A method and system for predicting and analyzing energy-saving effects of an unmanned aerial vehicle climbing process

Abstract

The application discloses a UAV climbing process energy-saving effect prediction analysis method and system, and belongs to the technical field of UAV control. In order to solve the problem of realizing actual application of the UAV energy-saving prediction analysis method, a main body layer of a performance prediction model of a UAV climbing process is constructed, a three-layer architecture of a power characteristic layer, a state response layer and an interlayer interaction layer is adopted, output indexes of the model are constructed, including a dynamic characteristic index group and a climbing ability index group, a full connection mode is adopted to establish the correlation between the model main body layer and the output indexes, a loss function is constructed, including a basic loss, a power dynamic mapping loss, a state migration loss and a response feature loss, and the overall optimization of the model is carried out through a comprehensive mapping loss, a UAV climbing process energy-saving effect prediction analysis method is constructed, including the establishment of a total energy efficiency factor, energy consumption characteristic evaluation, analysis of power system efficiency potential and flight state optimization potential, and establishment of an optimization decision mechanism.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle (UAV) control, and in particular relates to a method and system for predicting and analyzing the energy-saving effect of a UAV's climbing process. Background Art

[0002] As drone applications expand across various fields, energy consumption becomes increasingly prominent, particularly during the ascent phase. This phase consumes the most energy during a drone's flight, requiring the overcoming of gravitational potential energy changes and air resistance, placing extremely high demands on the power system's energy conversion efficiency. Currently, insufficient drone endurance has become a key bottleneck restricting its widespread adoption. There is an urgent need to conduct in-depth research on the energy consumption characteristics of the ascent process and establish scientific methods for predicting and analyzing energy-saving effects, providing a theoretical basis for optimizing flight strategies and improving energy efficiency.

[0003] Existing methods for predicting the energy efficiency of drones during climbs primarily suffer from a single prediction model. Most methods focus solely on single parameters such as battery discharge characteristics or motor efficiency, overlooking the coupled effects of the powertrain, flight attitude, and environmental conditions during the climb process. This simplified approach fails to accurately describe the energy conversion patterns during actual flight, resulting in significant discrepancies between predicted results and actual energy consumption. Especially under complex meteorological conditions, environmental factors such as temperature, humidity, and wind speed have a more significant impact on energy consumption, yet existing prediction methods lack comprehensive consideration of these factors.

[0004] Existing methods for evaluating energy-saving effects generally lack systematicity and dynamism. The evaluation index system is incomplete, making it difficult to fully reflect energy efficiency. The evaluation process lacks in-depth analysis of energy flow and conversion characteristics during flight. A mapping relationship between energy consumption status and flight parameters is not established. Furthermore, there is a lack of quantitative analysis of energy consumption differences under different climb strategies. These deficiencies result in low reliability of energy-saving effect evaluation results, making it difficult to provide effective guidance for climb strategy optimization.

[0005] Existing technologies still have significant limitations in the practical application of predictive analysis methods. Predictive models lack adaptability, making them difficult to adapt to diverse mission scenarios and environmental conditions; and prediction results often fail to directly reflect energy-saving potential. These issues severely impact the effectiveness of energy-saving prediction and analysis methods in practical applications, hindering the development and application of energy-saving technologies for drones. Summary of the Invention

[0006] The problem to be solved by the present invention is to realize the practical application of the energy-saving prediction and analysis method of unmanned aerial vehicle (UAV), and propose a method and system for predicting and analyzing the energy-saving effect of the climbing process of a UAV.

[0007] To achieve the above object, the present invention is implemented through the following technical solutions:

[0008] A method for predicting and analyzing the energy-saving effect of a UAV during a climbing process includes the following steps:

[0009] S1. Collect and normalize the input parameters for the performance prediction of the UAV during the climb process, including the power system input parameters, motion state input parameters, and environmental input parameters;

[0010] S2. Construct a performance prediction model for the UAV's climbing process, using a three-layer architecture consisting of a dynamic characteristics layer, a state response layer, and an inter-layer interaction layer.

[0011] S3. Based on the main layer of the performance prediction model for the UAV climbing process obtained in step S2, construct the model output indicators, including a dynamic characteristic indicator group and a climbing ability indicator group, and establish the association relationship between the main layer of the model and the output indicators using a fully connected approach;

[0012] S4. Construct a performance prediction model for a UAV during climb, including a loss function consisting of a base loss, a power dynamic mapping loss, a state transition loss, and a response feature loss. The model is then optimized using the integrated mapping loss.

[0013] S5. Construct a training set, a validation set, and a test set based on the input parameter data of the performance prediction of the drone climbing process determined in step S1 and the normalized output index data obtained in step S3;

[0014] The parameters of a constructed performance prediction model for the climbing process of a UAV are set, and then the performance prediction model for the climbing process of a UAV is trained using a training set, verified using a validation set, and tested using a test set.

[0015] S6. Denormalize the prediction results of the performance prediction model for the UAV's climb process obtained in step S5 to obtain data in physical units, and construct a prediction and analysis method for the energy-saving effect of the UAV's climb process, including establishing an overall energy efficiency factor, evaluating energy consumption characteristics, analyzing the power system efficiency potential and flight state optimization potential, and establishing an optimization decision-making mechanism.

[0016] Furthermore, the input parameters for the performance prediction of the UAV climbing process in step S1 are as follows:

[0017] The power system input parameters include motor input voltage A1, motor input current A2, propeller speed A3, measured thrust A4, motor surface temperature A5, motor remaining capacity A6, motor power factor A7 and motor battery internal resistance A8;

[0018] The motion state input parameters include vertical speed A9, horizontal speed A 10, instantaneous height A 11 , pitch angle A 12 Total weight A 13 , vertical acceleration A 14 , dynamic pressure A 15 and lift coefficient A 16 ;

[0019] Environmental input parameters include atmospheric pressure A 17 、Ambient temperature A 18 , relative humidity A 19 , horizontal wind speed A 20 , vertical wind speed A 21 、Air density A 22 , gravitational acceleration A 23 , air viscosity A 24 , and Reynolds number A 25 .

[0020] Furthermore, the specific implementation method of step S2 includes the following steps:

[0021] S2.1. Construct a power characteristics layer. Model the four dimensions of motor input, thrust-to-weight characteristics, energy efficiency characteristics, and thrust output, incorporating weight coefficients and bias terms. The motor input sublayer focuses on basic power output capability; the thrust-to-weight characteristics sublayer focuses on power distribution; the energy efficiency characteristics sublayer reflects energy conversion efficiency; and the thrust output sublayer reflects the final power output.

[0022] S2.2. Construct a state response layer to describe the UAV's response characteristics during the climb process, using the velocity response sublayer, acceleration response sublayer, attitude response sublayer, and energy efficiency sublayer. An LSTM structure is introduced to capture temporal evolution patterns.

[0023] S2.3. The inter-layer interaction layer first extracts environmental impact features, dynamic state features, and motion features, and then performs feature fusion.

[0024] Furthermore, the indicators in the dynamic characteristic index group in step S3 include thrust response time D1, attitude adjustment rate D2, speed tracking error D3, power fluctuation rate D4, thrust-to-weight ratio dynamic value D5 and attitude stability D6, and the indicators in the climbing ability index group include average climb rate D7, energy efficiency index D8, track keeping accuracy D9, power margin D10, and so on. 10 , lift-to-drag ratio D 11 and climb stability index D 12 .

[0025] Furthermore, the specific implementation method of step S4 includes the following steps:

[0026] S4.1. Constructing base losses;

[0027] The basic loss function adopts the form of mean square error, that is, the mean square error between the predicted value and the measured value of all output indicators is calculated, which is the basic loss ;

[0028] S4.2. Constructing power dynamic mapping loss;

[0029] During the climbing process of the UAV, the input voltage and current are converted electrically and mechanically to generate thrust output, accompanied by temperature changes and the dynamic evolution of thrust-to-weight characteristics. The power dynamic mapping loss is characterized and the expression is obtained as follows:

[0030] ;

[0031] in, Dynamic mapping loss for power; is the power deviation coefficient, is the temperature response coefficient, is the thrust-to-weight ratio coefficient;

[0032] S4.3. Constructing state transition loss;

[0033] The climbing process involves state space migration characteristics, including the coupling of multi-dimensional forces and the dynamic changes of velocity and acceleration. It is characterized by state migration loss and the expression is:

[0034] ;

[0035] in, is the state transition loss, is the cosine function, is the characteristic area, is the air speed, is the characteristic time interval, is the dynamic balance coefficient, is the speed characteristic coefficient;

[0036] S4.4. Constructing Response Feature Loss;

[0037] The system response characteristics of the UAV during climbing are characterized by a high-order nonlinear dynamic process, including the coupling of attitude dynamic characteristics, efficiency response characteristics, and stability characteristics. The expression is obtained by characterizing the response characteristic loss:

[0038] ;

[0039] in, For response feature loss; is the characteristic parameter, is the transfer coefficient; 、 、 are the weight coefficients of attitude dynamic characteristics, efficiency response characteristics and stability characteristics respectively; is the angle and acceleration conversion coefficient; is the stability and lift-to-drag ratio conversion coefficient;

[0040] S4.5. Constructing a comprehensive mapping loss.

[0041] Based on the unified description of dynamic characteristics, state characteristics and response characteristics, a comprehensive mapping loss function is constructed , the expression is:

[0042] ;

[0043] in, 、 、 、 are the weight coefficients of basic loss, power dynamic mapping loss, state transition loss and response feature loss respectively.

[0044] Furthermore, step S5 divides the data into a training set, a validation set, and a test set in a ratio of 8:1:1; the model training strategy is as follows:

[0045] The model is trained using the back-propagation algorithm. During the training process, the model calculates the predicted value through forward propagation, substitutes the predicted value and the true value into the loss function to calculate the loss, and then updates the network parameters through back-propagation. To prevent overfitting, an early stopping strategy is adopted, and training is stopped when the validation set loss does not decrease for 10 consecutive epochs.

[0046] After the model training is completed, the relationship between input and output is established, and the output result is restored to the actual physical quantity through denormalization.

[0047] Furthermore, the specific implementation method of step S6 includes the following steps:

[0048] S6.1. Combine the power conversion efficiency factor and the propulsion efficiency factor to establish an overall energy efficiency factor;

[0049] Power conversion efficiency factor The establishment of takes into account the coupling relationship between the motor working state and temperature influence, and reflects the energy conversion performance of the motor through the combination of voltage, current, power and temperature correction coefficient. The expression is:

[0050] ;

[0051] in, Characterizes the effective input power, Characterizes the power consumption of internal resistance, is the rated operating temperature, obtained from the design specifications; is the temperature effect correction factor, obtained from design instructions or expert experience;

[0052] Propulsion efficiency factor The establishment of takes the ratio of thrust output to power input as the basis, combines the wind field effect correction, and reflects the influence of aerodynamic characteristics on propulsion efficiency. The expression is:

[0053] ;

[0054] in, Characterizes the combined speed of horizontal wind speed and vertical wind speed; The wind field impact correction coefficient is used to adjust the intensity of environmental factors and is obtained from design specifications or expert experience. is the rated wind speed, obtained from the design specifications;

[0055] Overall energy efficiency factor Reflecting the complete energy conversion chain from input electrical energy to final thrust output, the expression is:

[0056] ;

[0057] S6.2. Energy Consumption Characteristic Assessment: Based on the understanding of basic efficiency indicators, further evaluate the energy consumption characteristics of the system during actual operation. Considering the dynamic characteristics of flight, including climb rate, power fluctuation, and power margin factors, two evaluation dimensions, namely the instantaneous energy consumption factor and the energy utilization efficiency factor, are established to comprehensively reflect the energy consumption characteristics of the system during dynamic operation.

[0058] Instantaneous energy consumption factor Based on the relationship between power input and power output, density correction and power-related correction terms are introduced to describe the instantaneous energy consumption characteristics of the system in the dynamic process. The expression is:

[0059] ;

[0060] in, is the standard atmospheric density, is the density influence coefficient, is the margin correction factor, which is determined by referring to the design instructions or expert experience; It is the reference power margin, determined with reference to the design specifications; This is the base power, determined by referring to the design specifications;

[0061] Energy efficiency factor Taking into account the influence of basic efficiency and dynamic fluctuation, the correction coefficient is used to adjust the influence of power fluctuation and climbing stability on energy utilization efficiency, and the expression is obtained as follows:

[0062] ;

[0063] in, is the reference power fluctuation rate, determined with reference to the design specifications; It is a reference climbing stability index, determined by reference to design specifications or expert experience; is the fluctuation influence coefficient, is the stability correction factor, determined by referring to the design instructions or expert experience;

[0064] S6.3. Analyze the potential for power system efficiency and flight optimization, and quantify the potential for system improvement by comparing current performance with theoretical optimal performance.

[0065] Powertrain efficiency potential factor In order to quantify the optimization space of the system in terms of power efficiency by comparing with the theoretical optimal efficiency and combining the speed deviation correction, the expression is:

[0066] ;

[0067] in, To refer to the optimal efficiency, it is determined by reference to the design specifications or expert experience; is the rated speed, refer to the design instructions; is the working point deviation correction factor, which is determined by referring to the design instructions or expert experience;

[0068] Flight status optimization potential factor Based on the relationship between energy consumption characteristics and posture parameters, the arctan function is used to describe the impact of posture deviation on optimization potential. The expression is:

[0069] ;

[0070] in, Refer to the optimal energy consumption, is the attitude deviation correction coefficient, which is determined by referring to the design instructions or expert experience;

[0071] S6.4. Construct optimization decision indicators;

[0072] Design the power optimization coefficient and flight optimization coefficient based on the overall energy efficiency factor, instantaneous energy consumption factor, energy utilization efficiency factor, power system efficiency potential factor, and flight state optimization potential factor, construct optimization decision indicators, quantify optimization requirements, and determine the optimal optimization direction and specific measures;

[0073] The expression of the power optimization coefficient POC is:

[0074] ;

[0075] in, is the adjustment factor;

[0076] The expression of flight optimization coefficient FOC is:

[0077] ;

[0078] in, is the reference power margin, is the reference climb stability index;

[0079] The optimization decision index ODI is to determine the direction that the system most needs to be optimized by comparing the minimum values ​​of the power optimization coefficient POC and the flight optimization coefficient FOC, and provide a quantitative basis for subsequent optimization decisions. The expression is:

[0080] ;

[0081] S6.5. Establish an optimization decision-making mechanism based on the optimization decision index (ODI). Determine the primary direction of system optimization by comparing the power optimization coefficient (POC) and the flight optimization coefficient (FOC). Furthermore, classify the optimization levels based on the specific ODI values ​​and formulate corresponding optimization measures.

[0082] set up To optimize the first threshold corresponding to the decision indicator ODI, To optimize the second threshold corresponding to the decision-making indicator ODI, it is determined by expert experience;

[0083] when If POC < FOC, you need to immediately check and adjust the motor operating voltage and current to improve the motor power factor and propeller speed. At the same time, monitor the motor temperature to ensure that the motor temperature is below 85% of the rated temperature. Evaluate the battery internal resistance. If it exceeds the standard, replace the battery. If POC ≥ FOC, you need to immediately adjust the pitch angle and optimize the vertical and horizontal speeds. At the same time, reduce the power fluctuation rate and maintain the power margin between 1.2 and 1.5.

[0084] when When the aircraft is in operation, preventive optimization should be carried out, key parameters of the power system and flight status should be checked weekly, and the overall performance of the system should be evaluated monthly;

[0085] when Just keep the current working status.

[0086] A system for predicting and analyzing the energy-saving effect of a drone's climbing process 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 method for predicting and analyzing the energy-saving effect of a drone's climbing process are implemented.

[0087] Beneficial effects of the present invention:

[0088] The present invention addresses the technical challenges of predicting and analyzing the energy-saving effects of a drone's climb process. A multi-level basic efficiency evaluation system, encompassing both power conversion efficiency and propulsion efficiency, was established, addressing the difficulty in accurately describing the energy conversion link. A method for evaluating instantaneous energy consumption characteristics, taking into account environmental factors such as temperature and humidity, was proposed, overcoming the drawback of traditional methods' insufficient prediction accuracy under dynamic conditions. A system potential analysis model based on energy utilization efficiency was constructed, enabling precise quantification of energy-saving optimization directions. A hierarchical optimization decision-making mechanism was designed, providing a scientific basis for the formulation and implementation of energy-saving strategies.

[0089] The energy-saving prediction and analysis method for the climb process of unmanned aerial vehicles (UAVs) described in this invention establishes a complete energy-saving prediction and analysis system with significant advantages. A multi-level basic efficiency indicator system accurately depicts the entire energy conversion process. An assessment method based on instantaneous energy consumption characteristics and energy utilization efficiency improves the accuracy and reliability of prediction results. The introduction of a system potential analysis model clarifies the direction of energy-saving optimization. The use of a hierarchical optimization decision-making mechanism ensures the scientific nature of optimization measures and improves implementation effectiveness. This method provides strong support for energy-saving optimization during the climb process of UAVs. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 This is a flow chart of a method for predicting and analyzing the energy-saving effect of a UAV climbing process according to the present invention;

[0091] Figure 2 This is a comparison chart of the ODI calculation results under different working conditions of the present invention. DETAILED DESCRIPTION

[0092] 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.

[0093] 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.

[0094] 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:

[0095] Example 1:

[0096] A method for predicting and analyzing the energy-saving effect of a UAV during a climbing process includes the following steps:

[0097] S1. Collect and normalize the input parameters for the performance prediction of the UAV during the climb process, including the power system input parameters, motion state input parameters, and environmental input parameters;

[0098] Furthermore, the input parameters for the performance prediction of the UAV climbing process in step S1 are as follows:

[0099] The power system input parameters include motor input voltage A1, motor input current A2, propeller speed A3, measured thrust A4, motor surface temperature A5, motor remaining capacity A6, motor power factor A7 and motor battery internal resistance A8;

[0100] The motion state input parameters include vertical speed A9, horizontal speed A 10 , instantaneous height A 11 , pitch angle A 12 Total weight A 13 , vertical acceleration A 14 , dynamic pressure A 15 and lift coefficient A 16 ;

[0101] Environmental input parameters include atmospheric pressure A 17 、Ambient temperature A 18 , relative humidity A 19 , horizontal wind speed A 20 , vertical wind speed A 21 、Air density A 22 , gravitational acceleration A 23 , air viscosity A 24 , and Reynolds number A 25 .

[0102] S2. Construct a performance prediction model for the UAV's climbing process, using a three-layer architecture consisting of a dynamic characteristics layer, a state response layer, and an inter-layer interaction layer.

[0103] Furthermore, the specific implementation method of step S2 includes the following steps:

[0104] S2.1. Construct a power characteristics layer. Model the four dimensions of motor input, thrust-to-weight characteristics, energy efficiency characteristics, and thrust output, incorporating weight coefficients and bias terms. The motor input sublayer focuses on basic power output capability; the thrust-to-weight characteristics sublayer focuses on power distribution; the energy efficiency characteristics sublayer reflects energy conversion efficiency; and the thrust output sublayer reflects the final power output.

[0105] S2.1.1. The design of the motor input sublayer uses the ReLU activation function for nonlinear mapping. It multiplies all the power system input parameters and environmental parameters, including atmospheric pressure, ambient temperature, and relative humidity, by their corresponding weight coefficients, and then adds a bias term. Finally, the output value of the motor input sublayer is obtained through the ReLU function. ;

[0106] S2.1.2. The thrust-weight characteristic sublayer uses the ReLU activation function to focus on the relationship between thrust and weight. Characterize the thrust-to-weight ratio characteristics, Characterize the relationship between power consumption and battery capacity, Characterize the nonlinear effect of wind speed on thrust-weight characteristics and obtain the output value of the thrust-weight characteristic sublayer , the expression is:

[0107] ;

[0108] in, is the bias term for the feature sub-layer, is the thrust-to-weight ratio influence weight coefficient, is the power consumption rate weight coefficient, is the weight coefficient of vertical acceleration to thrust-weight characteristic, is the weight coefficient of dynamic pressure on thrust-weight characteristics, is the wind field interference weight coefficient, is the weight coefficient of air pressure on thrust-weight characteristics, is the weight coefficient of gravitational acceleration on thrust-weight characteristics;

[0109] S2.1.3. The energy efficiency feature sublayer uses the sigmoid activation function to focus on energy conversion efficiency, using Characterize the effect of temperature gradient on energy conversion efficiency, Characterize the impact of aerodynamic effects on energy conversion and obtain the output value of the energy efficiency characteristic sublayer , the expression is:

[0110] ;

[0111] in, is the bias term of the energy efficiency characteristic sublayer; is the temperature difference loss weight coefficient, is the weight coefficient of the influence of the motor's remaining capacity on energy efficiency, is the weight coefficient of the influence of power factor on energy efficiency, is the weight coefficient of the influence of the motor battery internal resistance on energy efficiency, is the dynamic energy consumption weight coefficient, Aerodynamic efficiency weight coefficient, is the density correction weight coefficient;

[0112] S2.1.4. The thrust output sublayer uses the tanh function to focus on the final output effect, using Characterize the coupling effect of air and gravity loads and obtain the output value of the thrust output sublayer , the expression is:

[0113] ;

[0114] in, is the bias term of the inference output sublayer; is the weight coefficient of the influence of speed on thrust output, is the influence weight coefficient of the measured thrust on the thrust output, is the weight coefficient of the vertical speed on the thrust output, is the weight coefficient of the influence of horizontal velocity on thrust output, The weight coefficient of the influence of altitude on thrust output, is the weight coefficient of the effect of pitch angle on thrust output, is the weight coefficient of the influence of lift coefficient on thrust output, is the weight coefficient of air-gravity coupling effect;

[0115] S2.1.5. Constructing the output of dynamical feature layer fusion , the expression is:

[0116] ;

[0117] in, 、 、 、 are the weight coefficients corresponding to the motor input sublayer, thrust-weight characteristic sublayer, energy efficiency characteristic sublayer, and thrust output sublayer respectively;

[0118] S2.2. Construct a state response layer to describe the UAV's response characteristics during the climb process, using the velocity response sublayer, acceleration response sublayer, attitude response sublayer, and energy efficiency sublayer. An LSTM structure is introduced to capture temporal evolution patterns.

[0119] S2.2.1. The speed response sublayer integrates motor operating parameters, flight state parameters, and environmental parameters, and uses the ReLU activation function to capture the dynamic characteristics of the speed change process. Reflects the direct relationship between the motor input power and the speed response, and obtains the output value of the speed response sublayer , the expression is:

[0120] ;

[0121] in, is the bias term of the speed response sublayer; is the influence weight coefficient of vertical velocity, is the influence weight coefficient of horizontal velocity, is the weight coefficient of the influence of altitude on speed response, is the influence weight coefficient of horizontal wind speed on speed response, is the weight coefficient of the vertical wind speed on the speed response, is the weight coefficient of the influence of input power on speed response, is the influence weight coefficient of the velocity response of the thrust, is the weight coefficient of the influence of air density on speed response;

[0122] S2.2.2. The acceleration response sublayer uses the tanh function to describe the dynamic characteristics of the acceleration process, comprehensively considering the thrust system parameters and aerodynamic characteristics to achieve accurate modeling of the acceleration performance. Considering the impact of load changes on acceleration characteristics, the acceleration capability of the UAV is reflected and the output value of the acceleration response sublayer is obtained. , the expression is:

[0123] ;

[0124] in, is the bias term of the acceleration response sublayer; 、 、 、 、 、 、 are the weight coefficients of vertical acceleration, thrust-to-weight ratio, gravity acceleration, aerodynamic pressure, propeller speed, lift coefficient and air viscosity on acceleration characteristics;

[0125] S2.2.3. The attitude response sublayer uses the sigmoid function to characterize the attitude adjustment process, couples the flight attitude parameters with the influence of environmental factors, and constructs an attitude dynamic response model. Characterizes the intensity of horizontal wind disturbances experienced by the aircraft during vertical motion, which directly affects attitude stability; Reflects the interference intensity of rising or falling airflow on the horizontal motion of the aircraft, affects the attitude control requirements, and obtains the output value of the attitude response sublayer , the expression is:

[0126] ;

[0127] in, is the bias term of the attitude response sublayer; 、 、 、 、 、 are the weight coefficients of the influence of pitch angle, dynamic pressure, lateral interference, longitudinal interference, Reynolds number and atmospheric pressure on the attitude of the UAV;

[0128] S2.2.4. The energy efficiency sublayer establishes an energy efficiency model through the ReLU function, combines the motor characteristics, thrust characteristics and environmental parameters, reflects the efficiency change law in the energy conversion process, considers the energy conversion efficiency under environmental disturbance, and uses Reflects the effectiveness of thrust output under given wind conditions, that is, the ability of thrust output to counteract air kinetic energy loss, and obtains the output value of the energy efficiency sublayer , the expression is:

[0129] ;

[0130] in, is the bias term of the energy efficiency sublayer; 、 、 、 、 、 These are the weight coefficients of lift coefficient, kinetic energy compensation, motor remaining capacity, motor power factor, motor battery internal resistance, and relative humidity on energy efficiency;

[0131] S2.2.5. Use the LSTM network to process the output sequences of the speed response sublayer, acceleration response sublayer, posture response sublayer, and energy efficiency sublayer, and conduct in-depth mining of the state response time series features to obtain the output value of the time series feature embedding layer. , the expression is:

[0132] ;

[0133] in, It is a long short-term memory neural network module;

[0134] S2.2.6. Constructing the output of state-response layer fusion , the expression is:

[0135] ;

[0136] in, is the weight coefficient of the output value of the temporal feature embedding layer, is the weight coefficient for the feature fusion of the velocity response sublayer, acceleration response sublayer, attitude response sublayer and energy efficiency sublayer;

[0137] S2.3. The inter-layer interaction layer first extracts environmental impact features, dynamic state features, and motion features, and then performs feature fusion.

[0138] S2.3.1. The environmental impact feature sublayer assigns corresponding weight coefficients to the environmental input parameters and processes the sum of the weighted environmental input parameters through the ReLU function to obtain the output value E of the environmental impact feature sublayer.

[0139] S2.3.2. The power state feature sublayer assigns corresponding weight coefficients to the power system input parameters and processes the sum of the weighted power system input parameters through the ReLU function to obtain the output value F of the power state feature sublayer.

[0140] S2.3.3. The motion feature sublayer assigns corresponding weight coefficients to the motion state input parameters and processes the sum of the weighted motion state input parameters through the ReLU function to obtain the output value G of the motion feature sublayer.

[0141] S2.3.4. Constructing the output of inter-layer interaction fusion , the expression is:

[0142] ;

[0143] in, 、 、 、 、 They are the weight coefficients corresponding to the fusion output of the dynamic characteristic layer, the fusion output of the state response layer, the output of the environmental impact feature sub-layer, the output of the dynamic state feature sub-layer and the output of the motion feature sub-layer.

[0144] S3. Based on the main layer of the performance prediction model for the UAV climbing process obtained in step S2, construct the model output indicators, including a dynamic characteristic indicator group and a climbing ability indicator group, and establish the association relationship between the main layer of the model and the output indicators using a fully connected approach;

[0145] Furthermore, the indicators in the dynamic characteristic index group in step S3 include thrust response time D1, attitude adjustment rate D2, speed tracking error D3, power fluctuation rate D4, thrust-to-weight ratio dynamic value D5 and attitude stability D6, and the indicators in the climbing ability index group include average climb rate D7, energy efficiency index D8, track keeping accuracy D9, power margin D10, and so on. 10 , lift-to-drag ratio D 11 and climb stability index D 12 , the specific implementation method includes the following steps:

[0146] S3.1. Establish a correlation between the model main layer and the indicators in the dynamic characteristic indicator group;

[0147] ;

[0148] ;

[0149] ;

[0150] ;

[0151] ;

[0152] ;

[0153] in, 、 、 、 、 、 They are the full connections corresponding to thrust response time, attitude adjustment rate, speed tracking error, power fluctuation rate, thrust-to-weight ratio dynamic value and attitude stability;

[0154] S3.2. Establish a correlation between the model's main layer and the indicators in the climb capability indicator group;

[0155] ;

[0156] ;

[0157] ;

[0158] ;

[0159] ;

[0160] ;

[0161] in, 、 、 、 、 、 They are the full connections corresponding to the average climb rate, energy efficiency index, track keeping accuracy, power margin, lift-to-drag ratio and climb stability index.

[0162] S4. Construct a performance prediction model for a UAV during climb, including a loss function consisting of a base loss, a power dynamic mapping loss, a state transition loss, and a response feature loss. The model is then optimized using the integrated mapping loss.

[0163] Furthermore, the specific implementation method of step S4 includes the following steps:

[0164] S4.1. Constructing base losses;

[0165] The basic loss function adopts the form of mean square error, that is, the mean square error between the predicted value and the measured value of all output indicators is calculated, which is the basic loss ;

[0166] S4.2. Constructing power dynamic mapping loss;

[0167] During the climbing process of the UAV, the input voltage and current are converted electrically and mechanically to generate thrust output, accompanied by temperature changes and the dynamic evolution of thrust-to-weight characteristics. The power dynamic mapping loss is characterized and the expression is obtained as follows:

[0168] ;

[0169] in, Dynamic mapping loss for power; is the power deviation coefficient, is the temperature response coefficient, is the thrust-to-weight ratio coefficient;

[0170] S4.3. Constructing state transition loss;

[0171] The climbing process involves state space migration characteristics, including the coupling of multi-dimensional forces and the dynamic changes of velocity and acceleration. It is characterized by state migration loss and the expression is:

[0172] ;

[0173] in, is the state transition loss, is the cosine function, is the characteristic area, is the air speed, is the characteristic time interval, is the dynamic balance coefficient, is the speed characteristic coefficient;

[0174] S4.4. Constructing Response Feature Loss;

[0175] The system response characteristics of the UAV during climbing are characterized by a high-order nonlinear dynamic process, including the coupling of attitude dynamic characteristics, efficiency response characteristics, and stability characteristics. The expression is obtained by characterizing the response characteristic loss:

[0176] ;

[0177] in, For response feature loss; is the characteristic parameter, is the transfer coefficient; 、 、 are the weight coefficients of attitude dynamic characteristics, efficiency response characteristics and stability characteristics respectively; is the angle and acceleration conversion coefficient; is the stability and lift-to-drag ratio conversion coefficient;

[0178] S4.5. Constructing a comprehensive mapping loss.

[0179] Based on the unified description of dynamic characteristics, state characteristics and response characteristics, a comprehensive mapping loss function is constructed , the expression is:

[0180] ;

[0181] in, 、 、 、 are the weight coefficients of basic loss, power dynamic mapping loss, state transition loss and response feature loss respectively.

[0182] S5. Based on the input parameter data of the performance prediction of the drone climbing process determined in step S1 and the normalized output index data obtained in step S3, construct a training set, a validation set, and a test set;

[0183] The parameters of a constructed performance prediction model for the climbing process of a UAV are set, and then the performance prediction model for the climbing process of a UAV is trained using a training set, verified using a validation set, and tested using a test set.

[0184] Furthermore, step S5 divides the data into a training set, a validation set, and a test set in a ratio of 8:1:1; the model training strategy is as follows:

[0185] The model is trained using the back-propagation algorithm. During the training process, the model calculates the predicted value through forward propagation, substitutes the predicted value and the true value into the loss function to calculate the loss, and then updates the network parameters through back-propagation. To prevent overfitting, an early stopping strategy is adopted, and training is stopped when the validation set loss does not decrease for 10 consecutive epochs.

[0186] After the model training is completed, the relationship between input and output is established, and the output result is restored to the actual physical quantity through denormalization.

[0187] Furthermore, some input data of the performance prediction model of the UAV climbing process are shown in Table 1;

[0188] Table 1

[0189]

[0190] S6. Denormalize the prediction results of the performance prediction model for the UAV's climb process obtained in step S5 to obtain data in physical units, and construct a prediction and analysis method for the energy-saving effect of the UAV's climb process, including establishing an overall energy efficiency factor, evaluating energy consumption characteristics, analyzing the power system efficiency potential and flight state optimization potential, and establishing an optimization decision-making mechanism.

[0191] Furthermore, the system first evaluates basic energy conversion efficiency, including electrical energy conversion and propulsion efficiency. Secondly, it analyzes the system's energy consumption characteristics and assesses energy utilization efficiency. Finally, based on the initial analysis, it identifies the system's optimization potential and provides optimization recommendations. This hierarchical analysis approach ensures a comprehensive and systematic evaluation, effectively guiding energy-saving optimization of UAV systems.

[0192] Furthermore, the specific implementation method of step S6 includes the following steps:

[0193] S6.1. Combine the power conversion efficiency factor and the propulsion efficiency factor to establish an overall energy efficiency factor;

[0194] During a drone's ascent, energy conversion progresses from electrical energy to mechanical energy and then to propulsion energy. Electrical energy conversion efficiency is primarily affected by factors such as the motor's operating state and temperature, while propulsion efficiency is closely related to aerodynamic characteristics and environmental conditions. By establishing basic efficiency metrics, we can accurately evaluate the system's performance at each stage of energy conversion, providing foundational data for subsequent optimization.

[0195] Power conversion efficiency factor The establishment of takes into account the coupling relationship between the motor working state and temperature influence, and reflects the energy conversion performance of the motor through the combination of voltage, current, power and temperature correction coefficient. The expression is:

[0196] ;

[0197] in, Characterizes the effective input power, Characterizes the power consumption of internal resistance, is the rated operating temperature, obtained from the design specifications; is the temperature effect correction factor, obtained from design instructions or expert experience;

[0198] Propulsion efficiency factor The establishment of takes the ratio of thrust output to power input as the basis, combines the wind field effect correction, and reflects the influence of aerodynamic characteristics on propulsion efficiency. The expression is:

[0199] ;

[0200] in, Characterizes the combined speed of horizontal wind speed and vertical wind speed; The wind field impact correction coefficient is used to adjust the intensity of environmental factors and is obtained from design specifications or expert experience. is the rated wind speed, obtained from the design specifications;

[0201] Overall energy efficiency factor Reflecting the complete energy conversion chain from input electrical energy to final thrust output, the expression is:

[0202] ;

[0203] S6.2. Energy Consumption Characteristic Assessment: Based on the understanding of basic efficiency indicators, further evaluate the energy consumption characteristics of the system during actual operation. Considering the dynamic characteristics of flight, including climb rate, power fluctuation, and power margin factors, two evaluation dimensions, namely the instantaneous energy consumption factor and the energy utilization efficiency factor, are established to comprehensively reflect the energy consumption characteristics of the system during dynamic operation.

[0204] Instantaneous energy consumption factor Based on the relationship between power input and power output, density correction and power-related correction terms are introduced to describe the instantaneous energy consumption characteristics of the system in the dynamic process. The expression is:

[0205] ;

[0206] in, is the standard atmospheric density, is the density influence coefficient, is the margin correction factor, which is determined by referring to the design instructions or expert experience; It is the reference power margin, determined with reference to the design specifications; This is the base power, determined by referring to the design specifications;

[0207] Energy efficiency factor Taking into account the influence of basic efficiency and dynamic fluctuation, the correction coefficient is used to adjust the influence of power fluctuation and climbing stability on energy utilization efficiency, and the expression is obtained as follows:

[0208] ;

[0209] in, is the reference power fluctuation rate, determined with reference to the design specifications; It is a reference climbing stability index, determined by reference to design specifications or expert experience; is the fluctuation influence coefficient, is the stability correction factor, determined by referring to the design instructions or expert experience;

[0210] S6.3. Analyze the potential for power system efficiency and flight optimization, and quantify the potential for system improvement by comparing current performance with theoretical optimal performance.

[0211] Powertrain efficiency potential factor In order to quantify the optimization space of the system in terms of power efficiency by comparing with the theoretical optimal efficiency and combining the speed deviation correction, the expression is:

[0212] ;

[0213] in, To refer to the optimal efficiency, it is determined by reference to the design specifications or expert experience; is the rated speed, refer to the design instructions; is the working point deviation correction factor, which is determined by referring to the design instructions or expert experience;

[0214] Flight status optimization potential factor Based on the relationship between energy consumption characteristics and posture parameters, the arctan function is used to describe the impact of posture deviation on optimization potential. The expression is:

[0215] ;

[0216] in, Refer to the optimal energy consumption, is the attitude deviation correction coefficient, which is determined by referring to the design instructions or expert experience;

[0217] S6.4. Construct optimization decision indicators;

[0218] Design the power optimization coefficient and flight optimization coefficient based on the overall energy efficiency factor, instantaneous energy consumption factor, energy utilization efficiency factor, power system efficiency potential factor, and flight state optimization potential factor, construct optimization decision indicators, quantify optimization requirements, and determine the optimal optimization direction and specific measures;

[0219] The expression of the power optimization coefficient POC is:

[0220] ;

[0221] in, is the adjustment factor;

[0222] The expression of flight optimization coefficient FOC is:

[0223] ;

[0224] in, is the reference power margin, is the reference climb stability index;

[0225] The optimization decision index ODI is to determine the direction that the system most needs to be optimized by comparing the minimum values ​​of the power optimization coefficient POC and the flight optimization coefficient FOC, and provide a quantitative basis for subsequent optimization decisions. The expression is:

[0226] ;

[0227] S6.5. Establish an optimization decision-making mechanism based on the optimization decision index (ODI). Determine the primary direction of system optimization by comparing the power optimization coefficient (POC) and the flight optimization coefficient (FOC). Furthermore, classify the optimization levels based on the specific ODI values ​​and formulate corresponding optimization measures.

[0228] This decision-making method based on quantitative indicators can help quickly identify the weak links in the system and implement targeted optimization measures.

[0229] set up To optimize the first threshold corresponding to the decision indicator ODI, To optimize the second threshold corresponding to the decision-making indicator ODI, it is determined by expert experience;

[0230] when If POC < FOC, you need to immediately check and adjust the motor operating voltage and current to improve the motor power factor and propeller speed. At the same time, monitor the motor temperature to ensure that the motor temperature is below 85% of the rated temperature. Evaluate the battery internal resistance. If it exceeds the standard, replace the battery. If POC ≥ FOC, you need to immediately adjust the pitch angle and optimize the vertical and horizontal speeds. At the same time, reduce the power fluctuation rate and maintain the power margin between 1.2 and 1.5.

[0231] when When the aircraft is in operation, preventive optimization should be carried out, key parameters of the power system and flight status should be checked weekly, and the overall performance of the system should be evaluated monthly;

[0232] when When the current working state is maintained, the ODI calculation results under different working conditions are compared as shown in the figure below. Figure 2 shown.

[0233] Example 2:

[0234] A system for predicting and analyzing the energy-saving effect of a drone climbing process 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 method for predicting and analyzing the energy-saving effect of a drone climbing process as described in Example 1 are implemented.

[0235] 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.

[0236] 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 method for predicting and analyzing the energy-saving effect of a UAV during its climbing process, characterized in that: The steps include: S1. Collect and normalize the input parameters for the performance prediction of the UAV during the climb process, including the power system input parameters, motion state input parameters, and environmental input parameters; S2. Construct a performance prediction model for the UAV's climbing process, using a three-layer architecture consisting of a dynamic characteristics layer, a state response layer, and an inter-layer interaction layer. S3. Based on the main layer of the performance prediction model for the UAV climbing process obtained in step S2, construct the model output indicators, including a dynamic characteristic indicator group and a climbing ability indicator group, and establish the association relationship between the main layer of the model and the output indicators using a fully connected approach; S4. Construct a performance prediction model for a UAV during climb, including a loss function consisting of a base loss, a power dynamic mapping loss, a state transition loss, and a response feature loss. The model is then optimized using the integrated mapping loss. S5. Construct a training set, a validation set, and a test set based on the input parameter data of the performance prediction of the drone climbing process determined in step S1 and the normalized output index data obtained in step S3; S6. Denormalize the prediction results of the UAV climb performance prediction model obtained in step S5 to obtain data in physical units, and construct a prediction and analysis method for the energy-saving effect of the UAV climb process, including establishing an overall energy efficiency factor, evaluating energy consumption characteristics, analyzing the power system efficiency potential and flight state optimization potential, and establishing an optimization decision-making mechanism; The specific implementation method of step S6 includes the following steps: S6.

1. Combine the power conversion efficiency factor and the propulsion efficiency factor to establish an overall energy efficiency factor; Power conversion efficiency factor The establishment of the motor takes into account the coupling relationship between the working state and the temperature influence of the motor, and reflects the energy conversion performance of the motor through the combination of voltage, current, power and temperature correction coefficient; Propulsion efficiency factor The establishment of the ratio of thrust output to power input is taken as the basis, combined with the wind field effect correction, to reflect the impact of aerodynamic characteristics on propulsion efficiency; Overall energy efficiency factor Reflecting the complete energy conversion chain from input electrical energy to final thrust output, the expression is: ； S6.

2. Assessment of energy expenditure characteristics; On the basis of mastering the basic efficiency indicators, further evaluate the energy consumption characteristics of the system during actual operation; Taking into account the dynamic characteristics of the flight process, including climb rate, power fluctuation, and power margin factors, the instantaneous energy consumption factor and energy utilization efficiency factor are established as two evaluation dimensions to fully reflect the energy consumption characteristics of the system in the dynamic process; Instantaneous energy consumption factor Based on the relationship between power input and power output, density correction and power-related correction terms are introduced to describe the instantaneous energy consumption characteristics of the system in the dynamic process; Energy efficiency factor Comprehensively consider the basic efficiency and dynamic fluctuation effects, and adjust the impact of power fluctuation and climbing stability on energy utilization efficiency through correction coefficients; S6.

3. Analyze the potential for power system efficiency and flight optimization, and quantify the potential for system improvement by comparing current performance with theoretical optimal performance. Powertrain efficiency potential factor To quantify the optimization space of the system in terms of power efficiency by comparing with the theoretical optimal efficiency and combining the speed deviation correction; Flight status optimization potential factor Based on the relationship between energy consumption characteristics and posture parameters, the arctan function is used to describe the impact of posture deviation on optimization potential.

2. The energy-saving effect prediction and analysis method for a UAV climbing process according to claim 1 is characterized in that: The input parameters for the performance prediction of the UAV climbing process in step S1 are as follows: The power system input parameters include motor input voltage A1, motor input current A2, propeller speed A3, measured thrust A4, motor surface temperature A5, motor remaining capacity A6, motor power factor A7 and motor battery internal resistance A8; The motion state input parameters include vertical speed A9, horizontal speed A 10 , instantaneous height A 11 , pitch angle A 12 Total weight A 13 , vertical acceleration A 14 , dynamic pressure A 15 and lift coefficient A 16 ; Environmental input parameters include atmospheric pressure A 17 、Ambient temperature A 18 , relative humidity A 19 , horizontal wind speed A 20 , vertical wind speed A 21 、Air density A 22 , gravitational acceleration A 23 , air viscosity A 24 , and Reynolds number A 25 .

3. The energy-saving effect prediction and analysis method for a UAV climbing process according to claim 2 is characterized in that: The specific implementation method of step S2 includes the following steps: S2.

1. Construct a power characteristics layer to model the four dimensions of motor input, thrust-to-weight characteristics, energy efficiency characteristics, and thrust output. Weight coefficients and bias terms are added. The motor input sublayer focuses on basic power output capability. The thrust-weight characteristic sublayer focuses on the power distribution effect; the energy efficiency characteristic sublayer reflects the energy conversion efficiency; The thrust output sublayer reflects the final power effect; S2.1.

1. The design of the motor input sublayer uses the ReLU activation function for nonlinear mapping. It multiplies all the power system input parameters and environmental parameters, including atmospheric pressure, ambient temperature, and relative humidity, by their corresponding weight coefficients, and then adds a bias term. Finally, the output value of the motor input sublayer is obtained through the ReLU function. ; S2.1.

2. The thrust-weight characteristic sublayer uses the ReLU activation function to focus on the relationship between thrust and weight. Characterize the thrust-to-weight ratio characteristics, Characterize the relationship between power consumption and battery capacity, Characterize the nonlinear effect of wind speed on thrust-weight characteristics and obtain the output value of the thrust-weight characteristic sublayer ; S2.1.

3. The energy efficiency feature sublayer uses the sigmoid activation function to focus on energy conversion efficiency, using Characterize the effect of temperature gradient on energy conversion efficiency, Characterize the impact of aerodynamic effects on energy conversion and obtain the output value of the energy efficiency characteristic sublayer ; S2.1.

4. The thrust output sublayer uses the tanh function to focus on the final output effect, using Characterize the coupling effect of air and gravity loads and obtain the output value of the thrust output sublayer ; S2.1.

5. Constructing the output of dynamical feature layer fusion , the expression is: ； in, 、 、 、 are the weight coefficients corresponding to the motor input sublayer, thrust-weight characteristic sublayer, energy efficiency characteristic sublayer, and thrust output sublayer respectively; S2.

2. Construct a state response layer to describe the UAV's response characteristics during the climb process, using the velocity response sublayer, acceleration response sublayer, attitude response sublayer, and energy efficiency sublayer. An LSTM structure is introduced to capture temporal evolution patterns. S2.2.

1. The speed response sublayer integrates motor operating parameters, flight state parameters, and environmental parameters, and uses the ReLU activation function to capture the dynamic characteristics of the speed change process. Reflects the direct relationship between the motor input power and the speed response, and obtains the output value of the speed response sublayer ; S2.2.

2. The acceleration response sublayer uses the tanh function to describe the dynamic characteristics of the acceleration process, comprehensively considering the thrust system parameters and aerodynamic characteristics to achieve accurate modeling of the acceleration performance. Considering the impact of load changes on acceleration characteristics, the acceleration capability of the UAV is reflected and the output value of the acceleration response sublayer is obtained. ; S2.2.

3. The attitude response sublayer uses the sigmoid function to characterize the attitude adjustment process, couples the flight attitude parameters with the influence of environmental factors, and constructs an attitude dynamic response model. Characterizes the intensity of horizontal wind disturbances experienced by the aircraft during vertical motion, which directly affects attitude stability; Reflects the interference intensity of rising or falling airflow on the horizontal motion of the aircraft, affects the attitude control requirements, and obtains the output value of the attitude response sublayer ; S2.2.

4. The energy efficiency sublayer establishes an energy efficiency model through the ReLU function, combines the motor characteristics, thrust characteristics and environmental parameters, reflects the efficiency change law in the energy conversion process, considers the energy conversion efficiency under environmental disturbance, and uses It reflects the effectiveness of thrust output under given wind field conditions, that is, the ability of thrust output to counteract the loss of aerokinetic energy, and obtains the output value of the energy efficiency sub-layer; S2.2.

5. Use the LSTM network to process the output sequences of the speed response sublayer, acceleration response sublayer, posture response sublayer, and energy efficiency sublayer, and conduct in-depth mining of the state response time series features to obtain the output value of the time series feature embedding layer. , the expression is: ； in, It is a long short-term memory neural network module; S2.2.

6. Constructing the output of state-response layer fusion , the expression is: ； in, is the weight coefficient of the output value of the temporal feature embedding layer, is the weight coefficient for the feature fusion of the velocity response sublayer, acceleration response sublayer, attitude response sublayer and energy efficiency sublayer; S2.

3. The inter-layer interaction layer first extracts environmental impact features, dynamic state features, and motion features, and then performs feature fusion. S2.3.

1. The environmental impact feature sublayer assigns corresponding weight coefficients to the environmental input parameters and processes the sum of the weighted environmental input parameters through the ReLU function to obtain the output value E of the environmental impact feature sublayer. S2.3.

2. The power state feature sublayer assigns corresponding weight coefficients to the power system input parameters and processes the sum of the weighted power system input parameters through the ReLU function to obtain the output value F of the power state feature sublayer. S2.3.

3. The motion feature sublayer assigns corresponding weight coefficients to the motion state input parameters and processes the sum of the weighted motion state input parameters through the ReLU function to obtain the output value G of the motion feature sublayer. S2.3.

4. Constructing the output of inter-layer interaction fusion , the expression is: ； in, 、 、 、 、 They are the weight coefficients corresponding to the fusion output of the dynamic characteristic layer, the fusion output of the state response layer, the output of the environmental impact feature sub-layer, the output of the dynamic state feature sub-layer and the output of the motion feature sub-layer.

4. The energy-saving effect prediction and analysis method for a UAV climbing process according to claim 3 is characterized in that: The indicators in the dynamic characteristic index group in step S3 include thrust response time D1, attitude adjustment rate D2, speed tracking error D3, power fluctuation rate D4, thrust-to-weight ratio dynamic value D5 and attitude stability D6. The indicators in the climbing ability index group include average climb rate D7, energy efficiency index D8, track keeping accuracy D9, power margin D10, and so on. 10 , lift-to-drag ratio D 11 and climb stability index D 12 .

5. The energy-saving effect prediction and analysis method for a UAV climbing process according to claim 4 is characterized in that: The specific implementation method of step S4 includes the following steps: S4.

1. Constructing base losses; The basic loss function adopts the form of mean square error, that is, the mean square error between the predicted value and the measured value of all output indicators is calculated, which is the basic loss ; S4.

2. Constructing power dynamic mapping loss; Based on the fact that during the climbing process of the UAV, the input voltage and current are converted electrically and mechanically to generate thrust output, accompanied by temperature changes and dynamic evolution of thrust-weight characteristics, the power dynamic mapping loss is characterized. ; S4.

3. Constructing state transition loss; The climbing process involves state space migration characteristics, including the coupling of multi-dimensional forces and the dynamic changes of velocity and acceleration, which are characterized by state migration loss. ; S4.

4. Constructing Response Feature Loss; The system response characteristics of the UAV during climbing are characterized by a high-order nonlinear dynamic process, including the coupling of attitude dynamic characteristics, efficiency response characteristics and stability characteristics, which is characterized by the response characteristic loss. ; S4.

5. Constructing a comprehensive mapping loss. Based on the unified description of dynamic characteristics, state characteristics and response characteristics, a comprehensive mapping loss function is constructed .

6. The energy-saving effect prediction and analysis method for a UAV climbing process according to claim 5 is characterized in that: Step S5 divides the data into a training set, a validation set, and a test set in a ratio of 8:1:1; The parameters of a constructed performance prediction model for the climbing process of a UAV are set, and then the performance prediction model for the climbing process of a UAV is trained using a training set, verified using a validation set, and tested using a test set. The model training strategy is as follows: The model is trained using the backpropagation algorithm. During training, the model calculates the predicted value through forward propagation, substitutes the predicted value and the true value into the loss function to calculate the loss, and then updates the network parameters through backpropagation. To prevent overfitting, an early stopping strategy is adopted. Training is stopped when the validation set loss does not decrease for 10 consecutive epochs. After the model training is completed, the relationship between input and output is established, and the output result is restored to the actual physical quantity through denormalization.

7. The energy-saving effect prediction and analysis method for a UAV climbing process according to claim 6 is characterized in that: The specific implementation method of step S6 also includes the following steps: S6.

4. Construct optimization decision indicators; Design the power optimization coefficient and flight optimization coefficient based on the overall energy efficiency factor, instantaneous energy consumption factor, energy utilization efficiency factor, power system efficiency potential factor, and flight state optimization potential factor, construct optimization decision indicators, quantify optimization requirements, and determine the optimal optimization direction and specific measures; The expression of the power optimization coefficient POC is: ； in, is the adjustment factor; The expression of flight optimization coefficient FOC is: ； in, is the reference power margin, is the reference climb stability index; The optimization decision index ODI is to determine the direction that the system most needs to be optimized by comparing the minimum values ​​of the power optimization coefficient POC and the flight optimization coefficient FOC, and provide a quantitative basis for subsequent optimization decisions. The expression is: ； S6.

5. Establish an optimization decision-making mechanism based on the optimization decision index (ODI). Determine the direction of system optimization by comparing the power optimization coefficient (POC) and the flight optimization coefficient (FOC). Furthermore, classify the optimization level based on the specific value of the ODI and formulate corresponding optimization measures. set up To optimize the first threshold corresponding to the decision indicator ODI, To optimize the second threshold corresponding to the decision-making indicator ODI, it is determined by expert experience; when If POC < FOC, you need to immediately check and adjust the motor operating voltage and current to improve the motor power factor and propeller speed. At the same time, monitor the motor temperature to ensure that the motor temperature is below 85% of the rated temperature. Evaluate the battery internal resistance. If it exceeds the standard, replace the battery. If POC ≥ FOC, you need to immediately adjust the pitch angle and optimize the vertical and horizontal speeds. At the same time, reduce the power fluctuation rate and maintain the power margin between 1.2 and 1.

5. when When the aircraft is in operation, preventive optimization should be carried out, key parameters of the power system and flight status should be checked weekly, and the overall performance of the system should be evaluated monthly; when Just keep the current working status.

8. A prediction and analysis system for energy-saving effects during the climbing process of a UAV, characterized in that: The method comprises 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 the method for predicting and analyzing the energy-saving effect of a climbing process of a drone as described in any one of claims 1 to 7 are implemented.

Images