Test method, system, and storage medium for electric-governor-driven response of high-pressure unmanned aerial vehicle

Abstract

This invention provides a testing method, system, and storage medium for the electronic speed controller (ESC) drive response of a high-voltage unmanned aerial vehicle (UAV). First, the ESC is driven based on a preset PWM control signal sequence, and the pulse signal sequence is acquired and converted into a speed response curve. Next, dynamic feature groups are extracted from the curve. Then, each feature parameter is compared with a primary performance threshold to obtain a primary evaluation result, and further, the rate of change of the features is calculated for a secondary threshold comparison. Finally, the two levels of evaluation results are combined to generate a performance level report, which is then input into a pre-trained matching model to output specific diagnostic suggestions. This achieves a comprehensive and accurate evaluation of the ESC drive performance, effectively identifying potential performance defects and providing targeted optimization suggestions, thus improving the depth and reliability of the test.

Description

Technical Field

[0001] This invention relates to the field of high-voltage unmanned aerial vehicle (UAV) ESC performance testing, and more specifically, to a testing method, system, and storage medium for the ESC drive response of a high-voltage UAV. Background Technology

[0002] In the research and development and quality control of unmanned aerial vehicles (UAVs), performance testing of the power system is crucial. As the core component controlling motor speed, the electronic speed controller (ESC) directly determines the UAV's flight stability, maneuverability, and reliability through its drive response performance (such as response speed, stability, and tracking ability). Especially in the field of high-voltage UAVs (UAVs equipped with high-voltage motors and high-voltage ESCs, typically operating at 400V to 800V), current testing methods primarily focus on the performance of the motor itself. This mainly involves measuring parameters such as the motor's tension, speed, current, and voltage under different loads to evaluate the motor's power performance. While this comprehensively reflects the motor's operating state, the testing focus is on the motor's output capacity (tension) and efficiency, without delving into the dynamic response characteristics of the ESC as a control component.

[0003] Furthermore, as the core component controlling motor speed, the ESC's dynamic response characteristics directly determine the flight stability and handling performance of the UAV. However, existing testing methods have significant shortcomings. First, current technologies often test the ESC and motor as a whole, failing to effectively separate the ESC's control performance from the overall power system. This results in a vague test object and an inability to accurately assess the ESC's driving characteristics. Second, the evaluation dimensions are limited, currently relying mainly on static parameters such as tension and speed, lacking in-depth analysis of the dynamic response process. Third, existing methods lack effective quantification of the ESC's response process under dynamic conditions such as sudden changes in speed commands and load variations, failing to obtain key dynamic parameters such as response delay, overshoot, and settling time. Therefore, there is an urgent need for a technology that can directly and accurately test and evaluate the drive response performance of UAV ESCs. Summary of the Invention

[0004] In view of the above problems, the purpose of this invention is to provide a testing method, system, and storage medium for the electronic speed controller (ESC) drive response of a high-voltage unmanned aerial vehicle (UAV). By establishing a complete testing process and intelligent diagnostic mechanism, it achieves multi-dimensional evaluation of ESC performance and accurate problem localization. Specifically, firstly, an optimized signal acquisition scheme ensures high-quality acquisition of rotational speed signals, providing a reliable data foundation for subsequent analysis; simultaneously, precise rotational speed response curve generation accurately reproduces the dynamic response process under ESC drive; secondly, systematic feature parameter extraction comprehensively captures the key performance indicators of the ESC; then, primary performance threshold comparison enables rapid screening and defect identification of basic performance; next, secondary performance threshold comparison provides an in-depth evaluation of the ESC performance consistency under different operating conditions. This invention organically combines the above technical features to form an ESC drive response performance testing system, effectively solving the problem of testing accuracy for ESC drive response performance.

[0005] The first aspect of this invention provides a method for testing the electronically controlled drive response of a high-voltage unmanned aerial vehicle (UAV), the method comprising:

[0006] The ESC is driven by a preset PWM control signal sequence, and the pulse signal sequence is acquired through a signal conditioning module.

[0007] The rotational speed response curve is obtained based on the pulse signal sequence.

[0008] Based on the speed response curve, a dynamic feature set is obtained according to a preset feature extraction rule;

[0009] The various feature parameters of the dynamic feature group are compared with the preset primary performance thresholds to obtain the primary evaluation results, including primary pass features and primary defect features.

[0010] Calculate the rate of change of each characteristic parameter of the primary pass feature, compare it with the preset corresponding secondary performance threshold, and obtain the secondary evaluation results, including secondary pass features and secondary defect features;

[0011] Based on the primary and secondary assessment results, a performance level report is generated;

[0012] The performance level report is input into a pre-trained matching model, which outputs diagnostic suggestions.

[0013] In this solution, the step of driving the ESC based on a preset PWM control signal sequence, and acquiring the pulse signal sequence through a signal conditioning module, specifically includes:

[0014] According to the preset PWM control signal sequence, the signal generator sends a step-changing PWM control signal to the ESC under test, driving the ESC to control the blade rotation.

[0015] By aligning an infrared reflective sensor with a reflective sticker on the blade, a light pulse signal is obtained in response to changes in the light intensity of the reflective sticker;

[0016] The optical pulse signal is input to the signal conditioning module, and a pulse signal sequence is obtained through preset signal amplification and Schmitt shaping.

[0017] In this scheme, obtaining the speed response curve based on the pulse signal sequence specifically includes:

[0018] Based on the pulse signal sequence and a preset pulse frequency conversion rule, the rotational speed is calculated, wherein the rotational speed is proportional to the pulse frequency.

[0019] Based on the time axis recording of the speed value change, a speed response curve is generated, wherein the horizontal axis of the speed response curve is time and the vertical axis is speed.

[0020] In this scheme, obtaining the dynamic feature set based on the speed response curve and a preset feature extraction rule specifically includes:

[0021] Based on the speed response curve, and by comparing preset time feature points and deviation values, the response delay time, rise time, settling time, overshoot, and steady-state error are extracted.

[0022] in,

[0023] The response delay time is the time required from the moment the PWM control signal undergoes a step change to the time when the speed response curve first reaches the preset first proportion of the target speed value.

[0024] The rise time is the time required for the speed response curve to rise from a preset first proportion of the target speed value to a preset second proportion.

[0025] The overshoot is the percentage of the peak value exceeding the target speed value when the speed response curve first crosses the target speed value, to the target speed value itself.

[0026] The settling time is the time required for the speed response curve to enter and remain within a preset error band.

[0027] The steady-state error is the deviation between the average value of the rotational speed after it enters a steady state and the target rotational speed value.

[0028] In this scheme, the step of comparing each feature parameter of the dynamic feature group with a preset corresponding primary performance threshold to obtain a primary evaluation result specifically includes:

[0029] Based on each feature parameter in the dynamic feature group, each feature parameter is compared with a preset primary performance threshold range;

[0030] If the feature parameters are within the primary performance threshold range, they are marked as primary pass features;

[0031] If the feature parameter exceeds the primary performance threshold range, it is marked as a primary defect feature and the excess type is recorded;

[0032] Based on the comparison results of all special diagnostic parameters, a preliminary assessment result is obtained.

[0033] In this scheme, the calculation of the rate of change of each feature parameter of the primary pass feature is compared with the preset corresponding secondary performance threshold to obtain the secondary evaluation result, specifically including:

[0034] Based on the various feature parameters of the primary feature, the deviation between adjacent feature parameters is calculated to obtain the feature change rate;

[0035] The rate of change of the feature is compared with a preset secondary performance threshold.

[0036] If the rate of change of the feature is within the secondary performance threshold, it is marked as a secondary pass feature;

[0037] If the rate of change of the feature exceeds the secondary performance threshold, it is marked as a secondary defect feature and the deviation range is recorded;

[0038] The secondary evaluation results are obtained by combining the comparison results of the diagnostic parameters of all primary features.

[0039] A second aspect of the present invention provides a test system for the electronically controlled drive response of a high-voltage unmanned aerial vehicle (UAV), comprising a test method program for the electronically controlled drive response of a high-voltage UAV, wherein the test method program for the electronically controlled drive response of the high-voltage UAV, when executed by the processor, implements the following steps:

[0040] The ESC is driven by a preset PWM control signal sequence, and the pulse signal sequence is acquired through a signal conditioning module.

[0041] The rotational speed response curve is obtained based on the pulse signal sequence.

[0042] Based on the speed response curve, a dynamic feature set is obtained according to a preset feature extraction rule;

[0043] The various feature parameters of the dynamic feature group are compared with the preset primary performance thresholds to obtain the primary evaluation results, including primary pass features and primary defect features.

[0044] Calculate the rate of change of each characteristic parameter of the primary pass feature, compare it with the preset corresponding secondary performance threshold, and obtain the secondary evaluation results, including secondary pass features and secondary defect features;

[0045] Based on the primary and secondary assessment results, a performance level report is generated;

[0046] The performance level report is input into a pre-trained matching model, which outputs diagnostic suggestions.

[0047] A third aspect of the present invention provides a computer-readable storage medium comprising a test method program for the electronically controlled drive response of a high-voltage unmanned aerial vehicle (UAV), wherein when the test method program for the electronically controlled drive response of a high-voltage UAV is executed by a processor, the steps of the test method for the electronically controlled drive response of a high-voltage UAV as described in any of the preceding claims are implemented.

[0048] This invention provides a testing method, system, and storage medium for the electronic speed controller (ESC) drive response of a high-voltage unmanned aerial vehicle (UAV). First, the ESC is driven based on a preset PWM control signal sequence, and the pulse signal sequence is acquired and converted into a speed response curve. Next, dynamic feature groups are extracted from the curve. Then, each feature parameter is compared with a primary performance threshold to obtain a primary evaluation result, and further, the rate of change of the features is calculated for a secondary threshold comparison. Finally, the two levels of evaluation results are combined to generate a performance level report, which is then input into a pre-trained matching model to output specific diagnostic suggestions. This achieves a comprehensive and accurate evaluation of the ESC drive performance, effectively identifying potential performance defects and providing targeted optimization suggestions, thus improving the depth and reliability of the test. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope.

[0050] Figure 1 A connection diagram of a test apparatus for testing the ESC drive response of a high-voltage unmanned aerial vehicle is shown.

[0051] Figure 2 A flowchart of a test method for the electronically controlled drive response of a high-voltage unmanned aerial vehicle according to the present invention is shown;

[0052] Figure 3 A flowchart illustrating the acquisition process of a pulse electrical signal according to an embodiment of the present invention is shown.

[0053] Figure 4 A flowchart illustrating the generation of a rotational speed response curve according to an embodiment of the present invention is shown.

[0054] Figure 5 A schematic diagram of a speed response curve and dynamic characteristic parameters provided in an embodiment of the present invention is shown;

[0055] Figure 6A block diagram of a test system for the electronically controlled drive response of a high-voltage unmanned aerial vehicle (UAV) according to the present invention is shown. Detailed Implementation

[0056] 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.

[0057] Unless otherwise defined, all terms (including technical and scientific terms) used in embodiments of this invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in a common dictionary shall be interpreted as having a meaning consistent with their meaning in the context of the relevant art, and not as being interpreted in an idealized or highly formalized sense, unless expressly defined in this embodiment of the invention.

[0058] The terms "first," "second," and similar words used in the embodiments of this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "an," "a," or "the" do not indicate a quantity limitation, but rather indicate the presence of at least one. Similarly, terms such as "including" or "comprising" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The steps preceding or following the steps in the method of the embodiments of this invention are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0059] In addition, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0060] Figure 1 A connection diagram of a test apparatus for testing the ESC drive response of a high-voltage unmanned aerial vehicle is shown.

[0061] like Figure 1 As shown, a test device for testing the ESC drive response of a high-voltage unmanned aerial vehicle (UAV) includes an ESC 102, a motor 103, a propeller 104, a signal generator 101, an infrared reflective sensor 105, and a signal conditioning module 106.

[0062] The electronic speed controller drives the motor to rotate the blades;

[0063] Reflective stickers are affixed to the back of the blades;

[0064] The signal generator sends a preset PWM control signal to the ESC to drive the blades to rotate;

[0065] The signal output terminal of the infrared reflective sensor is connected to the signal conditioning module;

[0066] The signal conditioning module converts the optical signal into a pulsed electrical signal.

[0067] It should be noted that the ESC under test, the matching UAV motor, and the propellers are mounted on a test bench. Reflective stickers are affixed to the back of the propellers. A preset PWM control signal is sent to the ESC using a signal generator. Simultaneously, an infrared reflective sensor is mounted below the propellers, its optical path aligned with the rotation path of the reflective sticker. The signal output of the infrared reflective sensor is connected to a signal conditioning module, which converts the optical signal into a pulse electrical signal representing the rotational speed. Finally, this pulse electrical signal is measured using a probe from a signal measurement module such as an oscilloscope.

[0068] Figure 2 A flowchart of a test method for the electronically controlled drive response of a high-voltage unmanned aerial vehicle (UAV) according to the present invention is shown.

[0069] like Figure 2 As shown, the first aspect of this invention discloses a test method for the electronically controlled drive response of a high-voltage unmanned aerial vehicle (UAV), the method comprising:

[0070] S202 drives the ESC based on a preset PWM control signal sequence and acquires the pulse signal sequence through a signal conditioning module;

[0071] S204, Based on the pulse signal sequence, obtain the speed response curve;

[0072] S206, Based on the speed response curve and a preset feature extraction rule, obtain a dynamic feature group;

[0073] S208 compares each feature parameter of the dynamic feature group with the preset corresponding primary performance threshold to obtain the primary evaluation result, including primary pass feature and primary defect feature;

[0074] S210, calculate the rate of change of each characteristic parameter of the primary pass feature, compare it with the preset corresponding secondary performance threshold, and obtain the secondary evaluation result, including the secondary pass feature and the secondary defect feature.

[0075] S212, Generate a performance level report based on the primary evaluation results and secondary evaluation results;

[0076] S214, Input the performance level report into the pre-trained matching model and output diagnostic suggestions.

[0077] The dynamic feature group includes multiple feature parameters, including at least response delay time, rise time, settling time, overshoot, and steady-state error; the diagnostic suggestions include at least PID parameter adjustment suggestions and hardware module inspection suggestions.

[0078] It should be noted that in this embodiment, the ESC under test is first driven based on a preset PWM control signal sequence, and the pulse signal sequence generated during motor operation is acquired through a signal conditioning module. Secondly, based on the acquired pulse signal sequence, a response curve reflecting the dynamic changes in motor speed is obtained through frequency conversion processing. Next, based on this speed response curve and a preset feature extraction rule, a set of feature parameters containing multiple key performance indicators is extracted from the curve. Then, each parameter in the feature parameter set is compared and analyzed with a preset primary performance threshold, and qualified and defective features are distinguished based on the comparison results. Furthermore, the rate of change of qualified feature parameters under different test conditions is calculated, and these rates of change are compared with secondary performance thresholds to evaluate the consistency of the feature parameters. Finally, a comprehensive performance level report is generated by combining the primary and secondary evaluation results, and this report is input into a pre-trained intelligent diagnostic model to output targeted improvement suggestions. This embodiment, through the organic combination of multi-level threshold comparison and intelligent diagnosis, achieves a comprehensive evaluation and accurate diagnosis of ESC drive performance, ensuring the reliability of test results and providing a clear direction for product optimization.

[0079] Figure 3 A flowchart illustrating the acquisition process of a pulse electrical signal according to an embodiment of the present invention is shown.

[0080] According to embodiments of the present invention, such as Figure 3 As shown, the method of driving the ESC based on a preset PWM control signal sequence, and acquiring the pulse signal sequence through a signal conditioning module, specifically includes:

[0081] S302, according to the preset PWM control signal sequence, sends a step-changing PWM control signal to the ESC under test through the signal generator, driving the ESC to control the blade rotation;

[0082] S304 uses an infrared reflective sensor to align with the reflective sticker on the blade and receives a light pulse signal in response to changes in the light intensity of the reflective sticker;

[0083] S306, the optical pulse signal is input to the signal conditioning module, and a pulse signal sequence is obtained through preset signal amplification and Schmitt shaping.

[0084] It should be noted that in this embodiment, a preset PWM control signal sequence is generated by a signal generator and sent to the ESC under test, driving the ESC to control the motor and rotate the propeller. A reflective sticker is attached to the back of the propeller, and an infrared reflective sensor is aligned with the rotation path of the sticker. As the propeller rotates, each time the sticker passes the sensor, it causes a sudden change in light intensity, and the sensor generates a corresponding light pulse signal. The light pulse signal is transmitted to the signal conditioning module, where the weak signal is first amplified by an amplification circuit, and then the irregular waveform is shaped by a Schmitt trigger, finally outputting a regular pulse signal sequence. This embodiment uses a non-contact measurement principle, effectively avoiding interference from mechanical contact; at the same time, signal conditioning ensures the quality and stability of the acquired signal, providing a reliable data foundation for subsequent analysis.

[0085] Figure 4 A flowchart illustrating the generation of a speed response curve according to an embodiment of the present invention is shown.

[0086] According to embodiments of the present invention, such as Figure 4 As shown, obtaining the speed response curve based on the pulse signal sequence specifically includes:

[0087] S402, Based on the pulse signal sequence and a preset pulse frequency conversion rule, the rotational speed value is calculated, wherein the rotational speed value is proportional to the pulse frequency;

[0088] S404, Based on the time axis recording of the change in rotational speed value, a rotational speed response curve is generated, wherein the horizontal axis of the rotational speed response curve is time and the vertical axis is rotational speed.

[0089] It should be noted that in this embodiment, based on the pulse signal sequence output by the signal conditioning module, the corresponding real-time speed value is calculated by measuring the pulse frequency and according to a preset conversion relationship; wherein the speed value and the pulse frequency maintain a strict proportional relationship. Simultaneously with the speed value calculation, a coordinate system is established with time as the horizontal axis and speed as the vertical axis, continuously recording the speed values ​​at each time point to form a complete speed response curve. This embodiment, through precise frequency measurement and continuous data recording, reproduces the dynamic change process of the motor speed under ESC drive, providing an accurate data source for subsequent feature parameter extraction and ensuring the objectivity and accuracy of performance evaluation.

[0090] Figure 5 This diagram illustrates a speed response curve and dynamic characteristic parameters provided by an embodiment of the present invention.

[0091] According to an embodiment of the present invention, obtaining a dynamic feature set based on a preset feature extraction rule according to the rotational speed response curve specifically includes:

[0092] Based on the speed response curve, and by comparing preset time feature points and deviation values, the response delay time, rise time, settling time, overshoot, and steady-state error are extracted.

[0093] in,

[0094] The response delay time (Td) is the time required from the moment the PWM control signal undergoes a step change to the time when the speed response curve first reaches the target speed value at a preset first proportion.

[0095] The rise time (Tr) is the time required for the speed response curve to rise from a preset first proportion of the target speed value to a preset second proportion;

[0096] The overshoot (Mp) is the percentage of the peak value exceeding the target speed value when the speed response curve first crosses the target speed value, to the target speed value.

[0097] The settling time (Ts) is the time required for the speed response curve to enter and remain within the preset error band;

[0098] The steady-state error is the deviation between the average value of the rotational speed after it enters a steady state and the target rotational speed value.

[0099] It should be noted that in this embodiment, after obtaining the speed response curve, the starting moment of the step change in the PWM control signal is first determined. Then, key feature points on the curve are identified, including the moment when the curve first reaches a specific proportion (e.g., 10%) of the target speed value, the time period from a low proportion (e.g., 10%) to a high proportion (e.g., 90%), the peak point when the curve first crosses the target value, the starting moment when it enters and remains within a preset error range (e.g., ±2% of the target value), and the average speed during the steady-state phase. Based on the identification results of these feature points, characteristic parameters such as response delay time, rise time, overshoot, settling time, and steady-state error are calculated, which together form a dynamic feature set. This embodiment, through systematic feature point identification and parameter calculation, comprehensively captures various key performance indicators in the ESC drive response process, laying a solid foundation for multi-dimensional performance evaluation and making performance analysis more refined.

[0100] According to an embodiment of the present invention, the step of comparing each feature parameter of the dynamic feature group with a preset corresponding primary performance threshold to obtain a primary evaluation result specifically includes:

[0101] Based on each feature parameter in the dynamic feature group, each feature parameter is compared with a preset primary performance threshold range;

[0102] If the feature parameters are within the primary performance threshold range, they are marked as primary pass features;

[0103] If the feature parameter exceeds the primary performance threshold range, it is marked as a primary defect feature and the excess type is recorded;

[0104] Based on the comparison results of all special diagnostic parameters, a preliminary assessment result is obtained.

[0105] It should be noted that in this embodiment, each feature parameter in the dynamic feature group, including response delay time, rise time, settling time, overshoot, and steady-state error, is first compared and analyzed against a preset primary performance threshold range. During the comparison process, when the feature parameter value falls entirely within the corresponding threshold range, the parameter is marked as a primary pass feature; conversely, when the feature parameter value exceeds the threshold range, it is marked as a primary defect feature, and the specific type and degree of deviation are recorded in detail. Finally, the comparison results of all feature parameters are systematically integrated to form a complete primary evaluation result containing both the pass feature set and the defect feature set. The threshold comparison mechanism used in this embodiment enables rapid screening and classification of the basic performance characteristics of the ESC, providing a clear evaluation framework for subsequent in-depth analysis, thus improving testing efficiency.

[0106] According to an embodiment of the present invention, the calculation of the rate of change of each feature parameter of the primary pass feature, and the comparison with the preset corresponding secondary performance threshold to obtain the secondary evaluation result, specifically includes:

[0107] Based on the various feature parameters of the primary feature, the deviation between adjacent feature parameters is calculated to obtain the feature change rate;

[0108] The rate of change of the feature is compared with a preset secondary performance threshold.

[0109] If the rate of change of the feature is within the secondary performance threshold, it is marked as a secondary pass feature;

[0110] If the rate of change of the feature exceeds the secondary performance threshold, it is marked as a secondary defect feature and the deviation range is recorded;

[0111] The secondary evaluation results are obtained by combining the comparison results of the diagnostic parameters of all primary features.

[0112] It should be noted that, in this embodiment, based on the initial evaluation, the rate of change of all marked as passed feature parameters under adjacent test conditions is calculated; the rate of change reflects the stability of the feature parameter under different operating conditions. Then, the calculated rate of change is compared with a preset secondary performance threshold; when the rate of change remains within the threshold range, the feature is marked as a secondary pass feature; when the rate of change exceeds the threshold range, it is marked as a secondary defect feature, and the specific deviation range and fluctuation characteristics are recorded. Finally, the comparison results of the rate of change of all feature parameters are combined to form a secondary evaluation report for the system. This embodiment, by introducing the rate of change analysis dimension, effectively captures the performance consistency of the ESC under different operating conditions, and can identify potential problems that, although meeting basic requirements, exhibit unstable performance under dynamic changes.

[0113] It is worth mentioning that the process of inputting the performance level report into the pre-trained matching model and outputting diagnostic suggestions specifically includes:

[0114] Based on the performance level report and dynamic feature groups, the data is input into a pre-trained diagnostic matching model to generate the diagnostic recommendations;

[0115] The diagnostic matching model is trained based on machine learning algorithms, and the training data includes the correspondence between performance level reports from historical tests, dynamic feature groups and confirmed fault types.

[0116] The diagnostic matching model analyzes the combination patterns of each defect feature in the performance level report, and combines them with the specific values ​​of each parameter in the dynamic feature group to output diagnostic suggestions including the direction of PID parameter adjustment and the priority of hardware module inspection.

[0117] It should be noted that in this embodiment, the performance level report and dynamic feature group data are jointly input into a diagnostic matching model trained on a large amount of historical data. This matching model is built based on machine learning algorithms, and the training data includes the correspondence between performance characteristics and ultimately confirmed fault types from a large number of historical test cases. The matching model analyzes the combination patterns of various defect features in the current test, combined with the numerical characteristics of specific parameters in the dynamic feature group, to perform a multi-dimensional comprehensive judgment. The final output includes targeted diagnostic suggestions such as specific PID parameter adjustment directions and hardware module inspection priorities. This embodiment, through an intelligent diagnostic model, achieves everything from simple problem identification to in-depth root cause analysis, providing reliable technical support for quickly locating and resolving ESC performance problems.

[0118] It is worth mentioning that it also includes:

[0119] Obtain historical test data sets and environmental parameters;

[0120] The primary and secondary performance thresholds are dynamically adjusted based on the pre-trained machine learning model to obtain an optimized set of performance thresholds.

[0121] in,

[0122] The historical test data set includes feature parameter data and corresponding performance evaluation results recorded in multiple historical test cycles.

[0123] The current environmental parameters include at least the ambient temperature, humidity, and power supply voltage fluctuation values;

[0124] The machine learning model is based on a deep neural network. By analyzing the correlation between historical feature parameters and performance evaluation results, it outputs a performance threshold range optimized for the current test environment.

[0125] It should be noted that in this embodiment, feature parameter data and corresponding evaluation results accumulated during historical testing are continuously collected, while environmental parameters, including ambient temperature, humidity, and power supply voltage fluctuations, are monitored. This historical data and environmental parameters are input into a machine learning model built on a deep neural network. The machine learning model learns the optimal setting of performance thresholds under different environmental conditions by analyzing the complex correlation between historical feature parameters and performance evaluation results. Based on the learning results, the specific ranges of primary and secondary performance thresholds are dynamically adjusted to generate an optimized threshold set adapted to the current testing environment. This embodiment, through an adaptive threshold optimization mechanism, enables the testing system to intelligently adjust evaluation criteria according to different environmental conditions, improving the applicability and accuracy of the testing method, especially demonstrating good adaptability in complex and ever-changing application scenarios.

[0126] It is worth mentioning that it also includes:

[0127] Based on the dynamic characteristic group and the rotational speed response curve, frequency response characteristic parameters are extracted using frequency domain analysis.

[0128] The frequency response characteristic parameters are compared with a preset frequency domain performance threshold to obtain the frequency domain evaluation result;

[0129] Adjust the performance rating report based on the frequency domain evaluation results;

[0130] The frequency domain analysis includes performing a fast Fourier transform on the rotational speed response curve to extract the amplitude and phase characteristics of the main frequency components.

[0131] It should be noted that in this embodiment, based on time-domain analysis, the obtained speed response curve is processed by Fast Fourier Transform to transform it from the time domain to the frequency domain. The amplitude and phase characteristics of the main frequency components are extracted from the frequency-domain data as frequency response feature parameters. These frequency response feature parameters are compared and analyzed with preset frequency-domain performance thresholds to evaluate the response characteristics of the ESC system at different frequencies. Finally, the frequency-domain evaluation results are fused with the time-domain evaluation results to adjust and improve the original performance level report accordingly. This embodiment, by introducing a frequency-domain analysis dimension, compensates for the limitations of pure time-domain analysis; it can effectively identify dynamic problems such as resonance and oscillation at specific frequencies, providing a more comprehensive technical perspective for ESC performance evaluation.

[0132] Figure 6 A block diagram of a test system for the electronically controlled drive response of a high-voltage unmanned aerial vehicle (UAV) according to the present invention is shown.

[0133] like Figure 6 As shown, the second aspect of the present invention discloses a test system 6 for the electronically controlled drive response of a high-voltage unmanned aerial vehicle (UAV), comprising a memory 61 and a processor 62. The memory includes a test method program for the electronically controlled drive response of the high-voltage UAV. When the test method program for the electronically controlled drive response of the high-voltage UAV is executed by the processor, it performs the following steps:

[0134] The ESC is driven by a preset PWM control signal sequence, and the pulse signal sequence is acquired through a signal conditioning module.

[0135] The rotational speed response curve is obtained based on the pulse signal sequence.

[0136] Based on the speed response curve, a dynamic feature set is obtained according to a preset feature extraction rule;

[0137] The various feature parameters of the dynamic feature group are compared with the preset primary performance thresholds to obtain the primary evaluation results, including primary pass features and primary defect features.

[0138] Calculate the rate of change of each characteristic parameter of the primary pass feature, compare it with the preset corresponding secondary performance threshold, and obtain the secondary evaluation results, including secondary pass features and secondary defect features;

[0139] Based on the primary and secondary assessment results, a performance level report is generated;

[0140] The performance level report is input into a pre-trained matching model, which outputs diagnostic suggestions.

[0141] The dynamic feature group includes multiple feature parameters, including at least response delay time, rise time, settling time, overshoot, and steady-state error; the diagnostic suggestions include at least PID parameter adjustment suggestions and hardware module inspection suggestions.

[0142] It should be noted that in this embodiment, the ESC under test is first driven based on a preset PWM control signal sequence, and the pulse signal sequence generated during motor operation is acquired through a signal conditioning module. Secondly, based on the acquired pulse signal sequence, a response curve reflecting the dynamic changes in motor speed is obtained through frequency conversion processing. Next, based on this speed response curve and a preset feature extraction rule, a set of feature parameters containing multiple key performance indicators is extracted from the curve. Then, each parameter in the feature parameter set is compared and analyzed with a preset primary performance threshold, and qualified and defective features are distinguished based on the comparison results. Furthermore, the rate of change of qualified feature parameters under different test conditions is calculated, and these rates of change are compared with secondary performance thresholds to evaluate the consistency of the feature parameters. Finally, a comprehensive performance level report is generated by combining the primary and secondary evaluation results, and this report is input into a pre-trained intelligent diagnostic model to output targeted improvement suggestions. This embodiment, through the organic combination of multi-level threshold comparison and intelligent diagnosis, achieves a comprehensive evaluation and accurate diagnosis of ESC drive performance, ensuring the reliability of test results and providing a clear direction for product optimization.

[0143] According to an embodiment of the present invention, the step of driving the electronic speed controller based on a preset PWM control signal sequence, and acquiring the pulse signal sequence through a signal conditioning module, specifically includes:

[0144] According to the preset PWM control signal sequence, the signal generator sends a step-changing PWM control signal to the ESC under test, driving the ESC to control the blade rotation.

[0145] By aligning an infrared reflective sensor with a reflective sticker on the blade, a light pulse signal is obtained in response to changes in the light intensity of the reflective sticker;

[0146] The optical pulse signal is input to the signal conditioning module, and a pulse signal sequence is obtained through preset signal amplification and Schmitt shaping.

[0147] It should be noted that in this embodiment, a preset PWM control signal sequence is generated by a signal generator and sent to the ESC under test, driving the ESC to control the motor and rotate the propeller. A reflective sticker is attached to the back of the propeller, and an infrared reflective sensor is aligned with the rotation path of the sticker. As the propeller rotates, each time the sticker passes the sensor, it causes a sudden change in light intensity, and the sensor generates a corresponding light pulse signal. The light pulse signal is transmitted to the signal conditioning module, where the weak signal is first amplified by an amplification circuit, and then the irregular waveform is shaped by a Schmitt trigger, finally outputting a regular pulse signal sequence. This embodiment uses a non-contact measurement principle, effectively avoiding interference from mechanical contact; at the same time, signal conditioning ensures the quality and stability of the acquired signal, providing a reliable data foundation for subsequent analysis.

[0148] According to an embodiment of the present invention, obtaining the speed response curve based on the pulse signal sequence specifically includes:

[0149] Based on the pulse signal sequence and a preset pulse frequency conversion rule, the rotational speed is calculated, wherein the rotational speed is proportional to the pulse frequency.

[0150] Based on the time axis recording of the speed value change, a speed response curve is generated, wherein the horizontal axis of the speed response curve is time and the vertical axis is speed.

[0151] It should be noted that in this embodiment, based on the pulse signal sequence output by the signal conditioning module, the corresponding real-time speed value is calculated by measuring the pulse frequency and according to a preset conversion relationship; wherein the speed value and the pulse frequency maintain a strict proportional relationship. Simultaneously with the speed value calculation, a coordinate system is established with time as the horizontal axis and speed as the vertical axis, continuously recording the speed values ​​at each time point to form a complete speed response curve. This embodiment, through precise frequency measurement and continuous data recording, reproduces the dynamic change process of the motor speed under ESC drive, providing an accurate data source for subsequent feature parameter extraction and ensuring the objectivity and accuracy of performance evaluation.

[0152] According to an embodiment of the present invention, obtaining a dynamic feature set based on a preset feature extraction rule according to the rotational speed response curve specifically includes:

[0153] Based on the speed response curve, and by comparing preset time feature points and deviation values, the response delay time, rise time, settling time, overshoot, and steady-state error are extracted.

[0154] in,

[0155] The response delay time (Td) is the time required from the moment the PWM control signal undergoes a step change to the time when the speed response curve first reaches the target speed value at a preset first proportion.

[0156] The rise time (Tr) is the time required for the speed response curve to rise from a preset first proportion of the target speed value to a preset second proportion;

[0157] The overshoot (Mp) is the percentage of the peak value exceeding the target speed value when the speed response curve first crosses the target speed value, to the target speed value.

[0158] The settling time (Ts) is the time required for the speed response curve to enter and remain within the preset error band;

[0159] The steady-state error is the deviation between the average value of the rotational speed after it enters a steady state and the target rotational speed value.

[0160] It should be noted that in this embodiment, after obtaining the speed response curve, the starting moment of the step change in the PWM control signal is first determined. Then, key feature points on the curve are identified, including the moment when the curve first reaches a specific proportion (e.g., 10%) of the target speed value, the time period from a low proportion (e.g., 10%) to a high proportion (e.g., 90%), the peak point when the curve first crosses the target value, the starting moment when it enters and remains within a preset error range (e.g., ±2% of the target value), and the average speed during the steady-state phase. Based on the identification results of these feature points, characteristic parameters such as response delay time, rise time, overshoot, settling time, and steady-state error are calculated, which together form a dynamic feature set. This embodiment, through systematic feature point identification and parameter calculation, comprehensively captures various key performance indicators in the ESC drive response process, laying a solid foundation for multi-dimensional performance evaluation and making performance analysis more refined.

[0161] According to an embodiment of the present invention, the step of comparing each feature parameter of the dynamic feature group with a preset corresponding primary performance threshold to obtain a primary evaluation result specifically includes:

[0162] Based on each feature parameter in the dynamic feature group, each feature parameter is compared with a preset primary performance threshold range;

[0163] If the feature parameters are within the primary performance threshold range, they are marked as primary pass features;

[0164] If the feature parameter exceeds the primary performance threshold range, it is marked as a primary defect feature and the excess type is recorded;

[0165] Based on the comparison results of all special diagnostic parameters, a preliminary assessment result is obtained.

[0166] It should be noted that in this embodiment, each feature parameter in the dynamic feature group, including response delay time, rise time, settling time, overshoot, and steady-state error, is first compared and analyzed against a preset primary performance threshold range. During the comparison process, when the feature parameter value falls entirely within the corresponding threshold range, the parameter is marked as a primary pass feature; conversely, when the feature parameter value exceeds the threshold range, it is marked as a primary defect feature, and the specific type and degree of deviation are recorded in detail. Finally, the comparison results of all feature parameters are systematically integrated to form a complete primary evaluation result containing both the pass feature set and the defect feature set. The threshold comparison mechanism used in this embodiment enables rapid screening and classification of the basic performance characteristics of the ESC, providing a clear evaluation framework for subsequent in-depth analysis, thus improving testing efficiency.

[0167] According to an embodiment of the present invention, the calculation of the rate of change of each feature parameter of the primary pass feature, and the comparison with the preset corresponding secondary performance threshold to obtain the secondary evaluation result, specifically includes:

[0168] Based on the various feature parameters of the primary feature, the deviation between adjacent feature parameters is calculated to obtain the feature change rate;

[0169] The rate of change of the feature is compared with a preset secondary performance threshold.

[0170] If the rate of change of the feature is within the secondary performance threshold, it is marked as a secondary pass feature;

[0171] If the rate of change of the feature exceeds the secondary performance threshold, it is marked as a secondary defect feature and the deviation range is recorded;

[0172] The secondary evaluation results are obtained by combining the comparison results of the diagnostic parameters of all primary features.

[0173] It should be noted that, in this embodiment, based on the initial evaluation, the rate of change of all marked as passed feature parameters under adjacent test conditions is calculated; the rate of change reflects the stability of the feature parameter under different operating conditions. Then, the calculated rate of change is compared with a preset secondary performance threshold; when the rate of change remains within the threshold range, the feature is marked as a secondary pass feature; when the rate of change exceeds the threshold range, it is marked as a secondary defect feature, and the specific deviation range and fluctuation characteristics are recorded. Finally, the comparison results of the rate of change of all feature parameters are combined to form a secondary evaluation report for the system. This embodiment, by introducing the rate of change analysis dimension, effectively captures the performance consistency of the ESC under different operating conditions, and can identify potential problems that, although meeting basic requirements, exhibit unstable performance under dynamic changes.

[0174] It is worth mentioning that the process of inputting the performance level report into the pre-trained matching model and outputting diagnostic suggestions specifically includes:

[0175] Based on the performance level report and dynamic feature groups, the data is input into a pre-trained diagnostic matching model to generate the diagnostic recommendations;

[0176] The diagnostic matching model is trained based on machine learning algorithms, and the training data includes the correspondence between performance level reports from historical tests, dynamic feature groups and confirmed fault types.

[0177] The diagnostic matching model analyzes the combination patterns of each defect feature in the performance level report, and combines them with the specific values ​​of each parameter in the dynamic feature group to output diagnostic suggestions including the direction of PID parameter adjustment and the priority of hardware module inspection.

[0178] It should be noted that in this embodiment, the performance level report and dynamic feature group data are jointly input into a diagnostic matching model trained on a large amount of historical data. This matching model is built based on machine learning algorithms, and the training data includes the correspondence between performance characteristics and ultimately confirmed fault types from a large number of historical test cases. The matching model analyzes the combination patterns of various defect features in the current test, combined with the numerical characteristics of specific parameters in the dynamic feature group, to perform a multi-dimensional comprehensive judgment. The final output includes targeted diagnostic suggestions such as specific PID parameter adjustment directions and hardware module inspection priorities. This embodiment, through an intelligent diagnostic model, achieves everything from simple problem identification to in-depth root cause analysis, providing reliable technical support for quickly locating and resolving ESC performance problems.

[0179] It is worth mentioning that it also includes:

[0180] Obtain historical test data sets and environmental parameters;

[0181] The primary and secondary performance thresholds are dynamically adjusted based on the pre-trained machine learning model to obtain an optimized set of performance thresholds.

[0182] in,

[0183] The historical test data set includes feature parameter data and corresponding performance evaluation results recorded in multiple historical test cycles.

[0184] The current environmental parameters include at least the ambient temperature, humidity, and power supply voltage fluctuation values;

[0185] The machine learning model is based on a deep neural network. By analyzing the correlation between historical feature parameters and performance evaluation results, it outputs a performance threshold range optimized for the current test environment.

[0186] It should be noted that in this embodiment, feature parameter data and corresponding evaluation results accumulated during historical testing are continuously collected, while environmental parameters, including ambient temperature, humidity, and power supply voltage fluctuations, are monitored. This historical data and environmental parameters are input into a machine learning model built on a deep neural network. The machine learning model learns the optimal setting of performance thresholds under different environmental conditions by analyzing the complex correlation between historical feature parameters and performance evaluation results. Based on the learning results, the specific ranges of primary and secondary performance thresholds are dynamically adjusted to generate an optimized threshold set adapted to the current testing environment. This embodiment, through an adaptive threshold optimization mechanism, enables the testing system to intelligently adjust evaluation criteria according to different environmental conditions, improving the applicability and accuracy of the testing method, especially demonstrating good adaptability in complex and ever-changing application scenarios.

[0187] It is worth mentioning that it also includes:

[0188] Based on the dynamic characteristic group and the rotational speed response curve, frequency response characteristic parameters are extracted using frequency domain analysis.

[0189] The frequency response characteristic parameters are compared with a preset frequency domain performance threshold to obtain the frequency domain evaluation result;

[0190] Adjust the performance rating report based on the frequency domain evaluation results;

[0191] The frequency domain analysis includes performing a fast Fourier transform on the rotational speed response curve to extract the amplitude and phase characteristics of the main frequency components.

[0192] It should be noted that in this embodiment, based on time-domain analysis, the obtained speed response curve is processed by Fast Fourier Transform to transform it from the time domain to the frequency domain. The amplitude and phase characteristics of the main frequency components are extracted from the frequency-domain data as frequency response feature parameters. These frequency response feature parameters are compared and analyzed with preset frequency-domain performance thresholds to evaluate the response characteristics of the ESC system at different frequencies. Finally, the frequency-domain evaluation results are fused with the time-domain evaluation results to adjust and improve the original performance level report accordingly. This embodiment, by introducing a frequency-domain analysis dimension, compensates for the limitations of pure time-domain analysis; it can effectively identify dynamic problems such as resonance and oscillation at specific frequencies, providing a more comprehensive technical perspective for ESC performance evaluation.

[0193] A third aspect of the present invention provides a computer-readable storage medium comprising a test method program for the electronically controlled drive response of a high-voltage unmanned aerial vehicle (UAV), wherein when the test method program for the electronically controlled drive response of a high-voltage UAV is executed by a processor, the steps of the test method for the electronically controlled drive response of a high-voltage UAV as described in any of the preceding claims are implemented.

[0194] In summary, this invention provides a testing method, system, and storage medium for the electronic speed controller (ESC) drive response of a high-voltage unmanned aerial vehicle (UAV). First, the ESC is driven based on a preset PWM control signal sequence, and the pulse signal sequence is acquired and converted into a speed response curve. Next, dynamic feature groups are extracted from the curve. Then, each feature parameter is compared with a primary performance threshold to obtain a primary evaluation result, and further, the rate of change of the features is calculated for a secondary threshold comparison. Finally, the two levels of evaluation results are combined to generate a performance level report, which is then input into a pre-trained matching model to output specific diagnostic suggestions. This achieves a comprehensive and accurate evaluation of the ESC drive performance, effectively identifying potential performance defects and providing targeted optimization suggestions, thus improving the depth and reliability of the test.

[0195] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0196] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A test method for the electronic speed controller (ESC) drive response of a high-voltage unmanned aerial vehicle (UAV), applied to a test device for testing the ESC drive response of a high-voltage UAV, the device comprising an ESC, a motor, propellers, a signal generator, an infrared reflective sensor, and a signal conditioning module; The electronic speed controller drives the motor to rotate the blades; Reflective stickers are affixed to the back of the blades; The signal generator sends a preset PWM control signal to the ESC to drive the blades to rotate; An infrared reflective sensor is installed below the blade, with its optical path aligned with the rotation path of the reflective sticker; The signal output terminal of the infrared reflective sensor is connected to the signal conditioning module; The method is characterized by comprising: According to the preset PWM control signal sequence, the signal generator sends a step-changing PWM control signal to the ESC under test, driving the ESC to control the blade rotation. By aligning an infrared reflective sensor with a reflective sticker on the blade, a light pulse signal is obtained in response to changes in the light intensity of the reflective sticker; The optical pulse signal is input to the signal conditioning module, and a pulse signal sequence is obtained through preset signal amplification and Schmitt shaping. The rotational speed response curve is obtained based on the pulse signal sequence. Based on the speed response curve and a preset feature extraction rule, a dynamic feature set is obtained, specifically including: based on the speed response curve and a preset comparison of time feature points and deviation values, the response delay time, rise time, settling time, overshoot, and steady-state error are extracted; wherein, the response delay time is the time required from the moment of the step change in the PWM control signal to the moment the speed response curve first reaches a preset first proportion of the target speed value; the rise time is the time required for the speed response curve to rise from a preset first proportion of the target speed value to a preset second proportion; the overshoot is the percentage of the peak value exceeding the target speed value when the speed response curve first crosses the target speed value to the target speed value; the settling time is the time required for the speed response curve to enter and remain within a preset error band; and the steady-state error is the deviation between the average value of the speed after it enters a steady state and the target speed value. Each feature parameter of the dynamic feature group is compared with the preset corresponding primary performance threshold to obtain the primary evaluation result, which includes primary pass features and primary defect features. The rate of change of each characteristic parameter of the primary pass feature is calculated and compared with the corresponding preset secondary performance threshold to obtain the secondary evaluation result, which includes the secondary pass feature and the secondary defect feature. Based on the primary and secondary assessment results, a performance level report is generated; The performance level report is input into a pre-trained matching model, which outputs diagnostic suggestions.

2. The test method for the electronically controlled drive response of a high-voltage unmanned aerial vehicle according to claim 1, characterized in that, The step of obtaining the speed response curve based on the pulse signal sequence specifically includes: Based on the pulse signal sequence and a preset pulse frequency conversion rule, the rotational speed is calculated, wherein the rotational speed is proportional to the pulse frequency. Based on the time axis recording of the speed value change, a speed response curve is generated, wherein the horizontal axis of the speed response curve is time and the vertical axis is speed.

3. The test method for the electronically controlled drive response of a high-voltage unmanned aerial vehicle according to claim 1, characterized in that, The step of comparing each feature parameter of the dynamic feature group with a preset corresponding primary performance threshold to obtain a primary evaluation result specifically includes: Based on each feature parameter in the dynamic feature group, each feature parameter is compared with a preset primary performance threshold range; If the feature parameters are within the primary performance threshold range, they are marked as primary pass features; If the feature parameter exceeds the primary performance threshold range, it is marked as a primary defect feature and the excess type is recorded; Based on the comparison results of all feature parameters, a preliminary evaluation result is obtained.

4. The test method for the electronically controlled drive response of a high-voltage unmanned aerial vehicle according to claim 1, characterized in that, The calculation of the primary performance evaluation result involves comparing the rate of change of each feature parameter of the primary feature with a preset corresponding secondary performance threshold. This comparison yields the secondary evaluation result, specifically including: Based on the various feature parameters of the primary feature, the deviation between adjacent feature parameters is calculated to obtain the feature change rate; The rate of change of the feature is compared with a preset secondary performance threshold. If the rate of change of the feature is within the secondary performance threshold, it is marked as a secondary pass feature; If the rate of change of the feature exceeds the secondary performance threshold, it is marked as a secondary defect feature and the deviation range is recorded; The secondary evaluation results are obtained by combining the comparison results of the feature parameters of all primary features.

5. A test system for the electronic speed controller (ESC) drive response of a high-voltage unmanned aerial vehicle (UAV), applied to a test device for testing the ESC drive response of a high-voltage UAV, the device comprising an ESC, a motor, propellers, a signal generator, an infrared reflective sensor, and a signal conditioning module; The electronic speed controller drives the motor to rotate the blades; Reflective stickers are affixed to the back of the blades; The signal generator sends a preset PWM control signal to the ESC to drive the blades to rotate; An infrared reflective sensor is installed below the blade, with its optical path aligned with the rotation path of the reflective sticker; The signal output terminal of the infrared reflective sensor is connected to the signal conditioning module; Its features are, The system includes a memory and a processor. The memory includes a test method program for the electronically controlled drive response of a high-voltage unmanned aerial vehicle (UAV). When the processor executes the test method program for the electronically controlled drive response of the high-voltage UAV, it performs the following steps: According to the preset PWM control signal sequence, the signal generator sends a step-changing PWM control signal to the ESC under test, driving the ESC to control the blade rotation. By aligning an infrared reflective sensor with a reflective sticker on the blade, a light pulse signal is obtained in response to changes in the light intensity of the reflective sticker; The optical pulse signal is input to the signal conditioning module, and a pulse signal sequence is obtained through preset signal amplification and Schmitt shaping. The rotational speed response curve is obtained based on the pulse signal sequence. Based on the speed response curve and a preset feature extraction rule, a dynamic feature set is obtained, specifically including: based on the speed response curve and a preset comparison of time feature points and deviation values, the response delay time, rise time, settling time, overshoot, and steady-state error are extracted; wherein, the response delay time is the time required from the moment of the step change in the PWM control signal to the moment the speed response curve first reaches a preset first proportion of the target speed value; the rise time is the time required for the speed response curve to rise from a preset first proportion of the target speed value to a preset second proportion; the overshoot is the percentage of the peak value exceeding the target speed value when the speed response curve first crosses the target speed value to the target speed value; the settling time is the time required for the speed response curve to enter and remain within a preset error band; and the steady-state error is the deviation between the average value of the speed after it enters a steady state and the target speed value. Each feature parameter of the dynamic feature group is compared with the preset corresponding primary performance threshold to obtain the primary evaluation result, which includes primary pass features and primary defect features. The rate of change of each characteristic parameter of the primary pass feature is calculated and compared with the corresponding preset secondary performance threshold to obtain the secondary evaluation result, which includes the secondary pass feature and the secondary defect feature. Based on the primary and secondary assessment results, a performance level report is generated; The performance level report is input into a pre-trained matching model, which outputs diagnostic suggestions.

6. The test system for the electronically controlled drive response of a high-voltage unmanned aerial vehicle according to claim 5, characterized in that, The step of obtaining the speed response curve based on the pulse signal sequence specifically includes: Based on the pulse signal sequence and a preset pulse frequency conversion rule, the rotational speed is calculated, wherein the rotational speed is proportional to the pulse frequency. Based on the time axis recording of the speed value change, a speed response curve is generated, wherein the horizontal axis of the speed response curve is time and the vertical axis is speed.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer-readable storage medium includes a test method program for the electronically controlled drive response of a high-voltage unmanned aerial vehicle (UAV). When the test method program for the electronically controlled drive response of a high-voltage UAV is executed by a processor, it implements the steps of the test method for the electronically controlled drive response of a high-voltage UAV as described in any one of claims 1 to 4.

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