A method and system for optimizing the torque and tension of a UAV power system bench

Abstract

This invention relates to a method and system for optimizing the torque and tension of a drone power system test bench, relating to the field of drone testing. The method includes: obtaining multiple tension coefficients and multiple torque coefficients through test processing according to multiple preset test conditions; performing test stability analysis based on the multiple preset test conditions to obtain multiple test stability parameters; obtaining a first test tension, a first test torque, and a first test speed of the drone under extreme test conditions; calculating a second estimated tension and a second estimated torque based on a second estimated speed, multiple test stability parameters, multiple tension coefficients, and multiple torque coefficients; calculating a third estimated tension and a third estimated torque based on the drone's control parameters; and combining the first test tension, the first test torque, the second estimated tension, and the second estimated torque to calculate an optimized estimated tension and an optimized estimated torque. This invention achieves the technical effect of improving the accuracy of drone tension and torque testing.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) testing, and more particularly to a method and system for optimizing the torque and tension of a UAV power system test bench. Background Technology

[0002] With the promotion of low-altitude economic industries such as drone logistics, the promotion of drones is developing towards greater payload capacity. The larger the drone's propellers, the greater the tension and torque they generate. When testing drones, the test bench structure vibrates under the influence of large tension and torque, causing the torque sensor to have a large measurement error under high tension conditions. This results in inaccurate measurement of tension and torque for heavy-load drones, making it impossible to obtain accurate data. Summary of the Invention

[0003] This invention addresses the technical problem of inaccurate tensile and torque testing in heavy-load UAVs in the prior art by providing a method and system for optimizing the torque and tensile force of UAV power systems on a test bench.

[0004] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0005] In a first aspect, the present invention provides a method for optimizing the bench torque tension of an unmanned aerial vehicle (UAV) power system, comprising:

[0006] The UAV was tested using a test bench under multiple preset test conditions, and multiple tensile coefficients and multiple torque coefficients were obtained.

[0007] Based on the multiple preset test conditions, a test stability analysis is performed to obtain multiple test stability parameters;

[0008] The test obtains the first test pull force, first test torque and first test speed of the UAV under extreme test conditions. Based on the second estimated speed, multiple test stability parameters, multiple pull force coefficients and multiple torque coefficients, the second estimated pull force and the second estimated torque are calculated.

[0009] Based on the control parameters of the UAV, a third estimated pull force and a third estimated torque are calculated. Combining the first test pull force, the first test torque, the second estimated pull force, and the second estimated torque, an optimized estimated pull force and an optimized estimated torque are calculated.

[0010] Secondly, the present invention provides a bench torque and tension optimization system for a drone power system, comprising:

[0011] The multi-condition testing module is used to test the UAV on a test bench according to multiple preset test conditions, and process and obtain multiple tensile coefficients and multiple torque coefficients.

[0012] The test stability analysis module is used to perform test stability analysis based on the multiple preset test conditions and obtain multiple test stability parameters;

[0013] The extreme testing module is used to test and obtain the first test pull force, first test torque and first test speed of the UAV under extreme test conditions. Based on the second estimated speed, multiple test stability parameters, multiple pull force coefficients and multiple torque coefficients, the second estimated pull force and the second estimated torque are calculated.

[0014] The test optimization calculation module is used to calculate a third estimated pull force and a third estimated torque based on the control parameters of the UAV, and to calculate an optimized estimated pull force and optimized estimated torque by combining the first test pull force, the first test torque, the second estimated pull force, the second estimated torque, the third estimated pull force, and the third estimated torque.

[0015] Thirdly, this application provides an electronic device, comprising:

[0016] Memory, used to store computer programs;

[0017] A processor is used to read and execute the computer program, thereby implementing the UAV power system bench torque and tension optimization method described in the first aspect.

[0018] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the UAV power system bench torque and tension optimization method as described in the first aspect.

[0019] The beneficial effects of this invention are as follows: It proposes a method for optimizing the tension and torque of a UAV power system test bench. The method involves testing the UAV under multiple preset test conditions, collecting and processing multiple tension and torque coefficients, and combining historical test data for test stability analysis to calculate multiple test stability parameters, ensuring the reliability of the test results. During testing, by measuring the UAV's tension, torque, and rotational speed under extreme conditions, and combining the test stability parameters, tension coefficients, and torque coefficients, a second estimated tension and a second estimated torque are further calculated. Furthermore, based on the UAV's control parameters, a third estimated tension and a third estimated torque are calculated, and by combining the tension and torque at each stage, optimized estimated tension and estimated torque are finally obtained. This solution effectively solves the measurement error problem caused by vibration during the testing of heavy-load UAVs, significantly improving test accuracy and stability. It also provides high-quality test data support for UAV performance optimization and design improvement, laying a technical foundation for the development of the low-altitude economy industry. Attached Figure Description

[0020] Figure 1A flowchart illustrating the bench torque and tension optimization method for the UAV power system provided by this invention;

[0021] Figure 2 A schematic diagram of the structure for optimizing the torque and tension of the unmanned aerial vehicle power system test bench provided by the present invention;

[0022] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention;

[0023] Figure 4 This is a schematic diagram of the structure of a computer-readable storage medium provided by the present invention.

[0024] The components represented by each number in the attached diagram are explained below:

[0025] Multi-condition testing module 11, test stability analysis module 12, extreme test module 13, test optimization calculation module 14, electronic device 500, memory 510, processor 520, first computer program 511, computer-readable storage medium 600, and second computer program 611. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0028] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0029] Example 1:

[0030] like Figure 1 As shown, this embodiment of the invention provides a method for optimizing the bench torque and tension of an unmanned aerial vehicle (UAV) power system, including:

[0031] S10: The UAV is tested using a test bench according to multiple preset test conditions, and multiple tensile coefficients and multiple torque coefficients are obtained.

[0032] In this embodiment, under high-tension testing on the test bench, the test bench structure vibrates due to the large tension and torque, resulting in significant measurement errors from the torque sensor under high-tension conditions, thus affecting test accuracy. Under moderate-tension conditions, the test bench structure remains stable, leading to higher test accuracy. Therefore, testing the drone under moderate-tension conditions using a test bench, and based on the more accurate test results, processing and calibration yield multiple tension and torque coefficients for subsequent precise calculations of high tension and torque.

[0033] Step S10 in the method provided in this application embodiment includes:

[0034] Under the first preset test conditions, the UAV is tested using a test bench to obtain the first condition test tension, the first condition test torque and the first condition test speed, and the first tension coefficient and the first torque coefficient are obtained by processing and calculation.

[0035] The drone was tested under multiple preset test conditions, and multiple tensile coefficients and multiple torque coefficients were obtained.

[0036] This application embodiment uses a phased, multi-condition testing method to accurately measure and optimize the tensile force and torque of the UAV power system test bench.

[0037] First, under the first preset test conditions (e.g., test conditions under medium tensile conditions, under which the test bench structure is relatively stable and the vibration impact is small), the test bench is used to test the UAV to obtain the first condition test tensile force (e.g., 50N), the first condition test torque (e.g., 20Nm), and the first condition test speed (e.g., 3000rpm).

[0038] Based on the first-condition test pull force, first-condition test torque, and first-condition test speed, the first pull force coefficient and the first torque coefficient are calculated. The pull force coefficient and torque coefficient are proportional factors reflecting the relationship between pull force and torque and the square of the speed, and are used to describe the variation law of pull force and torque of the UAV at different speeds.

[0039] In this embodiment of the application, under the first preset test conditions, a test bench is used to test the UAV to obtain the first condition test tension, the first condition test torque, and the first condition test rotation speed. The first tension coefficient and the first torque coefficient are then processed and calculated, including:

[0040] Under the first preset test conditions, the UAV is tested using a test bench to obtain the first condition test tension, the first condition test torque, and the first condition test rotation speed;

[0041] Based on the first condition test tension, the first condition test torque, and the first condition test rotation speed, the first tension coefficient and the first torque coefficient are calculated as follows:

[0042] G=K G *V 2 ;

[0043] T=K T *V 2 ;

[0044] Where G is the test tension, T is the test torque, V is the test speed, and K is the test rotational speed. G K is the tensile coefficient. T This is the torque coefficient.

[0045] In this embodiment, under the first preset test conditions (e.g., a moderate tensile force, such as a tensile force of 50N, with a stable test bench structure and minimal vibration), the UAV is tested using a test bench. During the test, key parameters such as the first condition test tensile force (e.g., a measured tensile force of 50N), the first condition test torque (e.g., a measured torque of 20Nm), and the first condition test rotational speed (e.g., a measured rotational speed of 3000rpm) are collected by precision sensors. The first condition test tensile force, the first condition test torque, and the first condition test rotational speed reflect the UAV's power performance under specific operating conditions and are the basis for subsequent calculations.

[0046] Based on the first-condition test tension, first-condition test torque, and first-condition test speed obtained from the test, the first tension coefficient and the first torque coefficient are calculated as follows:

[0047] G=K G *V 2 ;

[0048] T=K T *V 2 ;

[0049] Where G is the test tensile force, such as 50N; T is the test torque, such as 20Nm; V is the test speed, such as 3000rpm; and K is the test torque. G This is the tensile strength coefficient, for example, calculated to be 5.56 × 10⁻⁶. -6 K T This is the torque coefficient, calculated for example as 2.22 × 10⁻⁶. -6 .

[0050] Furthermore, the drone is tested under multiple other preset test conditions. Based on the test data, multiple tensile coefficients and multiple torque coefficients are calculated and processed according to the above formula. The testing and calculation methods are the same as the processing steps for the first tensile coefficient and the first torque coefficient.

[0051] For example, multiple preset test conditions are test conditions near the medium tensile force of the drone, such as 40N, 45N, 50N, 55N and 60N respectively. Under these conditions, the drone is tested for conditional test tensile force, conditional test torque and conditional test speed, and multiple tensile force coefficients and multiple torque coefficients are calculated and obtained.

[0052] The tensile force and torque coefficients obtained through the above calculations accurately reflect the dynamic characteristics of the UAV under the test conditions and provide important parameter support for subsequent optimization calculations under high tensile and high torque conditions. The above steps effectively utilize the high-precision data from the test bench under moderate tensile conditions, avoid the impact of vibration on measurement accuracy, and significantly improve the overall reliability of the test and calculation.

[0053] S20: Based on the multiple preset test conditions, perform test stability analysis to obtain multiple test stability parameters;

[0054] In this embodiment, multiple preset test conditions are designed to minimize vibration of the test bench structure and ensure relatively accurate testing; however, they also introduce some error. Therefore, a test stability analysis is performed on these preset test conditions to assess the consistency and volatility of the UAV's test results across multiple tests under different preset conditions, thereby improving data reliability. This yields multiple test stability parameters for these preset test conditions, serving as the basis for subsequent accurate calculations of the UAV's test tension and torque under high tensile forces.

[0055] Step S20 in the method provided in this application embodiment includes:

[0056] Based on historical test data of similar UAVs under the first preset test conditions, obtain the historical first set of tension coefficients and the historical first set of torque coefficients;

[0057] Based on the historical first tensile force coefficient set and the historical first torque coefficient set, test stability calculation and analysis are performed to obtain the first test stability parameter;

[0058] Based on historical test data under the multiple preset test conditions, test stability calculation and analysis are performed to obtain multiple test stability parameters.

[0059] In this embodiment of the application, in order to ensure the stability and accuracy of the test data of the UAV power system bench test, the stability of the UAV test data under each preset test condition is evaluated, thereby optimizing the measurement accuracy of tension and torque.

[0060] Based on the first preset test conditions, historical test data of similar UAVs under these conditions are collected. This historical data typically comes from past experimental records or standard test data of similar UAV models. Specifically, the historical test data includes the first set of tension coefficients and the first set of torque coefficients obtained from multiple tests. The first set of tension coefficients and the first set of torque coefficients reflect the dynamic performance of the UAV under the first preset test conditions and are the basis for stability analysis.

[0061] Based on the historical first tensile force coefficient set and the historical first torque coefficient set, test stability calculation and analysis are performed. For example, the average fluctuation range of the historical first tensile force coefficient and the historical first torque coefficient within the historical first tensile force coefficient set and the historical first torque coefficient set is analyzed to obtain the first test stability parameter.

[0062] In this embodiment of the application, based on the historical first tensile coefficient set and the historical first torque coefficient set, test stability calculation and analysis are performed to obtain the first test stability parameter, including:

[0063] Multiple sets of historical first tensile force coefficients are randomly selected from the set of historical first tensile force coefficients, and the difference range is calculated to obtain multiple historical first tensile force difference ranges;

[0064] Calculate the average of the amplitudes of the multiple historical first tensile force differences, and subtract this average from 1 to obtain the stability parameter of the first tensile force test;

[0065] Multiple sets of historical first torque coefficients are randomly selected from the set of historical first torque coefficients, and the difference amplitude is calculated to obtain multiple historical first torque difference amplitudes;

[0066] Calculate the average of the amplitudes of the multiple historical first torque differences, and subtract this average from 1 to obtain the first torque test stability parameter;

[0067] The average value of the first tensile test stability parameter and the first torque test stability parameter is calculated to obtain the first test stability parameter.

[0068] In this embodiment of the application, firstly, multiple sets of historical first tensile force coefficients are randomly selected from the set of historical first tensile force coefficients. Each set of historical first tensile force coefficients includes two randomly selected historical first tensile force coefficients.

[0069] Furthermore, the fluctuations are assessed by calculating the magnitude of the difference between each set of historical first tensile force coefficients. For example, the absolute value of the difference between two historical first tensile force coefficients is calculated, and the ratio of this absolute value to the larger of the two historical first tensile force coefficients is calculated as the magnitude of the historical first tensile force difference. In this way, based on multiple sets of historical first tensile force coefficients, the magnitude of the difference between each set of historical first tensile force coefficients is calculated, resulting in multiple magnitudes of the historical first tensile force difference.

[0070] For example, the two historical first tensile coefficients were 5.56 × 10⁻⁶. -6 and 5.70×10 -6 The historical first tensile force difference was calculated to be 0.14 × 10⁻⁶. -6 Compared to 5.70×10 -6 It is 2.5%.

[0071] Furthermore, the mean of multiple historical first tensile force difference amplitudes is calculated as the average historical first tensile force difference amplitude, reflecting the average difference amplitude within the historical first tensile force coefficient set. The first tensile force test stability parameter is obtained by subtracting this mean from 1. The smaller the tensile force difference amplitude, the more stable the test data of similar UAVs under the first preset test conditions, and the larger the first tensile force test stability parameter.

[0072] Furthermore, using the same method, multiple sets of historical first torque coefficients are randomly selected from the historical first torque coefficient set, and the difference amplitude is calculated to obtain multiple historical first torque difference amplitudes. Then, the mean of multiple historical first torque difference amplitudes is calculated, and the first torque test stability parameter is obtained by subtracting the mean from 1.

[0073] Furthermore, to obtain comprehensive test stability results, the first tensile test stability parameter and the first torque test stability parameter are calculated together. Specifically, the average of the two is calculated to obtain the average stability parameter of the UAV under the first preset test conditions for tensile and torque tests, i.e., the first test stability parameter.

[0074] Using the same method described above, test stability calculations and analyses are performed based on historical test data from multiple other preset test conditions to obtain multiple test stability parameters.

[0075] This application embodiment performs test stability calculation and analysis based on historical test data under multiple preset test conditions to obtain multiple test stability parameters. This provides a unified standard for subsequent optimization calculations, helping to further improve the accuracy and reliability of UAV power systems under different test conditions. Through these steps, the stability of tension and torque during the test can be effectively evaluated, thereby providing more accurate and reliable data support for subsequent optimization calculations.

[0076] S30: The test obtains the first test pull force, first test torque and first test speed of the UAV under extreme test conditions. Based on the second estimated speed, multiple test stability parameters, multiple pull force coefficients and multiple torque coefficients, the second estimated pull force and the second estimated torque are calculated.

[0077] In this embodiment, the extreme test condition is the test condition with maximum tensile force, under which the vibration of the test bench structure is most severe and the test error is largest. In order to obtain the tensile force and torque of the UAV under extreme test conditions (i.e., the test condition with maximum tensile force), a fitting method using multiple data sources is adopted to improve the accuracy of the test calculation.

[0078] First, the drone was tested under maximum tensile force to obtain its performance under these conditions. Specifically, the drone's tensile force, torque, and rotational speed were measured to obtain the first test tensile force, first test torque, and first test rotational speed. The first test tensile force, first test torque, and first test rotational speed are parameters obtained directly from the tests and therefore have significant errors. The first test rotational speed is recorded as data but not used in subsequent calculations.

[0079] Furthermore, based on the second estimated rotational speed, and combining the aforementioned multiple test stability parameters, multiple tension coefficients, and multiple torque coefficients, the tension and torque of the UAV under extreme test conditions are estimated and calculated according to multiple tension coefficients and multiple torque coefficients under multiple preset test conditions, thus obtaining the second estimated tension and the second estimated torque. The second estimated tension and the second estimated torque are calculated based on multiple test stability parameters, multiple tension coefficients, and multiple torque coefficients under multiple preset test conditions, and are therefore relatively accurate. The second estimated rotational speed is the motor speed estimated under extreme test conditions based on motor control parameters. Since the first test rotational speed has a large error, the second estimated rotational speed is used for calculation.

[0080] Step S30 in the method provided in this application embodiment includes:

[0081] Based on the magnitude of the multiple test stability parameters, assign multiple conditional weights;

[0082] Based on the second estimated rotational speed, multiple conditional weights, multiple force coefficients, and multiple torque coefficients, the second estimated force and the second estimated torque are calculated as follows:

[0083]

[0084] in, For the second estimated tensile force, M is the number of multiple preset test conditions, and w i Let i be the condition weight of the i-th preset test condition. V is the tensile coefficient for the i-th preset test condition. D2 For the second estimated rotational speed, For the second estimated torque, is the torque coefficient for the i-th preset test condition.

[0085] In this embodiment, multiple test stability parameters are obtained from test stability analysis based on multiple preset test conditions to determine the weight of each preset test condition. The larger the test stability parameter, the greater the weight of the condition. The test stability parameter is an indicator that measures the stability of tensile and torque data under each test condition.

[0086] For example, the ratio of each test stability parameter to the sum of multiple test stability parameters is calculated, and this ratio is used as a condition weight. In this way, multiple condition weights are obtained through the calculation.

[0087] Based on the second estimated rotational speed, multiple conditional weights, multiple force coefficients, and multiple torque coefficients, the second estimated force and the second estimated torque are calculated as follows:

[0088]

[0089] in, For the second estimated tensile force, M is the number of multiple preset test conditions, and w i Let be the condition weight for the i-th preset test condition. Multiple preset test conditions correspond to multiple test stability parameters, and multiple test stability parameters correspond to multiple condition weights. V is the tensile coefficient calculated under the i-th preset test condition. D2 For the second estimated rotational speed, For the second estimated torque, is the torque coefficient for the i-th preset test condition.

[0090] The second estimated speed is the speed estimated by the motor position sensor. In practical applications, to save costs, the speed can also be estimated using a sensorless algorithm, as shown in the following formula:

[0091]

[0092] Where dθ is the difference in angle between the front and rear of the motor, and dt is the control cycle of the motor.

[0093] In this embodiment, the second estimated pull force and the second estimated torque are calculated based on the estimated second estimated rotational speed and the accurate pull force coefficient and torque coefficient under multiple test conditions. This process combines test stability parameters, pull force coefficient and torque coefficient, which can effectively estimate the performance of the UAV under extreme conditions.

[0094] By analyzing multiple preset test conditions and assigning condition weights according to test stability parameters, a more accurate second estimated pull force and second estimated torque of the UAV can be calculated based on the second estimated rotational speed. This process considers the pull force and torque coefficients calculated by the UAV under relatively accurate test conditions. This weighted calculation, combined with test stability parameters, is more accurate and improves the accuracy of high pull force and high torque tests on UAVs.

[0095] S40: Based on the control parameters of the UAV, calculate the third estimated pull force and the third estimated torque, and combine the first test pull force, the first test torque, the second estimated pull force, the second estimated torque, the third estimated pull force and the third estimated torque to calculate the optimized estimated pull force and the optimized estimated torque.

[0096] In this embodiment, the control parameters of the UAV include the motor control strategy (such as FOC vector control), which is generally achieved through FOC vector sensorless control. Taking a surface-mount motor as an example, the motor current (especially the q-axis current), the number of pole pairs, and motor parameters directly affect the UAV's pull and torque output. Based on the UAV's control parameters, a third estimated pull and a third estimated torque are calculated. These third estimated pull and torque are obtained based on the UAV's FOC vector sensorless control parameters.

[0097] Then, based on the first test tension and the first test torque obtained from direct testing, the second estimated tension and the second estimated torque calculated based on multiple tension coefficients and multiple torque coefficients under multiple preset test conditions, and the third estimated tension and the third estimated torque calculated based on the control parameters of the UAV, the three are combined to calculate the optimized estimated tension and the optimized estimated torque, which serve as the final optimized calculation of the UAV's tension and torque under extreme test conditions. By integrating multiple calculation and testing methods, the accuracy of the test is effectively improved.

[0098] Step S40 in the method provided in this application embodiment includes:

[0099] Based on the FOC vector sensorless control parameters of the UAV, the third estimated pull force and the third estimated torque are calculated as follows:

[0100]

[0101] in, For the third estimated torque, p is the number of motor pole pairs, phif is the motor parameter, iq is the q-axis current, and V is the torque value. D3 For the third estimated rotational speed, For the third estimated tension;

[0102] The first test tensile force, the second estimated tensile force, and the third estimated tensile force are weighted and calculated to obtain the optimized estimated tensile force. The first test torque, the second estimated torque, and the third estimated torque are weighted and calculated to obtain the optimized estimated torque.

[0103] In this embodiment, the UAV's control parameters, such as FOC (Field Oriented Control) vector sensorless control technology, are precisely adjusted to regulate the flight state by controlling the motor current. Based on these control parameters, a third estimated thrust and a third estimated torque are calculated. Key control parameters include the number of pole pairs of the motor, the motor parameter phif, and the iq-axis current in the control signal.

[0104] Based on the control principle of UAVs, the third estimated torque is first calculated as follows:

[0105]

[0106] in, The third estimated torque is given by p, which is the number of pole pairs of the motor, phif is the motor parameter, specifically the magnetic field angle between the stator and rotor of the motor, and iq is the q-axis current of the motor.

[0107] Then, based on this third estimated torque and combined with the formula mentioned above, the third estimated speed can be calculated as follows:

[0108]

[0109] Among them, V D3 The third estimated rotational speed is the rotational speed of the UAV under extreme test conditions, which is calculated based on the third estimated torque calculated from the UAV control parameters and combined with multiple conditional weights and multiple torque coefficients.

[0110] Optionally, the third estimated speed can also be the speed estimated by the motor position sensor, or it can be estimated using a sensorless algorithm, as shown in the following formula:

[0111]

[0112] Where dθ is the difference in angle between the front and rear of the motor, and dt is the control cycle of the motor.

[0113] Furthermore, based on the third estimated rotational speed and combined with the formulas mentioned above, the third estimated tension can be calculated as follows:

[0114]

[0115] in, The third estimated tension is obtained by substituting the third estimated rotational speed, multiple conditional weights, and multiple tension coefficients into the estimation calculation.

[0116] After obtaining the first test tension and the first test torque through direct testing, calculating the second estimated tension and the second estimated torque based on multiple tension coefficients and multiple torque coefficients under multiple preset test conditions, and calculating the third estimated tension and the third estimated torque based on the control parameters of the UAV, the three are combined to calculate the comprehensive and relatively accurate tension and torque of the UAV under extreme test conditions.

[0117] The first test tensile force, the second estimated tensile force, and the third estimated tensile force are weighted and calculated to obtain the optimized estimated tensile force. The first test torque, the second estimated torque, and the third estimated torque are weighted and calculated to obtain the optimized estimated torque.

[0118] The weights of the first test tensile force, the second estimated tensile force, and the third estimated tensile force, as well as the first test torque, the second estimated torque, and the third estimated torque, can be configured based on the accuracy of the three by those skilled in the art, for example, 0.3, 0.4, and 0.3.

[0119] Optionally, the average of the first test tension, the second estimated tension, and the third estimated tension can be calculated as the optimized estimated tension, and the average of the first test torque, the second estimated torque, and the third estimated torque can be calculated to obtain the optimized estimated torque.

[0120] By calculating the third estimated tension and the third estimated torque based on the FOC vector sensorless control parameters, and combining them with the first and second estimated tension and torque, an optimized estimated tension and optimized estimated torque are obtained using a weighted calculation method, which improves the accuracy of tension and torque testing of UAVs under heavy loads.

[0121] The method for optimizing the bench torque and tension of the UAV power system provided in this embodiment of the invention has at least the following technical effects:

[0122] This invention employs multiple preset test conditions to test drones, collecting and processing multiple tension and torque coefficients. Simultaneously, it combines historical test data for stability analysis, calculating multiple test stability parameters to ensure the reliability of the test results. During testing, by measuring the drone's tension, torque, and rotational speed under extreme conditions, and combining the test stability parameters, tension coefficients, and torque coefficients, a second estimated tension and a second estimated torque are further calculated. Furthermore, based on the drone's control parameters, a third estimated tension and a third estimated torque are calculated. Combining the tension and torque at each stage, optimized estimated tension and estimated torque are finally obtained. This solution effectively solves the measurement error problem caused by vibration during the testing of heavy-load drones, significantly improving test accuracy and stability. It also provides high-quality test data support for drone performance optimization and design improvement, laying a technical foundation for the development of the low-altitude economy industry.

[0123] Example 2:

[0124] like Figure 2 As shown, based on the same inventive concept as the UAV power system bench torque and tension optimization method provided in Embodiment 1, this embodiment of the invention also provides a UAV power system bench torque and tension optimization system. The explanation and limitation of the UAV power system bench torque and tension optimization method in Embodiment 1 also applies to the UAV power system bench torque and tension optimization system. The system includes:

[0125] The multi-condition test module 11 is used to test the UAV using a test bench according to multiple preset test conditions, and process to obtain multiple tensile coefficients and multiple torque coefficients.

[0126] The test stability analysis module 12 is used to perform test stability analysis based on the multiple preset test conditions to obtain multiple test stability parameters;

[0127] The extreme test module 13 is used to test and obtain the first test pull force, the first test torque and the first test speed of the UAV under extreme test conditions, and to calculate the second estimated pull force and the second estimated torque based on the second estimated speed, multiple test stability parameters, multiple pull force coefficients and multiple torque coefficients.

[0128] The test optimization calculation module 14 is used to calculate the third estimated pull force and the third estimated torque based on the control parameters of the UAV, and to calculate the optimized estimated pull force and the optimized estimated torque by combining the first test pull force, the first test torque, the second estimated pull force, the second estimated torque, the third estimated pull force and the third estimated torque.

[0129] The multi-condition testing module 11 is also used for:

[0130] Under the first preset test conditions, the UAV is tested using a test bench to obtain the first condition test tension, the first condition test torque and the first condition test speed, and the first tension coefficient and the first torque coefficient are obtained by processing and calculation.

[0131] The drone was tested under multiple preset test conditions, and multiple tensile coefficients and multiple torque coefficients were obtained.

[0132] The multi-condition testing module 11 is also used for:

[0133] Under the first preset test conditions, the UAV is tested using a test bench to obtain the first condition test tension, the first condition test torque, and the first condition test rotation speed;

[0134] Based on the first condition test tension, the first condition test torque, and the first condition test rotation speed, the first tension coefficient and the first torque coefficient are calculated as follows:

[0135] G=K G *V 2 ;

[0136] T=K T *V 2 ;

[0137] Where G is the test tension, T is the test torque, V is the test speed, and K is the test rotational speed. G K is the tensile coefficient. T This is the torque coefficient.

[0138] The stability analysis module 12 is also used for:

[0139] Based on historical test data of similar UAVs under the first preset test conditions, obtain the historical first set of tension coefficients and the historical first set of torque coefficients;

[0140] Based on the historical first tensile force coefficient set and the historical first torque coefficient set, test stability calculation and analysis are performed to obtain the first test stability parameter;

[0141] Based on historical test data under the multiple preset test conditions, test stability calculation and analysis are performed to obtain multiple test stability parameters.

[0142] The stability analysis module 12 is also used for:

[0143] Multiple sets of historical first tensile force coefficients are randomly selected from the set of historical first tensile force coefficients, and the difference range is calculated to obtain multiple historical first tensile force difference ranges;

[0144] Calculate the average of the amplitudes of the multiple historical first tensile force differences, and subtract this average from 1 to obtain the stability parameter of the first tensile force test;

[0145] Multiple sets of historical first torque coefficients are randomly selected from the set of historical first torque coefficients, and the difference amplitude is calculated to obtain multiple historical first torque difference amplitudes;

[0146] Calculate the average of the amplitudes of the multiple historical first torque differences, and subtract this average from 1 to obtain the first torque test stability parameter;

[0147] The average value of the first tensile test stability parameter and the first torque test stability parameter is calculated to obtain the first test stability parameter.

[0148] The extreme testing module 13 is also used for:

[0149] Based on the magnitude of the multiple test stability parameters, assign multiple conditional weights;

[0150] Based on the second estimated rotational speed, multiple conditional weights, multiple force coefficients, and multiple torque coefficients, the second estimated force and the second estimated torque are calculated as follows:

[0151]

[0152] in, For the second estimated tensile force, M is the number of multiple preset test conditions, and w i Let i be the condition weight of the i-th preset test condition. V is the tensile coefficient for the i-th preset test condition. D2 For the second estimated rotational speed, For the second estimated torque, is the torque coefficient for the i-th preset test condition.

[0153] The test optimization calculation module 14 is also used for:

[0154] Based on the FOC vector sensorless control parameters of the UAV, the third estimated pull force and the third estimated torque are calculated as follows:

[0155]

[0156] in, For the third estimated torque, p is the number of motor pole pairs, phif is the motor parameter, iq is the q-axis current, and V is the torque value. D3 For the third estimated rotational speed, For the third estimated tension;

[0157] The first test tensile force, the second estimated tensile force, and the third estimated tensile force are weighted and calculated to obtain the optimized estimated tensile force. The first test torque, the second estimated torque, and the third estimated torque are weighted and calculated to obtain the optimized estimated torque.

[0158] Example 3:

[0159] Please see Figure 3 , Figure 3 This is a schematic diagram illustrating an embodiment of the electronic device provided in this invention. For example... Figure 3 As shown, an embodiment of the present invention provides an electronic device 500, including a memory 510, a processor 520, and a first computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the first computer program 511, it implements a method for optimizing the bench torque tension of a UAV power system.

[0160] Example 4:

[0161] Please see Figure 4 , Figure 4 This is a schematic diagram illustrating an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. For example... Figure 4 As shown, this embodiment provides a computer-readable storage medium 600, on which a second computer program 611 is stored. When the second computer program 611 is executed by a processor, it implements the following steps: a method for optimizing the torque and tension of a UAV power system bench.

[0162] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0163] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0164] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0165] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0166] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0167] Although preferred embodiments of the invention have been described, those skilled in the art, once they have learned the basic inventive concept, can make other changes and modifications to these embodiments.

[0168] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for optimizing the torque and tension of a UAV power system test bench, characterized in that, The method includes: The UAV was tested using a test bench under multiple preset test conditions, and multiple tensile coefficients and multiple torque coefficients were obtained. Based on the multiple preset test conditions, a test stability analysis is performed to obtain multiple test stability parameters; The test obtains the first test pull force, first test torque and first test speed of the UAV under extreme test conditions. Based on the second estimated speed, multiple test stability parameters, multiple pull force coefficients and multiple torque coefficients, the second estimated pull force and the second estimated torque are calculated. Based on the control parameters of the UAV, a third estimated pull force and a third estimated torque are calculated. Combining the first test pull force, the first test torque, the second estimated pull force, and the second estimated torque, an optimized estimated pull force and an optimized estimated torque are calculated.

2. The method for optimizing the bench torque and tension of an unmanned aerial vehicle (UAV) power system according to claim 1, characterized in that, The drone was tested using a test bench under multiple preset test conditions, and multiple tensile force coefficients and multiple torque coefficients were obtained, including: Under the first preset test conditions, the UAV is tested using a test bench to obtain the first condition test tension, the first condition test torque and the first condition test speed, and the first tension coefficient and the first torque coefficient are obtained by processing and calculation. The drone was tested under multiple preset test conditions, and multiple tensile coefficients and multiple torque coefficients were obtained.

3. The method for optimizing the bench torque and tension of the UAV power system according to claim 2, characterized in that, Under the first preset test conditions, the UAV is tested using a test bench to obtain the first condition test pull force, the first condition test torque, and the first condition test rotation speed. The first pull force coefficient and the first torque coefficient are then processed and calculated, including: Under the first preset test conditions, the UAV is tested using a test bench to obtain the first condition test tension, the first condition test torque, and the first condition test rotation speed; Based on the first condition test tension, the first condition test torque, and the first condition test rotation speed, the first tension coefficient and the first torque coefficient are calculated as follows: G=K G *V 2 ; T=K T *V 2 ; Where G is the test tension, T is the test torque, V is the test speed, and K is the test rotational speed. G K is the tensile coefficient. T This is the torque coefficient.

4. The method for optimizing the bench torque and tension of an unmanned aerial vehicle (UAV) power system according to claim 1, characterized in that, Based on the multiple preset test conditions, a test stability analysis is performed to obtain multiple test stability parameters, including: Based on historical test data of similar UAVs under the first preset test conditions, obtain the historical first set of tension coefficients and the historical first set of torque coefficients; Based on the historical first tensile force coefficient set and the historical first torque coefficient set, test stability calculation and analysis are performed to obtain the first test stability parameter; Based on historical test data under the multiple preset test conditions, test stability calculation and analysis are performed to obtain multiple test stability parameters.

5. The method for optimizing the bench torque and tension of an unmanned aerial vehicle (UAV) power system according to claim 4, characterized in that, Based on the aforementioned historical first tensile force coefficient set and historical first torque coefficient set, test stability calculation and analysis are performed to obtain the first test stability parameters, including: Multiple sets of historical first tensile force coefficients are randomly selected from the set of historical first tensile force coefficients, and the difference range is calculated to obtain multiple historical first tensile force difference ranges; Calculate the average of the amplitudes of the multiple historical first tensile force differences, and subtract this average from 1 to obtain the stability parameter of the first tensile force test; Multiple sets of historical first torque coefficients are randomly selected from the set of historical first torque coefficients, and the difference amplitude is calculated to obtain multiple historical first torque difference amplitudes; Calculate the average of the amplitudes of the multiple historical first torque differences, and subtract this average from 1 to obtain the first torque test stability parameter; The average value of the first tensile test stability parameter and the first torque test stability parameter is calculated to obtain the first test stability parameter.

6. The method for optimizing the bench torque and tension of an unmanned aerial vehicle (UAV) power system according to claim 1, characterized in that, Based on the second estimated rotational speed, multiple test stability parameters, multiple tension coefficients, and multiple torque coefficients, the second estimated tension and the second estimated torque are calculated, including: Based on the magnitude of the multiple test stability parameters, assign multiple conditional weights; Based on the second estimated rotational speed, multiple conditional weights, multiple force coefficients, and multiple torque coefficients, the second estimated force and the second estimated torque are calculated as follows: in, For the second estimated tensile force, M is the number of multiple preset test conditions, and w i Let i be the condition weight of the i-th preset test condition. V is the tensile coefficient for the i-th preset test condition. D2 For the second estimated rotational speed, For the second estimated torque, is the torque coefficient for the i-th preset test condition.

7. The method for optimizing the bench torque and tension of an unmanned aerial vehicle (UAV) power system according to claim 6, characterized in that, Based on the control parameters of the UAV, a third estimated pull force and a third estimated torque are calculated. Combining the first test pull force, the first test torque, the second estimated pull force, and the second estimated torque, optimized estimated pull force and optimized estimated torque are calculated, including: Based on the FOC vector sensorless control parameters of the UAV, the third estimated pull force and the third estimated torque are calculated as follows: in, For the third estimated torque, p is the number of pole pairs of the motor, phif is the motor parameter, specifically the magnetic field angle between the stator and rotor, iq is the q-axis current, and V... D3 For the third estimated rotational speed, For the third estimated tension; The first test tensile force, the second estimated tensile force, and the third estimated tensile force are weighted and calculated to obtain the optimized estimated tensile force. The first test torque, the second estimated torque, and the third estimated torque are weighted and calculated to obtain the optimized estimated torque.

8. A bench torque and tension optimization system for a UAV power system, characterized in that, The system is used to execute the bench torque and tension optimization method for the UAV power system according to any one of claims 1-7, the system comprising: The multi-condition testing module is used to test the UAV on a test bench according to multiple preset test conditions, and process and obtain multiple tensile coefficients and multiple torque coefficients. The test stability analysis module is used to perform test stability analysis based on the multiple preset test conditions and obtain multiple test stability parameters; The extreme testing module is used to test and obtain the first test pull force, first test torque and first test speed of the UAV under extreme test conditions. Based on the second estimated speed, multiple test stability parameters, multiple pull force coefficients and multiple torque coefficients, the second estimated pull force and the second estimated torque are calculated. The test optimization calculation module is used to calculate a third estimated pull force and a third estimated torque based on the control parameters of the UAV, and to calculate an optimized estimated pull force and optimized estimated torque by combining the first test pull force, the first test torque, the second estimated pull force, the second estimated torque, the third estimated pull force, and the third estimated torque.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor is used to read and execute the computer program, thereby implementing the bench torque and tension optimization method for the UAV power system as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the unmanned aerial vehicle power system bench torque and tension optimization method as described in any one of claims 1-7.

Images