Unmanned aerial vehicle aerodynamic deceleration control safety assessment method and system

Abstract

The invention discloses an unmanned aerial vehicle aerodynamic deceleration control safety assessment method and system, and belongs to the technical field of unmanned aerial vehicle flight control. The problem of improving the flight safety performance of the unmanned aerial vehicle is solved. The method comprises the steps of constructing a hidden layer of an unmanned aerial vehicle aerodynamic deceleration control prediction model, and adopting a three-layer architecture of a dynamic perception layer, an aerodynamic characteristic layer and a control decision layer; constructing an output index of the model; constructing a loss function of an unmanned aerial vehicle aerodynamic deceleration control prediction model, wherein the loss function is a composite loss function of a basic loss function, a speed loss item function, a flight characteristic loss item function and a stability loss item function; a trained unmanned aerial vehicle aerodynamic deceleration control prediction model is obtained; and constructing an unmanned aerial vehicle aerodynamic deceleration control safety evaluation method based on the obtained M groups of input and output data, and comprehensively considering the dimensions of system stability, structural integrity, flight state, energy management, control characteristics and environmental adaptability to establish an evaluation system.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle (UAV) flight control, and in particular provides a safety assessment method and system for aerodynamic deceleration control of a UAV. Background Art

[0002] The aerodynamic deceleration control process for drones involves complex changes in aerodynamic characteristics and the coupling of multiple systems. Its safety is directly related to the reliable operation and mission completion of the drone. During the deceleration process, factors such as sudden changes in aerodynamic forces, enhanced unsteady aerodynamic effects, and the rapid response of the overall mechanism can all pose safety risks. Furthermore, the activation of the deceleration device causes aerodynamic redistribution, causing disturbances in the aircraft's attitude. Environmental factors such as changes in atmospheric conditions and wind field disturbances can also significantly affect the deceleration effect and safety. Therefore, establishing a scientific safety assessment system is of great significance to ensuring the reliability of drone deceleration control.

[0003] Current safety assessment methods for drone aerodynamic deceleration control suffer from the following major issues: First, most assessment methods focus solely on a single safety metric, such as stability or structural strength, lacking a comprehensive, multi-dimensional assessment of the system's overall safety. Second, existing methods often employ a static assessment model, which fails to effectively reflect the dynamic changes in various parameters during deceleration. Third, traditional assessment methods fail to adequately consider the impact of environmental factors, making it difficult to accurately predict and assess the impact of external environmental changes on deceleration safety. Finally, the lack of a systematic safety grading system and early warning mechanism limits the practical guidance value of the assessment results.

[0004] These issues can lead to serious safety hazards in practical applications. The assessment of a single indicator may overlook other important safety factors, resulting in a one-sided and incomplete safety assessment. Static assessment models are unable to promptly detect dynamic safety risks during deceleration, delaying early warning opportunities. Insufficient consideration of environmental factors can lead to misjudgments in complex weather conditions, affecting the selection of deceleration control strategies. Furthermore, the lack of a systematic safety level classification makes it difficult to formulate appropriate response measures for different risk levels, increasing the likelihood of safety accidents.

[0005] The limitations of traditional assessment methods also manifest themselves in the construction of evaluation indicator systems. Existing methods often rely on empirical judgments, lacking theoretical support and rigorous mathematical models, resulting in insufficient reliability and accuracy in the assessment results. Furthermore, the lack of a correlation mechanism between the various assessment indicators prevents them from reflecting the mutual influence and coupling effects between different safety factors, making it difficult for the assessment results to fully reflect the actual safety status of the system. These issues have severely hampered the improvement of the safety of drone deceleration control systems. Summary of the Invention

[0006] The problem to be solved by the present invention is to improve the safety performance of UAV flight, and propose a UAV aerodynamic deceleration control safety assessment method and system.

[0007] To achieve the above object, the present invention is implemented through the following technical solutions: A method for safety assessment of aerodynamic deceleration control of a UAV comprises the following steps: S1. Collect and normalize the input characteristic parameters of the UAV aerodynamic deceleration control prediction model, including flight state parameters, control input parameters, and environmental parameters; S2. Construct a hidden layer for a UAV aerodynamic deceleration control prediction model using a three-layer architecture consisting of a dynamics perception layer, an aerodynamic characteristics layer, and a control decision layer. S3. Output index of a hidden layer model constructed based on a UAV aerodynamic deceleration control prediction model obtained in step S2; S4. Construct a loss function for the UAV aerodynamic deceleration control prediction model that is a composite of the base loss function, the speed loss term, the flight characteristics loss term, and the stability loss term. S5. Based on the normalized input characteristic parameter data of the UAV aerodynamic deceleration control prediction model obtained in step S1 and the normalized output indicator data of the UAV aerodynamic deceleration control prediction model obtained in step S3, construct a training set and a test set, set parameters for the UAV aerodynamic deceleration control prediction model constructed in steps S2-S4, then train the UAV aerodynamic deceleration control prediction model using the training set and test it using the test set, thereby obtaining a trained UAV aerodynamic deceleration control prediction model. S6. After normalizing the input characteristic parameter sample points of the UAV aerodynamic deceleration control prediction model, the sample points are input into the UAV aerodynamic deceleration control prediction model trained in step S5. The corresponding M sets of output results are then denormalized to obtain dimensioned output parameters, thereby obtaining M sets of input and output data. S7. Based on the M sets of input and output data obtained in step S6, construct a safety assessment method for the UAV's aerodynamic deceleration control. This assessment system is established by comprehensively considering the dimensions of system stability, structural integrity, flight status, energy management, control characteristics, and environmental adaptability.

[0008] Furthermore, the input characteristic parameters of the UAV aerodynamic deceleration control prediction model in step S1 are as follows: Flight status parameters include airspeed X1, angle of attack X2, sideslip angle X3, altitude X4, Mach number X5, dynamic pressure X6, roll angle X7 and pitch angle X8; The control input parameters include aileron deflection angle X9, rudder deflection angle X 10 , elevator deflection angle X 11 , throttle opening X 12 , air brake plate deflection angle X 13 and the deployment area of ​​the parachute X 14 ; Environmental parameters include air density X 15 , temperature X 16 , wind speed X 17 、wind direction X 18 , atmospheric pressure X 19 and humidity X 20 .

[0009] Furthermore, the specific implementation method of step S2 includes the following steps: S2.1. Based on the consideration of airspeed and altitude among the flight state parameters, and the impact of environmental parameters on dynamic characteristics, a dynamic perception layer is constructed. The expression is: ; in, is the output of the dynamic perception layer, is the activation function, is the weight coefficient of the jth input characteristic parameter of the UAV aerodynamic deceleration control prediction model, is the jth input characteristic parameter of the UAV aerodynamic deceleration control prediction model, is the dynamic characteristic coefficient, is a high impact factor, is the characteristic time scale factor, which characterizes the characteristic response speed of the UAV during deceleration; is the characteristic height scale factor, which represents the sensitivity of the UAV to altitude changes; is the bias term of the dynamic perception layer, j=1~20; Dynamic characteristic coefficient The expression reflecting the dynamic characteristics of the UAV during deceleration is: ; in, For the quality of the drone, is the drag coefficient; High impact factor The relationship between altitude and atmospheric pressure is expressed as: ; in, is the reference height, is standard atmospheric pressure; S2.2. Based on the dynamic characteristics, the effects of angle of attack and dynamic pressure on aerodynamic forces are considered, and the Mach number effect is considered to construct the aerodynamic characteristic layer. The expression is: ; in, Output for the aerodynamic characteristics layer; is the bias term of the aerodynamic characteristic layer, is the lift characteristic coefficient; is the compressibility effect factor, is the characteristic angle of attack, which characterizes the response characteristics of the system to attitude changes during deceleration; is the characteristic dynamic pressure, which represents the adaptability of the UAV to aerodynamic changes; lift characteristic coefficient The aerodynamic characteristics are reflected by the ratio of dynamic pressure, parachute deployment area and gravity, and the expression is: ; Where g is the acceleration due to gravity; Compressibility effect factor Considering the influence of air density and Mach number on the compressibility effect, the expression is: ; S2.3. Construct a control decision layer. By establishing a direct information channel from the dynamics perception layer to the control decision layer, a rapid mapping of the initial dynamic state to the control decision is achieved. The expression is: ; in, To control the output of the decision-making layer; is the residual connection coefficient, determined by expert experience; is the control efficiency coefficient; is the stability influencing factor, To control the bias term of the decision layer; is the characteristic force, which represents the impact of the change of control force on the UAV; is the characteristic rudder deflection angle, which represents the effect of rudder surface deflection on the UAV; Control efficiency coefficient The control efficiency is reflected by considering the relationship between dynamic pressure and characteristic length. The product of dynamic pressure and the deployment area of ​​the parachute represents the aerodynamic control force. The expression is: ; in, is the characteristic length, is the mean aerodynamic chord length; Stability influencing factors The stability evaluation index is constructed by Mach number, and the expression is: .

[0010] Furthermore, the specific implementation method of step S3 is to perform a weighted combination of the outputs of the three hidden layers constructed in step S2, and introduce a nonlinear activation function and a characteristic scale factor of the corresponding indicator to achieve accurate prediction of the flight state prediction indicator and the control effect evaluation indicator. The nonlinear activation function introduced by the flight state prediction indicator is a hyperbolic tangent function, and the nonlinear activation function introduced by the control effect evaluation indicator is a sigmoid function, thereby obtaining the flight state prediction indicator and the control effect evaluation indicator; the flight state prediction indicator includes the predicted airspeed Y1, the predicted angle of attack Y2, the predicted altitude Y3, the predicted Mach number Y4 and the predicted dynamic pressure Y5; the control effect evaluation indicator includes the deceleration rate Y6, the aerodynamic drag coefficient Y7, the lift-to-drag ratio Y8, the energy loss rate Y9 and the stability index Y 10 ; Establish a mapping relationship between the model hidden layer and output indicators.

[0011] Furthermore, the specific implementation method of step S4 includes the following steps: S4.1. Design a basic loss function and use the mean square error to calculate all output indicators Y1~Y 10 The mean square error between the predicted value and the measured value is the basic loss ; S4.2. Design a speed loss function to quantify the deceleration effect by evaluating the difference between the predicted and actual airspeeds and the corresponding state transition efficiency. Taking into account the effects of air density and the deployment area of ​​the parachute, the resulting expression is: ; in, is the weight coefficient of speed loss term; is the state conversion efficiency weight coefficient; 、 Characterize the kinetic energy levels of the predicted state and the current state respectively; 、 Characterize the work level of predicted resistance and actual resistance respectively; S4.3. Design a flight performance loss function. Consider the impact of the Mach number effect on aerodynamic characteristics, evaluate the accuracy of lift, drag, and torque predictions, and construct a comprehensive evaluation index for flight quality by introducing characteristic length and aerodynamic parameters. The expression for the flight performance loss function is: ; in, is the Mach number effect coefficient, is the moment weight coefficient, 、 They represent the predicted aerodynamic intensity level and the actual aerodynamic intensity level respectively; 、 Respectively characterize the predicted torque level and the actual torque level; S4.4. Design a stability loss function. By evaluating altitude change, static stability, and dynamic stability, a complete stability evaluation system is constructed. The expression of the stability loss function is: ; in, is the height influence coefficient, is the stability weight coefficient, and They represent the mechanical energy level of the predicted state and the mechanical energy level of the current state respectively; and denote the predicted stability moment level and the actual stability moment level, respectively. is the static stability coefficient; is the characteristic energy factor; is the characteristic moment factor; S4.5. Construct a UAV aerodynamic deceleration control prediction model using a composite loss function consisting of speed loss, flight characteristics loss, and stability loss. The loss function is expressed as: ; in, 、 、 、 They are 、 、 、 The weight coefficient of .

[0012] Furthermore, the input characteristic parameter data of the UAV aerodynamic deceleration control prediction model after normalization in step S5 and the output index data of the UAV aerodynamic deceleration control prediction model after normalization obtained in step S3 are divided into a training set and a test set in a ratio of 8:2.

[0013] Furthermore, the specific implementation method of step S7 includes the following steps: S7.1. Considering aerodynamic efficiency and deceleration strength, the stability safety assessment index is obtained, expressed as: ; in, is a stability safety assessment indicator, is the aerodynamic efficiency coefficient, is the deceleration intensity adjustment coefficient, which is determined by expert experience or design instructions. The aerodynamic efficiency is Characterization: The deceleration intensity is characterized by the square of the ratio of the deceleration rate to the predicted airspeed; S7.2. Based on the air brake plate deflection angle X 13 and the deployment area of ​​the parachute X 14 For the deceleration device, the structural load influence during the deceleration process of the deceleration device is considered, and the structural load safety assessment index is obtained, which is expressed as: ; in, is the structural load safety assessment index, is the foundation load influence coefficient, is the device load distribution coefficient, is the dynamic load adjustment factor, determined by expert experience or design specifications; is the maximum deflection angle of the air brake plate, is the maximum deployment area of ​​the parachute, is the maximum value of dynamic pressure, determined by referring to expert experience or design specifications; S7.3. The flight envelope assessment during deceleration takes into account speed, angle of attack limitations, and dynamic pressure changes to derive a flight envelope safety assessment index, expressed as: ; in, is the flight envelope safety indicator, is the speed limit coefficient, is the angle of attack limitation coefficient, is the dynamic pressure limitation coefficient, determined by expert experience or design specifications; To limit the speed, is the maximum angle of attack, is the maximum dynamic pressure, determined by reference to expert experience or design specifications; S7.4. Based on the evaluation of the energy loss rate during the deceleration process and considering the impact of energy dissipation on system stability and control effectiveness, the energy management safety index is obtained, which is expressed as: ; in, is the energy management safety indicator, is the maximum energy loss rate, is the maximum deceleration rate, determined by reference to expert experience or design specifications; is the energy loss coefficient, is the deceleration efficiency coefficient, determined by expert experience or design specifications; S7.5. The coordinated operation of the actuators involved in the deceleration process, including ailerons, rudder, elevator, special deceleration device, air brake, parachute, and engine power regulation, is used to obtain the control input safety index. , the expression is: ; Where i=9, 10, 11, 12; The maximum permissible values ​​of the input parameters corresponding to i=9, 10, 11, and 12 are determined by referring to expert experience or design specifications; 、 These are the coordination coefficients for conventional control surfaces and special speed reduction devices, respectively, and are determined with reference to expert experience or design specifications; S7.6. Consider atmospheric parameter changes and wind field disturbances to obtain environmental adaptability safety indicators. , the expression is: ; in, is the atmospheric parameter influence coefficient, is the wind field disturbance coefficient, determined by expert experience or design specifications; is the atmospheric parameter coupling index, is the wind direction deviation attenuation coefficient, determined by expert experience or design specifications; is the standard air density; is standard atmospheric pressure; The reference wind speed is determined by reference to expert experience or historical data or design specifications; S7.7. Based on steps S7.1 to S7.6, and considering that when the structural load exceeds a certain threshold, the safety factor is rapidly reduced by exponential decay to prevent structural overload, and the comprehensive safety assessment index is obtained. , the expression is: ; in, is the stability weight coefficient, is the structural load weight coefficient, is the flight envelope weight coefficient, is the energy management weight coefficient, is the control input weight coefficient, is the environmental adaptation weight coefficient, is the structural load limit factor, To control the saturation suppression coefficient, the coefficients are determined by expert experience or design specifications; S7.8. Classify the comprehensive safety assessment indicators obtained in step S7.7 into safety levels and provide recommended measures: when To ensure safe operation, we continuously monitor various indicators and record performance data; when To identify areas of concern, we will increase the frequency of indicator monitoring and issue early warnings if any problems are discovered by predicting performance trend changes. when This is the warning zone. At this time, the aerodynamic deceleration is too rapid, and it is necessary to reduce the deceleration intensity, strengthen manual monitoring, and prepare emergency plans; when It is a dangerous area and the deceleration procedure needs to be terminated immediately, the safety state needs to be restored, and the emergency procedure needs to be initiated.

[0014] in, 、 、 It is a critical comprehensive safety assessment indicator corresponding to the safe operation area, attention area, warning area and danger area.

[0015] A safety assessment system for aerodynamic deceleration control of an unmanned aerial vehicle (UAV) includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed, the steps of a safety assessment method for aerodynamic deceleration control of an UAV are implemented.

[0016] Beneficial effects of the present invention: The UAV aerodynamic deceleration control safety assessment method described in this paper establishes a comprehensive, dynamic, and reliable UAV aerodynamic deceleration control safety assessment system. This system must be able to assess system safety from multiple dimensions, monitor and warn of safety risks in real time, accurately reflect the impact of environmental factors, and establish a scientific safety classification mechanism. Furthermore, the assessment method must possess both theoretical rigor and engineering practicality, providing a reliable basis for optimizing deceleration control strategies and safety management. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a safety assessment method for aerodynamic deceleration control of a UAV according to the present invention;

[0018] Figure 2 This is a comparison chart of the Sz calculation results under different working conditions of the present invention. DETAILED DESCRIPTION

[0019] In order to make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present invention and are not intended to limit the present invention. That is, the specific embodiments described herein are only some embodiments of the present invention, not all embodiments. Generally, the components of the specific embodiments of the present invention described and illustrated in the drawings herein can be arranged and designed in various different configurations, and the present invention can also have other embodiments.

[0020] Therefore, the following detailed description of the specific embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but is merely representative of selected specific embodiments of the present invention. All other specific embodiments obtained by those skilled in the art based on the specific embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] In order to further understand the content, features and effects of the present invention, the following specific embodiments are given as examples, and the attached Figure 1 and attached Figure 2 The detailed instructions are as follows:

[0022] Example 1: A method for safety assessment of aerodynamic deceleration control of a UAV, characterized by comprising the following steps: S1. Collect and normalize the input characteristic parameters of the UAV aerodynamic deceleration control prediction model, including flight state parameters, control input parameters, and environmental parameters; Furthermore, the input characteristic parameters of the UAV aerodynamic deceleration control prediction model in step S1 are as follows: Flight status parameters include airspeed X1, angle of attack X2, sideslip angle X3, altitude X4, Mach number X5, dynamic pressure X6, roll angle X7 and pitch angle X8; The control input parameters include aileron deflection angle X9, rudder deflection angle X 10 , elevator deflection angle X 11 , throttle opening X 12 , air brake plate deflection angle X 13 and the deployment area of ​​the parachute X 14 ; Environmental parameters include air density X 15 , temperature X 16 , wind speed X 17 、wind direction X 18 , atmospheric pressure X 19 and humidity X 20 .

[0023] S2. Construct a hidden layer for a UAV aerodynamic deceleration control prediction model using a three-layer architecture consisting of a dynamics perception layer, an aerodynamic characteristics layer, and a control decision layer. Furthermore, the specific implementation method of step S2 includes the following steps: S2.1. Based on the consideration of airspeed and altitude among flight state parameters, and the impact of environmental parameters on dynamic characteristics, a dynamic perception layer is constructed. The expression is: ; in, is the output of the dynamic perception layer, is the activation function, is the weight coefficient of the jth input characteristic parameter of the UAV aerodynamic deceleration control prediction model, is the jth input characteristic parameter of the UAV aerodynamic deceleration control prediction model, is the dynamic characteristic coefficient, is a high impact factor, is the characteristic time scale factor, which characterizes the characteristic response speed of the UAV during deceleration; is the characteristic height scale factor, which represents the sensitivity of the UAV to altitude changes; is the bias term of the dynamic perception layer, j=1~20; Dynamic characteristic coefficient The expression reflecting the dynamic characteristics of the UAV during deceleration is: ; in, For the quality of the drone, is the drag coefficient; High impact factor The relationship between altitude and atmospheric pressure is expressed as: ; in, is the reference height, is standard atmospheric pressure; S2.2. Based on the dynamic characteristics, the effects of angle of attack and dynamic pressure on aerodynamic forces are considered, and the Mach number effect is considered to construct the aerodynamic characteristic layer. The expression is: ; in, Output for the aerodynamic characteristics layer; is the bias term of the aerodynamic characteristic layer, is the lift characteristic coefficient; is the compressibility effect factor, is the characteristic angle of attack, which characterizes the response characteristics of the system to attitude changes during deceleration; is the characteristic dynamic pressure, which represents the adaptability of the UAV to aerodynamic changes; lift characteristic coefficient The aerodynamic characteristics are reflected by the ratio of dynamic pressure, parachute deployment area and gravity, and the expression is: ; Where g is the acceleration due to gravity; Compressibility effect factor Considering the influence of air density and Mach number on the compressibility effect, the expression is: ; S2.3. Construct a control decision layer. By establishing a direct information channel from the dynamics perception layer to the control decision layer, a rapid mapping of the initial dynamic state to the control decision is achieved. The expression is: ; in, To control the output of the decision-making layer; is the residual connection coefficient, determined by expert experience; is the control efficiency coefficient; is the stability influencing factor, To control the bias term of the decision layer; is the characteristic force, which represents the impact of the change of control force on the UAV; is the characteristic rudder deflection angle, which represents the effect of rudder surface deflection on the UAV; Control efficiency coefficient The control efficiency is reflected by considering the relationship between dynamic pressure and characteristic length. The product of dynamic pressure and the deployment area of ​​the parachute represents the aerodynamic control force. The expression is: ; in, is the characteristic length, is the mean aerodynamic chord length; Stability influencing factors The stability evaluation index is constructed by Mach number, and the expression is: .

[0024] S3. Output index of a hidden layer model constructed based on a UAV aerodynamic deceleration control prediction model obtained in step S2; Furthermore, the specific implementation method of step S3 is to perform a weighted combination of the outputs of the three hidden layers constructed in step S2, and introduce a nonlinear activation function and a characteristic scale factor of the corresponding indicator to achieve accurate prediction of the flight state prediction indicator and the control effect evaluation indicator. The nonlinear activation function introduced by the flight state prediction indicator is a hyperbolic tangent function, and the nonlinear activation function introduced by the control effect evaluation indicator is a sigmoid function, thereby obtaining the flight state prediction indicator and the control effect evaluation indicator; the flight state prediction indicator includes the predicted airspeed Y1, the predicted angle of attack Y2, the predicted altitude Y3, the predicted Mach number Y4 and the predicted dynamic pressure Y5; the control effect evaluation indicator includes the deceleration rate Y6, the aerodynamic drag coefficient Y7, the lift-to-drag ratio Y8, the energy loss rate Y9 and the stability index Y 10 ; Establish a mapping relationship between the model hidden layer and output indicators.

[0025] S4. Construct a loss function for the UAV aerodynamic deceleration control prediction model that is a composite of the base loss function, the speed loss term, the flight characteristics loss term, and the stability loss term. Furthermore, the specific implementation method of step S4 includes the following steps: S4.1. Design a basic loss function and use the mean square error to calculate all output indicators Y1~Y 10 The mean square error between the predicted value and the measured value is the basic loss ; S4.2. Design a speed loss function to quantify the deceleration effect by evaluating the difference between the predicted and actual airspeeds and the corresponding state transition efficiency. Taking into account the effects of air density and the deployment area of ​​the parachute, the resulting expression is: ; in, is the weight coefficient of speed loss term; is the state conversion efficiency weight coefficient; 、 Characterize the kinetic energy levels of the predicted state and the current state respectively; 、 Characterize the work level of predicted resistance and actual resistance respectively; The weight coefficient of the speed loss term reflects the change of the deceleration effect with the flight conditions. The air density predicted speed square term represents the change characteristics of kinetic energy. At the same time, considering the air density, the deployment area of ​​the parachute, and the mass factors, the expression is obtained as follows: ; The state conversion efficiency weight coefficient evaluates the energy conversion efficiency of the deceleration process. Considering the inertial characteristics of the UAV, the characteristics of the deceleration medium, the current motion state, and the characteristics of the deceleration device, the expression of the state conversion efficiency weight coefficient is obtained as follows: ; S4.3. Design a flight performance loss function. Consider the impact of the Mach number effect on aerodynamic characteristics, evaluate the accuracy of lift, drag, and torque predictions, and construct a comprehensive evaluation index for flight quality by introducing characteristic length and aerodynamic parameters. The expression for the flight performance loss function is: ; in, is the Mach number effect coefficient, is the moment weight coefficient, 、 They represent the predicted aerodynamic intensity level and the actual aerodynamic intensity level respectively; 、 Respectively characterize the predicted torque level and the actual torque level; The Mach number effect coefficient reflects the changes in aerodynamic characteristics during the flight speed change through the Mach number, and directly reflects the intensity of the Mach number effect. The expression is: ; The moment weight coefficient is obtained by considering the influence of environmental characteristics, parachute area and characteristic length. The characteristic length evaluates the influence of moment balance and the square of the predicted velocity reflects the dynamic pressure effect. The expression is: ; S4.4. Design a stability loss function. By evaluating altitude change, static stability, and dynamic stability, a complete stability evaluation system is constructed. The expression of the stability loss function is: ; in, is the height influence coefficient, is the stability weight coefficient, and They represent the mechanical energy level of the predicted state and the mechanical energy level of the current state respectively; and denote the predicted stability moment level and the actual stability moment level, respectively. is the static stability coefficient; is the characteristic energy factor; is the characteristic moment factor; The height influence coefficient represents the influence of height, and the expression is: ; The stability weight coefficient evaluates the influence of stability, and the square of the predicted velocity reflects the influence of dynamic pressure. At the same time, the influence of environmental characteristics, parachute area, average aerodynamic chord length and characteristic length are considered. The expression is: ; S4.5. Construct a UAV aerodynamic deceleration control prediction model using a composite loss function consisting of speed loss, flight characteristics loss, and stability loss. The loss function is expressed as: ; in, 、 、 、 They are 、 、 、 The weight coefficient of .

[0026] S5. Based on the normalized input characteristic parameter data of the UAV aerodynamic deceleration control prediction model obtained in step S1 and the normalized output indicator data of the UAV aerodynamic deceleration control prediction model obtained in step S3, construct a training set and a test set, set parameters for the UAV aerodynamic deceleration control prediction model constructed in steps S2-S4, then train the UAV aerodynamic deceleration control prediction model using the training set and test it using the test set, thereby obtaining a trained UAV aerodynamic deceleration control prediction model. Furthermore, the input characteristic parameter data of the UAV aerodynamic deceleration control prediction model after normalization in step S5 and the output index data of the UAV aerodynamic deceleration control prediction model after normalization obtained in step S3 are divided into a training set and a test set in a ratio of 8:2.

[0027] Further, data acquisition and processing: Data sources include commercial flight data obtained from the Civil Aviation Administration of China, test data from large drone manufacturers, design parameters from drone design manuals, experimental data from drone laboratory tests, and drone monitoring data from the drone monitoring and management platform. This data includes input data X1 to X20 and output data Y1 to Y10.

[0028] Model construction: Input layer construction: contains 20 neuron nodes, divided into flight state parameters, control input parameters, and environmental parameters; Model structure layer construction, including power perception layer, aerodynamic characteristics layer, and control decision layer; Output layer construction: Contains 10 neuron nodes, divided into flight status prediction indicators and control effect evaluation indicators; Loss function construction: It includes four parts: basic loss term, speed loss term, flight characteristic loss term, and stability loss term.

[0029] Parameter settings: Hyperparameters that need to be set manually based on expert experience include: network structure parameters, the number of neurons in each layer; Hyperparameters that need to be manually set during model training include: learning rate, batch size, training rounds, and weight coefficients in the loss function; The parameters that need to be determined by the model during training are: the weight coefficients and bias terms of each layer.

[0030] Training strategy: The model is trained using a backpropagation algorithm. During training, the model calculates predicted values ​​through forward propagation, substitutes the predicted values ​​and the true values ​​into the loss function to calculate the loss, and then updates the network parameters through backpropagation. To prevent overfitting, an early stopping strategy is used, stopping training when the test set loss does not decrease for 10 consecutive epochs.

[0031] After model training is complete, the relationship between input and output is established. By inputting the parameters corresponding to X1-X20, Y1-Y10 can be predicted. The output results are then restored to actual physical quantities through denormalization.

[0032] S6. After normalizing the input characteristic parameter sample points of the UAV aerodynamic deceleration control prediction model, the sample points are input into the UAV aerodynamic deceleration control prediction model trained in step S5. The corresponding M sets of output results are then denormalized to obtain dimensioned output parameters, thereby obtaining M sets of input and output data. S7. Based on the M sets of input and output data obtained in step S6, construct a safety assessment method for the UAV's aerodynamic deceleration control. This assessment system is established by comprehensively considering the dimensions of system stability, structural integrity, flight status, energy management, control characteristics, and environmental adaptability.

[0033] The aerodynamic deceleration process of a UAV involves complex changes in aerodynamic characteristics and the coupling of multiple systems. Its safety assessment requires comprehensive consideration of multiple dimensions, including system stability, structural integrity, flight status, energy management, control characteristics, and environmental adaptability. During the deceleration process, the system is prone to instability due to sudden changes in aerodynamic forces, the enhancement of unsteady aerodynamic effects, and the rapid response of various actuators. At the same time, the activation of the deceleration device causes a redistribution of aerodynamic forces, causing disturbances in the aircraft's attitude. Furthermore, environmental factors such as changing atmospheric conditions and wind field disturbances can affect the deceleration effect and safety. Therefore, it is necessary to establish a multi-level, multi-dimensional safety assessment system that comprehensively evaluates the safety of the deceleration process through a combination of quantitative indicators and qualitative analysis.

[0034] .Data acquisition

[0035] The UAV monitoring and management platform is used to obtain drone monitoring data, specifically input parameters X1 to X20, including flight state parameters, control input parameters, and environmental parameters. These 20 input parameters are fed into the previously constructed UAV aerodynamic deceleration control prediction model to obtain predicted output parameters Y1 to Y10. The predicted results are then restored to actual physical quantities through denormalization.

[0036] It should be noted that when the drone monitoring and management platform lacks the above data (X1~X20), it is necessary to consider combining multiple channels such as commercial flight data from the Civil Aviation Administration of China, test data from drone manufacturers, drone design manuals, and laboratory test data.

[0037] Furthermore, the specific implementation method of step S7 includes the following steps: S7.1. Considering aerodynamic efficiency and deceleration strength, the stability safety assessment index is obtained, which is expressed as follows: ; in, is a stability safety assessment indicator, is the aerodynamic efficiency coefficient, is the deceleration intensity adjustment coefficient, which is determined by expert experience or design instructions. The aerodynamic efficiency is Characterization: The deceleration intensity is characterized by the square of the ratio of the deceleration rate to the predicted airspeed; The stability of a drone during deceleration involves attitude stability, trajectory stability, and dynamic response characteristics. When the deceleration device is activated, the sudden change in aerodynamic force will cause attitude disturbance, while the reduction in speed will affect control effectiveness. In addition, the asymmetric deployment of the deceleration device (such as the parachute and aerodynamic brake) may cause unbalanced torque. It is necessary to comprehensively consider static stability (restoring torque characteristics) and dynamic stability (damping characteristics). S7.2. Based on the air brake plate deflection angle X 13 and the deployment area of ​​the parachute X 14 For the deceleration device, the structural load influence during the deceleration process of the deceleration device is considered, and the structural load safety assessment index is obtained, which is expressed as: ; in, is the structural load safety assessment index, is the foundation load influence coefficient, is the device load distribution coefficient, is the dynamic load adjustment factor, determined by expert experience or design specifications; is the maximum deflection angle of the air brake plate, is the maximum deployment area of ​​the parachute, is the maximum value of dynamic pressure, determined by referring to expert experience or design specifications; Structural loads during deceleration are primarily due to aerodynamic forces, inertial forces, and their coupled effects. Special attention should be paid to the loads borne by the deceleration device, the structural stresses caused by aerodynamic redistribution, and the vibration and impact caused by high-speed airflow. By establishing a structural load assessment model, stress states in key areas can be predicted to prevent structural failure.

[0038] S7.3. The flight envelope assessment during deceleration takes into account speed, angle of attack limitations, and dynamic pressure changes to derive a flight envelope safety assessment index, expressed as: ; in, is the flight envelope safety indicator, is the speed limit coefficient, is the angle of attack limitation coefficient, is the dynamic pressure limitation coefficient, determined by expert experience or design specifications; To limit the speed, is the maximum angle of attack, is the maximum dynamic pressure, determined by reference to expert experience or design specifications; Flight envelope assessment during deceleration requires consideration of factors such as speed, angle of attack limits, and dynamic pressure variations. Particular attention should be paid to critical conditions that may arise during deceleration, such as minimum safe speed and maximum allowable dynamic pressure. Real-time monitoring of the relationship between flight state parameters and safety margins ensures that the aircraft consistently operates within the safety envelope.

[0039] S7.4. Based on the evaluation of the energy loss rate during the deceleration process and considering the impact of energy dissipation on system stability and control effectiveness, the energy management safety index is obtained, which is expressed as: ; in, is the energy management safety indicator, is the maximum energy loss rate, is the maximum deceleration rate, determined by reference to expert experience or design specifications; is the energy loss coefficient, is the deceleration efficiency coefficient, determined by expert experience or design specifications; The deceleration process is essentially a rapid dissipation of energy. The energy loss rate must be assessed to ensure that the energy dissipation process is controllable. Furthermore, the impact of energy dissipation on system stability and control effectiveness must be considered.

[0040] S7.5. The coordinated operation of the actuators involved in the deceleration process, including ailerons, rudder, elevator, special deceleration device, air brake, parachute, and engine power regulation, is used to obtain the control input safety index. , the expression is: ; Where i=9, 10, 11, 12; The maximum permissible values ​​of the input parameters corresponding to i=9, 10, 11, and 12 are determined by referring to expert experience or design specifications; 、 These are the coordination coefficients for conventional control surfaces and special speed reduction devices, respectively, and are determined with reference to expert experience or design specifications; The deceleration process involves the coordinated operation of multiple actuators, including conventional aerodynamic control surfaces (ailerons, rudder, elevator), specialized deceleration devices (airbrakes, parachute), and engine power regulation. These control inputs exhibit complex aerodynamic interference and coupling effects. The operating status, response characteristics, and coordination of each actuator must be evaluated to ensure the rationality and effectiveness of the control inputs.

[0041] S7.6. Consider atmospheric parameter changes and wind field disturbances to obtain environmental adaptability safety indicators. , the expression is: ; in, is the atmospheric parameter influence coefficient, is the wind field disturbance coefficient, determined by expert experience or design specifications; is the atmospheric parameter coupling index, is the wind direction deviation attenuation coefficient, determined by expert experience or design specifications; is the standard air density; is standard atmospheric pressure; The reference wind speed is determined by reference to expert experience or historical data or design specifications; Environmental factors significantly impact the deceleration process, primarily changes in atmospheric parameters and wind field disturbances. It is necessary to assess the extent to which current environmental conditions affect deceleration effectiveness and predict the risks associated with environmental changes. Establishing an environmental adaptability assessment model will provide a basis for adjusting deceleration control strategies.

[0042] S7.7. Based on steps S7.1 to S7.6, and considering that when the structural load exceeds a certain threshold, the safety factor is rapidly reduced by exponential decay to prevent structural overload, and the comprehensive safety assessment index is obtained. , the expression is: ; in, is the stability weight coefficient, is the structural load weight coefficient, is the flight envelope weight coefficient, is the energy management weight coefficient, is the control input weight coefficient, is the environmental adaptation weight coefficient, is the structural load limit factor, To control the saturation suppression coefficient, the coefficients are determined by expert experience or design specifications; S7.8. Classify the comprehensive safety assessment indicators obtained in step S7.7 into safety levels and provide recommended measures: when To ensure safe operation, we continuously monitor various indicators and record performance data; when To identify areas of concern, we will increase the frequency of indicator monitoring and issue early warnings if any problems are discovered by predicting performance trend changes. when This is the warning zone. At this time, the aerodynamic deceleration is too rapid, and it is necessary to reduce the deceleration intensity, strengthen manual monitoring, and prepare emergency plans; when It is a dangerous area and the deceleration procedure needs to be terminated immediately, the safety state needs to be restored, and the emergency procedure needs to be initiated.

[0043] in, 、 、 It is a critical comprehensive safety assessment indicator corresponding to the safe operation area, attention area, warning area and danger area. Figure 2 2 is a comparison chart of the Sz calculation results under different working conditions of this embodiment.

[0044] Example 2: A drone aerodynamic deceleration control safety assessment system includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed, the steps of a drone aerodynamic deceleration control safety assessment method as described in Example 1 are implemented.

[0045] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0046] Although the present application has been described above with reference to specific embodiments, various modifications may be made thereto and components may be substituted with equivalents without departing from the scope of the present application. In particular, as long as there are no structural conflicts, the various features of the embodiments disclosed herein may be combined with each other in any manner, and the omission of an exhaustive description of these combinations in this specification is solely for the sake of space and resource conservation. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions within the scope of the claims.

Claims

1. A safety assessment method for aerodynamic deceleration control of an unmanned aerial vehicle, characterized in that: The steps include: S1. Collect and normalize the input characteristic parameters of the UAV aerodynamic deceleration control prediction model, including flight state parameters, control input parameters, and environmental parameters; S2. Construct a hidden layer for a UAV aerodynamic deceleration control prediction model using a three-layer architecture consisting of a dynamics perception layer, an aerodynamic characteristics layer, and a control decision layer. S3. Output index of a hidden layer model constructed based on a UAV aerodynamic deceleration control prediction model obtained in step S2; S4. Construct a loss function for the UAV aerodynamic deceleration control prediction model that is a composite of the base loss function, the speed loss term, the flight characteristics loss term, and the stability loss term. S5. Based on the normalized input characteristic parameter data of the UAV aerodynamic deceleration control prediction model obtained in step S1 and the normalized output indicator data of the UAV aerodynamic deceleration control prediction model obtained in step S3, construct a training set and a test set, set parameters for the UAV aerodynamic deceleration control prediction model constructed in steps S2-S4, then train the UAV aerodynamic deceleration control prediction model using the training set and test it using the test set, thereby obtaining a trained UAV aerodynamic deceleration control prediction model. S6. After normalizing the input characteristic parameter sample points of the UAV aerodynamic deceleration control prediction model, the sample points are input into the UAV aerodynamic deceleration control prediction model trained in step S5. The corresponding M sets of output results are then denormalized to obtain dimensioned output parameters, thereby obtaining M sets of input and output data. S7. Based on the M sets of input and output data obtained in step S6, construct a safety assessment method for the UAV's aerodynamic deceleration control. This assessment system is established by comprehensively considering the dimensions of system stability, structural integrity, flight status, energy management, control characteristics, and environmental adaptability.

2. The method for safety assessment of aerodynamic deceleration control of a UAV according to claim 1, characterized in that: The input characteristic parameters of the UAV aerodynamic deceleration control prediction model in step S1 are as follows: Flight status parameters include airspeed X1, angle of attack X2, sideslip angle X3, altitude X4, Mach number X5, dynamic pressure X6, roll angle X7 and pitch angle X8; The control input parameters include aileron deflection angle X9, rudder deflection angle X 10 , elevator deflection angle X 11 , throttle opening X 12 , air brake plate deflection angle X 13 and the deployment area of ​​the parachute X 14 ; Environmental parameters include air density X 15 , temperature X 16 , wind speed X 17 、wind direction X 18 , atmospheric pressure X 19 and humidity X 20 .

3. The method for safety assessment of aerodynamic deceleration control of a UAV according to claim 2, characterized in that: The specific implementation method of step S2 includes the following steps: S2.

1. Based on the consideration of airspeed and altitude among the flight state parameters, and the impact of environmental parameters on dynamic characteristics, a dynamic perception layer is constructed. The expression is: ； in, is the output of the dynamic perception layer, is the activation function, is the weight coefficient of the jth input characteristic parameter of the UAV aerodynamic deceleration control prediction model, is the jth input characteristic parameter of the UAV aerodynamic deceleration control prediction model, is the dynamic characteristic coefficient, is a high impact factor, is the characteristic time scale factor, which characterizes the characteristic response speed of the UAV during deceleration; is the characteristic height scale factor, which represents the sensitivity of the UAV to altitude changes; is the bias term of the dynamic perception layer, j=1~20; S2.

2. Based on the dynamic characteristics, the effects of angle of attack and dynamic pressure on aerodynamic forces are considered, and the Mach number effect is considered to construct the aerodynamic characteristic layer. The expression is: ； in, Output for the aerodynamic characteristics layer; is the bias term of the aerodynamic characteristic layer, is the lift characteristic coefficient; is the compressibility effect factor, is the characteristic angle of attack, which characterizes the response characteristics of the system to attitude changes during deceleration; is the characteristic dynamic pressure, which represents the adaptability of the UAV to aerodynamic changes; S2.

3. Construct a control decision layer. By establishing a direct information channel from the dynamics perception layer to the control decision layer, a rapid mapping of the initial dynamic state to the control decision is achieved. The expression is: ； in, To control the output of the decision-making layer; is the residual connection coefficient, determined by expert experience; is the control efficiency coefficient; is the stability influencing factor, To control the bias term of the decision layer; is the characteristic force, which represents the impact of the change of control force on the UAV; is the characteristic rudder deflection angle, which represents the impact of rudder surface deflection on the UAV.

4. The method for safety assessment of aerodynamic deceleration control of a UAV according to claim 3, characterized in that: The specific implementation method of step S3 is to perform a weighted combination of the outputs of the three hidden layers constructed in step S2, and introduce a nonlinear activation function and a characteristic scale factor of the corresponding indicator to achieve accurate prediction of the flight state prediction indicator and the control effect evaluation indicator. The nonlinear activation function introduced by the flight state prediction indicator is a hyperbolic tangent function, and the nonlinear activation function introduced by the control effect evaluation indicator is a sigmoid function, thereby obtaining the flight state prediction indicator and the control effect evaluation indicator; the flight state prediction indicators include the predicted airspeed Y1, the predicted angle of attack Y2, the predicted altitude Y3, the predicted Mach number Y4 and the predicted dynamic pressure Y5; the control effect evaluation indicators include the deceleration rate Y6, the aerodynamic drag coefficient Y7, the lift-to-drag ratio Y8, the energy loss rate Y9 and the stability index Y 10 ; Establish a mapping relationship between the model hidden layer and output indicators.

5. The method for safety assessment of aerodynamic deceleration control of a UAV according to claim 4, characterized in that: The specific implementation method of step S4 includes the following steps: S4.

1. Design a basic loss function and use the mean square error to calculate all output indicators Y1~Y 10 The mean square error between the predicted value and the measured value is the basic loss ; S4.

2. Design a speed loss function to quantify the deceleration effect by evaluating the difference between the predicted and actual airspeeds and the corresponding state transition efficiency. Taking into account the effects of air density and the deployment area of ​​the parachute, the resulting expression is: ； in, is the weight coefficient of speed loss term; is the state conversion efficiency weight coefficient; 、 Characterize the kinetic energy levels of the predicted state and the current state respectively; 、 characterize the work levels of the predicted and actual resistance, respectively; S4.

3. Design a flight performance loss function. Consider the impact of the Mach number effect on aerodynamic characteristics, evaluate the accuracy of lift, drag, and torque predictions, and construct a comprehensive evaluation index for flight quality by introducing characteristic length and aerodynamic parameters. The expression for the flight performance loss function is: ； in, is the Mach number effect coefficient, is the moment weight coefficient, 、 They represent the predicted aerodynamic intensity level and the actual aerodynamic intensity level respectively; 、 Respectively characterize the predicted torque level and the actual torque level; S4.

4. Design a stability loss function. By evaluating altitude change, static stability, and dynamic stability, a complete stability evaluation system is constructed. The expression of the stability loss function is: ； in, is the height influence coefficient, is the stability weight coefficient, and They represent the mechanical energy level of the predicted state and the mechanical energy level of the current state respectively; and denote the predicted stability moment level and the actual stability moment level, respectively. is the static stability coefficient; is the characteristic energy factor; is the characteristic moment factor; S4.

5. Construct a UAV aerodynamic deceleration control prediction model whose loss function is a composite loss function including speed loss term, flight characteristic loss term, and stability loss term.

6. The method for safety assessment of aerodynamic deceleration control of a UAV according to claim 5, characterized in that: The input characteristic parameter data of the UAV aerodynamic deceleration control prediction model after normalization in step S5 and the output index data of the UAV aerodynamic deceleration control prediction model after normalization obtained in step S3 are divided into a training set and a test set in a ratio of 8:

2.

7. The method for safety assessment of aerodynamic deceleration control of a UAV according to claim 6, characterized in that: The specific implementation method of step S7 includes the following steps: S7.

1. Considering aerodynamic efficiency and deceleration strength, the stability safety assessment index is obtained, which is expressed as follows: ； in, is a stability safety assessment indicator, is the aerodynamic efficiency coefficient, is the deceleration intensity adjustment coefficient, which is determined by expert experience or design instructions. The aerodynamic efficiency is Characterization: The deceleration intensity is characterized by the square of the ratio of the deceleration rate to the predicted airspeed; S7.

2. Based on the air brake plate deflection angle X 13 and the deployment area of ​​the parachute X 14 For the deceleration device, the structural load influence during the deceleration process of the deceleration device is considered, and the structural load safety assessment index is obtained, which is expressed as: ； in, is the structural load safety assessment index, is the foundation load influence coefficient, is the device load distribution coefficient, is the dynamic load adjustment factor, determined by expert experience or design specifications; is the maximum deflection angle of the air brake plate, is the maximum deployment area of ​​the parachute, is the maximum value of dynamic pressure, determined by referring to expert experience or design specifications; S7.

3. The flight envelope assessment during deceleration takes into account speed, angle of attack limitations, and dynamic pressure changes to derive a flight envelope safety assessment index, expressed as: ； in, is the flight envelope safety indicator, is the speed limit coefficient, is the angle of attack limitation coefficient, is the dynamic pressure limitation coefficient, determined by expert experience or design specifications; To limit the speed, is the maximum angle of attack, is the maximum dynamic pressure, determined by reference to expert experience or design specifications; S7.

4. Based on the evaluation of the energy loss rate during the deceleration process and considering the impact of energy dissipation on system stability and control effectiveness, the energy management safety index is obtained, which is expressed as: ； in, is the energy management safety indicator, is the maximum energy loss rate, is the maximum deceleration rate, determined by reference to expert experience or design specifications; is the energy loss coefficient, is the deceleration efficiency coefficient, determined by expert experience or design specifications; S7.

5. The coordinated operation of the actuators involved in the deceleration process, including ailerons, rudder, elevator, special deceleration device, air brake, parachute, and engine power regulation, is used to obtain the control input safety index. , the expression is: ； Where i=9, 10, 11, 12; The maximum permissible values ​​of the input parameters corresponding to i=9, 10, 11, and 12 are determined by referring to expert experience or design specifications; 、 These are the coordination coefficients for conventional control surfaces and special speed reduction devices, respectively, and are determined with reference to expert experience or design specifications; S7.

6. Consider atmospheric parameter changes and wind field disturbances to obtain environmental adaptability safety indicators. , the expression is: ； in, is the atmospheric parameter influence coefficient, is the wind field disturbance coefficient, determined by expert experience or design specifications; is the atmospheric parameter coupling index, is the wind direction deviation attenuation coefficient, determined by expert experience or design specifications; is the standard air density; is standard atmospheric pressure; The reference wind speed is determined by reference to expert experience or historical data or design specifications; S7.

7. Based on steps S7.1 to S7.6, and considering that when the structural load exceeds a certain threshold, the safety factor is rapidly reduced by exponential decay to prevent structural overload, and the comprehensive safety assessment index is obtained. ; S7.

8. Classify the comprehensive safety assessment indicators obtained in step S7.7 into safety levels and provide recommended measures: when To ensure safe operation, we continuously monitor various indicators and record performance data; when To identify areas of concern, we will increase the frequency of indicator monitoring and issue early warnings if any problems are discovered by predicting performance trend changes. when This is the warning zone. At this time, the aerodynamic deceleration is too rapid, and it is necessary to reduce the deceleration intensity, strengthen manual monitoring, and prepare emergency plans; when If it is a dangerous area, it is necessary to immediately terminate the deceleration procedure, restore the safety state, and start the emergency procedure; in, 、 、 It is a critical comprehensive safety assessment indicator corresponding to the safe operation area, attention area, warning area and danger area.

8. A UAV aerodynamic deceleration control safety assessment system, characterized in that: The invention comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed, the steps of the method for safety assessment of aerodynamic deceleration control of a drone as described in any one of claims 1 to 7 are implemented.

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