Ground-end multi-aircraft cluster flight energy efficiency measurement and analysis method and device

Abstract

The invention relates to the technical field of flapping-wing unmanned aerial vehicles, in particular to a ground-end multi-aircraft cluster flight energy efficiency measurement and analysis method and device. The method comprises the steps that real-time measurement and dynamic optimization of the energy utilization efficiency of the flapping-wing flying robot in the flying process are achieved through an eddy current imaging technology and a neural network optimization algorithm. By accurately calculating the energy exchange relation between the aircraft and the vortex field and adjusting flight parameters in combination with an intelligent optimization algorithm, the energy utilization efficiency of the aircraft is improved, the flight time is prolonged, the energy consumption is reduced, and the aircraft can flexibly and efficiently perform task execution in a complex flight environment.

Description

Technical Field

[0001] The present invention relates to the technical field of flapping-wing UAVs, and in particular to a method and device for measuring and analyzing the energy efficiency of multi-aircraft cluster flight on a ground side. Background Art

[0002] Flapping-wing flying robots, as biomimetic unmanned aerial vehicles, possess unique flight principles compared to traditional rotary-wing and fixed-wing aircraft. They mimic the flapping flight of birds and insects, utilizing mechanical structures to drive wings to generate lift and thrust, resulting in excellent flight efficiency and maneuverability. In particular, during swarm flight, the trailing aircraft utilizes the vortex fields generated by the flapping wings of the leading aircraft to reduce energy consumption, improve energy efficiency, and extend flight range. However, the vortex fields generated by the leading aircraft are complex and variable, composed of leading-edge vortices, trailing-edge vortices, and tip vortices. Existing aerodynamic theories struggle to accurately describe their structure and dynamics, making it difficult to clearly define the mechanism by which the trailing aircraft utilizes these vortex fields.

[0003] Currently, the key to improving energy efficiency in swarm flight lies in accurately detecting and describing the eddy current fields generated by the flapping wings of the leading aircraft, and revealing how the following aircraft can efficiently utilize these eddy current fields. In particular, in actual flight, the eddy current fields are affected by flapping amplitude, phase angle, relative position, and external wind factors. How to achieve real-time adjustments in swarm flight to cope with these changes remains a technical challenge that needs to be solved. Therefore, developing a ground-based method for measuring the aerodynamic performance parameters and optimizing energy efficiency of multiple aircraft, accurately measuring the eddy current field, and optimizing energy utilization in swarm flight has become the key to improving the practical application performance of flapping-wing flying robot swarms. Summary of the Invention

[0004] To address the technical problem of how to achieve real-time adjustment of swarm flight to cope with the changes in flapping amplitude, phase angle, relative position, and external wind factors that affect the eddy current field in actual flight, the present invention provides a ground-based multi-aircraft swarm flight energy efficiency measurement and analysis method and device. The technical solution is as follows:

[0005] On the one hand, a method for measuring and analyzing the energy efficiency of multi-aircraft cluster flight on the ground side is provided, characterized in that the method includes:

[0006] S1. Acquire status data of multiple flapping-wing flying robots and instrument system status data;

[0007] S2. The instrument speed and the flapping-wing flight robot motion state are set through the terminal control system. When the simulated incoming flow velocity reaches the preset experimental speed, tracer particles are scattered in the experimental space. The laser is used to illuminate the cross-section area of the eddy current field, and the camera is used to continuously capture multiple exposure images of the particles.

[0008] S3. Reconstruct the velocity vector map of the eddy current field through the three-dimensional projection relationship and the cross-correlation between multiple particle images, and further calculate the velocity vectors of each point in the eddy current field to obtain the eddy current field model of the flapping-wing flying robot during the motion simulation;

[0009] S4. Based on the velocity vectors of each point in the obtained eddy current field, calculate the interaction energy between the aircraft and the eddy current field during the stable period, and establish the mapping relationship between the energy utilization efficiency of the flapping-wing flying robot and the aircraft parameters in combination with the operation parameters of the aircraft;

[0010] S5. Repeat the test analyzer experiment, collect experimental data under multiple different rotational speed states, and perform data correction based on the neural network of orthogonal polynomials to obtain the multi-aircraft parameters with the highest energy utilization efficiency under the stable state.

[0011] Optionally, in S1, obtain the state data of three flapping-wing flying robots and the state data of the instrument system, including:

[0012] S11. Configure a flight control system on the flapping-wing flying robot;

[0013] S12. Obtain the state data of multiple flapping-wing flying robots and the state data of the instrument system through the test analyzer at the ground end.

[0014] Optionally, in S3, reconstruct the velocity vector map of the eddy current field through the three-dimensional projection relationship, and further calculate the velocity vectors of each point in the eddy current field to obtain the eddy current field model of the flapping-wing flying robot during the motion simulation, including;

[0015] Reconstruct the velocity vector map of the eddy current field through the three-dimensional projection relationship and the cross-correlation between multiple particle images, and further calculate the velocity vectors of each point in the eddy current field to obtain the eddy current field model of the flapping-wing flying robot during the motion simulation, including;

[0016] S31. After the instrument and the flapping-wing flying robot reach the preset operating state, scatter tracer particles in the experimental space, turn on the laser to irradiate the tracer particle area, and capture the tracer particle images through the camera;

[0017] S32. Discretize the three-dimensional space into a series of voxels distributed in the space, and calculate the gray values corresponding to the pixel points in the plane based on the camera projection relationship of the three-dimensional multi-camera measurement system;

[0018] S33. Arrange a search window at any spatial position in the particle image captured by the camera, delimit the matrix search area, and detect and extract the tracer particles;

[0019] S34. For any two adjacent particle images A and B at different times, obtain the three-dimensional particle field gray matrices corresponding to the particle images A and B, and calculate the correlation of the identified tracer particles according to the cross-correlation coefficient;

[0020] S35. By calculating the average displacement of the particles in the defined area and the exposure interval time of the camera, calculate the velocity vector at the coordinate point of the spatial area corresponding to the current tracer particle in the motion space of the flapping flight robot.

[0021] Optionally, in S4, based on the velocity vectors at each point of the obtained eddy current field, calculate the interaction energy between the aircraft and the eddy current field during the stable period, and combine the operation parameters of the aircraft to establish a mapping relationship between the energy utilization efficiency of the flapping flight robot and the aircraft parameters, including:

[0022] S41. Select one of the multiple flapping flight robots as the central flight robot of the multi-aircraft cluster, and the others as the following flight robots. According to the method described in S3, calculate the three-dimensional eddy current field of the cluster aircraft in space under the preset flight speed;

[0023] S42. Collect the real-time pose information of the central aircraft and the following aircraft, specifically including key aircraft parameters such as the angle of attack, flapping frequency, flapping amplitude, torsion angle, heading angle, and attitude angle of the aircraft. According to the wing area of the principle prototype to be measured based on the wing surface geometric parameters, and through coordinate transformation using the collected flapping pose information, calculate the projection of the wing surface perpendicular to the eddy current field to obtain the angle of attack area, and at the same time calculate the relative position relationship between the following aircraft and the central aircraft.

[0024] S43. According to the eddy current field information, aircraft flapping parameters, and the magnitude of the lift and thrust generated by the aircraft collected in steps S41 - S42, the average energy obtained by the following aircraft from the eddy current field within one flapping cycle can be calculated according to the following formula:

[0025] ;

[0026] S44. Repeat the experimental steps S41 - S43, fix the parameters of the central aircraft of the cluster, and adjust the parameters such as the angle of attack and flapping angle of the following aircraft. Use the aircraft parameters and relative pose as the input of the sample set, and the average energy calculated in step S43 as the output of the sample set. Complete the training of the sample set through the neural network based on orthogonal polynomials as follows. Through this sample set model, the aircraft parameters that can obtain the maximum energy under the current eddy current field model can be evaluated:

[0027] .

[0028] Optionally, the distribution information of the eddy current field includes the velocity vector field, the eddy current field intensity distribution, and the turbulent region boundary characteristic data.

[0029] Optionally, in S5, repeat the experiment of the test analyzer, collect experimental data under multiple different rotational speed states, and perform data correction based on the neural network of orthogonal polynomials to obtain the multi-robot parameters with the highest energy utilization efficiency under the stable state, including:

[0030] S51. Arbitrarily select one of the three flapping-wing flying robots as the central node of the multi-robot cluster, and set its preset cruising speed, flight altitude, and flight attitude;

[0031] S52. Assign random initial flight states to the remaining following aircraft, and set the task indicators for the multi-robot cluster flight;

[0032] S53. Start the test analyzer, and real-time monitor the flight states and energy utilization conditions of multiple flapping-wing flying robots through the terminal control system;

[0033] S54. Determine whether the system reaches the stable state based on the monitoring results; and repeat S51 - S53 to obtain multiple groups of experimental data;

[0034] S55. Record multiple groups of experimental data in the experiment, use the phase angle difference, angle of attack difference, and relative position between the aircraft as sample input data, use the calculated energy utilization efficiency as the sample output, and complete the training of the sample set through the neural network of orthogonal polynomials to further optimize the energy utilization efficiency of the aircraft.

[0035] Optionally, determining whether the system reaches the stable state based on the monitoring results includes:

[0036] When the monitoring results indicate that the system does not reach the stable state, based on the mapping relationship between the flight parameters and the available energy established in S4, calculate the flight parameters that the following aircraft need to adjust, send correction instructions to the relevant aircraft through the terminal control system, and continuously monitor the state changes of the aircraft until the stable conditions are met;

[0037] When the monitoring results indicate that the system reaches the stable state, export the relevant data of the current experiment, including the relative position between the aircraft, the energy utilization efficiency, and the results of other task indicators; after completing the experiment of the current task indicators, adjust the flight task parameters of the multi-robot cluster.

[0038] On the other hand, a ground-end multi-robot cluster flight energy efficiency measurement and analysis device is provided. This device is applied to the ground-end multi-robot cluster flight energy efficiency measurement and analysis method, and this device includes:

[0039] An initial data acquisition module, which is used to acquire the state data of multiple flapping-wing flying robots and the state data of the instrument system;

[0040] The eddy current imaging module is used to set the instrument rotation speed and the motion state of the flapping-wing flying robot through the terminal control system. After waiting for the system operation state to be stable, the eddy current imaging module is used to obtain the accurate position of the tracer particles in the image.

[0041] The eddy current field model construction module is used to reconstruct the velocity vector map of the eddy current field through the three-dimensional projection relationship and the cross-correlation between multiple particle images, and further calculate the velocity vectors of each point in the eddy current field to obtain the eddy current field model of the flapping-wing flying robot during the motion simulation process.

[0042] The mapping relationship establishment module is used to calculate the interaction energy between the aircraft and the eddy current field during the stable period based on the obtained velocity vectors of each point in the eddy current field, and combine the operation parameters of the aircraft to establish the mapping relationship between the energy utilization efficiency of the flapping-wing flying robot and the aircraft parameters.

[0043] The test analysis module is used to repeat the test analyzer experiment, collect experimental data under multiple different rotation speed states, and perform data correction based on the neural network of orthogonal polynomials to obtain the multi-aircraft parameters with the highest energy utilization efficiency under the stable state.

[0044] On the other hand, a ground-end multi-aircraft cluster flight energy efficiency measurement and analysis device is provided. The ground-end multi-aircraft cluster flight energy efficiency measurement and analysis device includes: a processor; a memory, and computer-readable instructions are stored on the memory. When the computer-readable instructions are executed by the processor, any one of the methods in the above-mentioned ground-end multi-aircraft cluster flight energy efficiency measurement and analysis method is implemented.

[0045] On the other hand, a computer-readable storage medium is provided. At least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by the processor to implement any one of the methods in the above-mentioned ground-end multi-aircraft cluster flight energy efficiency measurement and analysis method.

[0046] The beneficial effects brought by the technical solutions provided in the embodiments of the present invention at least include:

[0047] In the embodiments of the present invention, through the eddy current imaging technology and the neural network optimization algorithm, the real-time measurement and dynamic optimization of the energy utilization efficiency of the flapping-wing flying robot during the flight process are realized. By accurately calculating the energy exchange relationship between the aircraft and the eddy current field, and combining the intelligent optimization algorithm to adjust the flight parameters, the energy utilization efficiency of the aircraft is improved, the flight time is extended, the energy consumption is reduced, and the aircraft can flexibly and efficiently perform tasks in a complex flight environment. Description of the Drawings

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0049] Figure 1 It is a schematic flowchart of a method for measuring and analyzing the flight energy efficiency of a multi-aircraft cluster at the ground end provided by an embodiment of the present invention;

[0050] Figure 2 It is a schematic diagram of the working of the eddy current imaging module in the test analyzer provided by an example of the present invention;

[0051] Figure 3 It is a flowchart of a method for reconstructing the velocity vector of the eddy current field provided by an example of the present invention;

[0052] Figure 4 It is a flowchart of a method for establishing the relationship between the energy efficiency utilization efficiency of a multi-aircraft cluster and the flight parameters of a flapping-wing flying robot provided by an example of the present invention;

[0053] Figure 5 It is a working flowchart of the ground test analyzer provided by an example of the present invention;

[0054] Figure 6 It is a block diagram of a device for measuring and analyzing the flight energy efficiency of a multi-aircraft cluster at the ground end provided by an embodiment of the present invention;

[0055] Figure 7 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. Specific Embodiments

[0056] The following will describe the technical solutions in the present invention in conjunction with the accompanying drawings.

[0057] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of the word "example" aims to present concepts in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0058] In the embodiments of the present invention, sometimes subscripts such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning to be expressed is the same.

[0059] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail in conjunction with the accompanying drawings and specific embodiments.

[0060] An embodiment of the present invention provides a method for measuring and analyzing the flight energy efficiency of a multi-aircraft cluster at the ground end. This method can be implemented by a device for measuring and analyzing the flight energy efficiency of a multi-aircraft cluster at the ground end, and this device for measuring and analyzing the flight energy efficiency of a multi-aircraft cluster at the ground end can be a terminal or a server. As Figure 1 shown in the flowchart of the method for measuring and analyzing the flight energy efficiency of a multi-aircraft cluster at the ground end, as Figure 1 shown, the method for measuring and analyzing the flight energy efficiency of a multi-aircraft cluster proposed by the present invention, the processing flow of this method can include the following steps:

[0061] S1. Obtain the state data of multiple flapping-wing flying robots and the state data of the instrument system;

[0062] In a feasible implementation manner, in S1, obtaining the state data of three flapping-wing flying robots and the state data of the instrument system includes:

[0063] S11. Configure a flight control system on the flapping-wing flying robot;

[0064] S12. Obtain the state data of three flapping-wing flying robots and the state data of the instrument system through a test analyzer at the ground end.

[0065] In a feasible implementation manner, this device supports deploying up to three unmanned aerial vehicles. For each unmanned aerial vehicle, aerodynamic data and flight simulation parameters can be collected, and for two or more unmanned aerial vehicles, subsequent multi-aircraft collaborative energy utilization efficiency optimization algorithms can be applied.

[0066] S2. Set the instrument rotation speed and the motion state of the flapping-wing flying robot through the terminal control system. After waiting for the system operation state to be stable, use the eddy current imaging module to obtain the accurate position of the tracer particles in the image.

[0067] In a feasible implementation manner, as Figure 2 shown is a schematic diagram of the working of the eddy current imaging module.

[0068] Next, Figure 2 specifically describe each component of the eddy current imaging module:

[0069] Among them, 1001 is a laser source, 1002 is a light guide arm, 1003 is a three-dimensional camera, 1004 is a mobile control terminal, and 1005 is a prototype to be tested for the principle.

[0070] Among them, 1001 is a laser source, which is the light source generating device of the entire eddy current imaging module. It can emit laser beams with specific wavelengths and powers, illuminate the scattered tracer particles in the experimental area, provide basic illumination for the subsequent imaging process, irradiate the cross-sectional area of the eddy current field through the laser, and continuously capture particle images with multiple exposures through the camera. 1002 is a light guide arm, whose function is to efficiently transmit and direct the laser generated by the laser source 1001 to ensure that the laser is accurately irradiated to the area to be tested. 1003 is a three-dimensional camera, which is used to capture the laser information reflected by the tracer particles. 1004 is a mobile control terminal, which is used to receive imaging data and control the eddy current imaging module. 1005 is a prototype to be tested for the principle.

[0071] In a feasible implementation, the entire device can be divided into two parts: an instrument and a prototype to be tested for the principle. The flight control system refers to the inertial navigation system carried by the flapping-wing flying robot to be tested, which is usually used to collect the position and attitude data of the flapping-wing flying robot itself and control the flapping-wing flying robot itself; the instrument is divided into a terminal control system, a rotation simulation system, and an eddy current imaging module (system). Among them, the terminal control system is the central part of the device, responsible for collecting data from each sensor and interacting with the instrument and the prototype to be tested; the rotation simulation system is responsible for driving the prototype to be tested to perform circular motion, so as to simulate the motion state of the flapping-wing flying robot at the ground end. The eddy current imaging module consists of a laser and a camera, and is responsible for reconstructing the three-dimensional eddy current field during the flapping process of the flapping-wing flying robot.

[0072] S3. Reconstruct the velocity vector map of the eddy current field through the three-dimensional projection relationship and the cross-correlation between multiple particle images, and further calculate the velocity vectors of each point in the eddy current field to obtain the eddy current field model of the flapping-wing flying robot during the motion simulation process;

[0073] In a feasible implementation, in S3, reconstruct the velocity vector map of the eddy current field through the three-dimensional projection relationship, and further calculate the velocity vectors of each point in the eddy current field to obtain the eddy current field model of the flapping-wing flying robot during the motion simulation process, including:

[0074] S31. After the instrument and the flapping-wing flying robot reach the preset operating state, scatter tracer particles in the experimental space, turn on the laser to irradiate the tracer particle area, and capture tracer example images through the camera;

[0075] S32. Discretize the three-dimensional space into a series of voxels distributed in the space. Based on the camera projection relationship of the three-dimensional multi-camera measurement system, the formula for calculating the gray value corresponding to the pixel point in the plane is as follows in formula (1):

[0076] (1)

[0077] Among them, corresponds to a planar image The total number of voxels contributing to the location, these adjacent voxels are projected into the same camera plane along a direction, is the weight of each voxel’s contribution to the pixel grayscale value, is a voxel in three-dimensional space, is the grayscale value of the image;

[0078] S33. To reduce errors caused by factors such as ambient optics and mechanical vibration, a search window is arranged at a certain spatial position in the particle image captured by the camera, and a matrix search area is defined to detect and extract adjacent images;

[0079] S34. For any two adjacent voxel images A and B, assume that their corresponding three-dimensional particle field grayscale matrices are and , the correlation of identified tracer particles can be calculated according to the following mutual correlation coefficient calculation formula (2):

[0080] (2)

[0081] in, is the three-dimensional mutual correlation coefficient, are the average grayscale values of the three-dimensional particle field of images A and B, They are the step lengths in the three axes at two different times, is the coordinate index of the three-dimensional grayscale matrix, Is to query the three-dimensional gray matrix in Size in direction;

[0082] S35. Calculate the velocity vector of the coordinate point of the current tracer particle corresponding to the spatial region in the flapping-wing flying robot's motion space through the particle displacement and the camera exposure interval, as shown in the following formula (3):

[0083] (3)

[0084] in, The particles are Speed in direction, are the coordinate values of the marked tracer particles on the two images, is the absolute time when the image is captured by the camera system.

[0085] In a feasible implementation, the process of the method for reconstructing the eddy current field velocity vector is as follows: Figure 3 The image average in this embodiment is the tracer particle image, which is an example image captured by the laser-illuminated camera after the instrument and the flapping-wing flying robot reach a preset operating state.

[0086] A voxel is a concept in the analysis method, used to divide three-dimensional space, and can be understood as a point in space.

[0087] S4. Based on the velocity vectors of each point in the obtained eddy current field, calculate the interaction energy between the aircraft and the eddy current field during the stable period, and establish a mapping relationship between the energy utilization efficiency of the flapping-wing flying robot and the aircraft parameters in combination with the operation parameters of the aircraft;

[0088] In a feasible implementation, the process of establishing the relationship between the energy efficiency utilization of the multi-aircraft cluster and the flight parameters of the flapping-wing flying robot is as Figure 4 shown; S4. Calculate the energy that the flapping-wing flying robot can obtain in the eddy current field during a flapping cycle, including:

[0089] S41. Select one of the multi flapping-wing flying robots as the central flying robot of the multi-aircraft cluster, and the rest as the following flying robots. According to the method described in S3, calculate the three-dimensional eddy current field of the cluster aircraft in space under the preset flight speed;

[0090] S42. Collect the real-time pose information of the central aircraft and the following aircraft, specifically including key aircraft parameters such as the angle of attack, flapping frequency, flapping amplitude, torsion angle, heading angle, and attitude angle of the aircraft. According to the wing surface geometric parameters of the prototype to be measured, obtain the wing area. After coordinate transformation using the collected flapping pose information, calculate the projection of the wing surface perpendicular to the eddy current field to obtain the angle of attack area, and at the same time calculate the relative position relationship between the following aircraft and the central aircraft

[0091] S43. According to the eddy current field information, aircraft flapping parameters, and the magnitude of the lift and thrust generated by the aircraft collected in steps S41~S42, the average energy obtained by the following aircraft from the eddy current field during a flapping cycle can be calculated according to the following formula (4):

[0092] (4);

[0093] S44. Repeat the experimental steps S41~S43, fix the parameters of the cluster central aircraft, adjust the parameters such as the angle of attack and flapping angle of the following aircraft, use the aircraft parameters and relative pose as the input of the sample set, and the average energy calculated in step S43 as the output of the sample set. Complete the training of the sample set through the following neural network based on orthogonal polynomials. Through this sample set model, the aircraft parameters that can obtain the maximum energy under the current eddy current field model can be evaluated, as shown in formula (5):

[0094] (5)

[0095] Among them, is the degree of the orthogonal polynomial, is the weight vector from the input layer to the hidden layer of the neural network, is the excitation vector from the input layer to the hidden layer of the neural network.

[0096] In a feasible implementation, the distribution information of the eddy current field includes characteristic data such as the velocity vector field, the eddy current field intensity distribution, and the boundary of the turbulent region.

[0097] In a feasible implementation, calculating the average energy obtained by the following aircraft from the eddy current field within a flapping cycle includes:

[0098] Calculating the average energy obtained by the following aircraft from the eddy current field within a flapping cycle according to the following formula (4), and the variables involved in the formula include the eddy current field velocity vector, the force-bearing area of the aircraft surface, the flapping frequency, and the periodic thrust distribution;

[0099] (4)

[0100] Wherein, is the energy that the aircraft can obtain, is a motion cycle, is the average contact area, is the eddy current field representation.

[0101] S5. Repeat the experiment of the test analyzer, collect experimental data under multiple different rotational speed states, and perform data correction based on the neural network of orthogonal polynomials to obtain the multi-aircraft parameters with the highest energy utilization efficiency under the stable state.

[0102] In a feasible implementation, the working process of the ground test analyzer is as Figure 5 shown; S5, repeat the experiment of the test analyzer, collect experimental data under multiple different rotational speed states, and perform data correction based on the neural network of orthogonal polynomials to obtain the multi-aircraft parameters with the highest energy utilization efficiency under the stable state, including:

[0103] S51. Arbitrarily select one of the three flapping-wing flying robots as the central node of the multi-aircraft cluster, and set its preset cruise speed, flight altitude, and flight attitude;

[0104] S52. Assign random initial flight states to the remaining following aircraft, and set the task indicators for the multi-aircraft cluster flight;

[0105] S53. Start the test analyzer, and real-time monitor the flight states and energy utilization conditions of multiple flapping-wing flying robots through the terminal control system;

[0106] S54. Determine whether the system reaches the stable state based on the monitoring results; and repeat the execution of S51 - S53 to obtain multiple groups of experimental data;

[0107] S55. Record multiple sets of experimental data in the experiment. Use the phase angle difference, angle of attack difference, and relative position between the aircraft as sample input data, and use the calculated energy utilization efficiency as the sample output. Complete the training of the sample set through the neural network of orthogonal polynomials to further optimize the energy utilization efficiency of the aircraft.

[0108] In a feasible implementation, determine whether the system reaches a stable state based on the monitoring results, including:

[0109] When the monitoring results indicate that the system does not reach a stable state, based on the mapping relationship between the flight parameters and the available energy established in S4, calculate the flight parameters that the following aircraft needs to adjust, send correction instructions to the relevant aircraft through the terminal control system, and continuously monitor the state changes of the aircraft until the stable conditions are met;

[0110] When the monitoring results indicate that the system reaches a stable state, export the relevant data of the current experiment, including the relative position between the aircraft, the energy utilization efficiency, and the results of other mission indicators; after completing the current mission indicator experiment, adjust the multi-aircraft cluster flight mission parameters.

[0111] In the embodiment of the present invention, through the eddy current imaging technology and the neural network optimization algorithm, the real-time measurement and dynamic optimization of the energy utilization efficiency of the flapping-wing flying robot during flight are realized. By accurately calculating the energy exchange relationship between the aircraft and the eddy current field and combining the intelligent optimization algorithm to adjust the flight parameters, the energy utilization efficiency of the aircraft is improved, the flight time is extended, the energy consumption is reduced, and the aircraft can flexibly and efficiently perform tasks in a complex flight environment.

[0112] Figure 6 It is a block diagram of a ground-end multi-aircraft cluster flight energy efficiency measurement and analysis device 300 shown according to an exemplary embodiment. The device 300 is used for the ground-end multi-aircraft cluster flight energy efficiency measurement and analysis method. Refer to Figure 6 , the device includes an initial data acquisition module 310, an eddy current imaging module 320, an eddy current field model construction module 330, a mapping relationship establishment module 340, and a test analysis module 350. Among them:

[0113] The initial data acquisition module 310 is used to acquire the state data of multiple flapping-wing flying robots and the state data of the instrument system;

[0114] The eddy current imaging module 320 is used to set the instrument rotation speed and the motion state of the flapping-wing flying robot through the terminal control system. After waiting for the system operation state to be stable, use the eddy current imaging module to obtain the accurate position of the tracer particles in the image;

[0115] The eddy current field model construction module 330 is used to reconstruct the velocity vector map of the eddy current field through the three-dimensional projection relationship and the cross-correlation between multiple particle images, and further calculate the velocity vectors of each point in the eddy current field to obtain the eddy current field model of the flapping-wing flying robot during the motion simulation process;

[0116] The mapping relationship establishment module 340 is used to calculate the interaction energy between the aircraft and the eddy current field during the stable period based on the velocity vectors of each point in the obtained eddy current field, and establish the mapping relationship between the energy utilization efficiency of the flapping-wing flying robot and the aircraft parameters in combination with the operation parameters of the aircraft;

[0117] The test and analysis module 350 is used to repeat the test analyzer experiment, collect experimental data under multiple different rotational speed states, and perform data correction based on the neural network of orthogonal polynomials to obtain the multi-aircraft parameters with the highest energy utilization efficiency under the stable state.

[0118] Optionally, the initial data acquisition module 310 is used to configure a flight control system on the flapping-wing flying robot;

[0119] Obtain the state data of multiple flapping-wing flying robots and the state data of the instrument system through the test analyzer at the ground end.

[0120] Optionally, the eddy current field model construction module 330 is used to reconstruct the velocity vector map of the eddy current field through the three-dimensional projection relationship and the cross-correlation between multiple particle images, and further calculate the velocity vectors of each point in the eddy current field to obtain the eddy current field model of the flapping-wing flying robot during the motion simulation process, including;

[0121] After the instrument and the flapping-wing flying robot reach the preset operating state, spread tracer particles in the experimental space, turn on the laser to irradiate the tracer particle area, and capture the tracer particle images through the camera;

[0122] Discretize the three-dimensional space into a series of voxels distributed in the space, and calculate the gray value corresponding to the pixel points in the plane based on the camera projection relationship of the three-dimensional multi-camera measurement system;

[0123] Arrange a search window at any spatial position in the particle image captured by the camera, delimit a matrix search area, and detect and extract the tracer particles;

[0124] For any two adjacent-time particle images A and B, obtain the three-dimensional particle field gray matrices corresponding to the particle images A and B, and calculate the correlation of the identified tracer particles according to the cross-correlation coefficient;

[0125] Calculate the average displacement of the particles in the delimited area and the exposure interval time of the camera, and calculate the velocity vector at the coordinate point of the corresponding spatial area of the current tracer particle in the motion space of the flapping-wing flying robot.

[0126] Optionally, the mapping relationship establishment module 340 is configured to select one of multiple flapping-wing flying robots as the central flying robot of the multi-robot cluster, and the rest as following flying robots. According to the method described in S3, calculate the three-dimensional eddy current field of the cluster aircraft in space at a preset flight speed;

[0127] Collect the real-time pose information of the central aircraft and the following aircraft, specifically including key aircraft parameters such as the angle of attack, flapping frequency, flapping amplitude, torsion angle, heading angle, and attitude angle of the aircraft. Obtain the wing area according to the wing surface geometric parameters of the prototype to be measured. After coordinate transformation with the collected flapping pose information, calculate the projection of the wing surface vertical eddy current field to obtain the angle of attack area, and at the same time calculate the relative position relationship between the following aircraft and the central aircraft;

[0128] According to the collected eddy current field information, aircraft flapping parameters, and the magnitude of the lift and thrust generated by the aircraft, the average energy obtained by the following aircraft from the eddy current field within one flapping cycle can be calculated according to the following formula,

[0129] 2];

[0130] Repeat the experimental steps, fix the parameters of the cluster central aircraft, adjust the parameters such as the angle of attack and flapping angle of the following aircraft, use the aircraft parameters and relative pose as the input of the sample set, and the average energy calculated in step S43 as the output of the sample set. Complete the training of the sample set through the following neural network based on orthogonal polynomials. Through this sample set model, the aircraft parameters that can obtain the maximum energy under the current eddy current field model can be evaluated:

[0131] .

[0132] Optionally, the distribution information of the eddy current field includes velocity vector field, eddy current field intensity distribution, and turbulent region boundary characteristic data.

[0133] Optionally, the test analysis module 350 is configured to arbitrarily select one of three flapping-wing flying robots as the central node of the multi-robot cluster, and set its preset cruise speed, flight altitude, and flight attitude;

[0134] Assign random initial flight states to the remaining following aircraft, and set the mission indicators for the multi-robot cluster flight;

[0135] Start the test analyzer, and monitor the flight states and energy utilization of multiple flapping-wing flying robots in real time through the terminal control system;

[0136] Judge whether the system reaches a stable state through the monitoring results; and repeat S51 - S53 to obtain multiple groups of experimental data;

[0137] Record multiple sets of experimental data in the experiment. Use the phase angle difference, angle of attack difference, and relative position between the aircraft as sample input data, and use the calculated energy utilization efficiency as the sample output. Complete the training of the sample set through the neural network of orthogonal polynomials to further optimize the energy utilization efficiency of the aircraft.

[0138] Optionally, determine whether the system reaches a stable state based on the monitoring results, including:

[0139] When the monitoring results indicate that the system does not reach a stable state, based on the mapping relationship between the flight parameters and the available energy size established in S4, calculate the flight parameters that the following aircraft need to adjust, send correction instructions to the relevant aircraft through the terminal control system, and continuously monitor the state changes of the aircraft until the stable conditions are met;

[0140] When the monitoring results indicate that the system reaches a stable state, export the relevant data of the current experiment, including the relative position between the aircraft, the energy utilization efficiency, and the results of other mission indicators; after completing the current mission indicator experiment, adjust the multi-aircraft cluster flight mission parameters.

[0141] In the embodiments of the present invention, through the eddy current imaging technology and the neural network optimization algorithm, the real-time measurement and dynamic optimization of the energy utilization efficiency of the flapping flight robot during flight are realized. By accurately calculating the energy exchange relationship between the aircraft and the eddy current field and combining the intelligent optimization algorithm to adjust the flight parameters, the energy utilization efficiency of the aircraft is improved, the flight time is extended, the energy consumption is reduced, and the aircraft can flexibly and efficiently perform tasks in a complex flight environment.

[0142] Figure 7 It is a schematic structural diagram of a ground-end multi-aircraft cluster flight energy efficiency measurement and analysis device provided by the embodiments of the present invention, as Figure 7 shown. The ground-end multi-aircraft cluster flight energy efficiency measurement and analysis device may include the above-mentioned Figure 6 ground-end multi-aircraft cluster flight energy efficiency measurement and analysis device shown. Optionally, the ground-end multi-aircraft cluster flight energy efficiency measurement and analysis device 410 may include a first processor 2001.

[0143] Optionally, the ground-end multi-aircraft cluster flight energy efficiency measurement and analysis device 410 may further include a memory 2002 and a transceiver 2003.

[0144] Among them, the first processor 2001 is connected to the memory 2002 and the transceiver 2003, such as through a communication bus.

[0145] The following combines Figure 7Specifically introduce each component of the ground - end multi - aircraft cluster flight energy efficiency measurement and analysis device 410:

[0146] Among them, the first processor 2001 is the control center of the ground - end multi - aircraft cluster flight energy efficiency measurement and analysis device 410. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or it can be an application - specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. For example: one or more digital signal processors (DSPs), or one or more field - programmable gate arrays (FPGAs).

[0147] Optionally, the first processor 2001 can execute various functions of the ground - end multi - aircraft cluster flight energy efficiency measurement and analysis device 410 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0148] In a specific implementation, as an embodiment, the first processor 2001 can include one or more CPUs, such as Figure 7 CPU0 and CPU1 shown in

[0149] In a specific implementation, as an embodiment, the ground - end multi - aircraft cluster flight energy efficiency measurement and analysis device 410 can also include multiple processors, such as Figure 7 the first processor 2001 and the second processor 2004 shown in. Each of these processors can be a single - core processor (single - CPU) or a multi - core processor (multi - CPU). Here, the processor can refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0150] Among them, the memory 2002 is used to store the software program for implementing the solution of the present invention and is controlled by the first processor 2001 for execution. The specific implementation method can refer to the above - mentioned method embodiments and will not be elaborated here.

[0151] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or may be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001 or may exist independently and be coupled to the first processor 2001 through an interface circuit ( Figure 7 not shown) of the ground multi-machine cluster flight energy efficiency measurement and analysis device 410. The embodiments of the present invention do not make specific limitations thereto.

[0152] The transceiver 2003 is configured to communicate with a network device or communicate with a terminal device.

[0153] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 7 not shown separately). Among them, the receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0154] Optionally, the transceiver 2003 may be integrated with the first processor 2001 or may exist independently and be coupled to the first processor 2001 through an interface circuit ( Figure 7 not shown) of the ground multi-machine cluster flight energy efficiency measurement and analysis device 410. The embodiments of the present invention do not make specific limitations thereto.

[0155] It should be noted that Figure 7 the structure of the ground multi-machine cluster flight energy efficiency measurement and analysis device 410 shown does not constitute a limitation to the router. The actual knowledge structure recognition device may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0156] In addition, the technical effects of the ground multi-machine cluster flight energy efficiency measurement and analysis device 410 may refer to the technical effects of the ground multi-machine cluster flight energy efficiency measurement and analysis method described in the above method embodiments, and will not be elaborated herein.

[0157] It should be understood that the first processor 2001 in the embodiments of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0158] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0159] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable sensors. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0160] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be understood specifically by referring to the context before and after.

[0161] It should be understood that in various embodiments of the present invention, the magnitudes of the sequence numbers of the above processes do not indicate the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0162] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this document can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0163] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed over multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0164] In addition, in each embodiment of the present invention, each functional unit may be integrated in a processing unit, may exist physically separately for each unit, or two or more units may be integrated in one unit.

[0165] If the above function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of the technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention.

[0166] As mentioned above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for measuring and analyzing the flight energy efficiency of a multi-aircraft cluster at the ground end, characterized in that The method includes: S1. Obtain the state data of multiple flapping-wing flying robots and the state data of the instrument system; S2. Set the instrument rotation speed and the motion state of the flapping-wing flying robot through the terminal control system. When waiting for the simulated oncoming flow speed to reach the preset experimental rotation speed, spread tracer particles in the experimental space, irradiate the cross-sectional area of the eddy current field with a laser, and continuously capture multiple exposed particle images through a camera; S3. Reconstruct the velocity vector diagram of the eddy current field through the three-dimensional projection relationship and the cross-correlation between multiple particle images, and further calculate the velocity vectors of each point in the eddy current field to obtain the eddy current field model of the flapping-wing flying robot during the motion simulation process; S4. Based on the obtained velocity vectors of each point in the eddy current field, calculate the interaction energy between the aircraft and the eddy current field during the stable period, and combine the operation parameters of the aircraft to establish a mapping relationship between the energy utilization efficiency of the flapping-wing flying robot and the aircraft parameters; S5. Repeat the test analyzer experiment, collect experimental data under multiple different rotation speed states, and perform data correction based on the neural network of orthogonal polynomials to obtain the multi-aircraft parameters with the highest energy utilization efficiency under the stable state.

2. The ground - end multi - aircraft cluster flight energy efficiency measurement and analysis method according to claim 1, characterized in that, In the above S1, obtaining the state data of multiple flapping-wing flying robots and the state data of the instrument system includes: S11. Configure a flight control system on the flapping-wing flying robot; S12. Obtain the state data of multiple flapping-wing flying robots and the state data of the instrument system through the test analyzer at the ground end.

3. The method for measuring and analyzing the flight energy efficiency of a multi-aircraft cluster at the ground end according to claim 1, wherein In the above S3, reconstructing the velocity vector diagram of the eddy current field through the three-dimensional projection relationship and the cross-correlation between multiple particle images, and further calculating the velocity vectors of each point in the eddy current field to obtain the eddy current field model of the flapping-wing flying robot during the motion simulation process includes: S31. After the instrument and the flapping-wing flying robot reach the preset operating state, spread tracer particles in the experimental space, turn on the laser to irradiate the tracer particle area, and capture the tracer example images through a camera; S32. Discretize the three-dimensional space into a series of voxels distributed in the space, and calculate the gray values corresponding to the pixel points in the plane based on the camera projection relationship of the three-dimensional multi-camera measurement system; S33. Arrange a search window at any spatial position in the particle image captured by the camera, delimit the matrix search area, and detect and extract the tracer particles; S34. For any two adjacent-time particle images A and B, obtain the three-dimensional particle field gray matrices corresponding to the particle images A and B, and calculate the correlation of the identified tracer particles according to the cross-correlation coefficient; S35. Calculate the velocity vector at the coordinate point of the spatial area corresponding to the current tracer particle in the motion space of the flapping-wing flying robot by calculating the average displacement of the particles in the delimited area and the exposure interval time of the camera.

4. The ground - end multi - aircraft cluster flight energy efficiency measurement and analysis method according to claim 2, wherein In the above S4, based on the obtained velocity vectors of each point in the eddy current field, calculating the interaction energy between the aircraft and the eddy current field during the stable period, and combining the operation parameters of the aircraft to establish a mapping relationship between the energy utilization efficiency of the flapping-wing flying robot and the aircraft parameters includes: S41. Select one of the multiple flapping-wing flying robots as the central flying robot of the multi-robot cluster, and the rest as the following flying robots. According to the method described in S3, calculate the three-dimensional eddy current field of the cluster aircraft in space under the preset flight speed; S42. Collect the real-time pose information of the central aircraft and the following aircraft, specifically including key aircraft parameters such as the angle of attack, flapping frequency, flapping amplitude, torsion angle, heading angle, and attitude angle of the aircraft. Obtain the wing area according to the wing surface geometric parameters of the prototype to be measured. After coordinate transformation using the collected flapping pose information mentioned above, calculate the projection of the wing surface vertical eddy current field to obtain the angle of attack area, and at the same time calculate the relative position relationship between the following aircraft and the central aircraft. S43. According to the eddy current field information, aircraft flapping parameters, and the magnitude of the lift and thrust generated by the aircraft collected in steps S41 - S42, the average energy obtained by the following aircraft from the eddy current field within one flapping cycle can be calculated according to the following formula. ； S44. Repeat the experimental steps S41 - S43, fix the parameters of the cluster central aircraft, adjust the parameters such as the angle of attack and flapping angle of the following aircraft, use the aircraft parameters and relative pose as the sample set input, and the average energy calculated in step S43 as the sample set output. Complete the training of the sample set through the following neural network based on orthogonal polynomials. Through this sample set model, the aircraft parameters that can obtain the maximum energy under the current eddy current field model can be evaluated. 。 5. The method for measuring and analyzing the flight energy efficiency of a multi-aircraft cluster at the ground end according to claim 3, wherein The distribution information of the eddy current field includes velocity vector field, eddy current field intensity distribution, and turbulent region boundary characteristic data.

6. The ground terminal multi-aircraft cluster flight energy efficiency measurement and analysis method according to claim 4, characterized in that S5. Repeat the test analyzer experiment, collect experimental data under multiple different rotational speed states, and perform data correction through the neural network based on orthogonal polynomials to obtain the multi-robot parameters with the highest energy utilization efficiency in the stable state, including: S51. Arbitrarily select one of the three flapping-wing flying robots as the central node of the multi-robot cluster, and set its preset cruise speed, flight altitude, and flight attitude. S52. Assign random initial flight states to the remaining following aircraft and set the mission indicators for the multi-robot cluster flight. S53. Start the test analyzer and real-time monitor the flight states and energy utilization of multiple flapping-wing flying robots through the terminal control system. S54. Determine whether the system reaches the stable state based on the monitoring results; and repeat S51 - S53 to obtain multiple groups of experimental data. S55. Record multiple groups of experimental data in the experiment, use the phase angle difference, angle of attack difference, and relative position between the aircraft as the sample input data, use the calculated energy utilization efficiency as the sample output, and complete the training of the sample set through the neural network of orthogonal polynomials to further optimize the energy utilization efficiency of the aircraft.

7. The ground - end multi - aircraft cluster flight energy efficiency measurement and analysis method according to claim 5, characterized in that The determination of whether the system reaches the stable state based on the monitoring results includes: When the monitoring results indicate that the system has not reached the stable state, based on the mapping relationship between the flight parameters and the energy that can be obtained established in S4, calculate the flight parameters that the following aircraft need to adjust, send correction instructions to the relevant aircraft through the terminal control system, and continuously monitor the state changes of the aircraft until the stable conditions are met. When the monitoring results indicate that the system reaches a stable state, export the relevant data of the current experiment, including the relative positions between the aircraft, the energy utilization efficiency, and the results of other mission metrics; after completing the current mission metric experiment, adjust the multi-aircraft cluster flight mission parameters.

8. A ground - based multi - aircraft cluster flight energy efficiency measurement and analysis device, which is used to implement the ground - based multi - aircraft cluster flight energy efficiency measurement and analysis method according to any one of claims 1 - 7, and is characterized in that, The device includes: An initial data acquisition module, configured to acquire the state data of multiple flapping flight robots and the state data of the instrument system; An eddy current imaging module, configured to set the instrument rotation speed and the motion state of the flapping flight robot through the terminal control system, and after waiting for the system operation state to be stable, use the eddy current imaging module to obtain the accurate position of the tracer particles in the image; An eddy current field model construction module, configured to reconstruct the velocity vector diagram of the eddy current field through the three-dimensional projection relationship and the cross-correlation between multiple particle images, and further calculate the velocity vectors of each point in the eddy current field to obtain the eddy current field model of the flapping flight robot during the motion simulation process; A mapping relationship establishment module, configured to calculate the interaction energy between the aircraft and the eddy current field during the stable period based on the velocity vectors of each point in the obtained eddy current field, and establish a mapping relationship between the energy utilization efficiency of the flapping flight robot and the aircraft parameters in combination with the operation parameters of the aircraft; A test and analysis module, configured to repeat the test analyzer experiment, collect experimental data under multiple different rotation speed states, and perform data correction based on the neural network of orthogonal polynomials to obtain the multi-aircraft parameters with the highest energy utilization efficiency under the stable state.

9. A ground-end multi-aircraft cluster flight energy efficiency measurement and analysis device, the ground-end multi-aircraft cluster flight energy efficiency measurement and analysis device includes: A processor; A memory, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by the processor, any one of the methods in the ground-end multi-aircraft cluster flight energy efficiency measurement and analysis method according to any one of claims 1-7 is implemented.

10. A computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is loaded and executed by the processor to implement any one of the methods in the ground-end multi-aircraft cluster flight energy efficiency measurement and analysis method according to any one of claims 1-7.

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