Wind speed and wind direction estimation method and device based on attitude change of unmanned aerial vehicle, unmanned aerial vehicle equipment and storage medium

Abstract

The invention relates to the technical field of wind speed and direction estimation. The invention discloses a wind speed and wind direction estimation method and device based on unmanned aerial vehicle attitude change, unmanned aerial vehicle equipment and a storage medium. The accuracy of wind speed and wind direction estimation can be improved. The wind speed and wind direction estimation method based on the attitude change of the unmanned aerial vehicle comprises the steps of obtaining attitude parameters and physical parameters of the unmanned aerial vehicle; performing wind direction estimation processing on the attitude parameters by adopting a wind direction estimation algorithm to obtain a wind direction; and performing wind speed estimation processing on the attitude parameters and the physical parameters by adopting a wind speed estimation algorithm to obtain the wind speed.

Description

Technical Field

[0001] The present application relates to the technical field of wind speed and direction estimation. More specifically, the present application relates to a method, apparatus, drone equipment, and storage medium for estimating wind speed and direction based on drone attitude changes. Background Art

[0002] Traditional methods for estimating wind speed and direction involve integrating a wind sensor onto a drone. After the drone, carrying the wind sensor, flies to a predetermined location, the wind sensor directly measures (or estimates) wind direction and speed. However, the airflow generated by the drone's rotor blades can significantly disrupt the aerodynamic field around the aircraft, causing measurement errors in the wind sensor. Consequently, the wind direction and speed estimates generated by existing technologies remain inaccurate, and further improvement is needed. Summary of the Invention

[0003] The purpose of the embodiments of the present application is to provide a method, apparatus, drone device, and storage medium for estimating wind speed and direction based on drone attitude changes, which can improve the accuracy of estimated wind speed and direction. The embodiments of the present application are mainly achieved through the following technical solutions: A first aspect of the embodiments of the present application provides a method for estimating wind speed and direction based on a change in the attitude of a drone, comprising: Get the attitude parameters and physical parameters of the drone; Using a wind direction estimation algorithm to perform wind direction estimation processing on the attitude parameters to obtain the wind direction; A wind speed estimation algorithm is used to perform wind speed estimation processing on the posture parameters and the physical parameters to obtain the wind speed.

[0004] According to one embodiment of the present application, a wind direction estimation algorithm is used to perform wind direction estimation processing on the posture parameter, and the step of obtaining the wind direction includes: Using a first preset algorithm in the wind direction estimation algorithm to perform flight direction estimation processing on the attitude parameters to obtain the flight direction of the UAV; A second preset algorithm in the wind direction estimation algorithm is used to perform wind direction estimation processing on the flight direction to obtain the wind direction.

[0005] According to one embodiment of the present application, the step of performing flight direction estimation processing on the attitude parameters using the first preset algorithm in the wind direction estimation algorithm to obtain the flight direction of the UAV includes: performing angle estimation processing on the roll angle and the pitch angle of the attitude parameters using the angle estimation algorithm of the first preset algorithm to obtain a target angle, where the target angle is the angle between the observation direction of the UAV and the projection of a target vector on the XY plane, and the target vector is obtained by multiplying the vector of the roll angle by the vector of the pitch angle; Performing position estimation processing on the roll angle and pitch angle of the attitude parameters using the azimuth estimation algorithm of the first preset algorithm to obtain a target position, wherein the target position is relative to the observation direction, and the target vector is located at the position of the UAV; The flight direction estimation algorithm of the first preset algorithm is used to perform flight direction estimation processing on the heading angle of the attitude parameters, the target angle and the target position to obtain the flight direction of the UAV.

[0006] According to one embodiment of the present application, after the step of performing wind direction estimation processing on the attitude parameters using a wind direction estimation algorithm to obtain the wind direction, the wind speed and direction estimation method based on the attitude change of the drone further includes: A difference calculation process is performed on the heading angle and the wind direction to obtain a relative wind direction.

[0007] According to one embodiment of the present application, a wind speed estimation algorithm is used to perform wind speed estimation processing on the posture parameter and the physical parameter, and the step of obtaining the wind speed includes: Using a third preset algorithm of the wind speed estimation algorithm to perform resistance estimation processing on the attitude parameters and the physical parameters to obtain a target resistance of the UAV; A fourth preset algorithm of the wind speed estimation algorithm is used to perform wind speed estimation processing on the target resistance to obtain the wind speed.

[0008] According to one embodiment of the present application, the step of performing resistance estimation processing on the attitude parameters and the physical parameters using the third preset algorithm of the wind speed estimation algorithm to obtain the target resistance of the drone includes: Performing angle calculation processing on the attitude parameters to obtain the inclination angle of the UAV; A resistance estimation process is performed on the gravitational acceleration of the physical parameter, the total mass of the physical parameter, and the inclination angle to obtain the target resistance.

[0009] According to one embodiment of the present application, the fourth preset algorithm of the wind speed estimation algorithm is used to perform wind speed estimation processing on the target resistance, and the step of obtaining the wind speed includes: Get the air density; A wind speed estimation process is performed on the air density, the target resistance, the drag coefficient of the physical parameter, and the projection area of the UAV of the physical parameter to obtain the wind speed.

[0010] A second aspect of the embodiments of the present application provides a device for estimating wind speed and direction based on a change in the attitude of a drone, comprising: Parameter acquisition module, used to obtain the attitude parameters and physical parameters of the drone; A wind direction estimation module is used to perform wind direction estimation processing on the posture parameters using a wind direction estimation algorithm to obtain the wind direction; The wind speed estimation module is used to perform wind speed estimation processing on the posture parameters and the physical parameters using a wind speed estimation algorithm to obtain the wind speed.

[0011] The third aspect of the embodiments of the present application provides a drone device for estimating wind speed and direction based on drone attitude changes, including: an inertial measurement unit, a microcomputer controller and a memory, wherein the inertial measurement unit is used to obtain the attitude parameters of the drone and transmit the attitude parameters to the microcomputer controller, and the microcomputer controller calls and runs the computer program stored in the memory to execute the steps of the wind speed and direction estimation method based on drone attitude changes provided in the first aspect of the embodiments of the present application.

[0012] In a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which is used to store a computer program, and the computer program enables a computer to execute the steps of the wind speed and direction estimation method based on drone attitude changes provided in the first aspect of the embodiment of the present application.

[0013] The beneficial effects of the embodiments of the present application include: The embodiments of the present application obtain the attitude parameters and physical parameters of the drone; use a wind direction estimation algorithm to estimate the wind direction of the attitude parameters to obtain the wind direction; and use a wind speed estimation algorithm to estimate the wind speed of the attitude parameters and physical parameters to obtain the wind speed. Compared with the existing technology that uses wind sensors to measure wind direction and wind speed, the embodiments of the present application use the attitude parameters and physical parameters of the drone itself to estimate wind direction and wind speed. Because the attitude parameters and physical parameters of the drone itself are less affected by the airflow caused by the operation of the drone, the measurement error of wind direction and wind speed caused by the strong disturbance of the aerodynamic field around the drone body can be effectively reduced, thereby improving the accuracy of the estimated wind speed and wind direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the conventional technology, the following briefly introduces the drawings required for use in the embodiments or the conventional technology descriptions. 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 creative work.

[0015] Figure 1 This is a flowchart of a method for estimating wind speed and direction based on a change in the attitude of a drone according to some embodiments of the present application; Figure 2This is the reference map for the actual north wind speed and the predicted north wind speed in this application; Figure 3 This is the reference map for the actual and predicted easterly wind speeds in this application; Figure 4 A reference diagram showing the relationship between the tilt angle of the drone and the wind speed; Figure 5 This is a principle block diagram of a device for estimating wind speed and direction based on the attitude change of a drone according to some embodiments of the present application; Figure 6 This is a principle block diagram of some embodiments of the drone device for estimating wind speed and direction based on drone attitude changes of the present application. DETAILED DESCRIPTION

[0016] To make the above-mentioned objects, features, and advantages of the present application more clearly understood, the specific embodiments of the present application are described in detail below with reference to the accompanying drawings. The following description sets forth many specific details to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar improvements without violating the scope of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0017] It should be noted that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. In the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0018] The terms "exemplary" or "for example" are used to indicate an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0019] The terms "comprises," "comprising," or any other variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, product, or apparatus.

[0020] Unless otherwise defined, all technical and scientific terms used in the specification of this application have the same meaning as commonly understood by those skilled in the art to which this application belongs. 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. The term "and / or" used in the specification of this application includes any and all combinations of one or more of the relevant listed items.

[0021] The specific implementation of this application is further described below with reference to the accompanying drawings.

[0022] refer to Figure 1 FIG. 1 is a flow chart of a method for estimating wind speed and direction based on the change of the attitude of a drone according to the first aspect of the embodiment of the present application. Figure 1 The wind speed and direction estimation method based on the change of the UAV attitude includes: S1. Obtain the attitude parameters and physical parameters of the drone.

[0023] The attitude parameters include roll angle, pitch angle and heading angle.

[0024] The physical parameters include gravitational acceleration, total mass, drag coefficient, and the projected area of the drone. These physical parameters are the physical parameters of the drone itself. In other embodiments, the physical parameters may also include other content, which can be specifically set by those skilled in the art based on actual needs.

[0025] S2. Using a wind direction estimation algorithm to perform wind direction estimation processing on the posture parameters to obtain wind direction.

[0026] The wind direction estimation algorithm includes a first preset algorithm and a second preset algorithm. The first preset algorithm is used to estimate the flight direction of the UAV based on the attitude parameters to obtain the flight direction of the UAV; the second preset algorithm is used to estimate the wind direction based on the flight direction to obtain the wind direction. The wind direction estimation algorithm can be understood as a wind estimation method based on the relationship between the inclination angle and the wind vector function.

[0027] Furthermore, step S2 includes: using the first preset algorithm in the wind direction estimation algorithm to perform flight direction estimation processing on the attitude parameters to obtain the flight direction of the drone; using the second preset algorithm in the wind direction estimation algorithm to perform wind direction estimation processing on the flight direction to obtain the wind direction.

[0028] Furthermore, the first preset algorithm in the wind direction estimation algorithm is used to perform flight direction estimation processing on the attitude parameters, and the step of obtaining the flight direction of the UAV includes: using the angle estimation algorithm of the first preset algorithm to perform angle estimation processing on the roll angle and pitch angle of the attitude parameters to obtain the target angle, where the target angle is the angle between the observation direction of the UAV and the projection of the target vector on the XY plane, and the target vector is obtained by multiplying the vector of the roll angle and the vector of the pitch angle; using the azimuth estimation algorithm of the first preset algorithm to perform position estimation processing on the roll angle and pitch angle of the attitude parameters to obtain the target position, where the target position is the position of the target vector located on the UAV relative to the observation direction; using the flight direction estimation algorithm of the first preset algorithm to perform flight direction estimation processing on the heading angle of the attitude parameters, the target angle and the target position to obtain the flight direction of the UAV.

[0029] Furthermore, the angle estimation algorithm of the first preset algorithm is used to perform angle estimation processing on the roll angle and pitch angle of the attitude parameter, and the calculation formula for obtaining the target angle step is: ;in, is the target angle; is the observation direction of the UAV, ; Is perpendicular to the ground flat; is the target vector; is the vector of the roll angle; is the roll angle; is the vector of the pitch angle; is the pitch angle; Is parallel to the ground flat. It can also be understood as perpendicular to the ground The normal unit vector of the plane. The target angle is in the interval [0°, 180°].

[0030] Furthermore, the calculation formula of the roll angle vector is: .

[0031] Furthermore, the calculation formula of the pitch angle vector is: .

[0032] Furthermore, the position estimation algorithm of the first preset algorithm is used to perform position estimation processing on the roll angle and pitch angle of the attitude parameter, and the calculation formula for the step of obtaining the target position is: ; in, is the target location; The target vector is located on the left side of the UAV relative to the observation direction; The target vector is located to the right of the UAV relative to the observation direction; Except and All locations except yes The normal unit vector of the plane, ; yes flat.

[0033] Furthermore, the flight direction estimation algorithm of the first preset algorithm is used to perform flight direction estimation processing on the heading angle of the attitude parameter, the target angle, and the target position, and the calculation formula for the step of obtaining the flight direction of the UAV is: ; in, is the flight direction of the drone; is the heading angle.

[0034] Furthermore, the wind direction estimation process is performed on the flight direction using the second preset algorithm in the wind direction estimation algorithm, and the calculation formula for obtaining the wind direction step is: ; in, is the wind direction.

[0035] The wind calculated in the embodiment of the present application is a plane wind. When the drone is in a hovering state or has no speed in the X and Y directions, the opposite direction of the drone's flight direction is the direction of the wind. .

[0036] Furthermore, after the step of performing wind direction estimation processing on the attitude parameters using a wind direction estimation algorithm to obtain the wind direction, the wind speed and direction estimation method based on the attitude change of the drone also includes: performing difference calculation processing on the heading angle and the wind direction to obtain the relative wind direction.

[0037] Furthermore, the heading angle and the wind direction are subjected to difference calculation processing to obtain the relative wind direction. The calculation formula is: ; in, is the relative wind direction. The relative wind direction is the wind direction relative to the nose of the drone.

[0038] Furthermore, the coefficients of the first function expression are different under different relative wind directions, and the first function expression is: ;in, is the first coefficient; is the second coefficient. For example, when the relative wind direction is 0°, the first coefficient is 2.29, the second coefficient is 0.71; when the relative wind direction is 45°, the first coefficient is 2.99, the second coefficient is 0.58; when the relative wind direction is 90°, the first coefficient is 1.94, the second coefficient is 0.82; when the relative wind direction is 180°, the first coefficient is 2.57, the second coefficient is 1.23; when the relative wind direction is 225°, the first coefficient is 0.53, the second coefficient is 1.21; when the relative wind direction is 270°, the first coefficient is 0.58, the second coefficient In other embodiments, the relative wind direction, the relationship between the first coefficient and the second coefficient, and their specific values can be set by those skilled in the art according to actual needs.

[0039] The present embodiment employs a variety of functional models to fit and analyze the relationship between wind speed and inclination angle. The fitting methods used include polynomial functions, exponential functions, logarithmic functions, and power function models based on the least squares method. By comparing and evaluating the various fitting results, the power function model with the best fitting effect was ultimately selected as the functional expression for calculating wind speed, i.e., the first functional expression.

[0040] S3. Use a wind speed estimation algorithm to perform wind speed estimation processing on the posture parameters and the physical parameters to obtain wind speed.

[0041] The wind speed estimation algorithm includes a third preset algorithm and a fourth preset algorithm. The third preset algorithm is used to perform drag estimation processing on the attitude parameters and the physical parameters to obtain the target drag of the drone; the fourth preset algorithm is used to perform wind speed estimation processing on the target drag to obtain the wind speed. The wind speed estimation algorithm can be understood as a wind estimation method based on force balance.

[0042] Furthermore, step S3 includes: using the third preset algorithm of the wind speed estimation algorithm to perform resistance estimation processing on the attitude parameters and the physical parameters to obtain the target resistance of the drone; using the fourth preset algorithm of the wind speed estimation algorithm to perform wind speed estimation processing on the target resistance to obtain the wind speed.

[0043] Furthermore, the third preset algorithm of the wind speed estimation algorithm is used to perform resistance estimation processing on the attitude parameters and the physical parameters, and the step of obtaining the target resistance of the UAV includes: performing angle calculation processing on the attitude parameters to obtain the inclination angle of the UAV; performing resistance estimation processing on the gravitational acceleration of the physical parameters, the total mass of the physical parameters and the inclination angle to obtain the target resistance.

[0044] Furthermore, the attitude parameters are subjected to angle calculation processing to obtain the inclination angle of the drone using the following calculation formula: ; in, is the tilt angle of the drone; is the normal unit vector of the XY plane parallel to the ground, .

[0045] Furthermore, resistance estimation processing is performed on the gravity acceleration of the physical parameter, the total mass of the physical parameter, and the inclination angle, and a calculation formula for obtaining the target resistance is: ; in, is the target resistance; is the acceleration due to gravity; is the total mass. The total mass refers to the total mass of the entire drone.

[0046] Furthermore, the fourth preset algorithm of the wind speed estimation algorithm is used to perform wind speed estimation processing on the target resistance, and the steps of obtaining the wind speed include: obtaining air density; performing wind speed estimation processing on the air density, the target resistance, the drag coefficient of the physical parameters and the projected area of the drone of the physical parameters to obtain the wind speed.

[0047] Furthermore, the air density, the target resistance, the drag coefficient of the physical parameter, and the projected area of the UAV of the physical parameter are subjected to wind speed estimation processing, and the calculation formula for obtaining the wind speed step is: ; in, is the wind speed; is the air density; is the projected area of the UAV; is the drag coefficient.

[0048] During hovering and vertical takeoff and landing, the drone has no ground speed, so the drone's flight speed is equal to the wind speed. Furthermore, the drone's projected area and drag coefficient must meet pre-set conditions, which are calculated using the following formula: ;in, is a key coefficient obtained from a flight experiment of the UAV.

[0049] The setting of the preset conditions can reduce the errors caused by the calculation of the projection area of the UAV and the drag coefficient.

[0050] The calculation formula of the target resistance, the calculation formula of the wind speed and the calculation formula of the preset conditions constitute the key coefficients and inclination The functional relationship between the two makes it possible for the embodiment of the present application to be able to The key coefficients can be accurate estimate of .

[0051] In the above-mentioned embodiment, the embodiment of the present application realizes the estimation of wind direction and wind speed through the attitude parameters and physical parameters of the drone itself. Since the attitude parameters and physical parameters of the drone itself are less affected by the airflow caused during the operation of the drone, the measurement error of wind direction and wind speed caused by the strong disturbance generated by the aerodynamic field around the drone body can be effectively reduced, thereby improving the accuracy of estimating wind speed and wind direction.

[0052] Furthermore, the drone of the embodiment of the present application does not require additional load wind direction sensors and wind speed sensors, which can reduce the structural burden of the drone, reduce energy consumption, and improve the duration and maneuverability of the drone in estimating wind direction and wind speed.

[0053] In the embodiment of the present application, the accuracy of the wind speed estimation can reach within ±1m / s, and the accuracy of the wind direction estimation can reach within ±30°.

[0054] In some embodiments, the key coefficient and the tilt angle satisfy the following second functional equation: ; in, is the third coefficient; The natural constant An exponential function with base ; is the fourth coefficient; is the fifth coefficient.

[0055] In some embodiments, the method for estimating wind speed and direction based on drone attitude changes further includes inputting the attitude parameters into a machine learning model for estimation processing to obtain the wind direction and wind speed. The method of inputting the attitude parameters into a machine learning model for estimation processing to obtain the wind direction and wind speed can be understood as a wind estimation method based on machine learning.

[0056] The machine learning model is a random forest model.

[0057] The machine learning model is trained on a historical data set, and only the data when the drone is in the hovering period is retained. 80% of the data set in the historical data is selected for training the machine learning model, and the remaining 20% is retained for testing the machine learning model. The trained model is applied to field data, and the field data is collected at a different time than the historical data, and the field data has the same data format as the historical data. Then, the prediction results of the machine learning model are compared with the wind direction and wind speed obtained in steps S2 and S3 to evaluate the accuracy and stability of the machine learning model. In addition, the embodiment of the present application also uses the mean and standard deviation of the correlation coefficient and the root mean square error as evaluation indicators of the machine learning model.

[0058] Figure 2 and Figure 3 The performance of the machine learning model trained using the historical dataset is demonstrated when the drone is in a hovering state. Figure 2 and Figure 3 The results show that the machine learning model has good predictive ability, and the training set in the historical data set (i.e. Figure 2 and Figure 3 The “training” in the historical dataset and the test set in the historical dataset (i.e. Figure 2 and Figure 3 The coefficient of determination ( ) are all greater than 0.9.

[0059] When the root mean square error is used to evaluate the performance of the machine learning model, the root mean square error value obtained for the north-south wind prediction is 1.02, and the root mean square error value obtained for the east-west wind prediction is 0.93.

[0060] This embodiment uses the RandomForestRegressor function in scikit-learn (an open-source machine learning library based on Python) in Python (Python is a computer programming language) as the random forest model. In the configuration of the random forest model, the number of trees is set to 100 to ensure sufficient ensemble diversity, the maximum depth of each tree is unrestricted to allow for sufficient growth and capture of complex patterns in the data, and the random seed is fixed at 42 to ensure reproducible results.

[0061] The wind speed and direction estimation method based on drone attitude changes proposed in the embodiment of the present application can not only achieve wind field estimation with higher temporal and spatial resolution, and is suitable for dynamic detection of turbulence and local wind fields in complex terrain or urban environments, but also can perform real-time wind field detection in areas that are susceptible to interference or cannot be deployed (such as above forests, high altitudes or dangerous areas), and has good adaptability and deployment flexibility.

[0062] In some embodiments, the method for estimating wind speed and direction based on the change in the attitude of the drone further includes using a wind sensor to measure the wind direction and the wind speed. When the wind sensor is used to measure the wind speed, the corresponding relationship between the tilt angle of the drone and the wind speed can be referred to Figure 4 As shown, in Figure 4 In FIG, 0°, 45°, 90°, 180°, 225°, and 270° represent relative wind directions. In other embodiments, the correspondence between the tilt angle of the drone and the wind speed can also be other data, which can be specifically set based on actual experiments by those skilled in the art.

[0063] The embodiments of the present application use wind tunnel experiments to verify the data measured by the wind sensor. In the wind tunnel experiment, there is a certain deviation between the data measured by the wind sensor and the actual wind speed. This deviation is mainly attributed to the interference effect generated by the drone rotor. Through systematic analysis of the experimental data, it was found that this interference has a predictable regularity and can be quantified using a mathematical function. Under different load conditions and different relative wind directions, the wind speed measured by the wind sensor and the actual wind speed set by the wind tunnel experiment show a stable linear relationship.

[0064] During the wind tunnel test, the wind speed can be from 1 m / s to 10 m / s, and the wind speed level can be set to ten levels. In order to reduce the attitude response differences caused by changes in the direction of the drone's nose when evaluating the wind direction, the wind tunnel test also set six relative wind direction conditions, namely relative wind directions of 0°, 45°, 90°, 180°, 225° and 270°. Under all experimental conditions, the drone was in a stable hovering state and continued to fly for more than one minute under various wind speed and direction combinations. Through this wind tunnel test, the calculation formulas for the target angle, the roll angle vector, the pitch angle vector, the target position, the flight direction of the drone, the wind direction, the relative wind direction, the tilt angle of the drone, the target resistance, the wind speed and the preset conditions were verified.

[0065] In other embodiments, the specific settings of the wind speed, wind speed level and relative wind direction used in the wind tunnel experiment can be set by those skilled in the art according to actual needs, and this application does not impose further restrictions on this.

[0066] In the wind tunnel experiment, the actual wind speed of the wind tunnel experiment is used to perform linear fitting on the readings of the wind sensor, and the data measured by the wind sensor is calibrated using the fitted linear equation.

[0067] refer to Figure 5 The figure shows a principle block diagram of a wind speed and direction estimation device based on the attitude change of a drone according to the second aspect of the embodiment of the present application. Figure 5 In the embodiment, the wind speed and direction estimation device 100 based on the change of the attitude of the drone includes: Parameter acquisition module 101, used to obtain the attitude parameters and physical parameters of the UAV; A wind direction estimation module 102 is configured to perform wind direction estimation processing on the posture parameters using a wind direction estimation algorithm to obtain a wind direction; The wind speed estimation module 103 is configured to perform wind speed estimation processing on the posture parameters and the physical parameters using a wind speed estimation algorithm to obtain wind speed.

[0068] The attitude parameters include roll angle, pitch angle and heading angle. The physical parameters include gravitational acceleration, total mass, drag coefficient and projected area of the UAV.

[0069] refer to Figure 6 As shown in FIG, it is a principle block diagram of a drone device for estimating wind speed and direction based on the change of drone attitude provided by the third aspect of the embodiment of the present application. Figure 6In the embodiment, the drone device 200 for estimating wind speed and direction based on drone attitude changes includes: an inertial measurement unit 201, a microcomputer controller 202 and a memory 203. The inertial measurement unit 201 is used to obtain the attitude parameters of the drone (including roll angle, pitch angle and compass angle) and transmit the attitude parameters to the microcomputer controller 202. The microcomputer controller 202 calls and runs the computer program stored in the memory 203 to execute the steps of the wind speed and direction estimation method based on drone attitude changes provided in the first aspect of the above-mentioned embodiment of the present application.

[0070] In a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which is used to store a computer program, and the computer program enables a computer to execute the steps of the wind speed and direction estimation method based on drone attitude changes provided in the first aspect of the embodiment of the present application.

[0071] The technical features of the above embodiments can be combined without changing the basic principles of this application. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0072] The above embodiments merely illustrate several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of patent protection for the present application shall be determined by the appended claims.

Claims

1. A method for estimating wind speed and direction based on the attitude change of a drone, characterized in that: include: Get the attitude parameters and physical parameters of the drone; Using a wind direction estimation algorithm to perform wind direction estimation processing on the attitude parameters to obtain the wind direction; A wind speed estimation algorithm is used to perform wind speed estimation processing on the posture parameters and the physical parameters to obtain the wind speed.

2. The method for estimating wind speed and direction based on the attitude change of a drone according to claim 1, characterized in that: The wind direction estimation algorithm is used to perform wind direction estimation processing on the posture parameters, and the steps of obtaining the wind direction include: Using a first preset algorithm in the wind direction estimation algorithm to perform flight direction estimation processing on the attitude parameters to obtain the flight direction of the UAV; A second preset algorithm in the wind direction estimation algorithm is used to perform wind direction estimation processing on the flight direction to obtain the wind direction.

3. The method for estimating wind speed and direction based on the change of UAV attitude according to claim 2, characterized in that: The step of performing flight direction estimation processing on the attitude parameters using the first preset algorithm in the wind direction estimation algorithm to obtain the flight direction of the UAV includes: performing angle estimation processing on the roll angle and the pitch angle of the attitude parameters using the angle estimation algorithm of the first preset algorithm to obtain a target angle, where the target angle is the angle between the observation direction of the UAV and the projection of a target vector on the XY plane, and the target vector is obtained by multiplying the vector of the roll angle by the vector of the pitch angle; Performing position estimation processing on the roll angle and pitch angle of the attitude parameters using the azimuth estimation algorithm of the first preset algorithm to obtain a target position, wherein the target position is relative to the observation direction, and the target vector is located at the position of the UAV; The flight direction estimation algorithm of the first preset algorithm is used to perform flight direction estimation processing on the heading angle of the attitude parameters, the target angle and the target position to obtain the flight direction of the UAV.

4. The method for estimating wind speed and direction based on the change of the attitude of the UAV according to claim 3 is characterized in that: After the step of performing wind direction estimation processing on the attitude parameters using a wind direction estimation algorithm to obtain the wind direction, the method for estimating wind speed and direction based on the attitude change of the drone further includes: A difference calculation process is performed on the heading angle and the wind direction to obtain a relative wind direction.

5. The method for estimating wind speed and direction based on the attitude change of a drone according to claim 1, characterized in that: The wind speed estimation algorithm is used to perform wind speed estimation processing on the posture parameters and the physical parameters, and the step of obtaining the wind speed includes: Using a third preset algorithm of the wind speed estimation algorithm to perform resistance estimation processing on the attitude parameters and the physical parameters to obtain a target resistance of the UAV; A fourth preset algorithm of the wind speed estimation algorithm is used to perform wind speed estimation processing on the target resistance to obtain the wind speed.

6. The method for estimating wind speed and direction based on the change of the attitude of the UAV according to claim 5, characterized in that: The step of performing resistance estimation processing on the attitude parameters and the physical parameters using the third preset algorithm of the wind speed estimation algorithm to obtain the target resistance of the UAV includes: Performing angle calculation processing on the attitude parameters to obtain the inclination angle of the UAV; A resistance estimation process is performed on the gravitational acceleration of the physical parameter, the total mass of the physical parameter, and the inclination angle to obtain the target resistance.

7. The method for estimating wind speed and direction based on the attitude change of a drone according to claim 6, characterized in that: The step of performing wind speed estimation processing on the target resistance using the fourth preset algorithm of the wind speed estimation algorithm to obtain the wind speed includes: Get the air density; A wind speed estimation process is performed on the air density, the target resistance, the drag coefficient of the physical parameter, and the projection area of the UAV of the physical parameter to obtain the wind speed.

8. A wind speed and direction estimation device based on the attitude change of a drone, characterized in that: include: Parameter acquisition module, used to obtain the attitude parameters and physical parameters of the drone; A wind direction estimation module is used to perform wind direction estimation processing on the posture parameters using a wind direction estimation algorithm to obtain the wind direction; The wind speed estimation module is used to perform wind speed estimation processing on the posture parameters and the physical parameters using a wind speed estimation algorithm to obtain the wind speed.

9. A UAV device for estimating wind speed and direction based on the change of UAV attitude, characterized in that: include: An inertial measurement unit, a microcomputer controller, and a memory, wherein the inertial measurement unit is used to obtain the attitude parameters of the drone and transmit the attitude parameters to the microcomputer controller, and the microcomputer controller calls and runs the computer program stored in the memory to execute the steps of the wind speed and direction estimation method based on drone attitude changes as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Used to store a computer program, which enables a computer to execute the steps of the wind speed and direction estimation method based on the change of the attitude of the drone as described in any one of claims 1 to 7.

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