Control method of unmanned aerial vehicle

Abstract

The invention discloses a control method of an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle control, and the system comprises the following components: S1, fluid medium sensing, S2, parameter calculation of a data processing and control unit, S3, transmission of a control signal and action of an adjusting mechanism, and S4, adjustment of the surface structure of the unmanned aerial vehicle. The flow velocity, pressure, temperature and density parameter information of the fluid medium around the unmanned aerial vehicle is collected in real time, and the adjustment parameters of the fluid dynamic adjustment mechanism are calculated according to the internally stored ideal fluid dynamic parameters and the corresponding adjustment mechanism control strategy; according to the method, the shape or texture of the wing surface structure of the unmanned aerial vehicle can be adjusted in real time according to the change of the surrounding environment, so that the flow characteristic of airflow is optimized, and the flight stability and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle control, and in particular to a method for controlling an unmanned aerial vehicle. Background Art

[0002] With the continuous development of UAV technology, it has been widely used in many fields such as aerial photography, logistics distribution, and agricultural plant protection. The flight performance of UAV plays a decisive role in these applications, and the characteristics of the fluid medium around the UAV have a crucial influence on the flight performance.

[0003] Traditional UAV control methods mainly focus on the control of conventional parameters such as flight attitude, speed, altitude and heading. However, when dealing with complex and changing flight environments, these methods often fail to fully consider the dynamic interaction between the UAV surface and the surrounding fluid medium, and thus fail to achieve optimal control of fluid dynamic characteristics.

[0004] In view of the above-mentioned shortcomings, it is particularly important that the present invention proposes a control method for a drone. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and to provide a control method for an unmanned aerial vehicle. The method collects the flow rate, pressure, temperature, and density parameter information of the fluid medium around the unmanned aerial vehicle in real time, and calculates the adjustment parameters of the fluid dynamics regulation mechanism based on the ideal fluid dynamics parameters stored internally and the corresponding regulation mechanism control strategy.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for controlling a drone, the specific steps of the method are:

[0007] S1, Fluid Media Sensing

[0008] The fluid medium sensing is used to collect the flow rate, pressure, temperature and density parameter information of the fluid medium around the UAV in real time. The fluid medium sensing adopts a combined sensor array based on pressure sensor, wind speed sensor and temperature sensor, and each sensor is arranged in a specific layout on the fuselage and wing of the UAV;

[0009] S2, data processing and control unit parameter calculation

[0010] The collected fluid information is transmitted to the data processing and control unit, which processes the ideal fluid dynamics parameters under different flight states stored in the unit, including ideal flow rate, ideal pressure, ideal temperature, ideal density and the corresponding control strategy of the regulating mechanism, and uses the adaptive weighted fusion algorithm to calculate the adjustment parameters of the fluid dynamics regulating mechanism. The collected flow rate, pressure, temperature and density parameters are respectively formed into vector forms: Where n is the number of acquisitions, and the vector corresponding to the ideal parameters is:

[0011] Calculate the deviation vector of each parameter from the ideal parameter:

[0012]

[0013] Introducing the weight vector To measure the importance of each parameter deviation to the adjustment of the regulating mechanism;

[0014] The adjustment parameter vector of the fluid dynamics adjustment mechanism is calculated according to the following formula:

[0015]

[0016] Among them, k is an overall scaling factor, which is determined by comparing the actual adjustment effect and the expected adjustment effect of the UAV in typical flight scenarios. The purpose is to enable the calculated adjustment parameters to accurately drive the adjustment mechanism to achieve the expected adjustment effect in actual applications.

[0017] S3, control signal transmission and regulation mechanism action

[0018] The control signal corresponding to the adjustment parameter vector calculated by the data processing and control unit is transmitted to the fluid dynamics adjustment mechanism through the mechanical transmission device;

[0019] S4, adjustment of the drone surface structure

[0020] The fluid dynamics control mechanism adjusts the surface structure of the UAV wing through its mechanical device according to the control signal received, changing the shape or texture of the surface. The control mechanism can control the airflow by changing the curvature of the leading edge of the wing, the deployment angle of the trailing edge flaps, and the roughness of the micro-texture on the wing surface.

[0021] Furthermore, when the fluid medium sensing sensor is arranged on the fuselage and wings of the drone, in addition to the pressure sensor, wind speed sensor and temperature sensor, a humidity sensor is also added. The arrangement position of the humidity sensor is determined according to the climate characteristics of the drone flight area and the factors that may affect the fluid medium characteristics. In different climate areas, the parameters have different degrees of influence. By adding humidity sensors, the perception of the fluid medium characteristics around the drone can be further improved. The collected humidity data H will be transmitted to the data processing and control unit as supplementary information together with the flow velocity v, pressure p, temperature T, and density p parameters for comprehensive analysis. In the data processing and control unit, the processing method for the humidity data is similar to that for other parameters, and its deviation vector from the ideal humidity is calculated. According to the predetermined humidity weight vector Its determination method is similar to the flow velocity weight w vi , pressure weight w pi , temperature weight w ti , density weight w ri Similarly, the humidity deviation is incorporated into the overall adjustment parameter calculation based on the specific formula calculated based on the flight altitude h, flight speed u, and fuselage material factors of the drone. The specific calculation formula is:

[0022]

[0023] This allows the adjustment parameters of the fluid dynamics control mechanism to be calculated more comprehensively and accurately, enabling more precise adjustments to the surface structure of the drone, thereby optimizing airflow and related flight parameters.

[0024] Furthermore, the data processing and control unit is subdivided for different types of UAV tasks when storing the ideal fluid dynamics parameters and the corresponding control strategies of the regulating mechanism under different flight states. For aerial photography tasks, its ideal fluid dynamics parameters and control strategies focus on maintaining the stable flight of the UAV to obtain clear and stable shooting images. For logistics and distribution tasks, it focuses more on reducing the energy consumption of the UAV while carrying a certain load and improving its endurance.

[0025] According to the specific mission type, the ideal fluid dynamics parameters and corresponding control strategies of the regulating mechanism under different flight states under each mission type are determined through the analysis of a large amount of actual flight data and the results of simulated flight experiments. A special mission type identification module is set in the data processing and control unit. When the UAV starts a specific mission, it can quickly identify the mission type and call the corresponding ideal fluid dynamics parameters and control strategies for subsequent calculation and control operations, so that the UAV can achieve more accurate and effective fluid dynamics adjustment according to its own mission characteristics when performing different tasks, and optimize flight performance;

[0026] Throughout the entire process, relevant calculations and comparison operations are still performed based on the flow rate (v), pressure (p), temperature (T), density (ρ) parameters involved in claim 1 and the ideal flow rate (v_ideal), ideal pressure (p_ideal), ideal temperature (T_ideal), and ideal density (ρ_ideal) parameters to ensure that the control methods under different task types are consistent with the overall parameter system, so as to accurately achieve effective control and performance optimization of the drone.

[0027] Furthermore, when the mechanical transmission device transmits the control signal output by the data processing and control unit to the fluid dynamics adjustment mechanism, the flexible transmission mechanism is composed of a series of bendable and deformable transmission elements. During the transmission process, a strain sensor is set on the flexible transmission mechanism to monitor its deformation in real time, and feedback control is performed on the transmission process according to the monitoring data. When the strain sensor detects that the deformation of the transmission element exceeds a preset threshold, the connection mode between the transmission elements is adjusted or a certain compensation force is applied to ensure the accuracy and stability of the transmission, thereby achieving effective adjustment of the surface structure of the UAV, optimizing the airflow and related flight parameters. In this process, the parameter vector is still adjusted. As the basis of the control signal, it is accurately transmitted through the flexible transmission mechanism, so that the fluid dynamics regulation mechanism can perform corresponding adjustment operations based on the received signal to achieve the consistency and effectiveness of the overall control process.

[0028] Furthermore, when the fluid dynamics adjustment mechanism adjusts the surface structure of the drone wing according to the received control signal, it adopts a layered adjustment method to divide the surface structure of the drone wing into three layers: an outer layer, a middle layer and an inner layer. The outer layer is mainly responsible for adjusting the macroscopic flow characteristics of the airflow, which is achieved by changing the shape of the leading edge and the trailing edge of the wing and the large-scale texture of the surface. The middle layer focuses on adjusting the microscopic flow characteristics of the airflow, which is achieved by changing some deformable structures inside the wing, such as the opening and closing state of the micro-channel. The inner layer mainly optimizes the airflow characteristics of the connection between the wing and the fuselage, which is achieved by adjusting the shape and roughness of the connection. When performing layered adjustment, each layer is adjusted in a targeted manner according to different elements in the control signal and the preset thresholds and proportional coefficients of each layer adjustment. When the parameter vector is adjusted When an element in the outer layer meets the conditions for adjustment, the outer layer is adjusted first to observe the changes in airflow. If the expected effect is not achieved, the middle layer or inner layer is further adjusted according to the situation. Through this layered adjustment method, the airflow can be more finely regulated to optimize the lift, drag and torque flight parameters of the drone. In the entire adjustment process, the parameter vector is always adjusted. As a basis, ensure the consistency of the adjustment action with the overall control logic, so as to achieve effective adjustment of the UAV surface structure and performance optimization.

[0029] Furthermore, when calculating the adjustment parameters of the fluid dynamics regulating mechanism, the data processing and control unit adopts a time series analysis method to regard the collected fluid medium parameters as a time series data, that is, for the flow rate (v), there are time series data {v(t1), v(t2), ..., v}(tn)}, where t1, t2, ..., tn are different collection time points. By analyzing these time series data, the autoregressive moving average model method is adopted to predict the possible values ​​of each parameter at the next moment, and then the predicted value is compared with the ideal parameter to calculate the prediction deviation vector. The prediction deviation vector for the flow rate is in To predict the velocity vector, the actual deviation vector and the predicted deviation vector are weighted and fused according to a certain ratio to obtain the final deviation vector for calculating the adjustment parameter, that is, the adjustment parameter vector By introducing the time series analysis method, it is possible to predict the changing trend of fluid medium parameters in advance, so as to more accurately calculate the adjustment parameters of the fluid dynamics regulation mechanism, achieve more precise adjustment of the UAV surface structure, optimize airflow and related flight parameters, and throughout the calculation process, always operate around parameters and calculation logic to ensure the consistency and effectiveness of the method.

[0030] Furthermore, the fluid medium sensing adopts a multi-frequency sampling method when collecting parameter information of the fluid medium around the drone, that is, different sampling frequencies are set for different sensors and different fluid medium parameters. For the flow rate (v) sensor, a higher sampling frequency is set during the take-off and landing stages of the drone due to the drastic changes in the airflow, while in the cruising stage, the airflow is relatively stable and a lower sampling frequency is set. For the pressure (p) sensor, the temperature (T) sensor, and the density (p) sensor, different sampling frequencies are set according to the importance of their impact on flight performance and the stability factors of the airflow in different flight stages. Through this multi-frequency sampling method, it is possible to obtain sufficiently detailed fluid medium parameter information in the critical flight stage, and to reduce unnecessary data collection and transmission in the cruising stage, thereby improving data processing efficiency, thereby more accurately calculating the adjustment parameters of the fluid dynamics adjustment mechanism, realizing precise adjustment of the surface structure of the drone, optimizing the airflow and related flight parameters, and in the entire sampling process, always performing relevant sampling frequency settings and data collection operations based on the flow rate (v), pressure (p), temperature (T), and density (p) parameters to ensure that the collected data is consistent with the overall control method, so as to accurately achieve effective control and performance optimization of the drone.

[0031] Furthermore, the fluid dynamic adjustment mechanism adopts a memory alloy when changing the shape of the surface structure of the UAV wing or changing the shape or texture of the surface structure of the UAV wing. The memory alloy has the property of being able to recover to a preset shape under certain temperature conditions. In the fluid dynamic adjustment mechanism, the memory alloy is made into an element of a specific shape. When a control signal is received, a certain current or temperature change is applied to the memory alloy element to cause it to deform, thereby achieving adjustment of the wing surface structure.

[0032] Compared with the prior art, a control method for a drone has the following beneficial effects:

[0033] 1. The present invention collects the flow rate, pressure, temperature, and density parameter information of the fluid medium around the UAV in real time, and calculates the adjustment parameters of the fluid dynamics adjustment mechanism based on the ideal fluid dynamics parameters stored internally and the corresponding adjustment mechanism control strategy. This method enables the UAV to adjust the shape or texture of its wing surface structure in real time according to changes in the surrounding environment, thereby optimizing the flow characteristics of the airflow and improving the stability and efficiency of the flight.

[0034] 2. The present invention improves the accuracy and efficiency of the UAV control method by introducing time series analysis method and multi-frequency sampling technology. The time series analysis method can predict the changing trend of fluid medium parameters, thereby adjusting the surface structure of the UAV in advance to avoid potential airflow interference and performance degradation, while the multi-frequency sampling technology can obtain sufficiently detailed fluid medium parameter information in the critical flight phase, while reducing unnecessary data collection and transmission in the cruising phase, thereby improving the efficiency of data processing. The combination of these technologies makes the UAV control method of the present invention more intelligent and efficient, providing strong support for the widespread application of UAVs. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0036] Figure 1 An operation flow chart of a control method for a drone;

[0037] Figure 2 Operational flow chart of fluid medium sensing and parameter calculation of data processing and control unit. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Embodiment 1

[0040] This embodiment describes a drone being dispatched to perform an aerial photography mission. Its flight area covers a variety of complex terrains such as mountains, lakes and cities, and the climatic conditions are changeable, including high temperature, strong wind, low pressure and different humidity environments. These complex climatic and terrain conditions pose severe challenges to the drone's flight stability and shooting quality.

[0041] The fluid medium sensing equipped on the drone uses carefully arranged pressure sensors, wind speed sensors, temperature sensors and humidity sensors to collect key parameters of flow rate, pressure, temperature and humidity of the fluid medium around the drone in real time. In mountains and near lakes, due to the influence of terrain and climate, the sensors can capture significant changes in wind speed and humidity differences, while in urban areas, the sensors can cope with the challenges of high temperature and low air pressure. The sensors use multi-frequency sampling to ensure that sufficiently detailed fluid medium parameter information is obtained during critical flight stages, while reducing unnecessary data collection and transmission during the cruise stage.

[0042] The collected fluid information is quickly transmitted to the data processing and control unit, which uses the adaptive weighted fusion algorithm to calculate the adjustment parameters according to the ideal fluid dynamics parameters stored under the aerial photography mission and the corresponding control strategy of the regulating mechanism. The collected flow velocity, pressure, temperature, and density parameters are respectively formed into vector forms: Where n is the number of acquisitions, and the vector corresponding to the ideal parameters is:

[0043] Calculate the deviation vector of each parameter from the ideal parameter:

[0044]

[0045] Introducing the weight vector To measure the importance of each parameter deviation to the adjustment of the regulating mechanism;

[0046] The adjustment parameter vector of the fluid dynamics adjustment mechanism is calculated according to the following formula:

[0047]

[0048] Among them, k is an overall scaling factor, which is determined by comparing and analyzing the actual adjustment effect and the expected adjustment effect of the UAV in a typical flight scenario. The purpose is to enable the calculated adjustment parameters to accurately drive the adjustment mechanism to achieve the expected adjustment effect in practical applications. In particular, since humidity has a significant impact on flight performance, the data processing unit will comprehensively consider the humidity data, calculate its deviation from the ideal humidity, and calculate the adjustment parameters based on the predetermined humidity weight vector. The data processing and control unit also introduces a time series analysis method to predict the possible values ​​of each fluid medium parameter at the next moment, and weightedly fuse the predicted deviation with the actual deviation to more accurately calculate the adjustment parameters.

[0049] The control signal corresponding to the calculated adjustment parameter vector is transmitted to the fluid dynamics adjustment mechanism through the flexible transmission mechanism. These adjustment mechanisms are made of memory alloy and have the property of being able to restore to a preset shape under specific temperature conditions. When receiving the control signal, the memory alloy element will deform accordingly according to the change of the signal, thereby achieving adjustment of the UAV wing surface structure. This adjustment includes changing the curvature of the leading edge of the wing, the deployment angle of the trailing edge flap, and the roughness of the micro-texture on the wing surface, so as to optimize the airflow characteristics and improve flight stability.

[0050] The fluid dynamics adjustment mechanism performs layered adjustment on the surface structure of the UAV wing according to the control signal received. The layered adjustment includes three levels: outer layer, middle layer and inner layer, which are responsible for adjusting the macro flow characteristics, micro flow characteristics and air flow characteristics of the connection between the wing and the fuselage respectively. Through this sophisticated layered adjustment method, the UAV can better adapt to complex and changeable climate and terrain conditions, ensuring the smooth completion of aerial photography missions.

[0051] Embodiment 2

[0052] This embodiment describes that in inter-city logistics delivery tasks, the drone needs to carry a certain load and complete the delivery in the shortest possible time with the lowest energy consumption. Due to the complex urban environment, there are different climatic conditions, building layouts and traffic conditions, which will affect the flight performance of the drone.

[0053] The UAV collects the flow rate, pressure and temperature information of the surrounding environment in real time through fluid medium perception. According to the characteristics of the urban flight environment, the sensors are arranged in the fuselage and wings of the UAV in a specific layout. The layout is determined through three-dimensional modeling and simulation analysis based on the aerodynamic shape of the UAV and the expected main flight posture. In areas where humidity changes may have a significant impact on flight performance, humidity sensors are added to collect humidity data in real time. Different sampling frequencies are set for different sensors and different fluid medium parameters. When the UAV takes off, lands and passes through complex environments, the sampling frequency is increased to obtain more detailed fluid medium information. During the cruising stage, the airflow is relatively stable, and the sampling frequency is reduced to reduce the data processing burden.

[0054] According to the characteristics of logistics distribution tasks, the ideal fluid dynamics parameters and control strategies stored in the data processing and control unit focus on reducing energy consumption. For drones carrying loads, special attention is paid to their energy efficiency in different flight stages (such as take-off, cruising, and landing). The collected fluid information is compared with the ideal parameters, the deviation vector is calculated, and the weight vector is introduced to measure the importance of each parameter deviation to the adjustment of the drone flight state. According to the adaptive weighted fusion algorithm and the overall scaling coefficient, the adjustment parameter vector of the fluid dynamics adjustment mechanism is calculated. The collected flow velocity, pressure, temperature, and density parameters are respectively composed of vector forms: Where n is the number of acquisitions, and the vector corresponding to the ideal parameters is:

[0055] Calculate the deviation vector of each parameter from the ideal parameter:

[0056]

[0057] Introducing the weight vector To measure the importance of each parameter deviation to the adjustment of the regulating mechanism;

[0058] The adjustment parameter vector of the fluid dynamics adjustment mechanism is calculated according to the following formula:

[0059]

[0060] Among them, k is an overall scaling factor, which is determined by comparing and analyzing the actual adjustment effect and the expected adjustment effect of the UAV in a typical flight scenario. The purpose is to enable the calculated adjustment parameters to accurately drive the adjustment mechanism to achieve the expected adjustment effect in practical applications. The time series analysis method is used to predict the collected flow rate, pressure, and temperature data to obtain the possible values ​​of each parameter at the next moment. The predicted deviation vector and the actual deviation vector are weighted and fused to obtain the final deviation vector, which is used to more accurately calculate the adjustment parameters.

[0061] The control signal corresponding to the adjustment parameter is transmitted to the fluid dynamics adjustment mechanism through the flexible transmission mechanism. The flexible transmission mechanism is composed of a series of bendable and deformable transmission elements, which can adapt to the deformation requirements of the UAV under different flight postures. During the signal transmission process, a check code or redundant transmission method is used to ensure the accurate transmission of the control signal.

[0062] The fluid dynamics adjustment mechanism adopts a layered adjustment method, dividing the surface structure of the drone wing into three layers: the outer layer, the middle layer and the inner layer. The outer layer is mainly responsible for adjusting the macroscopic flow characteristics of the airflow, the middle layer focuses on adjusting the microscopic flow characteristics of the airflow, and the inner layer mainly optimizes the airflow characteristics at the connection between the wing and the fuselage. In the adjustment mechanism, memory alloy elements are used. By applying a certain current or temperature change, the memory alloy is deformed, thereby adjusting the surface structure of the wing. The deformation process of the memory alloy is reversible and can be restored to its original shape after the control signal is revoked. When the adjustment mechanism performs the adjustment action, it monitors the adjustment effect in real time through the built-in sensor and transmits the feedback signal back to the data processing and control unit. The unit fine-tunes the adjustment parameters according to the feedback signal to achieve more accurate energy consumption optimization.

[0063] In logistics and distribution tasks, the UAV is flight tested to evaluate its energy consumption performance, record energy consumption data, flight time and flight distance information during the flight, input the flight test data into the data processing and control unit for analysis, compare the energy consumption in different flight phases and under different load conditions, find out the potential energy consumption optimization points, and fine-tune the ideal fluid dynamics parameters and control strategies according to the data analysis results to improve the energy consumption performance of the UAV. At the same time, the sensor layout, sampling frequency and adjustment mechanism design are continuously optimized to adapt to different flight environments and mission requirements.

[0064] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for controlling an unmanned aerial vehicle, characterized in that: The specific steps of this method are: S1, Fluid Media Sensing The flow rate, pressure, temperature and density parameter information of the fluid medium around the UAV are collected in real time by using fluid medium sensing. The fluid medium sensing adopts a combined sensor array based on pressure sensors, wind speed sensors and temperature sensors, and each sensor is arranged in a specific layout on the fuselage and wings of the UAV; S2, data processing and control unit parameter calculation The collected fluid information is transmitted to the data processing and control unit, which processes the ideal fluid dynamics parameters under different flight states stored in the unit, including ideal flow rate, ideal pressure, ideal temperature, ideal density and the corresponding control strategy of the regulating mechanism, and uses the adaptive weighted fusion algorithm to calculate the adjustment parameters of the fluid dynamics regulating mechanism. The collected flow rate, pressure, temperature and density parameters are respectively formed into vector forms: Where n is the number of acquisitions, and the vector corresponding to the ideal parameters is: Calculate the deviation vector of each parameter from the ideal parameter: Introducing the weight vector To measure the importance of each parameter deviation to the adjustment of the regulating mechanism; The adjustment parameter vector of the fluid dynamics adjustment mechanism is calculated according to the following formula: Among them, k is an overall scaling factor; S3, control signal transmission and regulation mechanism action The control signal corresponding to the adjustment parameter vector calculated by the data processing and control unit is transmitted to the fluid dynamics adjustment mechanism through the mechanical transmission device; S4, adjustment of the drone surface structure The fluid dynamics control mechanism adjusts the surface structure of the UAV wing through its mechanical device according to the control signal received, changing the shape or texture of the surface. The control mechanism can control the airflow by changing the curvature of the leading edge of the wing, the deployment angle of the trailing edge flaps, and the roughness of the micro-texture on the wing surface.

2. The method for controlling a drone according to claim 1, characterized in that: When the fluid medium sensing device is arranged on the fuselage and wings of the drone, in addition to the pressure sensor, wind speed sensor and temperature sensor, a humidity sensor is also added. The arrangement position of the humidity sensor is determined according to the climate characteristics of the drone flight area and the factors that may affect the fluid medium characteristics. In different climate areas, the parameters have different degrees of influence. By adding humidity sensors, the collected humidity data H will be transmitted to the data processing and control unit as supplementary information together with the flow velocity v, pressure p, temperature T, and density p parameters for comprehensive analysis. In the data processing and control unit, the processing method for humidity data is similar to other parameters, and its deviation vector from the ideal humidity is calculated. According to the predetermined humidity weight vector Its determination method is similar to the flow velocity weight w vi , pressure weight w pi , temperature weight w ti , density weight w ri Similarly, the humidity deviation is incorporated into the overall adjustment parameter calculation based on the specific formula calculated based on the flight altitude h, flight speed u, and fuselage material factors of the drone. The specific calculation formula is: Thereby, the adjustment parameters of the fluid dynamics regulating mechanism can be calculated more comprehensively and accurately.

3. The method for controlling a drone according to claim 1, characterized in that: When storing the ideal fluid dynamics parameters and corresponding control strategies of the regulating mechanisms under different flight conditions, the data processing and control unit is subdivided according to different types of UAV tasks. For aerial photography tasks, the ideal fluid dynamics parameters and control strategies focus on maintaining the stable flight of the UAV. For logistics and distribution tasks, more attention is paid to reducing the energy consumption of the UAV while carrying a load.

4. The method for controlling a drone according to claim 1, characterized in that: The mechanical transmission device adopts a flexible transmission mechanism when transmitting the control signal output by the data processing and control unit to the fluid dynamics regulating mechanism. The flexible transmission mechanism is composed of a transmission element that can be bent and deformed.

5. The method for controlling a drone according to claim 1, characterized in that: When the fluid dynamics adjustment mechanism adjusts the surface structure of the UAV wing according to the received control signal, it adopts a layered adjustment method to divide the surface structure of the UAV wing into three layers: an outer layer, a middle layer and an inner layer. The outer layer is mainly responsible for adjusting the macroscopic flow characteristics of the airflow, the middle layer focuses on adjusting the microscopic flow characteristics of the airflow, and the inner layer mainly optimizes the airflow characteristics at the connection between the wing and the fuselage. When performing layered adjustment, targeted adjustments are made to each layer according to different elements in the control signal and pre-set thresholds and proportional coefficients of each layer adjustment.

6. The method for controlling a drone according to claim 1, characterized in that: When calculating the adjustment parameters of the fluid dynamics regulating mechanism, the data processing and control unit adopts the time series analysis method to regard the collected fluid medium parameters as a time series data, that is, for the flow rate (v), there are time series data {v(t1), v(t2), ..., v}(tn)}, where t1, t2, ..., tn are different collection time points. By analyzing these time series data, the autoregressive moving average model method is adopted to predict the possible values ​​of each parameter at the next moment, and then the predicted value is compared with the ideal parameter to calculate the prediction deviation vector. The prediction deviation vector for the flow rate is in To predict the flow velocity vector, the actual deviation vector and the predicted deviation vector are weighted and fused to obtain the final deviation vector for calculating the adjustment parameter, that is, the adjustment parameter vector 7. The method for controlling a drone according to claim 1, characterized in that: The fluid medium sensing adopts a multi-frequency sampling method when collecting parameter information of the fluid medium around the drone, that is, different sampling frequencies are set for different sensors and different fluid medium parameters. For the flow velocity (v) sensor, a higher sampling frequency is set during the take-off and landing stages of the drone due to the drastic changes in the airflow, while in the cruising stage, the airflow is relatively stable and a lower sampling frequency is set. For the pressure (p) sensor, temperature (T) sensor, and density (p) sensor, different sampling frequencies are set according to their importance to the flight performance and the stability factors of the airflow in different flight stages.

8. The method for controlling a drone according to claim 1, characterized in that: The fluid dynamics adjustment mechanism adopts a memory alloy when changing the shape of the surface structure of the UAV wing or changing the shape or texture of the surface structure of the UAV wing. The memory alloy has the characteristic of being able to recover to a preset shape under temperature conditions. In the fluid dynamics adjustment mechanism, the memory alloy is made into an element of a specific shape. When a control signal is received, a certain current or temperature change is applied to the memory alloy element to cause it to deform, thereby achieving adjustment of the wing surface structure.

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