Wind speed calculation method, wind speed estimator and drone

Abstract

The present invention relates to a wind speed measurement method, a wind speed estimator, and an unmanned aerial vehicle (UAV). The wind speed measurement method includes determining the UAV's current wind resistance interference through system identification based on flight data and attribute data; and calculating the wind speed of the UAV's flight environment based on the wind resistance interference and the UAV's inherent wind resistance. Utilizing the principles of system identification, the method implements wind speed measurement through parameter identification without relying on additional wind speed sensors or external databases. This method saves hardware costs while avoiding additional computing power and real-time performance issues. The method is simple and cost-effective.

Description

Technical field

[0001] The present invention relates to the technical field of unmanned aerial vehicles (UAVs), and in particular to a wind speed measurement method, a wind speed estimator, and a UAV. [Background Technology]

[0002] As a highly adaptable, low-cost, and fast and convenient hovering aerial vehicle, drones are widely used in many different situations. They can play an important role by carrying different types of functional components.

[0003] During flight, drones are subject to wind interference. When wind speeds or forces are low, the flight control system's inherent robustness can withstand wind interference and ensure stable flight. However, the range of wind forces that a flight control system can adjust to or resist is limited.

[0004] When wind speeds exceed the upper limit of a drone's tolerance, the stability of the flight control system becomes difficult to maintain, making it easy for the drone to be unable to return home or even crash. This is especially true for aerial photography drones, as the quality of their footage can be severely affected when wind speeds are high.

[0005] Therefore, wind speed detection is a very important function. Based on the wind speed detection and estimation of drones, it can provide drone users with better early warning and effectively avoid accidents.

[0006] Current wind speed detection or estimation methods can be broadly categorized into two types: direct airflow velocity measurement using wind speed sensors and estimation using pre-built databases or big data-based methods. However, direct airflow velocity measurement using wind speed or wind force sensors requires adding additional sensors to the drone, increasing its production cost. Database-building or big data calculation methods, on the other hand, consume significant computing power, increasing the operational burden on the flight control system. Furthermore, loading the database onto the aircraft consumes significant memory and is time-consuming, significantly impacting the real-time performance of wind speed measurement.

[0007] Therefore, new low-cost wind speed detection methods are urgently needed. [Summary of the invention]

[0008] In order to solve the above technical problems, embodiments of the present invention provide a wind speed measurement method, a wind speed estimator, and a drone that do not rely on a database or a new wind speed sensor.

[0009] To solve the above technical problems, an embodiment of the present invention provides a wind speed calculation method. The wind speed calculation method includes:

[0010] Based on the flight data and attribute data of the UAV, the current wind resistance interference of the UAV is determined by system identification; the flight data includes: attitude angle, flight speed, acceleration and flight altitude of the UAV;

[0011] The attribute data includes: the mass of the UAV, the inherent drag coefficient, and a nonlinear function for calculating the frontal area;

[0012] The wind speed of the flight environment of the UAV is calculated according to the wind resistance interference and the inherent wind resistance of the UAV.

[0013] Optionally, determining the current wind resistance interference of the drone through system identification based on the flight data and attribute data of the drone includes:

[0014] Constructing a system identification model of the UAV, wherein the parameter to be identified in the system identification model is the current equivalent drag coefficient of the UAV;

[0015] By using an online identification method, solving the corresponding equivalent drag coefficient according to the current flight data of the UAV and the attribute data;

[0016] The calculating the wind speed of the flight environment of the UAV according to the wind resistance interference and the inherent wind resistance of the UAV includes:

[0017] The wind speed of the flight environment of the UAV is calculated according to the difference between the equivalent drag coefficient and the inherent drag coefficient of the UAV.

[0018] Optionally, solving the equivalent drag coefficient corresponding to the current flight data and attribute data by an online identification method specifically includes:

[0019] Discretizing the system identification model to form corresponding discrete equations;

[0020] Recursively calculate the equivalent wind resistance of the drone based on the preset initial values, the drone's current attitude angle, flight speed, and acceleration;

[0021] Converting the equivalent wind resistance into an equivalent drag coefficient according to the current frontal area and air density of the UAV;

[0022] The frontal area is obtained by calculating the current attitude angle of the UAV and a nonlinear function for calculating the frontal area, and the air density is obtained by calculating the current flight altitude of the UAV.

[0023] Optionally, the equivalent drag coefficient is represented by an equivalent drag coefficient component in the x-direction and an equivalent drag coefficient component in the y-direction, and the wind speed is represented by a wind speed component in the x-direction and a wind speed component in the y-direction; the x-direction and the y-direction are perpendicular to each other and are in the same plane as the drone.

[0024] Optionally, calculating the wind speed of the flight environment of the UAV based on the wind resistance interference and the inherent wind resistance of the UAV specifically includes:

[0025] The wind speed of the UAV's flight environment is calculated using the following formula:

[0026]

[0027] Among them, V wx is the wind speed component in the x direction of the flight environment of the UAV, V wy V is the wind speed component in the y direction of the UAV’s flight environment, x is the speed of the UAV in the x direction, V y is the speed of the UAV in the y direction, ρ is the air density at the flight altitude, S fb is the frontal area of ​​the UAV when it flies in the x direction, S rl is the frontal area of ​​the UAV when it flies in the y direction, C x is the equivalent drag coefficient component in the x direction, C y is the equivalent drag coefficient component in the y direction, C dx is the inherent drag coefficient of the UAV in the x direction, C dy is the inherent drag coefficient of the UAV in the y direction.

[0028] Optionally, the inherent drag coefficient of the UAV in the x-direction and the inherent drag coefficient in the y-direction are determined by least squares fitting based on flight data of the UAV in a windless room.

[0029] Optionally, the system identification model is expressed by the following formula:

[0030]

[0031] in, is the acceleration of the drone in the x direction, is the acceleration of the drone in the y direction, V x is the speed of the UAV in the x direction, V y is the speed of the UAV in the y direction,

[0032] T is the propeller thrust, θ is the pitch angle, φ is the roll angle, ρ is the air density at the flight altitude, Sfb is the frontal area of ​​the UAV when it flies in the x direction, S rl is the frontal area of ​​the UAV when it flies in the y direction, C x is the equivalent drag coefficient component in the x direction, C y is the equivalent drag coefficient component in the y direction, m is the mass of the UAV, and w x is the model uncertainty in the x direction, w y is the model uncertainty in the y direction.

[0033] Optionally, the frontal area is calculated and determined by the following formula:

[0034] S fb =S fb0 (1+f fb (θ,φ))

[0035] S rl =S rl0 (1+f rl (θ,φ))

[0036] Among them, S fb is the frontal area of ​​the UAV when it flies in the x direction, S rl is the windward area of ​​the UAV when it flies in the x direction; S fb0 is the windward area of ​​the UAV when it flies along the x direction when the attitude angle is 0; S rl0 is the windward area of ​​the UAV when it flies along the y direction when the attitude angle is 0; f fb (θ,φ) and f rl (θ, φ) is a nonlinear function; θ is the pitch angle; φ is the roll angle.

[0037] Optionally, the propeller thrust is calculated using the following formula:

[0038]

[0039] Among them, a z is the acceleration of the drone in the z direction, g is the acceleration due to gravity; the z direction is perpendicular to the plane formed by the x direction and the y direction; θ is the pitch angle; φ is the roll angle; and m is the mass of the drone.

[0040] Optionally, the method further includes:

[0041] The wind direction is calculated based on the wind speed components in the x and y directions using the following formula:

[0042] β=ψ+arctan2(-V wx ,-V wy )

[0043] Among them, ψ is the yaw angle of the UAV, β is the wind direction, V wx is the wind speed component in the x direction, v wy is the wind speed component in the y direction.

[0044] Optionally, the method further includes: issuing a warning signal when the wind speed in the flight environment of the UAV meets a preset warning condition.

[0045] Optionally, when the wind speed in the flight environment of the UAV meets a preset warning condition, issuing a warning signal includes:

[0046] The following calculation formula is used to determine whether the preset warning conditions are met:

[0047]

[0048] Among them, V wx is the wind speed component in the x direction, V wy is the wind speed component in the y direction, V thr is the safety wind speed threshold;

[0049] When the preset warning conditions are met, a warning signal is issued;

[0050] When the preset warning condition is not met, the wind speed of the flight environment of the UAV continues to be detected.

[0051] Another embodiment of the present invention provides a wind speed estimator, wherein the wind speed estimator includes:

[0052] A system identification unit, the system identification unit being configured to receive flight data and attribute data of the UAV and identify and determine a current wind resistance interference of the UAV based on the flight data and attribute data;

[0053] The flight data includes: the attitude angle, flight speed, acceleration and flight altitude of the UAV; the attribute data includes: the mass of the UAV, the inherent wind resistance coefficient and the nonlinear function used to calculate the frontal area;

[0054] A wind speed estimation unit is connected to the system identification unit and is used to calculate the wind speed of the flight environment of the drone based on the wind resistance interference and the inherent wind resistance of the drone.

[0055] Optionally, a preset system identification model is stored in the system identification unit, and the parameter to be identified of the system identification model is an equivalent drag coefficient;

[0056] The system identification unit is used to solve the corresponding equivalent drag coefficient according to the current flight data and the attribute data through an online identification method.

[0057] Optionally, the system identification unit is further configured to:

[0058] Discretizing the system identification model to form corresponding discrete equations;

[0059] Recursively calculate the equivalent wind resistance of the drone based on the preset initial values, the drone's current attitude angle, flight speed, and acceleration;

[0060] Converting the equivalent wind resistance into an equivalent drag coefficient according to the current frontal area and air density of the UAV;

[0061] The frontal area is obtained by calculating the current attitude angle of the UAV and a nonlinear function for calculating the frontal area, and the air density is obtained by calculating the current flight altitude of the UAV.

[0062] Optionally, the equivalent drag coefficient is represented by an equivalent drag coefficient component in the x-direction and an equivalent drag coefficient component in the y-direction, and the wind speed is represented by a wind speed component in the x-direction and a wind speed component in the y-direction; the x-direction and the y-direction are perpendicular to each other and are in the same plane as the drone.

[0063] Optionally, the system identification model is expressed by the following formula:

[0064]

[0065] in, is the acceleration of the drone in the x direction, is the acceleration of the drone in the y direction, V x is the speed of the UAV in the x direction, V y is the speed of the UAV in the y direction,

[0066] T is the propeller thrust, θ is the pitch angle, φ is the roll angle, ρ is the air density at the flight altitude, S fb is the frontal area of ​​the UAV when it flies in the x direction, S rl is the frontal area of ​​the UAV when it flies in the y direction, C x is the equivalent drag coefficient component in the x direction, C y is the equivalent drag coefficient component in the y direction, m is the mass of the UAV, and w x is the model uncertainty in the x direction, w y is the model uncertainty in the y direction.

[0067] Optionally, the wind speed estimation unit is further configured to receive the current attitude angle, flight speed, flight altitude, inherent drag coefficient, and a nonlinear function for calculating the frontal area of ​​the UAV, and calculate the wind speed of the flight environment of the UAV using the following formula:

[0068]

[0069] Among them, V wx is the wind speed component in the x direction of the flight environment of the UAV, V wy V is the wind speed component in the y direction of the UAV’s flight environment, x is the speed of the UAV in the x direction, V y is the speed of the UAV in the y direction, ρ is the air density at the flight altitude, S fb is the frontal area of ​​the UAV when it flies in the x direction, S rl is the frontal area of ​​the UAV when it flies in the y direction, C x is the equivalent drag coefficient component in the x direction, C y is the equivalent drag coefficient component in the y direction, C dx is the inherent drag coefficient of the UAV in the x direction, C dy is the inherent drag coefficient of the UAV in the y direction.

[0070] Optionally, the wind speed estimator further comprises: an early warning unit;

[0071] The early warning unit is used to send a warning signal when the wind speed in the flight environment of the UAV meets a preset warning condition.

[0072] Optionally, the early warning unit is further configured to:

[0073] The following calculation formula is used to determine whether the preset warning conditions are met:

[0074]

[0075] Among them, V wx is the wind speed component in the x direction, V wy is the wind speed component in the y direction, V thr is the safety wind speed threshold;

[0076] When the preset warning conditions are met, a warning signal is issued;

[0077] When the preset warning condition is not met, the wind speed of the flight environment of the UAV continues to be detected.

[0078] Another embodiment of the present invention provides an unmanned aerial vehicle (UAV). The UAV includes a main body, one or more sensors disposed on the main body, a memory, and a flight control system. The memory stores computer-executable program instructions, which, when invoked by the flight control system, cause the flight control system to use flight data acquired from the sensors and attribute data from the memory to execute the wind speed measurement method described above.

[0079] Optionally, the flight control system is further configured to convert the wind speed of the flight environment of the UAV into wind direction, and display the wind speed and wind direction on a remote control device corresponding to the UAV.

[0080] Compared with the existing technology, the wind speed measurement method provided in the embodiment of the present invention utilizes the principle of system identification. Without relying on new wind speed sensors and external databases, it realizes the wind speed measurement process in the form of identification parameters, which not only saves the cost of hardware equipment, but also does not bring additional computing power burden and real-time problems. The method is simple and low-cost.

[0081] Furthermore, the results of wind speed measurement can be applied to the early warning function to prompt or alarm the user, effectively reducing the probability of flight accidents.

Brief Description of the Drawings

[0082] One or more embodiments are exemplarily illustrated by pictures in the corresponding drawings. These exemplifications do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings are represented as similar elements. Unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0083] Figure 1 A schematic diagram of an application environment of an embodiment of the present invention;

[0084] Figure 2 A functional block diagram of a drone provided by an embodiment of the present invention;

[0085] Figure 3 A schematic diagram of the display interface of an RC remote controller provided in an embodiment of the present invention;

[0086] Figure 4 A schematic diagram of a display interface of a smart terminal provided in an embodiment of the present invention;

[0087] Figure 5 A functional block diagram of a wind speed estimator provided by an embodiment of the present invention;

[0088] Figure 6 A flow chart of a method for calculating wind speed provided by an embodiment of the present invention;

[0089] Figure 7 A flow chart of a method for recursive calculation of identification parameters provided by an embodiment of the present invention;

[0090] Figure 8 A flow chart of a method for calculating wind speed provided by another embodiment of the present invention;

[0091] Figure 9 A flowchart of a method for a calculation process performed by a flight control system provided by an embodiment of the present invention;

[0092] Figure 10 A graph showing wind speed changes over time provided by an embodiment of the present invention;

[0093] Figure 11 A graph showing wind direction changing over time, provided by an embodiment of the present invention. [Specific implementation method]

[0094] For ease of understanding of the present invention, the present invention will be described in more detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that when an element is described as being "fixed to" another element, it can be directly on the other element, or there can be one or more centered elements therebetween. When an element is described as being "connected to" another element, it can be directly connected to the other element, or there can be one or more centered elements therebetween. The orientation or positional relationship indicated by the terms "upper", "lower", "inner", "outer", "bottom" etc. used in this specification is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0095] Unless otherwise defined, all technical and scientific terms used in this specification have the same meanings as those commonly understood by those skilled in the art to which this invention belongs. The terms used in this specification and in the description of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" as used in this specification includes any and all combinations of one or more of the relevant listed items.

[0096] In addition, the technical features involved in different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0097] System identification is a method of system control that relies on the system's input-output time functions to determine and describe the data model of the system's behavior, thereby enabling prediction of the system's behavior. During the system identification process, the specific data model is determined based on prior knowledge and combined with a parameter identification process. Using a number of calculated parameters to be identified, it is possible to estimate external disturbances to the entire system, and then obtain the required parameters through a series of appropriate transformation methods. This external disturbance can be an interference imposed on a specific motion system, such as the impact of wind disturbances on a drone during flight.

[0098] Figure 1 The application environment provided by the embodiment of the present invention. Figure 1 As shown, the application environment takes a drone system as an example, including a drone 10, a remote control device 20 and a wireless network 30.

[0099] The drone 10 can be any type of unmanned aerial vehicle powered by any type of power (e.g., electricity), including but not limited to quadcopter drones, fixed-wing aircraft, and helicopter models. In this embodiment, a quadcopter drone is used as an example. The drone 10 can be equipped with a number of different functional modules. These modules can be software modules, hardware modules, or a combination of software and hardware, modular devices that implement one or more functions.

[0100] The remote control device 20 can be any type of device used to establish a communication connection with the drone and control the drone, such as an RC remote controller. The RC remote controller can be equipped with one or more different user interaction devices to collect user commands or display and feedback information to the user based on these user interaction devices, thereby enabling interaction between the user and the drone.

[0101] These interactive devices include, but are not limited to, buttons, scroll wheels, display screens, touch screens, mice, speakers, and joysticks. For example, the remote control device 20 may be equipped with a display screen to receive user remote control commands for the drone and display aerial images to the user via the display screen, or present a corresponding simulated driving interface to the user, displaying one or more flight parameters, such as flight speed, heading, or remaining battery power.

[0102] In other embodiments, the remote control device 20 may also be implemented by a smart terminal. Such smart terminals include, but are not limited to, smartphones, tablet computers, laptops, and wearable devices. The smart terminal establishes a communication connection with the drone by running a specially configured app client or webpage, enabling data transmission and reception between the drone and the drone, thereby functioning as the remote control device 20.

[0103] Wireless network 30 can be a wireless communication network based on any type of data transmission principle for establishing a data transmission channel between two nodes. For example, a Bluetooth network, a WiFi network, a wireless cellular network, or a combination thereof, operating in different signal frequency bands. The specific frequency band or network type used by wireless network 30 depends on the communication equipment used by drone 10 and remote control device 20.

[0104] Figure 2 This is a functional block diagram of the drone 10 provided in an embodiment of the present invention. In some embodiments, Figure 2 As shown, in order to achieve the most basic flight requirements of the drone 10 , the functional modules carried by the drone 10 at least include: a sensor 11 , a memory 12 and a flight control system 13 .

[0105] Sensors 11 are installed within the main body of the drone and are used to detect the motion parameters of the drone during flight. These sensors, such as a six-axis gyroscope and an accelerometer, are essential for the design and manufacture of drone 10 and are used to monitor the current motion state of drone 10 and effectively control its flight.

[0106] The memory 12 is a non-volatile computer-readable storage medium, such as at least one disk storage device, flash memory device, or other non-volatile solid-state memory device. It has a program storage area and a data storage area, each for storing corresponding data information, such as non-volatile software programs, non-volatile computer executable programs and modules stored in the program storage area, and computational processing results and captured image information stored in the data storage area.

[0107] The flight control system 13 is the core of the drone's flight control. It can utilize any type of processor as the core for logic processing and computation. It is responsible for acquiring data, performing logical operations, and distributing the results. It also changes the drone's flight state based on user commands and ensures a safe and controllable flight state.

[0108] On the one hand, the flight control system 13 can obtain one or more types of collected data from the sensor 11 and, through a predetermined data fusion or analysis method, analyze and determine a number of data related to the drone (such as attitude angle, acceleration, and flight speed) as the basis for controlling the drone's motion state. On the other hand, the flight control system 13 is also connected to the memory 12 and calls the corresponding software program or computer executable program in the memory 12 to perform the corresponding logical operations and make corresponding calculations and judgments.

[0109] For example, to realize the function of wind speed warning, during the flight of the UAV, the flight control system 13 can read the data information related to the UAV, use the principle of system identification to estimate the wind interference currently suffered by the UAV, and output the corresponding wind speed estimation value. Then, the output wind speed estimation value is compared with the preset warning condition to determine whether a warning signal needs to be triggered at this time.

[0110] After triggering the warning signal, the drone 10 will feed back to the remote control device 20 via the wireless network 30. After receiving the warning signal, the remote control device 20 can display corresponding warning information through the interactive device to remind the operator to pay attention to flight safety and land at a suitable location in time.

[0111] For example, when the remote control device 20 is an RC remote controller, the following may be used: Figure 3 The display interface shown in FIG. 1 prompts the user in the center of the simulated driving interface that “the wind speed is high”. When the remote control device 20 is a smart terminal, such as Figure 4 As shown, a prompt message (Tips) can be displayed in a local area of ​​the display screen of the smart terminal. Alternatively, a special alarm tone can be played through the speaker of the remote control device 20 to prompt a warning of the current excessive wind speed.

[0112] Based on the wind speed of the flight environment of the UAV provided by the wind speed estimator, in other embodiments, the flight control system 13 can also convert it into wind direction data and provide it to the remote control device 20, and the wind speed and wind direction are displayed by interactive devices such as the display screen of the remote control device 20, so that the operator can promptly know the current wind conditions in the flight airspace.

[0113] exist Figure 1 In the application environment shown, only the application of wind speed observation and warning function on the UAV system is shown. It can be understood by those skilled in the art that the functional module that realizes the wind speed observation and warning function can also be carried on other types of mobile vehicles (such as remote control vehicles), and the same or similar warning functions mentioned above can be realized by receiving data information related to the mobile vehicle and calculating the motion interference of the mobile vehicle. The inventive concept disclosed in the embodiment of the present invention is not limited to Figure 1 Applied on the UAV system shown.

[0114] Based on the inventive concept of using a wind speed estimator to calculate wind speed interference in the flight environment of a drone, as disclosed in the embodiments of the present invention, those skilled in the art may readily consider adjusting, replacing, or changing one or more steps or parameters to construct alternative models, depending on practical needs or the drone's usage scenario. These alternative models are obtained by those skilled in the art through reasonable deduction based on the present invention and considering different aspects of the drone.

[0115] For example, the intensity of interference can be quantitatively observed by monitoring the change in the drone's attitude angle while hovering. Alternatively, methods such as the force balance principle and the interference observation principle can be used to determine the amount of wind interference on the drone's operation, thereby estimating the wind speed to which the drone is subject.

[0116] The following details the process of wind speed measurement based on the system identification principle. Figure 5 This is a functional block diagram of a wind speed estimator provided by an embodiment of the present invention. Figure 5 As shown, the wind speed estimator includes a system identification unit 1311 and a wind speed estimation unit 1312 .

[0117] The system identification unit 1311 is configured to receive flight data and attribute data related to the drone and determine the drone's current wind resistance interference through system identification. The system identification unit 1311 can be implemented by a processor capable of executing logical judgment steps (e.g., a flight control system) by invoking computer software program instructions related to system identification pre-stored in memory.

[0118] The flight data includes: the attitude angle, flight speed, acceleration and flight altitude of the UAV; the attribute data includes: the mass of the UAV, the inherent drag coefficient and the nonlinear function used to calculate the frontal area.

[0119] System identification unit 1311 uses system identification to quantitatively determine the interference experienced by the drone. Generally, it can be assumed that the interference experienced by the drone primarily comes from wind in the flight airspace. Therefore, the interference experienced can be treated as equivalent to wind resistance, thereby calculating the wind resistance interference.

[0120] Specifically, the system identification unit 1311 uses a system identification model constructed by some prior knowledge (such as the dynamic change equation of the speed of the drone, etc.). In the system identification model, the parameter to be identified can be the equivalent drag coefficient.

[0121] The equivalent drag coefficient is a parameter related to wind resistance interference, used to characterize the relationship between a drone and the wind resistance it experiences. Specifically, once the equivalent drag coefficient is determined, combined with various data related to the drone's properties (i.e., attribute data) and motion state (i.e., flight data) collected by the drone's sensors, the wind resistance at that moment can be calculated.

[0122] The wind speed estimation unit 1312 is connected to the system identification unit 1311 and receives the wind resistance interference and calculates the wind speed of the UAV's flight environment based on the change in wind resistance relative to the UAV's inherent wind resistance. The specific wind speed calculation process can be determined based on the form of the input wind resistance interference and can be completed using any type of conversion method.

[0123] The wind speed estimation unit 1312 can be implemented by a processor capable of executing logic judgment steps (such as a flight control system) by calling computer software program instructions related to wind speed calculation pre-stored in a memory.

[0124] In some embodiments, to facilitate calculation and representation, two mutually perpendicular x and y directions can be constructed on the plane where the drone is located, and the equivalent drag coefficient component and wind speed component in these two directions can be calculated respectively to complete the wind speed estimation process.

[0125] Specifically, based on the prior knowledge of the UAV's force, speed dynamics, etc., we can construct the system identification model shown in the following formula (1):

[0126]

[0127] in, is the rate of change of the UAV’s velocity in the x direction (i.e., acceleration), is the velocity change rate of the UAV in the y direction, V x is the speed of the UAV in the x direction, V y is the speed of the drone in the y direction, T is the propeller thrust, θ is the pitch angle, φ is the roll angle, ρ is the air density at the flight altitude, S fb is the frontal area of ​​the UAV when it flies in the x direction, S rl is the frontal area of ​​the UAV when it flies in the y direction, C x is the equivalent drag coefficient component in the x direction, C y is the equivalent drag coefficient component in the y direction, m is the mass of the UAV, and w x is the model uncertainty in the x direction, w y is the model uncertainty in the y direction.

[0128] Different altitudes have corresponding air densities. The air density at the drone's flight altitude can usually be obtained by looking up the air density table. Of course, when the air density changes slightly, you can also use a fixed value to ignore small changes in air density.

[0129] Model uncertainty is the adjustment component used to compensate for discrepancies between the established system model and the actual motion conditions. As an empirical value or function, it can be determined and adjusted through appropriate statistical methods such as experiments or data analysis.

[0130] The frontal area is a parameter that changes with the flight attitude of the UAV. In some embodiments, it can be approximately considered as a nonlinear function related to the attitude angle. For example, the nonlinear function can be expressed as shown in the following equations (2) and (3):

[0131] S fb =S fb0 (1+f fb (θ,φ))(2)

[0132] S rl =S rl0 (1+f rl (θ,φ))(3)

[0133] Among them, S fb0 is the windward area of ​​the UAV when it flies along the x direction when the attitude angle is 0; S rl0 is the frontal area of ​​the UAV when it flies along the y direction when the attitude angle is 0.

[0134] The propeller thrust is related to the motor's output power, which is manifested as the acceleration of the drone. Generally, a greater acceleration also means a higher output propeller thrust. In some embodiments, the propeller thrust can be calculated using the following formula (4):

[0135]

[0136] Among them, a z is the acceleration of the drone in the z direction, g is the acceleration due to gravity; the z direction is perpendicular to the plane formed by the x direction and the y direction.

[0137] In the system identification model shown in formula (1), based on the changes in the attitude angle, flight speed, and acceleration of the drone, which are the input and output of the entire drone motion system, the system identification unit 1311 can complete the parameter identification process and obtain the current equivalent drag coefficient to reflect the wind resistance interference of the drone's flight environment.

[0138] Corresponding to the identification parameters, the inherent wind resistance of the drone is represented by the inherent wind resistance coefficient of the drone. Specifically, the wind speed estimation unit 1312 can calculate the wind speed of the flight environment of the drone by the following formula (5):

[0139]

[0140] Among them, V wx is the wind speed component in the x direction of the flight environment of the UAV, V wy V is the wind speed component in the y direction of the UAV’s flight environment, x is the speed of the UAV in the x direction, V y is the speed of the UAV in the y direction, ρ is the air density at the flight altitude, S fb is the frontal area of ​​the UAV when it flies in the x direction, S rl is the frontal area of ​​the UAV when it flies in the y direction, C x is the equivalent drag coefficient component in the x direction, C y is the equivalent drag coefficient component in the y direction, C dx is the inherent drag coefficient of the UAV in the x direction, C dy is the inherent drag coefficient of the UAV in the y direction.

[0141] The inherent drag coefficient is a mathematical parameter determined by the drone's shape and structure in a windless environment. It can be determined by fitting multiple sets of experimental data collected from windless indoor flights or other ideal experimental environments before the drone leaves the factory using statistical methods such as the least squares method.

[0142] It should be noted that the above-mentioned method of calculating the inherent drag coefficient is an offline calculation process, which can be completed in advance and recorded and stored in the memory of the drone, and called by the wind speed estimation unit 1312 without having to be performed on each drone.

[0143] According to the formula disclosed in the above embodiment, those skilled in the art will understand that when the system identification unit performs system identification and determines the wind resistance interference currently experienced by the drone, the drone-related data information that needs to be used includes at least: the drone's attitude angle, flight speed, acceleration, flight altitude, drone mass, inherent drag coefficient, and a nonlinear function for calculating the frontal area.

[0144] The drone's attitude angle, flight speed, acceleration, and altitude are all parameters that change with the drone's motion state. These parameters can be obtained through one or more methods, such as data fusion, based on sampled data collected by a series of basic sensors installed on the drone.

[0145] The drone's mass, inherent drag coefficient, and the nonlinear function used to calculate frontal area are parameters determined by the drone's inherent properties. They can be pre-calculated offline through experiments or other methods, then stored and recalled in memory.

[0146] Please continue reading Figure 5In some other embodiments, the wind speed estimator may further include an early warning unit 1313 .

[0147] The warning unit 1313 is connected to the wind speed estimation unit 1312, and is used to receive the wind speed of the current flight environment provided by the wind speed estimation unit 1312, and when the wind speed of the flight environment in which the UAV is located meets the preset warning condition, send a warning signal to realize the wind speed warning function.

[0148] The warning unit 1313 can be implemented by a processor that can execute logical judgment steps (such as a flight control system) by calling computer software program instructions related to the warning conditions pre-stored in the memory.

[0149] That is, the system identification unit, wind speed estimation unit and warning unit can all be implemented by the flight control system of the embodiment of the present invention by respectively calling computer software program instructions of corresponding functional steps.

[0150] It should be noted that Figure 5 Taking the functional block diagram as an example, the structure of the wind speed estimator provided by the embodiment of the present invention is described in detail. Based on the inventive concepts, steps to be performed, and functions to be implemented disclosed in the specification, those skilled in the art can choose to implement the functions of the wind speed estimator using software, hardware, or a combination of software and hardware, according to the actual requirements (e.g., chip power consumption, heat generation limitations, silicon wafer cost, or chip volume). For example, using more software components can reduce chip cost and circuit area occupied, and facilitate modification. Using more hardware circuits can improve reliability and computing speed.

[0151] exist Figure 5 Based on the structural framework of the wind speed estimator shown, embodiments of the present invention also provide a complete wind speed calculation method used by the wind speed estimator. The wind speed estimator and wind speed calculation method provided in embodiments of the present invention are implemented based on the same inventive concept. Therefore, one or more specific steps in the wind speed calculation method embodiment can also be applied to the wind speed estimator and implemented by corresponding functional modules. For simplicity, they are not repeated here.

[0152] Figure 6 The flow chart of the method for calculating wind speed provided by the embodiment of the present invention is shown in FIG. In this embodiment, the wind speed calculation method can be performed by Figure 1 The drone shown in the figure executes to obtain the wind speed information in the drone's current flight environment. Specifically, Figure 2 The flight control system shown is implemented by calling the data information provided by the memory and sensors.

[0153] like Figure 6 As shown, the method includes the following steps:

[0154] 601. Based on the flight data and attribute data of the UAV, determine the current wind resistance interference of the UAV through system identification.

[0155] System identification estimates the interference to the entire motion system (all equivalent to wind resistance interference) based on the changes in the input and output data of the UAV motion system over time.

[0156] Flight data is real-time data that changes with the drone's flight state (e.g., the drone's attitude angle, flight speed, flight altitude, and acceleration). Attribute data is pre-set data determined by the drone's inherent properties (e.g., the drone's mass, inherent drag coefficient, and the nonlinear function used to calculate frontal area).

[0157] 602. Calculate the wind speed of the flight environment of the UAV based on the wind resistance interference and the inherent wind resistance of the UAV.

[0158] It can be understood that the wind resistance interference estimated by the system identification is actually composed of the constant resistance of the drone itself in a windless state and the wind resistance interference imposed by the external wind in the flight airspace.

[0159] Therefore, according to the change of the complete wind resistance interference relative to the inherent wind resistance, a corresponding conversion calculation can be performed to determine the wind speed of the current flight environment of the UAV.

[0160] Based on the wind speed measurement results, in some embodiments, please continue to refer to Figure 6 , the wind speed calculation method further includes:

[0161] 603. Calculate the wind direction according to the wind speed components in the x-direction and the y-direction of the flight environment of the UAV.

[0162] The x-direction and the y-direction are two mutually perpendicular directions located in the plane where the drone is located. The process of calculating the wind direction according to the wind speed component is completed by the following formula (6):

[0163] β=ψ+arctan2(-V wx ,-V wy ) (6)

[0164] Among them, ψ is the yaw angle of the UAV, β is the wind direction, V wx is the wind speed component in the x direction, V wy is the wind speed component in the y direction.

[0165] In some embodiments, the system identification model used for system identification is the current equivalent drag coefficient of the UAV (the system identification model is constructed in an offline process). During system identification, an online identification method is used to solve the equivalent drag coefficient corresponding to the current flight data and attribute data.

[0166] The equivalent drag coefficient is a mathematical parameter determined by equating all disturbances a drone experiences during motion to wind disturbances. It represents the relationship between the drone's current operating state and the wind resistance it experiences.

[0167] According to actual use requirements or the structural structure of the model, technicians can use any appropriate online identification method to complete the system identification process. Figure 7 The flowchart of the online identification method provided by the embodiment of the present invention is as follows. Figure 7 As shown, the online identification method includes the following steps:

[0168] 701. Discretize the system identification model to form a corresponding discrete equation.

[0169] Since mathematical models are generally represented as systems of equations, their values ​​are always continuous over time. Therefore, if a computer or other device is required to solve the state equation of a continuous-time system, it must first be converted into a discrete equation.

[0170] 702. Recursively calculate the equivalent wind resistance of the UAV based on the preset initial value, the current attitude angle, flight speed, and acceleration of the UAV.

[0171] Recursive calculation is a frequently used method in mathematical operations. When an initial value and a recursive relationship between two items are given, the target result can be obtained through multiple recursive calculations.

[0172] Equivalent wind drag is the sum of all resistances experienced by a drone during flight, as determined by system identification and estimation. Because the primary drag on a drone comes from wind, it can be equated to wind drag.

[0173] 703. Convert the equivalent wind resistance into an equivalent drag coefficient according to the current frontal area and air density of the UAV.

[0174] As described in the above embodiment, the equivalent drag coefficient is a mathematical parameter related to wind resistance. Therefore, it can be determined through appropriate conversion calculation steps based on the known flight data and attribute data of the drone.

[0175] The frontal area is obtained by calculating the current attitude angle of the UAV and a nonlinear function for calculating the frontal area, and the air density is obtained by calculating the current flight altitude of the UAV.

[0176] The following takes the system identification equation shown in formula (1) as an example to describe in detail the specific solution process of the parameter to be identified (i.e., the equivalent drag coefficient):

[0177] 1) The UAV velocity dynamic equation of equation (1) can be simplified as shown in equation (7):

[0178]

[0179] According to the x-direction or y-direction to be calculated, the corresponding equation is: x = V x or V y , or c=-0.5C x ρS fb or -0.5C y ρS rl and w=w x or w y .

[0180] 2) Assume that the sampling step is T (minimum value), k = 0, 1, 2, etc., and T*k = t. The dynamic equation of the UAV velocity f(t) that changes with time can be written as the discrete equation shown in the following formula (8):

[0181] x(k+1)-x(k)-Tu(k)=Tc(k)x 2 (k)+Tw(k) (8)

[0182] 3) Further construct parameters y(k)=x(k+1)-x(k)-Tu(k), h(k)=Tx 2 (k) and υ(k)=Tw(k) 2 Formula (7) can be further simplified as shown in Formula (9):

[0183] y(k)=h(k)c(k)+υ(k) (9)

[0184] 4) Construct the recursive formula shown in the following formula (10) and calculate the parameter c(k) by recursion:

[0185]

[0186] Wherein, P(0) and c(0) are both initial values, which are set to 1 and c0 respectively in this embodiment. The technicians can also set and use appropriate values ​​as initial values ​​to solve the calculation parameter c(k) according to the actual needs.

[0187] 5) Since c = -0.5C x ρS fb or -0.5C y ρS rl Therefore, after calculating the value of the parameter c(k), the corresponding equivalent drag coefficient C can be calculated based on the air density at the current time t (i.e. T*k) and the windward area of ​​the drone. x and C y .

[0188] In accordance with the equivalent drag coefficient, the inherent wind resistance is also represented by the inherent drag coefficient of the UAV. In step 602, the wind speed of the UAV's flight environment can be specifically calculated and determined by formula (5).

[0189] In the method of wind speed measurement, in addition to the steps that need to be performed online during the operation of the drone, such as real-time detection of flight data and online identification, it also includes some offline steps, such as determining the inherent drag coefficient of the drone in the x-direction and the inherent drag coefficient in the y-direction, fitting the nonlinear function required to calculate the frontal area, and determining the mass of the drone.

[0190] It should be noted that the offline steps above do not need to be repeated on every drone. Offline experimental calculations only need to be recorded in the drone's memory after completion. Furthermore, drones with the same or similar physical structures can directly use existing data and omit one or more of the offline steps above.

[0191] The wind speed measurement method provided by the present invention uses a parameter identification process based on system identification to estimate the current wind resistance interference of a UAV and further infer the wind speed of the flight environment. This method does not require additional wind speed or wind force sensors, nor does it rely on a large database. It has low implementation costs and excellent real-time performance, making it widely applicable to UAV systems.

[0192] When the drone is in flight, the flight control system can periodically run the wind speed measurement method provided by the embodiment of the present invention according to a set period to obtain an estimated value of the current wind speed and / or wind direction. Since the flight speed of the drone is mainly affected by wind interference during flight, the impact of other interferences is relatively small. Therefore, the estimated values ​​of wind speed and / or wind force calculated and determined under the above equivalent settings can be basically considered to be relatively accurate and can basically meet the use needs of early warning.

[0193] Figure 8 This is a flow chart of a wind speed calculation method provided by another embodiment of the present invention.

[0194] like Figure 8 As shown, the method includes:

[0195] 801. Obtain the flight data and attribute data of the drone.

[0196] The flight data and attribute data required depends on the variables required to calculate the theoretical flight speed of the drone. Those skilled in the art may adjust or modify these data based on actual needs, preferences, or accuracy requirements.

[0197] Specifically, the flight data will change with the motion state of the drone, and can be obtained by calculating the sampling data of the drone's own sensors through a data fusion algorithm.

[0198] The attribute data is inherent to the drone, determined by its structure and other factors, and does not change with its motion state. It can be pre-set and recorded in memory through experiments and other means, and retrieved from the memory when needed.

[0199] 802. Based on the system identification principle, calculate and obtain the wind speed of the flight environment of the UAV.

[0200] By periodically executing step 802, the wind speed of the drone's flight environment can be continuously updated to ensure timely warning. The specific update period is an empirical value that can be adjusted or set according to actual conditions, such as a period of 1 minute or longer.

[0201] 803. Determine whether the wind speed meets the preset warning condition. If so, execute step 804; if not, return to step 802 and update the wind speed.

[0202] The preset warning conditions are pre-set criteria based on experience or the actual conditions of the drone (for example, its wind speed tolerance). They can consist of one or more conditions and are used to measure the probability of a drone losing control. In other words, when the preset warning conditions are met, it indicates that the drone's flight control system has essentially reached its design limits under wind interference, and the possibility of an anomaly or accident is very high.

[0203] In some embodiments, the warning condition can be a preset alarm threshold. Monitor 132 can continuously monitor whether the wind speed has reached the alarm threshold and, when the wind speed reaches the alarm threshold, send a warning signal to remote control device 20. The alarm threshold is also an empirical value and can be determined or set by technicians based on the specific operating status of the drone through experimental testing or other methods.

[0204] 804. Issue a warning signal.

[0205] The warning signal may be represented by any suitable form or type of identification, such as a warning flag simply represented by 1 or 0. When the value of the warning flag is 1, it indicates that a warning signal has been issued, and when the value of the warning flag is 0, it indicates that no warning signal has been issued.

[0206] Specifically, under the preset alarm threshold, the logic of the monitor 132 triggering the warning signal can be expressed by the following formula (11):

[0207]

[0208] Among them, V wx is the wind speed in the x direction, V wy is the wind speed in the y direction, and flag is the value of the warning signal flag. That is, when the sum of the squares of the wind speeds in the x and y directions is greater than or equal to the square of the preset alarm threshold, the monitor 132 will determine that the wind speed meets the preset warning condition and issue a warning signal.

[0209] Of course, the judgment logic shown in formula (11) is only for illustration and is not intended to limit the steps for monitor 132 to send a warning signal. Those skilled in the art may also use other different warning conditions to determine whether the wind speed of the drone is too high and the aircraft control system cannot effectively control it according to actual needs.

[0210] After receiving the warning signal, the remote control device 20 can feedback corresponding warning information to the user through a display screen or other interactive devices to remind the user to stop the drone flight in time and land it in a safe and controllable position.

[0211] Specific warning information can be configured based on actual circumstances, including but not limited to text or images. For example, the words "Current wind speed is too high" can be highlighted on the remote control display interface, or an icon in a specific color can be used to indicate that the current wind speed exceeds the limit. Furthermore, a voice prompt can be broadcast through the speaker.

[0212] The wind speed detection method provided in the embodiment of the present invention can be applied to drones to effectively solve the problem that existing ordinary drones cannot make forecasts, resulting in users / operators not having enough time to operate and the drone crashing due to excessive wind speed. When the wind speed is too high, the user can be prompted to fly cautiously or choose a safe place to land.

[0213] Based on the recursive calculation process disclosed in the embodiment of the present invention, the flight control system can specifically execute the following Figure 9 The steps shown in the figure can realize the wind speed measurement and early warning of UAV without relying on wind speed sensors and databases.

[0214] In this embodiment, the drone has one or more basic sensors that can at least collect flight data such as the drone's attitude angle (including pitch angle, roll angle and heading angle), acceleration and flight speed in real time.

[0215] The x-direction and y-direction are two mutually perpendicular vectors in the plane the drone is in. The x-direction is the direction the drone moves forward and backward, and the y-direction is the direction the drone flies left and right.

[0216] In addition, the mass of the UAV, the frontal area in the x-direction and the y-direction and the nonlinear function of the attitude angle f fb (θ,φ) and f rl (θ, φ) and the inherent drag coefficient of the drone in the x direction and the inherent drag coefficient in the y direction are measured and determined, and the records are stored in the memory of the drone.

[0217] The nonlinear function of the frontal area and attitude angle and the inherent drag coefficient can be determined by fitting using the least squares method using experimental data obtained under ideal conditions (for example, collecting multiple sets of flight data of a drone flying in an indoor windless environment).

[0218] like Figure 9 As shown, the calculation process performed by the flight control system includes:

[0219] 901. Given initial values ​​P(0) and c(0), and k=1.

[0220] It should be noted that the initial values ​​required for the recursive calculation can be set or initialized according to actual conditions. For example, these initial values ​​can be simply initialized to 0.

[0221] 902. Update the parameter P(k) according to the recursive formula shown in formula (10).

[0222] 903. Based on the parameter P(k), update the parameter c(k) according to the recursive formula shown in formula (10).

[0223] 904. Convert the parameter c(k) into an equivalent drag coefficient.

[0224] The specific conversion method can be determined based on the relationship between the parameter c(k) and the equivalent drag coefficient (i.e. c = -0.5C x ρS fb or -0.5C y ρS rl ).

[0225] 905. Use formula (5) to calculate the wind speed currently experienced by the UAV.

[0226] Figure 10 The graph of wind speed changing with time provided by the embodiment of the present invention is as follows. Figure 10 As shown, when using the system identification model disclosed in the embodiment of the present invention, the wind speed components in the x-direction and the y-direction can be calculated separately, and the corresponding two can be combined into the wind speed currently experienced by the drone to obtain a curve of wind speed changes over time.

[0227] 906. Determine whether to issue a warning signal using the judgment logic shown in formula (11).

[0228] The judgment logic uses the alarm threshold as a judgment condition to determine whether a warning signal needs to be issued. Figure 8 The alarm threshold is a pre-set empirical value. When the wind speed exceeds the alarm threshold, an alarm signal is issued, indicating that the wind speed is strong and the user or operator needs to pay attention.

[0229] During the flight of the drone, the wind speed needs to be updated periodically. When updating the wind speed, k can be set to k+1, and steps 902 and 903 can be re-executed to calculate and update the wind speed of the flight environment of the drone.

[0230] Figure 11 A graph showing wind direction changing over time provided by an embodiment of the present invention. Figure 11 Therefore Figure 10 The wind speed curve shown is used as a basis, and the corresponding wind direction curve is obtained by conversion using formula (6). The calculated wind direction angle can also be transmitted to the remote control device 20 and displayed to the user through the interactive device of the remote control device 20 (such as a display screen).

[0231] To sum up, the wind speed measurement method provided by the embodiment of the present invention and the drone early warning method implemented based on this method do not require the use of wind speed-related sensors and the creation of a huge database. Based on the existing information, the wind speed can be estimated by algorithm and the corresponding wind speed and / or wind direction can be determined.

[0232] Since it does not rely on wind speed related sensors and databases, it effectively reduces the hardware implementation cost of drones and avoids the shortcomings of large database computation, large memory requirements, and large time delay, and has good application prospects.

[0233] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Under the concept of the present invention, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the present invention as described above. For the sake of simplicity, they are not provided in detail. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in this field should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wind speed calculation method, characterized in that: include: According to the flight data and attribute data of the UAV, the current wind resistance interference of the UAV is solved by an online identification method; Calculating the wind speed of the flight environment of the drone based on the wind resistance interference and the inherent wind resistance of the drone; The determining of the current wind resistance interference of the UAV through online identification based on the flight data and attribute data of the UAV includes: Constructing a system identification model of the UAV, wherein the parameter to be identified in the system identification model is the current equivalent drag coefficient of the UAV; By using an online identification method, solving the corresponding equivalent drag coefficient according to the current flight data of the UAV and the attribute data; The calculating the wind speed of the flight environment of the UAV according to the wind resistance interference and the inherent wind resistance of the UAV includes: The wind speed of the flight environment of the UAV is calculated according to the difference between the equivalent drag coefficient and the inherent drag coefficient of the UAV.

2. The wind speed calculation method according to claim 1, characterized in that: The method of solving the corresponding equivalent drag coefficient based on the current flight data of the UAV and the attribute data by an online identification method includes: Discretizing the system identification model to form corresponding discrete equations; Recursively calculate the equivalent wind resistance of the drone based on the preset initial values, the drone's current attitude angle, flight speed, and acceleration; Converting the equivalent wind resistance into an equivalent drag coefficient according to the current frontal area and air density of the UAV; The frontal area is obtained by calculating the current attitude angle of the UAV and a nonlinear function for calculating the frontal area, and the air density is obtained by calculating the current flight altitude of the UAV; Calculating the wind speed of the flight environment of the UAV based on the difference between the equivalent drag coefficient and the inherent drag coefficient of the UAV specifically includes: The wind speed of the UAV's flight environment is calculated using the following formula: in, is the wind speed component in the x direction of the flight environment of the UAV, is the wind speed component in the y direction of the flight environment of the UAV, is the speed of the UAV in the x direction, is the speed of the UAV in the y direction, ρ is the air density at the flight altitude, is the frontal area of ​​the UAV when it flies in the x direction, is the frontal area of ​​the UAV when it flies along the y direction, is the equivalent drag coefficient component in the x direction, is the equivalent drag coefficient component in the y direction, is the inherent drag coefficient of the UAV in the x direction, is the inherent drag coefficient of the UAV in the y direction; The system identification model is expressed by the following formula: in, is the acceleration of the drone in the x direction, is the acceleration of the UAV in the y direction, is the speed of the UAV in the x direction, is the speed of the UAV in the y direction, T is the propeller thrust, θ is the pitch angle, φ is the roll angle, ρ is the air density at the flight altitude, is the frontal area of ​​the UAV when it flies in the x direction, is the frontal area of ​​the UAV when it flies along the y direction, is the equivalent drag coefficient component in the x direction, is the equivalent drag coefficient component in the y direction, m is the mass of the UAV, is the model uncertainty in the x-direction, is the model uncertainty in the y direction.

3. The wind speed calculation method according to claim 2, characterized in that: The equivalent drag coefficient is represented by an equivalent drag coefficient component in the x-direction and an equivalent drag coefficient component in the y-direction, and the wind speed is represented by a wind speed component in the x-direction and a wind speed component in the y-direction; the x-direction and the y-direction are perpendicular to each other and are in the same plane as the drone.

4. The wind speed calculation method according to claim 2, characterized in that: The inherent drag coefficient of the UAV in the x-direction and the inherent drag coefficient in the y-direction are determined by least squares fitting based on flight data of the UAV in a windless room.

5. The wind speed calculation method according to claim 2, characterized in that: The frontal area is determined by the following calculation formula: in, is the frontal area of ​​the UAV when it flies in the x direction, is the frontal area of ​​the UAV when flying along the y direction; is the frontal area of ​​the UAV when it flies along the x direction when the attitude angle is 0; is the frontal area of ​​the UAV when it flies along the y direction when the attitude angle is 0; and is a nonlinear function; θ is the pitch angle; φ is the roll angle.

6. The wind speed calculation method according to claim 5, characterized in that: The propeller thrust is calculated by the following formula: in, is the acceleration of the drone in the z direction, g is the acceleration due to gravity; the z direction is perpendicular to the plane formed by the x direction and the y direction; θ is the pitch angle; φ is the roll angle; and m is the mass of the drone.

7. The wind speed calculation method according to claim 2, characterized in that: The method further comprises: The wind direction is calculated based on the wind speed components in the x and y directions using the following formula: Among them, ψ is the yaw angle of the UAV, β is the wind direction, is the wind speed component in the x direction, is the wind speed component in the y direction.

8. The wind speed calculation method according to any one of claims 2 to 4, characterized in that: The method further includes: When the wind speed in the flight environment of the UAV meets a preset warning condition, a warning signal is issued.

9. The wind speed calculation method according to claim 8, characterized in that: When the wind speed in the flight environment of the UAV meets a preset warning condition, issuing a warning signal includes: The following calculation formula is used to determine whether the preset warning conditions are met: in, is the wind speed component in the x direction, is the wind speed component in the y direction, is the safety wind speed threshold; When the preset warning conditions are met, a warning signal is issued; When the preset warning condition is not met, the wind speed of the flight environment of the UAV continues to be detected.

10. A wind speed estimator, characterized in that The wind speed estimator comprises: A system identification unit, the system identification unit being configured to receive flight data and attribute data of the UAV and, based on the flight data and attribute data, to determine the current wind resistance interference of the UAV through an online identification method; A wind speed estimation unit, the wind speed estimation unit being connected to the system identification unit and configured to calculate the wind speed of the flight environment of the drone based on the wind resistance interference and the inherent wind resistance of the drone; The system identification unit stores a preset system identification model, wherein the parameter to be identified of the system identification model is the equivalent drag coefficient; The system identification unit is used to: solve the corresponding equivalent drag coefficient according to the current flight data and the attribute data through an online identification method; The wind speed estimation unit is used to calculate the wind speed of the flight environment of the UAV according to the difference between the equivalent drag coefficient and the inherent drag coefficient of the UAV.

11. The wind speed estimator according to claim 10, characterized in that The system identification unit is further configured to: Discretizing the system identification model to form corresponding discrete equations; Recursively calculate the equivalent wind resistance of the drone based on the preset initial values, the drone's current attitude angle, flight speed, and acceleration; Converting the equivalent wind resistance into an equivalent drag coefficient according to the current frontal area and air density of the UAV; The frontal area is obtained by calculating the current attitude angle of the UAV and a nonlinear function for calculating the frontal area, and the air density is obtained by calculating the current flight altitude of the UAV; The system identification model is expressed by the following formula: in, is the acceleration of the drone in the x direction, is the acceleration of the UAV in the y direction, is the speed of the UAV in the x direction, is the speed of the UAV in the y direction, T is the propeller thrust, θ is the pitch angle, φ is the roll angle, ρ is the air density at the flight altitude, is the frontal area of ​​the UAV when it flies in the x direction, is the frontal area of ​​the UAV when it flies along the y direction, is the equivalent drag coefficient component in the x direction, is the equivalent drag coefficient component in the y direction, m is the mass of the UAV, is the model uncertainty in the x-direction, is the model uncertainty in the y direction; The wind speed estimation unit is further configured to receive the current attitude angle, flight speed, flight altitude, inherent drag coefficient, and a nonlinear function for calculating the frontal area of ​​the UAV, and calculate the wind speed of the UAV's flight environment using the following formula: in, is the wind speed component in the x direction of the flight environment of the UAV, is the wind speed component in the y direction of the flight environment of the UAV, is the speed of the UAV in the x direction, is the speed of the UAV in the y direction, ρ is the air density at the flight altitude, is the frontal area of ​​the UAV when it flies in the x direction, is the frontal area of ​​the UAV when it flies along the y direction, is the equivalent drag coefficient component in the x direction, is the equivalent drag coefficient component in the y direction, is the inherent drag coefficient of the UAV in the x direction, is the inherent drag coefficient of the UAV in the y direction.

12. The wind speed estimator according to claim 11, characterized in that The equivalent drag coefficient is represented by an equivalent drag coefficient component in the x-direction and an equivalent drag coefficient component in the y-direction, and the wind speed is represented by a wind speed component in the x-direction and a wind speed component in the y-direction; the x-direction and the y-direction are perpendicular to each other and are in the same plane as the drone.

13. The wind speed estimator according to any one of claims 11-12, characterized in that The wind speed estimator further includes: an early warning unit; The early warning unit is used to send a warning signal when the wind speed in the flight environment of the UAV meets a preset warning condition.

14. The wind speed estimator according to claim 13, characterized in that The early warning unit is also used for: The following calculation formula is used to determine whether the preset warning conditions are met: in, is the wind speed component in the x direction, is the wind speed component in the y direction, is the safety wind speed threshold; When the preset warning conditions are met, a warning signal is issued; When the preset warning condition is not met, the wind speed of the flight environment of the UAV continues to be detected.

15. A drone, characterized in that: The UAV includes a fuselage body, one or more sensors arranged on the fuselage body, a memory and a flight control system; the memory stores computer-executable program instructions, and when the computer-executable program instructions are called by the flight control system, the flight control system obtains flight data from the sensors and obtains attribute data from the memory, and executes the wind speed measurement method according to any one of claims 1 to 9.

16. The drone according to claim 15, characterized in that: The flight control system is further configured to convert the wind speed of the flight environment of the UAV into wind direction, and display the wind speed and wind direction on a remote control device corresponding to the UAV.

Images