A method for testing and evaluating an unmanned aerial vehicle equipment

Abstract

The present application provides a method for testing and evaluating an unmanned aerial vehicle (UAV) equipment. The method includes: obtaining air density data in a plateau area, and calculating the power attenuation coefficient of the UAV in a plateau environment in combination with the engine power model of the UAV; extracting the temperature fluctuation range from the historical temperature change data in the plateau area, and predicting the performance attenuation trend of the battery at extreme temperatures in combination with the discharge characteristic curve of the battery; integrating the power attenuation coefficient and the performance attenuation trend to identify and evaluate the overall dynamic performance of the UAV equipment. When evaluating the performance of the UAV, the present invention not only considers the engine power attenuation caused by the thin air in the plateau area, but also considers the influence of extreme temperatures on the battery performance. By integrating these factors, the actual performance of the UAV in a complex plateau environment can be evaluated more comprehensively and accurately, avoiding the deviation caused by single-factor evaluation.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and particularly to a method for testing and evaluating unmanned aerial vehicle (UAV) equipment. Background Art

[0002] During the process of testing and evaluating UAV equipment, technical contradictions are mainly reflected in the conflict between performance testing and environmental adaptability. The performance testing of UAVs usually includes key indicators such as flight stability, payload capacity, endurance time, and communication link reliability. However, in actual tests, environmental factors often have a significant impact on these performance indicators. For example, in complex terrains or adverse weather conditions, the flight stability of UAVs may decrease due to sudden changes in airflows, and communication links may also be interrupted due to electromagnetic interference or signal attenuation. In this case, the contradiction between the accuracy of performance testing and environmental adaptability becomes more prominent.

[0003] Specifically, when a UAV is tested in a plateau area, the thin air will cause a decrease in engine power, which in turn affects its climbing ability and endurance time. At the same time, the temperature changes drastically in the plateau area, which may lead to a sharp decline in battery performance, further exacerbating the endurance problem. This poses higher requirements for the response speed and stability of the flight control system. However, existing performance testing standards are often designed based on conventional environments and are difficult to fully simulate the special conditions in the plateau area, resulting in a large deviation between the test results and the actual application scenarios. Summary of the Invention

[0004] Aiming at the deficiencies in the prior art, the purpose of the present invention is to propose a method for testing and evaluating UAV equipment to solve the problems mentioned in the above background art section.

[0005] The present invention provides a method for testing and evaluating UAV equipment, and the method includes:

[0006] Obtain the air density data of the plateau area, and combine it with the engine power model of the UAV to calculate the power attenuation coefficient of the UAV in the plateau environment;

[0007] Extract the temperature fluctuation range from the historical temperature change data of the plateau area, and combine it with the discharge characteristic curve of the battery to predict the performance attenuation trend of the battery under extreme temperatures;

[0008] Integrate the power attenuation coefficient and the performance attenuation trend to identify and evaluate the overall dynamic performance of the UAV equipment.

[0009] As a further improvement, the step of obtaining the air density data of the plateau area, combining it with the engine power model of the UAV, and calculating the power attenuation coefficient of the UAV in the plateau environment specifically includes:

[0010] The power attenuation coefficient (Cp It is calculated by the following formula:

[0011]

[0012] where ρ 高原 is the air density in the plateau area; ρ 标准 is the rated power of the drone under standard atmospheric conditions, and k is an empirical coefficient.

[0013] As a further improvement, the empirical coefficient k is obtained through iterative calculation by the gradient descent method.

[0014] As a further improvement, the iterative calculation of the empirical coefficient k by the gradient descent method specifically includes:

[0015] Select an initial value k0; set the learning rate η; set the maximum number of iterations T;

[0016] Then perform iterative update as follows:

[0017] For each iteration step t, calculate the gradient of the objective function at the current k t :

[0018]

[0019] Then update k:

[0020] .

[0021] Stop the iteration when the number of iterations reaches the maximum value T or the absolute value of the gradient is less than a preset threshold, and η represents the learning rate.

[0022] As a further improvement, extracting the temperature fluctuation range from the historical temperature change data in the plateau area and combining it with the discharge characteristic curve of the battery to predict the performance decay trend of the battery under extreme temperatures specifically includes:

[0023] ;

[0024] ;

[0025] where C(T) is the discharge capacity of the battery at temperature T; C 标准 is the discharge capacity of the battery at the standard temperature; γ(D) is the influence coefficient of the discharge rate on the battery performance, and α(T) is the temperature influence coefficient.

[0026] As a further improvement, the method further includes:

[0027] Extract topographic data from the plateau region and evaluate the impact of the topographic data on the signal attenuation degree.

[0028] As a further improvement, the extraction of topographic data from the plateau region and the evaluation of the impact of the topographic data on the signal attenuation degree specifically include:

[0029] Comprehensively consider free space attenuation, terrain occlusion attenuation, and terrain reflection attenuation, and establish a comprehensive evaluation model for the impact of the plateau region terrain on the signal attenuation degree as follows:

[0030]

[0031] L total is the total signal attenuation amount, with the unit of decibel (dB); L ref is the reflection loss; L dif is the signal diffraction loss; L fs is the attenuation amount of the free space attenuation model; k1, k2, k3: weight coefficients, respectively representing the relative contributions of free space attenuation, terrain occlusion attenuation, and terrain reflection attenuation in the total signal attenuation, and satisfying k1 + k2 + k3 = 1.

[0032] The technical solution provided by the embodiments of the present invention may include the following beneficial effects: When evaluating the performance of the drone, the present invention not only considers the engine power attenuation caused by the thin air in the plateau region, but also considers the impact of extreme temperatures on the battery performance and the impact of complex terrain on the flight stability. By integrating these factors, the actual performance of the drone in the plateau complex environment can be evaluated more comprehensively and accurately, avoiding the deviation caused by single-factor evaluation. During the identification process, by extracting the actual topographic data and the historical data of temperature changes in the plateau region, and combining with the specific parameters of the drone for simulation and calculation, the evaluation results are closer to the situations that the drone may encounter in actual applications, providing a more reliable basis for the selection, deployment, and mission planning of the drone. Brief Description of the Drawings

[0033] Figure 1 It is a flowchart of a method for testing and identifying a drone equipment provided in an embodiment of the present invention.

[0034] Figure 2 It is a flowchart of a method for testing and identifying a drone equipment provided in another embodiment of the present invention. Detailed Description of the Embodiment

[0035] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this specification without creative efforts shall fall within the scope of protection of this specification.

[0036] As Figure 1 , a method for testing and evaluating an unmanned aerial vehicle (UAV) equipment in this embodiment may specifically include:

[0037] S10. Obtain the air density data in the plateau area, and combine it with the engine power model of the UAV to calculate the power attenuation coefficient of the UAV in the plateau environment;

[0038] S11. Extract the temperature fluctuation range from the historical temperature change data in the plateau area, and combine it with the discharge characteristic curve of the battery to predict the performance attenuation trend of the battery under extreme temperatures;

[0039] S12. Integrate the power attenuation coefficient and the performance attenuation trend to identify and evaluate the overall dynamic performance of the UAV equipment.

[0040] The calculation model of the power attenuation coefficient of the UAV in the plateau environment is mainly based on the influence of air density on the engine power. The air is thin and the air density is low in the plateau area, which will cause a reduction in the intake air volume of the UAV engine, thereby affecting the power output of the engine. Specifically, in step S10, the obtaining of the air density data in the plateau area and combining it with the engine power model of the UAV to calculate the power attenuation coefficient of the UAV in the plateau environment specifically includes:

[0041] The power attenuation coefficient (C p ) can be calculated by the following formula:

[0042]

[0043] Among them, P 高原 is the actual power output of the UAV in the plateau environment; P 标准 is the rated power of the UAV under standard atmospheric conditions.

[0044] The ratio of the air density (ρ 高原 ) in the plateau area to the air density (ρ 标准 ) under standard atmospheric conditions is a key factor. The air density (ρ 高原 ) in the plateau area can be calculated through meteorological station data or an atmospheric model (such as the International Standard Atmosphere ISA). The air density ratio α can be calculated by the following formula:

[0045]

[0046] Since engine power is usually directly proportional to air density, it can be assumed that:

[0047]

[0048] where k is an empirical coefficient, usually determined according to the type and characteristics of the engine. For most piston engines, the value of k is between 0.5 and 1.0 and needs to be calibrated according to the specific engine type and experimental data.

[0049] Substituting the above formula into the calculation formula of the power attenuation coefficient, we get:

[0050]

[0051] Exemplarily, assume that:

[0052] The air density under standard atmospheric conditions is: 1.225 kg / m 3 ; the air density in the plateau area is 0.7 kg / m 3 ; the empirical coefficient k = 0.8; then the power attenuation coefficient is:

[0053]

[0054] This means that the power output of the UAV in the plateau environment is about 62% of that under standard conditions. This model provides a basic framework and may need to be adjusted and optimized according to the specific engine characteristics and plateau environmental conditions in actual applications.

[0055] In other embodiments, to obtain an accurate empirical coefficient k, iterative calculation can be performed by the gradient descent method as follows:

[0056] Select an initial value k0 (e.g., 0.8); set the learning rate η (e.g., 0.01); set the maximum number of iterations T (e.g., 1000).

[0057] Then perform iterative update as follows:

[0058] For each iteration step t, calculate the gradient of the objective function at the current k t :

[0059]

[0060] Then update k:

[0061] .

[0062] Stop the iteration when the number of iterations reaches the maximum value T or the absolute value of the gradient is less than a preset threshold (e.g., 10 −6 ), where η represents the learning rate. If the learning rate η is set too large, it may lead to too large a step size for each update, thus skipping the optimal solution and even causing the model not to converge. If the learning rate η is set too small, although the optimal solution can be found more precisely, it may lead to a very slow convergence rate and require more iterations.

[0063] In a plateau environment, extreme temperatures can have a significant impact on the battery performance of drones. To predict the performance degradation trend of the battery under extreme temperatures, a calculation model based on the battery discharge characteristic curve and temperature change data can be established. Specifically, in step S10, the extraction of the temperature fluctuation range from the historical temperature change data in the plateau area and the combination with the battery discharge characteristic curve to predict the performance degradation trend of the battery under extreme temperatures specifically includes:

[0064] Consider the influence of the discharge rate D on the battery performance. The discharge rate is usually expressed as D = C / I, where I is the discharge current and C is the battery capacity.

[0065] Combining the influence of temperature and discharge rate, define a comprehensive performance degradation coefficient β(T, D):

[0066]

[0067] where: γ(D) is the influence coefficient of the discharge rate on the battery performance, which can be obtained by fitting experimental data. α(T) is the temperature influence coefficient, representing the influence of temperature on the battery capacity.

[0068] Predict the performance degradation trend of the battery under extreme temperatures as follows:

[0069] , where: C(T) is the discharge capacity of the battery at temperature T; C 标准 is the discharge capacity of the battery at the standard temperature (e.g., 25°C).

[0070] To improve the accuracy of the model, more experimental data can be collected, especially data at extreme temperatures. Or use curve fitting or machine learning methods to optimize the calculation of the temperature influence coefficient and the discharge rate influence coefficient. Of course, the aging effect of the battery can also be considered. Over time, the battery performance will gradually decline. Through this model, the performance degradation trend of the drone battery under extreme plateau temperatures can be predicted, providing a scientific basis for the use and maintenance of drones.

[0071] In step S12, integrating the power attenuation coefficient and the performance attenuation trend to identify and evaluate the overall dynamic performance of the UAV equipment specifically includes:

[0072] The overall dynamic performance evaluation index (K) comprehensively identifies and evaluates the dynamic performance of the UAV in the plateau environment. The overall dynamic performance evaluation index (K) comprehensively considers the effects of power attenuation and battery performance attenuation on the dynamic performance of the UAV.

[0073] Among them, the overall dynamic performance evaluation index (K) satisfies:

[0074]

[0075] C p : Power attenuation coefficient, with a value range between 0 and 1; β: Battery performance attenuation coefficient, with a value range between 0 and 1; K0: Benchmark value of the dynamic performance of the UAV in the standard environment, determined according to actual tests or data provided by the manufacturer. w1 and w2 are weight coefficients, respectively representing the relative importance of power attenuation and battery performance attenuation in the overall dynamic performance evaluation, satisfying w1 + w2 = 1.

[0076] Please refer to Figure 2 As shown, in other embodiments, it may further include:

[0077] S13, Extracting terrain data from the plateau area and evaluating the impact of the terrain data on the signal attenuation degree.

[0078] Specifically, obtaining the terrain data of the plateau area from a Geographic Information System (GIS) or other terrain databases, including information such as altitude, terrain slope, and terrain roughness. Represent the terrain data as a two-dimensional matrix H(x, y), where x and y are horizontal coordinates, and H(x, y) represents the altitude of the corresponding point. Normalize the extracted terrain data, mapping data such as altitude to the [0, 1] interval for subsequent calculations. The normalization formula is:

[0079]

[0080] Among them, H min and H max are respectively the minimum and maximum values of the terrain data in this area.

[0081] In an ideal space without obstacles, the signal strength decays with the increase of distance, and the attenuation amount (L fs ) of the free space attenuation model can be expressed as:

[0082]

[0083] Where d is the distance between the transmitter and the receiver in kilometers, and f is the signal frequency in megahertz.

[0084] However, the terrain blocking attenuation model can use the knife-edge diffraction model to estimate this attenuation. Assuming the terrain blocking height is h, its signal diffraction loss (L dif ) can be expressed as:

[0085]

[0086] Where λ is the signal wavelength, λ = c / f, and c is the speed of light.

[0087] Furthermore, it also includes a terrain reflection attenuation model, that is, the reflection from the terrain surface will also cause signal attenuation. ref ) is related to the reflection coefficient (Γ), which can be expressed as:

[0088]

[0089] Where Z1 and Z2 are the wave impedances of the incident medium and the reflecting medium, respectively. For the reflection of electromagnetic signals on the terrain surface, the reflection coefficient can be calculated based on the electrical properties of the terrain (such as the dielectric constant), and then the reflection loss can be obtained.

[0090] Taking into account free space attenuation, terrain occlusion attenuation and terrain reflection attenuation, a comprehensive evaluation model of the impact of terrain on signal attenuation in plateau areas is established, as shown below:

[0091]

[0092] L total is the total signal attenuation in decibels (dB); k1, k2, k3 are weight coefficients, which respectively represent the relative contributions of free space attenuation, terrain occlusion attenuation and terrain reflection attenuation to the total signal attenuation, satisfying k1+ k2+ k3= 1.

[0093] Through the above mathematical model, terrain data can be effectively extracted from plateau areas and its impact on the degree of signal attenuation can be evaluated, providing an important basis for the evaluation of communication link reliability in the test and identification of UAV equipment.

[0094] It should be noted that the above examples are only some specific embodiments of the present invention. Obviously, the present invention is not limited to the above embodiments, and there are many variations. All variations that can be directly derived or associated with the content disclosed by a person skilled in the art should be considered as the protection scope of the present invention.

Claims

1. A method for testing and evaluating an unmanned aerial vehicle equipment, characterized in that, The method includes: Obtain the air density data in the plateau area, and combine it with the engine power model of the drone to calculate the power attenuation coefficient of the drone in the plateau environment, specifically including: the power attenuation coefficient C p Calculate through the following formula: where ρ 高原 is the air density in the plateau area; ρ 标准 is the rated power of the UAV under standard atmospheric conditions, and k is an empirical coefficient; Extract the temperature fluctuation range from the historical temperature change data in the plateau area, and combine it with the discharge characteristic curve of the battery to predict the performance attenuation trend of the battery under extreme temperatures; Integrate the power attenuation coefficient and the performance attenuation trend to identify and evaluate the overall dynamic performance of the UAV equipment; The overall dynamic performance evaluation index K comprehensively identifies and evaluates the dynamic performance of the UAV in the plateau environment. The overall dynamic performance evaluation index K comprehensively considers the impacts of power attenuation and battery performance attenuation on the dynamic performance of the UAV; Among them, the overall dynamic performance evaluation index K satisfies: ； C p : Power attenuation coefficient, with a value range between 0 and 1; β: Battery performance attenuation coefficient, with a value range between 0 and 1, K0: Benchmark value of the dynamic performance of the UAV in the standard environment, determined according to actual tests or data provided by the manufacturer; w1 and w2 are weight coefficients, representing the relative importance of power attenuation and battery performance attenuation in the overall dynamic performance evaluation respectively, satisfying w1 + w2 = 1.

2. The method according to claim 1, wherein The empirical coefficient k is obtained through iterative calculation by the gradient descent method.

3. The method according to claim 2, characterized in that The empirical coefficient k is obtained through iterative calculation by the gradient descent method, which specifically includes: Select an initial value k0; set the learning rate η; set the maximum number of iterations T; Then perform iterative update as follows: For each iteration step t, compute the gradient of the objective function at the current k t : Then update k: Stop the iteration when the number of iterations reaches the maximum value T or the absolute value of the gradient is less than a preset threshold, where η represents the learning rate, and P 高原 is the actual power output of the UAV in the plateau environment; P 标准 is the rated power of the UAV under standard atmospheric conditions.

4. The method according to claim 3, wherein The extracting the temperature fluctuation range from the historical temperature change data in the plateau area and combining it with the discharge characteristic curve of the battery to predict the performance attenuation trend of the battery under extreme temperatures specifically includes: ； ； where C(T) is the discharge capacity of the battery at temperature T; C 标准 is the discharge capacity of the battery at the standard temperature; γ(D) is the influence coefficient of the discharge rate on the battery performance, and α(T) is the temperature influence coefficient.

5. The method according to claim 4, characterized in that It further includes: Extract terrain data from the plateau area and evaluate the impact of the terrain data on the signal attenuation degree.

6. The method according to claim 5, characterized in that The extracting the terrain data from the plateau area and evaluating the impact of the terrain data on the signal attenuation degree specifically includes: Comprehensively consider free space attenuation, terrain occlusion attenuation, and terrain reflection attenuation, and establish a comprehensive evaluation model for the impact of the terrain in the plateau area on the signal attenuation degree, as follows: L total is the total signal attenuation, in decibels (dB); L ref is the reflection loss; L dif is the signal diffraction loss; L fs is the attenuation of the free space attenuation model; k1, k2, k3: weighting coefficients, representing the relative contributions of free space attenuation, terrain obstruction attenuation, and terrain reflection attenuation to the total signal attenuation respectively, and satisfying k1 + k2 + k3 = 1.

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