100 is communicatively connected to the control terminal 200 .

[0028] In the process of realizing wind condition prediction, the unmanned aerial vehicle 100 determines the target flight speed of the unmanned aerial vehicle 100 under the target control instruction based on the mapping relationship between the control instruction and the flight speed. When the flight speed of the unmanned aerial vehicle 100 reaches the target flight speed under the condition of satisfying the preset environmental conditions (without considering the influence of aerodynamic resistance and environmental wind), the unmanned aerial vehicle 100 detects the target attitude parameters of the unmanned aerial vehicle 100. The unmanned aerial vehicle 100 calculates the theoretical thrust of the unmanned aerial vehicle 100 in the horizontal direction based on the target attitude parameters. When flying under the target control instruction, the unmanned aerial vehicle 100 obtains the aerodynamic resistance and actual thrust of the unmanned aerial vehicle 100 in the horizontal direction during the flight process, predicts the wind condition information based on the theoretical thrust, aerodynamic resistance and actual thrust, and sends the wind condition information to the control terminal 200. The above-mentioned wind condition prediction method does not need to rely on the experience of the driver, and in strong wind weather, it can effectively improve the prediction accuracy of the wind condition information, thereby helping to make timely flight adjustments to the unmanned aerial vehicle and reduce the risk of damage to the unmanned aerial vehicle.

[0029] In some embodiments, the control terminal 200 receives wind condition information sent by the unmanned aerial vehicle 100, and displays the wind condition information in an attitude ball displayed on the display screen of the control terminal 200, thereby helping the pilot to understand the wind condition information in a timely manner, thereby facilitating timely flight adjustments to the unmanned aerial vehicle and reducing the risk of damage to the unmanned aerial vehicle.

[0030] In some embodiments, the unmanned aerial vehicle 100 may be a multi-rotor drone, an unmanned aircraft, etc. The control terminal 200 may be a remote controller, or a user terminal, such as any one or more of a mobile phone, a tablet computer, a wearable device, etc. The embodiments of the present application do not limit the specific device types of the unmanned aerial vehicle 100 and the control terminal 200.

[0031] In some embodiments, the communication connection method includes but is not limited to wireless communication connection. For example, the wireless channel from the unmanned aerial vehicle 100 to the control terminal 200 can be used to transmit data collected by the unmanned aerial vehicle 100, such as videos, pictures, sensor data, and status information of the unmanned aerial vehicle 100. The wireless channel from the control terminal 200 to the unmanned aerial vehicle 100 can be used to transmit remote control data, such as flight control instructions and control instructions such as return instructions, photo instructions, and video recording instructions.

[0032] Figure 1 The scenarios shown are only illustrative examples, and the wind condition prediction method or wind condition prompt method provided in this application can also be applied in other scenarios. The embodiments of this application do not limit the specific application scenarios of the wind condition prediction method and the wind condition prompt method.

[0033] See also Figure 2 The figure is a flowchart of the implementation of the wind condition prediction method provided in the embodiment of the present application. The method is applied to unmanned aerial vehicles. The embodiment of the present application is based on the method applied to Figure 1 The method is described by taking the unmanned aerial vehicle 100 in FIG.

[0034] S11: Determine the target flight speed of the UAV under the target control command based on the mapping relationship between the control command and the flight speed.

[0035] In some embodiments, during the flight of the UAV, the main factors affecting the flight speed of the UAV include air resistance, resistance caused by ambient wind, and control instructions for controlling the flight of the UAV.

[0036] Without considering the resistance caused by air resistance and ambient wind, the flight of the UAV is mainly controlled by the control instructions from the control terminal. In this case, the control instructions are transmitted to the UAV via radio signals, directly guiding the flight action and speed of the UAV. The flight control system (FCS) of the UAV receives the control instructions and parses them. The flight control system adjusts the motor speed, propeller disc tilt angle, etc. of the UAV according to the information contained in the parsed control instructions, such as the position (or angle) of the joystick, thereby changing the flight speed. This process follows the laws of physics, and without considering the resistance caused by air resistance and ambient wind, there is a mapping relationship between the control instructions and the flight speed of the UAV.

[0037] In some embodiments, without considering the air resistance (aerodynamic resistance) and the resistance caused by the ambient wind, the UAV can determine the mapping relationship between the control command and the flight speed through modeling. In this process, the UAV can obtain the flight speed of the UAV under different control commands, and determine the mapping relationship between the control command and the flight speed based on the flight speed of the UAV under different control commands and the pre-established model.

[0038] In some embodiments, the UAV may determine a target flight speed corresponding to a target control instruction based on a mapping relationship between the control instruction and the flight speed.

[0039] S12: When the preset environmental conditions are met, if the flight speed of the UAV reaches the target flight speed, the target attitude parameters of the UAV are detected.

[0040] In some embodiments, the attitude of the unmanned aerial vehicle is usually described by attitude angles, wherein the attitude angles include pitch angle, roll angle and yaw angle. The pitch angle represents the angle between the body axis (along the nose direction) of the unmanned aerial vehicle and the horizontal plane. Usually, when the nose of the unmanned aerial vehicle is up, the pitch angle is a positive value; when the nose is down, the pitch angle is a negative value, and the pitch angle can range from -90° to 90°. The roll angle is mainly used to control the left and right tilt and side flight of the unmanned aerial vehicle. It represents the angle of rotation of the unmanned aerial vehicle around its longitudinal axis and reflects the degree of roll of the unmanned aerial vehicle in the horizontal plane. The yaw angle is mainly used to control the steering and heading of the aircraft, and represents the angle between the actual heading of the unmanned aerial vehicle and the planned heading. The target attitude parameters include target pitch angle, target roll angle, target yaw angle and other data.

[0041] In some embodiments, the preset environmental condition includes a windless environment or a flight environment with a wind speed lower than a preset value, wherein the preset value can be set by a user.

[0042] In some embodiments, the UAV may detect the target attitude parameters through sensors. The specific method of detecting the target attitude parameters is not limited in the embodiments of the present application.

[0043] When the preset environmental conditions are met, the UAV responds to the target control command to adjust the motor speed, propeller disc tilt angle, etc., thereby adjusting the flight speed. When the flight speed of the UAV reaches the target flight speed, the UAV can detect the current attitude parameters and use the current attitude parameters as the target attitude parameters. Based on the target attitude parameters, the UAV can determine the component of the total thrust projected in the horizontal direction, which can also be called the thrust in the horizontal direction. Among them, the total thrust represents the total thrust generated by the UAV adjusting the motor speed, propeller disc tilt angle, etc. under the target control command.

[0044] S13: Based on the target attitude parameters, calculate the theoretical thrust of the unmanned aerial vehicle in the horizontal direction.

[0045] In some embodiments, the horizontal direction refers to a direction parallel to the ground. The theoretical thrust of the UAV in the horizontal direction refers to the component force in the horizontal direction of the total thrust of the UAV under the target control instruction when the preset environmental conditions are met.

[0046] In some embodiments, the unmanned aerial vehicle can calculate the total thrust of the unmanned aerial vehicle under the target control instruction based on the target control instruction and the power system parameters of the unmanned aerial vehicle. For example, the power system parameters may include data such as motor current and speed. The unmanned aerial vehicle can calculate the total thrust of the unmanned aerial vehicle based on data such as motor speed and power corresponding to the target control instruction, as well as the thrust-to-weight ratio of the unmanned aerial vehicle (the ratio between the total thrust of the motor and the weight of the unmanned aerial vehicle), the weight of the unmanned aerial vehicle, and other data.

[0047] In some embodiments, the UAV may calculate the horizontal component of the total thrust, or the theoretical horizontal thrust of the UAV, based on the attitude angle and the total thrust in the target attitude parameters.

[0048] S14: When flying under the target control instruction, obtaining the aerodynamic resistance and actual thrust of the unmanned aerial vehicle in the horizontal direction during the flight.

[0049] In some embodiments, the aerodynamic drag of the UAV during flight refers to the horizontal component of the force that hinders the forward movement of the UAV due to the interaction between the UAV and the air when the UAV is flying.

[0050] In actual scenarios, factors that affect the flight of unmanned aerial vehicles include not only control commands, but also external factors such as air resistance (or aerodynamic resistance) and ambient wind. In order to overcome the influence of external factors such as air resistance and ambient wind and maintain or change the motion state of the unmanned aerial vehicle, the power system of the unmanned aerial vehicle (such as propellers, motors, etc.) will generate thrust.

[0051] In some embodiments, the actual thrust represents the horizontal component of the real thrust that drives the UAV to maintain or change its motion state while overcoming external factors such as air resistance and ambient wind.

[0052] In some embodiments, the theoretical thrust represents the horizontal thrust of the unmanned aerial vehicle under preset environmental conditions (for example, not considering the influence of aerodynamic drag and ambient wind). Therefore, based on the theoretical thrust, aerodynamic drag and actual thrust, the unmanned aerial vehicle can determine whether the unmanned aerial vehicle is affected by the ambient wind and calculate the wind condition information.

[0053] In some embodiments of the present application, the aerodynamic drag of an unmanned aerial vehicle in the horizontal direction during flight is obtained, including: predicting the aerodynamic drag parameters of the unmanned aerial vehicle using a preset model based on the flight speed of the unmanned aerial vehicle during flight; and calculating the aerodynamic drag based on the flight speed and the aerodynamic drag parameters.

[0054] In some embodiments, the aerodynamic drag parameter represents a characteristic value of the drag generated by the interaction between the fluid and the surface of the UAV when the UAV flies in a fluid (such as air), and is used to measure the magnitude of the drag generated when the UAV moves in the fluid.

[0055] In some embodiments, the flight speed of the UAV is an important factor affecting the aerodynamic drag. As the flight speed increases, the interaction between the UAV and the surrounding air will also increase, resulting in greater aerodynamic drag. Based on this, the UAV can train a preset model based on the flight speed of the UAV in multiple directions, use the flight speed of the UAV as input, and use the preset model to predict the aerodynamic drag parameters of the UAV. After determining the aerodynamic drag parameters, the UAV can calculate the aerodynamic drag in the horizontal direction based on the flight speed of the UAV in the horizontal direction and the aerodynamic drag parameters. Or the UAV can calculate the actual aerodynamic drag suffered by the UAV during the flight process based on the actual flight speed of the UAV and the corresponding aerodynamic drag parameters, calculate the component of the actual aerodynamic drag in the horizontal direction, and determine the aerodynamic drag of the UAV in the horizontal direction. The embodiments of the present application do not limit the specific method of determining the aerodynamic drag of the UAV in the horizontal direction.

[0056] In some embodiments of the present application, the unmanned aerial vehicle can determine the preset model in the following manner: under the condition of satisfying the preset environmental conditions, collect the sample flight data of the unmanned aerial vehicle, the sample flight data at least including the flight speed of the unmanned aerial vehicle in different flight directions and the corresponding flight resistance; call the initial model, and predict the corresponding sample resistance parameters according to the sample flight data. Use the sample flight data to train the initial model, predict the sample resistance parameters corresponding to the sample flight data, and calculate the prediction error; if the prediction error is less than the preset error threshold, obtain the preset model based on the trained initial model.

[0057] In some embodiments, the initial model may be a deep learning model (eg, a regression model).

[0058] In some other embodiments, the initial model may also be a mathematical model (eg, a computational fluid dynamics (CFD) model), which is not limited in the embodiments of the present application.

[0059] In some embodiments of the present application, obtaining the actual thrust of the UAV in the horizontal direction during flight includes: obtaining the thrust acceleration of the UAV in the horizontal direction; and calculating the actual thrust according to the thrust acceleration.

[0060] In some embodiments, the thrust acceleration represents the horizontal acceleration of the UAV due to the actual thrust in the horizontal direction.

[0061] In some embodiments, the UAV may detect the thrust acceleration through a sensor (such as an accelerometer, a gyroscope, etc.), or calculate it through an algorithm within a flight control system. For example, the thrust acceleration in the horizontal direction may be calculated by detecting the flight speed of the UAV in the horizontal direction and by differential speed.

[0062] In some embodiments, the UAV may detect the horizontal flight speed of the UAV through a sensor (eg, a Global Positioning System).

[0063] In some embodiments, after acquiring the thrust acceleration of the UAV in the horizontal direction, the UAV can calculate the actual thrust according to the thrust acceleration and the mass of the UAV.

[0064] S15: Based on the theoretical thrust, aerodynamic drag and actual thrust, predict wind condition information, and send the wind condition information to the control terminal.

[0065] In some embodiments, wind condition information includes wind speed, wind direction, wind speed level, etc.

[0066] In some embodiments, the theoretical thrust represents the thrust of the UAV in the horizontal direction when the preset environmental conditions are met (for example, the influence of aerodynamic drag and ambient wind is not considered), and the actual thrust represents the thrust of the UAV in the horizontal direction after overcoming the air resistance and the resistance generated by the ambient wind. Therefore, the UAV can predict the wind condition information based on the theoretical thrust, aerodynamic drag and actual thrust.

[0067] In some embodiments, after the unmanned aerial vehicle obtains the predicted wind condition information, the wind condition information can be sent to the control terminal. After receiving the wind condition information, the control terminal can display the wind condition information on a display screen so that the user can understand the wind condition information in time, thereby facilitating timely flight adjustments of the unmanned aerial vehicle and reducing the risk of damage to the unmanned aerial vehicle.

[0068] In some embodiments of the present application, wind condition information is predicted based on theoretical thrust, aerodynamic drag and actual thrust, including: calculating a first difference between theoretical thrust and aerodynamic drag, and calculating a second difference between the first difference and actual thrust; predicting wind speed and wind direction based on the second difference and aerodynamic drag parameters; and determining wind condition information based on the wind speed and wind direction.

[0069] In some embodiments, the unmanned aerial vehicle can determine the remaining thrust in the horizontal direction after the unmanned aerial vehicle overcomes the air resistance based on the first difference between the theoretical thrust and the aerodynamic resistance. If the first difference is consistent with the actual thrust, it indicates that there is no wind in the environment, and the flight of the unmanned aerial vehicle is not affected by the environmental wind or is less affected. If the first difference is inconsistent with the actual thrust, it indicates that there is wind in the environment, and the flight of the unmanned aerial vehicle is affected by the environmental wind. Due to the influence of the environmental wind, a second difference is generated between the first difference and the actual thrust. The second difference is used to indicate the degree to which the flight of the unmanned aerial vehicle is affected by the environmental wind, which can also be called wind resistance. The size of the second difference indicates the size of the wind resistance, and the sign of the second difference reflects the direction of the wind resistance. For example, if the second difference is negative, it indicates that the direction of the wind resistance is consistent with the direction of the corresponding theoretical thrust; if the second difference is positive, it indicates that the direction of the wind resistance is opposite to the direction of the corresponding theoretical thrust.

[0070] In some embodiments, the UAV may use a mathematical model to predict wind speed and wind direction based on the second difference and the aerodynamic drag parameter. For example, in the process of predicting wind speed, the UAV may predict wind speed based on the formula F=1 / 2×ρ×V²×A×CD, where F represents wind resistance, ρ represents air density, V represents wind speed, A represents the reference area of ​​the UAV (usually the cross-sectional area or projected area of ​​the UAV), and CD represents the aerodynamic drag parameter. The UAV may determine the wind direction based on the direction of the wind speed.

[0071] In other embodiments, a mapping relationship between wind speed level and wind speed magnitude is pre-stored in the unmanned aerial vehicle, so that after the unmanned aerial vehicle determines the wind speed in the wind condition information, it can determine the corresponding wind speed level according to the preset mapping relationship between wind speed level and wind speed magnitude.

[0072] In the wind condition prediction method provided in the embodiment of the present application, the target flight speed of the UAV under the target control instruction is determined based on the mapping relationship between the control instruction and the flight speed; if the flight speed of the UAV reaches the target flight speed under the preset environmental conditions, the target attitude parameters of the UAV are detected; based on the target attitude parameters, the theoretical thrust of the UAV in the horizontal direction is calculated; when flying under the target control instruction, the aerodynamic drag and actual thrust of the UAV in the horizontal direction during the flight are obtained; based on the theoretical thrust, aerodynamic drag and actual thrust, the wind condition information is predicted, which can effectively improve the prediction accuracy of the wind condition information, thereby helping to make timely flight adjustments to the UAV and reduce the risk of damage to the UAV. By sending the wind condition information to the control terminal, it helps the driver to understand the impact of the ambient wind on the UAV in a timely manner, thereby making flight adjustments to the UAV and further reducing the risk of damage to the UAV.

[0073] See also Figure 3 The figure is a flowchart of the implementation of the wind condition prompt method provided in the embodiment of the present application. The method is applied to the control terminal. The embodiment of the present application is applied to the method in Figure 1 The control terminal 200 in FIG. 1 is used as an example for explanation. The method comprises the following steps.

[0074] S21: Receive wind condition information sent by the unmanned aerial vehicle.

[0075] In some embodiments, wind condition information includes wind speed, wind direction, wind speed level, etc.

[0076] S22: Displaying wind condition information in a posture ball displayed on a display screen of the control terminal.

[0077] In some embodiments, the wind condition information includes at least information such as wind speed, wind direction, and wind speed level corresponding to the wind speed.

[0078] In some embodiments, the wind condition information can be intuitively presented on the display screen of the control terminal through preset dynamic icons, for example, using an attitude ball to display the flight attitude of the UAV. The attitude ball is used to display the direction, attitude and relative position of the UAV in real time, which helps the pilot to accurately understand the flight status of the UAV. Figure 4As shown, the attitude ball 10 is a circular icon, in which there is an aircraft icon 12 representing the unmanned aerial vehicle, a compass 11, and an attitude line 13. Among them, the aircraft icon 12 is used to indicate the relative direction of the unmanned aerial vehicle, the compass 11 is used to indicate the change of the heading attitude of the unmanned aerial vehicle, and the attitude line 13 is used to indicate the flight attitude of the unmanned aerial vehicle (such as pitch attitude, roll attitude). Through the changes of the compass 11 and the attitude line 13 displayed in the attitude ball, the user can intuitively determine the direction, attitude, etc. of the unmanned aerial vehicle, thereby ensuring accurate control of the unmanned aerial vehicle. In other embodiments, the icons used to represent objects such as attitude balls or unmanned aerial vehicles can be adjusted according to actual needs. Figure 4 The above is only an example and is not limited to this in practical applications.

[0079] In some embodiments, the control terminal displays wind condition information relative to the UAV in the attitude ball based on the direction, attitude, and relative position of the UAV displayed in the attitude ball.

[0080] In some embodiments, the control terminal can display any one or more of the data such as wind speed, wind direction, wind speed level, etc. in the attitude ball. For example, the wind speed level and wind direction can be displayed in the attitude ball, or the wind speed and wind direction can be displayed in the attitude ball, or the wind speed, wind direction and wind speed level can be displayed in the attitude ball. The embodiments of the present application are not limited to this.

[0081] See also Figure 4 As shown in FIG. 1 , an example diagram of the interface of the posture ball 10 provided in the embodiment of the present application is shown. Figure 4 As shown, 11 represents a compass or a compass, which is used to indicate the change of the heading attitude of the unmanned aerial vehicle; 12 represents an aircraft icon, which is used to indicate the relative direction of the unmanned aerial vehicle. In the attitude ball 10, the relative direction of the aircraft icon is used as a reference to display other relative position and direction information; 13 represents an attitude line, which is used to indicate the flight attitude of the unmanned aerial vehicle (such as pitch attitude, roll attitude); 14 represents the control terminal icon displayed in the attitude ball 10, which is used to indicate the position and direction of the control terminal relative to the unmanned aerial vehicle; 15 represents the return point displayed in the attitude ball 10 and the position and direction of the return point relative to the unmanned aerial vehicle. 16 represents the wind speed level and wind direction relative to the unmanned aerial vehicle, wherein the number in 16 represents the wind speed level, the wind speed level is level 3, and the direction of the icon 16 represents the wind direction.

[0082] In some embodiments, the direction of the compass 11 is based on the relative direction of the aircraft icon. When the heading attitude of the UAV changes, the compass 11 will rotate around the aircraft icon 12 displayed in the attitude ball 10.

[0083] In some embodiments, the horizontal tilt state of the attitude line 13 (or the shaded area) in the attitude sphere 10 indicates the rolling attitude of the UAV. When the UAV maintains a horizontal flight attitude, the attitude line 13 is a horizontal straight line and divides the attitude sphere 10 into two equal semicircles. When the attitude line 13 tilts horizontally, it indicates that the rolling attitude of the UAV is tilted, and the tilt angle of the attitude line 13 is the same as the tilt angle of the rolling attitude. Figure 4 As shown, the UAV is affected by the ambient wind from the left side. In order to maintain the flight stability of the UAV, the UAV tilts to the left at a certain angle (the attitude line 13 or the shaded area in the attitude sphere 10 tilts to the left) to resist the influence of the ambient wind on the flight of the UAV.

[0084] In some embodiments, the display area of ​​the shaded area in the attitude ball 10 indicates the pitch attitude of the UAV. For example, if the display area of ​​the shaded area is larger than half of the area of ​​the attitude ball 10, it indicates that the nose of the UAV is tilted downward; if the display area of ​​the shaded area is smaller than half of the area of ​​the attitude ball 10, it indicates that the nose of the UAV is tilted upward. Figure 4 As shown, the display area of ​​the shaded area is smaller than half of the area of ​​the attitude ball 10, indicating that the nose of the UAV is tilted upward.

[0085] In some embodiments of the present application, if the wind speed is greater than or equal to a preset wind speed threshold, the wind condition information is displayed in the attitude ball; if the wind speed is less than the preset wind speed threshold, the wind condition information is not displayed in the attitude ball.

[0086] In some embodiments, when the wind speed of the ambient wind in the flight environment of the UAV is relatively low, the ambient wind has less impact on the flight of the UAV. Since environmental factors (such as air density, temperature, humidity, etc.) may also affect the accuracy of the calculated wind speed value during the calculation of the wind speed, such errors may be more significant when the wind speed is relatively low, resulting in poor calculation accuracy of the wind speed value.

[0087] To solve the above problem, the control terminal can set a preset wind speed threshold, and when it is determined that the wind speed is greater than or equal to the preset wind speed threshold, the wind condition information is displayed in the attitude ball. When the wind speed is less than the preset wind speed threshold, the wind condition information is not displayed in the attitude ball.

[0088] In some embodiments, the preset wind speed threshold can be customized, for example, the preset wind speed threshold can be set to 3.4 m / s. The embodiments of the present application do not limit the preset wind speed threshold.

[0089] In other embodiments, if the wind speed level is greater than or equal to the preset level threshold, the control terminal displays the wind condition information in the attitude ball. If the wind speed level is less than the preset level threshold, the control terminal does not display the wind condition information in the attitude ball. The preset level threshold can be set by user, for example, the preset level threshold can be set to level 3, and the embodiments of the present application do not limit the preset level threshold.

[0090] In some embodiments of the present application, the control terminal can provide a risk warning for the unmanned aerial vehicle so as to adjust the flight of the unmanned aerial vehicle in a timely manner to avoid damage to the unmanned aerial vehicle. For example, the control terminal can provide a risk warning by adjusting the color of the attitude mark corresponding to the flight attitude displayed on the display screen. Specifically, the control terminal can determine the predicted risk level of the unmanned aerial vehicle based on wind information and / or flight attitude; determine the color parameters corresponding to the predicted risk level based on the correspondence between the preset risk level and the color parameters; and update the color of the attitude mark corresponding to the flight attitude displayed on the display screen based on the determined color parameters.

[0091] In some embodiments, if the wind speed in the flying environment of the UAV is too high or the attitude angle of the UAV is too large, the safety and stability of the UAV flight will be affected, and even the UAV may be damaged.

[0092] In order to solve the above problems, the control terminal can pre-establish a mapping relationship between wind condition information and / or flight attitude and predicted risk level. When the predicted risk level is determined to be high based on the wind condition information and / or the flight attitude of the UAV, the control terminal can issue a risk warning to facilitate timely flight adjustments to the UAV and reduce the probability of damage to the UAV.

[0093] Taking the establishment of a mapping relationship between flight attitude and predicted risk level as an example, when the attitude angle is less than or equal to the first threshold, it indicates that the flight attitude of the UAV is in an ideal control range, and the corresponding preset risk level can be set to low risk; when the attitude angle is greater than the first threshold and less than or equal to the second threshold, it indicates that the flight attitude of the UAV is large, and the UAV may be in a maneuvering flight or strong wind state, and the corresponding preset risk level can be set to medium risk; when the attitude angle is greater than the second threshold, it indicates that the flight attitude of the UAV is close to or exceeds the maximum safe value of the attitude angle, and the flight attitude of the UAV may change drastically, and the corresponding preset risk level can be set to high risk.

[0094] The specific setting of the mapping relationship between the flight attitude and the predicted risk level is not limited in the present application embodiment. For example, the preset risk level may be further set to a range of attitude angles corresponding to medium-low risk, medium-high risk, etc.

[0095] In some embodiments, the control terminal can provide risk warnings by updating the color of the attitude mark corresponding to the flight attitude displayed on the display screen. Among them, the control terminal can pre-establish a corresponding relationship between the risk level and the color parameter. For example, when the preset risk level is low risk, the corresponding color parameter can be set to green; when the preset risk level is medium risk, the corresponding color parameter can be set to yellow; when the preset risk level is high risk, the corresponding color parameter can be set to red. By observing the color of the attitude mark corresponding to the flight attitude displayed on the display screen, the driver can estimate the risk level of the current flight process of the unmanned aerial vehicle, and when it is determined that the predicted risk level is high, timely adjust the flight status of the unmanned aerial vehicle, etc., to reduce the probability of damage to the unmanned aerial vehicle.

[0096] In some embodiments, the control terminal may use the attitude line displayed in the attitude ball as an attitude mark corresponding to the flight attitude, so that the control terminal may provide a risk warning by updating the color of the attitude line.

[0097] The wind condition prompt method provided in the embodiment of the present application helps to intuitively remind the driver of the wind condition information of the flight environment of the unmanned aerial vehicle by displaying the wind condition information in the attitude ball displayed on the display screen of the control terminal, thereby facilitating timely flight adjustments of the unmanned aerial vehicle, reducing the risk of damage to the unmanned aerial vehicle, and improving the flight safety of the unmanned aerial vehicle.

[0098] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0099] See also Figure 5 As shown in FIG. 1 , it is a structural diagram of the wind condition prediction device provided in the embodiment of the present application, which can realize the details of the wind condition prediction method in the above embodiment and achieve the same effect. Figure 5 As shown, the wind condition prediction device 500 can be applied to an unmanned aerial vehicle with a data processing function, and the wind condition prediction device 500 includes: a determination module 501, which is used to determine the target flight speed of the unmanned aerial vehicle under the target control instruction based on the mapping relationship between the control instruction and the flight speed; a detection module 502, which is used to detect the target attitude parameters of the unmanned aerial vehicle if the flight speed of the unmanned aerial vehicle reaches the target flight speed under the condition of meeting the preset environmental conditions; a calculation module 503, which is used to calculate the theoretical thrust of the unmanned aerial vehicle in the horizontal direction based on the target attitude parameters; an acquisition module 504, which is used to acquire the aerodynamic resistance and actual thrust of the unmanned aerial vehicle in the horizontal direction during the flight when flying under the target control instruction; a prediction module 505, which is used to predict wind condition information based on the theoretical thrust, aerodynamic resistance and actual thrust, and send the wind condition information to the control terminal.

[0100] For the specific definition of the wind condition prediction device 500, please refer to the definition of the wind condition prediction method above, which will not be repeated here. Each module in the above-mentioned wind condition prediction device 500 can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the unmanned aerial vehicle in the form of hardware, or can be stored in the memory in the unmanned aerial vehicle in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0101] See also Figure 6 As shown in FIG. 1 , the structure diagram of the wind condition prompting device provided in the embodiment of the present application can realize the details of the wind condition prompting method in the above embodiment and achieve the same effect. Figure 6 As shown, the wind condition prompting device 600 can be applied to a control terminal with a data processing function. The wind condition prompting device 600 includes: a receiving module 601: used to receive wind condition information sent by an unmanned aerial vehicle; a display module 602, used to display the wind condition information in a posture ball displayed on the display screen of the control terminal.

[0102] For the specific definition of the wind condition prompting device 600, please refer to the definition of the wind condition prompting method above, which will not be repeated here. Each module in the above-mentioned wind condition prompting device 600 can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the control terminal in the form of hardware, or can be stored in the memory in the control terminal in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0103] See also Figure 7 , Figure 7 The figure shows a schematic diagram of the structure of an unmanned aerial vehicle provided in an embodiment of the present application. The unmanned aerial vehicle 100 includes but is not limited to any one of a multi-rotor drone, an unmanned aerial vehicle, etc. The network where the unmanned aerial vehicle 100 is located includes but is not limited to the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN), etc.

[0104] like Figure 7 As shown, the UAV 100 includes a communication module 101, a memory 102, a processor 103, an input / output interface 104 and a bus 105. The processor 103 is coupled to the communication module 101, the memory 102 and the input / output interface 104 through the bus 105.

[0105] The communication module 101 can be a wireless communication module or a mobile communication module. The wireless communication module can provide wireless communication solutions including wireless local area networks (WLAN) (for example, wireless fidelity (Wi-Fi) network), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication technology (NFC), infrared technology (IR), etc., which are applied to the unmanned aerial vehicle 100. The mobile communication module can provide wireless communication solutions including 2G / 3G / 4G / 5G, etc., which are applied to the unmanned aerial vehicle 100.

[0106] The memory 102 may include one or more random access memories (RAM) and one or more non-volatile memories (NVM). The random access memory can be directly read and written by the processor 103, and can be used to store executable programs (such as machine instructions) of the operating system or other running programs, and can also be used to store user and application data. The random access memory may include static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM, for example, the fifth generation DDR SDRAM is generally called DDR5 SDRAM), etc.

[0107] The non-volatile memory may also store executable programs and user and application data, etc., and may be loaded into the random access memory in advance for direct reading and writing by the processor 103. The non-volatile memory may include a disk storage device and a flash memory.

[0108] The memory 102 is used to store one or more computer programs. The one or more computer programs are configured to be executed by the processor 103. The one or more computer programs include a plurality of instructions, and when the plurality of instructions are executed by the processor 103, the wind condition prompt method executed on the unmanned aerial vehicle 100 can be implemented.

[0109] In other embodiments, the UAV 100 further includes an external memory interface for connecting to an external memory to expand the storage capacity of the UAV 100 .

[0110] The processor 103 may include one or more processing units, for example, the processor 103 may include an application processor (AP), a modem processor, a graphics processor (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Different processing units may be independent devices or integrated into one or more processors.

[0111] The processor 103 provides computing and control capabilities. For example, the processor 103 is used to execute a computer program stored in the memory 102 to implement the above-mentioned wind condition prompt method.

[0112] The input / output interface 104 is used to provide a channel for user input or output. For example, the input / output interface 104 can be used to connect various input and output devices, such as a mouse, keyboard, touch device, display screen, etc., so that the user can enter information or visualize information.

[0113] The bus 105 is at least used to provide a channel for mutual communication among the communication module 101 , the memory 102 , the processor 103 , and the input / output interface 104 in the UAV 100 .

[0114] It is to be understood that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the unmanned aerial vehicle 100. In other embodiments of the present application, the unmanned aerial vehicle 100 may include more or fewer components than shown in the figure, or combine some components, or separate some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0115] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. The computer program includes program instructions. The method implemented when the program instructions are executed can refer to the wind condition prompt method in the above-mentioned embodiments of the present application.

[0116] The computer-readable storage medium may be the internal memory of the UAV described in the above embodiment, such as the hard disk or memory of the UAV. The computer-readable storage medium may also be an external storage device of the UAV, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the UAV.

[0117] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the unmanned aerial vehicle, etc.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical solution of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present application.

Claims

1. A wind condition prediction method, applied to an unmanned aerial vehicle, characterized in that: The unmanned aerial vehicle is in communication connection with a control terminal, and the wind condition prediction method comprises: Determining a target flight speed of the unmanned aerial vehicle under the target control command based on a mapping relationship between the control command and the flight speed; Under the condition that the preset environmental conditions are met, if the flight speed of the unmanned aerial vehicle reaches the target flight speed, detecting the target attitude parameters of the unmanned aerial vehicle; Calculating the theoretical thrust of the unmanned aerial vehicle in the horizontal direction based on the target attitude parameters; When flying under the target control command, obtaining the aerodynamic drag and actual thrust of the unmanned aerial vehicle in the horizontal direction during the flight; Based on the theoretical thrust, the aerodynamic drag and the actual thrust, the wind condition information is predicted, including: calculating a first difference between the theoretical thrust and the aerodynamic drag, and calculating a second difference between the first difference and the actual thrust; based on the second difference and an aerodynamic drag parameter, the wind speed and wind direction are predicted; according to the wind speed and the wind direction, the wind condition information is determined, and the wind condition information is sent to the control terminal.

2. The wind condition prediction method according to claim 1, characterized in that: Obtaining the aerodynamic drag of the unmanned aerial vehicle in the horizontal direction during flight, including: Based on the flight speed of the unmanned aerial vehicle during the flight, predicting the aerodynamic drag parameters of the unmanned aerial vehicle using a preset model; The aerodynamic drag is calculated based on the flight speed and the aerodynamic drag parameter.

3. The wind condition prediction method according to claim 2, characterized in that: The method further comprises: Under the condition that preset environmental conditions are met, collecting sample flight data of the unmanned aerial vehicle, the sample flight data at least including the flight speed of the unmanned aerial vehicle in different flight directions and the corresponding flight resistance; Calling the initial model to predict corresponding sample drag parameters according to the sample flight data; Using sample flight data to train the initial model, predicting sample drag parameters corresponding to the sample flight data, and calculating prediction errors; If the prediction error is less than a preset error threshold, the preset model is obtained based on the trained initial model.

4. The wind condition prediction method according to claim 1, characterized in that: Obtaining the actual thrust of the unmanned aerial vehicle in the horizontal direction during flight, including: Obtaining the thrust acceleration of the unmanned aerial vehicle in the horizontal direction; The actual thrust is calculated according to the thrust acceleration.

5. A wind condition prompt method, applied to a control terminal, characterized in that: The control terminal is communicatively connected to the unmanned aerial vehicle according to any one of claims 1 to 4, and the wind condition prompting method comprises: receiving wind condition information sent by the unmanned aerial vehicle; The wind condition information is displayed in a posture ball displayed on a display screen of the control terminal, and the wind condition information includes one or more of wind direction, wind speed, and wind speed level.

6. The wind condition prompting method according to claim 5, characterized in that: The wind condition information includes wind speed, and the method further includes: If the wind speed is greater than or equal to a preset wind speed threshold, the wind condition information is displayed in the attitude ball; If the wind speed is less than the preset wind speed threshold, the wind condition information is not displayed in the attitude ball.

7. The wind condition prompting method according to claim 5, characterized in that: The method further comprises: Determining a predicted risk level of the unmanned aerial vehicle according to the wind condition information and / or the flight attitude of the unmanned aerial vehicle; Determining the color parameter corresponding to the predicted risk level according to the preset correspondence between the risk level and the color parameter; According to the determined color parameters, the color of the posture mark corresponding to the flight posture displayed on the display screen is updated.

8. An unmanned aerial vehicle, characterized in that: The method comprises a memory, a processor and computer-readable instructions stored in the memory and executable on the processor, wherein the computer-readable instructions, when executed by the processor, implement the wind condition prediction method according to any one of claims 1 to 4.

9. A control terminal, characterized in that: The method comprises a memory, a processor and computer-readable instructions stored in the memory and executable on the processor, wherein the computer-readable instructions, when executed by the processor, implement the wind condition prompting method as described in any one of claims 5 to 7.

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