Methods and devices for identifying abnormalities in the power kit of unmanned aerial vehicles (UAVs), storage media and UAVs

Abstract

This application discloses a method and device for identifying abnormalities in the power kit of a drone, a storage medium, and a drone. It relates to the field of drone power system anomaly detection technology. The method and device for identifying abnormalities in the power kit of a drone, a storage medium, and a drone are disclosed. By applying pulse voltage to the windings of a stationary three-phase motor and analyzing the inductance parameters, and combining the difference threshold, it determines whether the three-phase motor is abnormal and the type of abnormality. This solves the problems of traditional detection methods requiring motor operation, being susceptible to temperature interference, and being unable to identify hidden faults. Thus, it achieves high-precision detection of the drone power kit and has strong anti-interference capabilities, which can improve the safety of the drone.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) power system anomaly detection technology, and in particular to a method, device, storage medium, and UAV for identifying anomalies in UAV power systems. Background Technology

[0002] With the widespread application of drones in surveying, inspection, plant protection, and logistics, the reliability of their power kits (including core components such as three-phase motors and electronic speed controllers) directly impacts flight safety. Current mainstream power kit anomaly detection methods have several technical limitations: traditional flux linkage identification technology requires the motor to be running to complete the detection, which conflicts with the safety standard that requires the power kit to remain stationary during drone power-on self-tests; resistance-based detection methods are significantly affected by temperature changes, especially since the temperature coefficient of the copper resistance in the motor windings is as high as 0.0039 / ℃, easily leading to misjudgments; existing technologies are insufficient in identifying latent defects such as moisture intrusion into the motor, a significant hazard for plant protection drones during pesticide spraying; some detection methods even require disassembling the load for offline testing, making rapid self-testing at power-on impossible. These methods, when detecting the power kit's operating status or verifying motor model compatibility, are easily affected by safety restrictions, temperature fluctuations, and electromagnetic interference, leading to reduced reliability of self-test results and consequently affecting the normal use and flight safety of drones.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this application is to provide a method, device, storage medium, and drone for identifying abnormalities in the power kit of a drone, with the aim of improving the safety of drones.

[0005] To achieve the above objectives, this application proposes a method for identifying anomalies in a drone power kit, wherein the drone power kit includes a three-phase motor and an electronic speed controller, the three-phase motor and the electronic speed controller being electrically connected, and the method comprising:

[0006] The electronic speed controller outputs a pulse voltage of a preset duration to two phase windings of the three-phase motor, obtains the corresponding response current, and obtains the line inductance value between the two phase windings based on the preset duration, the pulse voltage, and the response current.

[0007] Repeat the above steps to obtain the line inductance values ​​between each pair of the windings of the three-phase motors.

[0008] The average line inductance value is calculated based on the three sets of line inductance values, and then the stator inductance value of the three-phase motor is calculated based on the average line inductance value.

[0009] The difference between the stator inductance value and the preset nominal inductance value is calculated to obtain the inductance difference value, and the abnormality of the drone power kit is determined based on the magnitude of the inductance difference value.

[0010] In one embodiment, the preset duration is less than the reciprocal of the internal switching frequency of the electronic speed controller.

[0011] In one embodiment, the step of obtaining the line inductance value between the two-phase windings based on the preset duration, pulse voltage, and response current includes calculation according to the following formula:

[0012] ;

[0013] in, express Phase winding and The line inductance between phase windings Indicates pulse voltage. Indicates the preset duration. This represents the response current.

[0014] In one embodiment, the nominal inductance value is the motor inductance value corresponding to the sensorless FOC control module of the electronic speed controller. The step of calculating the difference between the stator inductance value and the preset nominal inductance value, and determining the abnormal condition of the UAV power kit based on the magnitude of the difference, includes:

[0015] If the inductance difference is greater than a first percentage of the nominal inductance value, it is determined that the drone power kit is abnormal.

[0016] Otherwise, the drone's power kit is deemed to be normal.

[0017] In one embodiment, the step of determining that the drone power kit is abnormal when the inductance difference is greater than a first percentage of the nominal inductance value includes:

[0018] If the inductance difference is greater than a first percentage of the nominal inductance value and the inductance difference is less than a second percentage of the nominal inductance value, then it is determined that the drone power sleeve has a moisture ingress fault.

[0019] If the inductance difference is greater than a second percentage of the nominal inductance value, it is determined that the drone power kit has a motor fault.

[0020] In one embodiment, a first percentage of the nominal inductance value is 10% of the nominal inductance value; and a second percentage of the nominal inductance value is 20% of the nominal inductance value.

[0021] In one embodiment, the UAV power kit further includes a warning module, and the method further includes:

[0022] When an abnormality is detected in the drone's power kit, an abnormality alert is issued via the warning module; the abnormality alert includes at least one of an audio alert and a light alert.

[0023] In addition, when it is determined that there is an abnormality in the power kit of the UAV, the identified water vapor ingress fault or motor fault is recorded by the electronic speed controller.

[0024] Furthermore, when an abnormality is detected in the drone power kit, the drone power kit shall be prohibited from starting operation.

[0025] In addition, to achieve the above objectives, this application also proposes a drone power kit anomaly identification device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the drone power kit anomaly identification method.

[0026] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the above-described UAV power kit anomaly identification method.

[0027] In addition, to achieve the above objectives, this application also proposes a drone that uses the aforementioned drone power kit anomaly identification method or that includes the aforementioned drone power kit anomaly identification device.

[0028] The UAV power kit anomaly identification method, device, storage medium, and UAV proposed in this application apply pulse voltage to the windings of a stationary three-phase motor and analyze the inductance parameters. Combined with the difference threshold, it determines whether the three-phase motor is abnormal and the type of abnormality. This solves the problems of traditional detection methods requiring motor operation, being susceptible to temperature interference, and being unable to identify hidden faults. Thus, it achieves high-precision detection of UAV power kits and has strong anti-interference capabilities, which can improve the safety of UAVs. Attached Figure Description

[0029] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0030] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating an embodiment of the UAV power kit anomaly identification method of this application;

[0032] Figure 2 This is a flowchart illustrating another embodiment of the UAV power kit anomaly identification method of this application;

[0033] Figure 3 for Figure 2 Detailed flowchart of step S420;

[0034] Figure 4 A flowchart illustrating yet another embodiment of the UAV power sleeve anomaly identification method of this application;

[0035] Figure 5 This is a schematic diagram of a structural feature of an embodiment of the UAV power kit anomaly identification device of this application.

[0036] Label:

[0037] Explanation of icon numbers:

[0038] 10. Memory; 20. Processor.

[0039] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0040] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of protection of this application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0041] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0042] In existing technologies, anomaly detection methods for UAV power kits generally rely on flux linkage identification or resistance measurement. Flux linkage identification requires the motor to be running, which cannot meet the safety requirement that the power kit must remain stationary during power-on self-testing. Resistance identification is easily affected by temperature fluctuations; for example, the high temperature coefficient of copper resistors may lead to false positives. Furthermore, existing technologies struggle to identify latent defects such as moisture intrusion into the motor, especially in agricultural operations where pesticide spraying environments easily allow moisture to enter the motor, and traditional methods cannot effectively detect such problems. Some detection methods even require offline load testing, making rapid self-testing impossible.

[0043] To address the aforementioned issues, the inventors discovered a strong correlation between changes in inductance value and moisture intrusion or motor malfunction, while inductance measurement is less affected by temperature. Based on this, they considered how to achieve anomaly detection through static inductance measurement. Furthermore, they proposed applying a short-duration pulse voltage to the motor windings and collecting the response current, then calculating the line inductance value using time parameters, thus avoiding the need for motor operation. By repeatedly measuring the inductance values ​​of different winding combinations and calculating the average, single-measurement errors can be eliminated, and the anomaly type can be determined by comparing the differences with the nominal values.

[0044] Based on this, embodiments of this application provide a method for identifying anomalies in the power kit of a UAV, referring to... Figure 1 The method for identifying anomalies in the UAV power kit includes steps S100 to S400, wherein:

[0045] Step S100: Based on the electronic speed controller outputting a pulse voltage of a preset duration to the two-phase windings of the three-phase motor, obtain the corresponding response current, and based on the preset duration, the pulse voltage and the response current, obtain the line inductance value between the two-phase windings.

[0046] Step S200: Repeat the above steps to obtain the line inductance values ​​between each pair of the windings of the three sets of three-phase motors.

[0047] Step S300: Calculate the average line inductance value based on the three sets of line inductance values, and then calculate the stator inductance value of the three-phase motor based on the average line inductance value;

[0048] Step S400: Calculate the difference between the stator inductance value and the preset nominal inductance value to obtain the inductance difference value, and determine the abnormality of the UAV power kit based on the magnitude of the inductance difference value.

[0049] In this embodiment, the pulse voltage refers to the short-time voltage excitation signal output by the electronic speed controller. Specifically, the DC bus voltage can be used as the output source, and its amplitude is determined by the supply voltage of the electronic speed controller. The preset duration must be less than the reciprocal of the switching frequency of the electronic speed controller, and can be set to a microsecond-level time window to prevent the motor from rotating. The response current refers to the transient current generated by the winding under the action of the pulse voltage, which can be collected in real time through the current sampling circuit. The line inductance value characterizes the equivalent inductance parameter between two-phase windings, and is specifically obtained by calculating the ratio of voltage, time, and current. The average line inductance value is used to eliminate single measurement deviations, and is specifically processed by the arithmetic mean or weighted average of three sets of line inductance values. The stator inductance value reflects the overall inductance characteristics of the motor, and is specifically derived through the average line inductance value conversion formula. The nominal inductance value refers to the reference inductance parameter of the motor under normal conditions, and is specifically stored in the control module of the electronic speed controller.

[0050] In this embodiment, the electronic speed controller applies a pulse voltage of a preset duration to two selected phase windings, while the third phase winding remains open. The current sampling module synchronously records the peak or integral value of the response current. Based on Ohm's law and the inductance characteristic equation, the line inductance value between the two phase windings is calculated using the pulse voltage amplitude, application time, and response current. This measurement process is repeated to obtain the line inductance values ​​for phases AB, BC, and CA respectively. The three sets of line inductance values ​​are averaged and converted using a formula to obtain the stator inductance value. The difference between this value and the preset nominal value is calculated; if the difference exceeds a threshold range, an anomaly detection mechanism is triggered. The entire process is completed while the motor is stationary, achieving power system condition assessment without mechanical movement.

[0051] Compared to existing technologies, current flux linkage identification methods require motor rotation to complete parameter identification. This solution avoids safety risks by using static inductance measurement. Furthermore, traditional resistance detection methods are susceptible to temperature drift interference; this solution utilizes the temperature stability of inductance parameters to improve detection reliability. Simultaneously, this solution can effectively detect winding moisture or insulation degradation through changes in inductance value. Moreover, existing offline testing requires load removal; this solution directly utilizes the connection between the electronic speed controller and the motor to achieve in-situ testing. Through the above technical solutions, this application can quickly complete power sleeve anomaly detection during the UAV's power-on self-test phase, identifying moisture intrusion or winding faults without motor operation. The static detection mode complies with safe operating procedures, avoiding misjudgments caused by temperature fluctuations in traditional methods. By measuring multiple sets of inductance values ​​and averaging them, the stability and accuracy of the detection results are effectively improved, providing reliable anomaly identification capabilities for special applications such as plant protection.

[0052] In one feasible implementation, the preset duration is less than the reciprocal of the internal switching frequency of the electronic speed controller. In this embodiment, the preset duration refers to the time period during which pulse voltages are applied to two phase windings of a three-phase motor, which can be achieved by setting a timer or counter. This time period needs to be shorter than the duty cycle of the power switching devices inside the electronic speed controller. The reciprocal of the internal switching frequency of the electronic speed controller corresponds to a complete switching cycle, which can be measured using a Hall sensor or a PWM signal generator. This parameter reflects the number of times the power device switches on and off per unit time.

[0053] Understandably, when an electronic speed controller applies a pulse voltage to the motor windings, if the preset duration exceeds the switching cycle, the switching action of the power device will interfere with the current response waveform, leading to errors in the calculation of the line inductance value. By strictly controlling the preset duration within a single switching cycle, it can be ensured that the current sampling point is within the conduction range of the power device, avoiding interference from switching noise on the current rising edge. For example, when the electronic speed controller's switching frequency is 20kHz, its cycle is 50 microseconds. In this case, the preset duration can be set to 30 microseconds, ensuring that the pulse voltage application process always occurs within a single switching cycle.

[0054] In this embodiment, by establishing a constraint relationship between a preset duration and the switching cycle, the interference of power device switching action on current sampling is effectively isolated, making the calculated line inductance value closer to the true value. This solves the technical problem of inaccurate inductance measurement caused by switching noise, ensuring that the motor winding inductance parameters can still be accurately obtained when the electronic speed controller is in operation. This technique enables the UAV power kit to complete accurate anomaly detection during the power-on self-test phase, avoiding the risk of misjudgment caused by measurement errors in traditional methods.

[0055] In one feasible implementation, the step of obtaining the line inductance value between the two-phase windings based on the preset duration, pulse voltage, and response current includes calculation according to the following formula:

[0056] ;

[0057] in, express Phase winding and The line inductance between phase windings Indicates pulse voltage. Indicates the preset duration. This represents the response current.

[0058] In this embodiment, when the electronic speed controller outputs a DC pulse voltage to the two-phase windings, an inductance calculation model is established using the linear relationship between the voltage application time and the current change. By measuring the current rise within a fixed time window and combining it with the known voltage amplitude, the equivalent inductance value between the windings is directly derived. For example, when the voltage is applied to the two-phase windings... Harmony When a 300V pulse voltage is applied for 50 microseconds, if the measured peak current is 6A, the line inductance is calculated as 300 × 0.00005 / 6 = 0.0025H. This calculation is performed within the ESC control chip and does not depend on the motor's rotation status.

[0059] In this embodiment, by using DC pulse excitation and transient response analysis, the inductance parameters can be directly obtained when the motor is stationary. The inductance parameters are significantly less sensitive to temperature changes than the resistance parameters, resulting in higher stability of the detection results and avoiding the safety hazards of requiring the motor to be running in traditional methods.

[0060] In one feasible implementation, the nominal inductance value is the motor inductance value corresponding to the sensorless FOC control module of the electronic speed controller, with reference to... Figure 2 Step S400 includes steps S410 to S420, wherein:

[0061] Step S410: When the inductance difference is greater than a first percentage of the nominal value of the inductance, it is determined that the drone power kit is abnormal.

[0062] Step S420, otherwise determine that there is no abnormality in the drone power kit.

[0063] In this embodiment, the nominal inductance value refers to the motor inductance value corresponding to the sensorless FOC control module of the electronic speed controller. Specifically, the reference inductance value of the motor under normal conditions can be determined through calibration experiments and used as a reference standard for anomaly judgment. The first percentage refers to the preset anomaly judgment threshold, which can be set according to the motor type and environmental factors, for example, 10% or 20%. When the inductance deviation exceeds this threshold, the anomaly judgment mechanism is triggered.

[0064] In this embodiment, during the UAV's power-on self-test, abnormal inductance parameters can be quickly identified by comparing the deviation between the stator inductance value calculated in real time and the preset nominal value. For example, when the detected inductance difference exceeds 10% of the nominal value, a potential fault in the power sleeve can be determined. This determination method does not rely on the motor's operating state and can complete the detection in a stationary state, avoiding the safety hazards caused by the traditional method requiring motor operation. At the same time, by setting a percentage threshold, it is possible to effectively distinguish between normal parameter fluctuations and abnormal faults, such as inductance reduction caused by moisture intrusion or inductance abnormalities caused by short circuits in the motor windings.

[0065] In this embodiment, static detection is achieved through inductance parameter comparison, eliminating temperature interference and effectively identifying hidden defects such as moisture intrusion. For example, in plant protection operations, when pesticide spraying causes water to enter the motor, the winding inductance value will drop significantly. By setting a reasonable percentage threshold, such faults can be accurately identified. This enables rapid self-testing of the drone's power kit while it is stationary, overcoming the safety hazards of traditional detection methods that require motor operation. Inductance parameter comparison effectively eliminates temperature interference, improving the reliability of anomaly detection.

[0066] In one feasible implementation, refer to Figure 3 Step S410 includes steps S411 to S412, wherein:

[0067] Step S411: When the inductance difference is greater than a first percentage of the nominal inductance value and the inductance difference is less than a second percentage of the nominal inductance value, it is determined that there is a water vapor ingress fault in the drone power sleeve.

[0068] Step S412: When the inductance difference is greater than a second percentage of the nominal inductance value, it is determined that the drone power kit has a motor fault.

[0069] In this embodiment, the first percentage and the second percentage refer to preset threshold ratios, such as 10% and 20% of the nominal inductance value, used to distinguish different fault types. A moisture ingress fault refers to a decrease in winding insulation performance caused by excessively high ambient humidity or liquid seepage, which can be specifically identified by a decrease in inductance value. A motor fault refers to a significant shift in inductance value caused by winding short circuits, open circuits, or magnetic circuit abnormalities, which can be identified by an inductance value exceeding a preset range.

[0070] In this embodiment, after calculating the stator inductance value, the difference between the measured value and the nominal value is compared with two preset thresholds. When the difference is between the first and second thresholds, for example, the difference exceeds 10% but does not exceed 20%, it can be determined as a slight inductance decay caused by moisture intrusion; when the difference exceeds the second threshold, for example, the difference exceeds 20%, it is determined as structural damage to the winding or magnetic circuit. This establishes a graded diagnostic mechanism, establishing a correspondence between the degree of inductance abnormality and specific fault types.

[0071] In this embodiment, by setting multiple threshold levels and utilizing the correlation between the inductance change amplitude and the severity of the fault, accurate identification of fault types is achieved. This is particularly suitable for application scenarios such as agricultural drones that are susceptible to moisture corrosion. It effectively distinguishes between temporary faults caused by environmental factors and structural faults of the device itself, avoiding misjudgment of motor damage due to the device recovering on its own after moisture evaporation. At the same time, it prevents serious faults such as winding short circuits from being misjudged as environmental interference, thereby improving the accuracy of fault diagnosis and ensuring the safe operation of the drone's power system.

[0072] In one feasible implementation, the UAV power kit further includes an early warning module, as referenced. Figure 4 The method further includes step S500: when it is determined that there is an abnormality in the drone power kit, an abnormality prompt is given through the early warning module; the abnormality prompt includes at least one of sound prompt and light prompt; and when it is determined that there is an abnormality in the drone power kit, the identified water vapor ingress fault or motor fault is recorded through the electronic speed controller; and when it is determined that there is an abnormality in the drone power kit, the drone power kit is prohibited from starting operation.

[0073] In this embodiment, the early warning module refers to the hardware component used to issue abnormal signals. Specifically, it can be implemented using devices such as a buzzer, speaker, or LED light. Its function is to provide operators with intuitive alarm information when an anomaly is detected. The audible alert can be provided by a buzzer emitting an alarm sound of a specific frequency, while the visual alert can be provided by flashing or constantly lit LED lights of different colors, ensuring that operators can promptly detect anomalies under different environmental conditions. Recording water vapor ingress faults or motor faults refers to storing the fault type and occurrence time in the non-volatile memory of the electronic speed controller, facilitating the retrieval of historical fault data for analysis during subsequent maintenance. Prohibiting start-up operation refers to cutting off the motor drive signal through the control logic of the electronic speed controller. Specifically, this can be achieved by locking the output enable signal of the ESC through software settings, ensuring that the power unit cannot be activated under abnormal conditions, thus avoiding potential safety risks.

[0074] In this embodiment, when an anomaly is detected in the UAV's power kit, the warning module immediately triggers an audible or visual alert, such as a continuous buzzer or a flashing red LED, to remind the operator to address the issue promptly. Simultaneously, the electronic speed controller records the current fault type (e.g., moisture intrusion or motor winding damage) and the time of occurrence in its internal memory, creating a traceable fault log. Furthermore, the electronic speed controller automatically enters a protection mode, preventing the motor from starting by disabling the PWM signal output or cutting off the power circuit, thereby avoiding damage to the device or a flight accident caused by forced operation under abnormal conditions.

[0075] Understandably, traditional methods typically rely solely on software alarms or simple shutdowns upon detecting anomalies, lacking multimodal alarm mechanisms and fault data storage capabilities, and failing to clearly distinguish fault types. This solution enhances alarm reliability through dual audible and visual cues, supports precise maintenance by recording specific fault types, and significantly improves the safety and operability of anomaly handling by locking the power sleeve operation through hardware and software collaboration. Thus, this application can rapidly trigger multi-dimensional alarms upon detecting a power sleeve anomaly, helping operators identify potential hazards immediately; record the fault type and time to provide data support for subsequent fault diagnosis; and effectively prevent dangerous operation of the drone under abnormal conditions by forcibly prohibiting power sleeve operation, making it particularly suitable for applications such as agricultural spraying where there is a risk of moisture intrusion, ensuring the safety of both the equipment and personnel.

[0076] In this embodiment, the UAV power kit anomaly identification method applies pulse voltage to the windings of a stationary three-phase motor and analyzes the inductance parameters. It then uses the difference threshold to determine whether the three-phase motor is abnormal and the type of abnormality. This solves the problems of traditional detection methods, such as requiring the motor to be running, being susceptible to temperature interference, and being unable to identify hidden faults. As a result, it achieves high-precision detection of the UAV power kit and has strong anti-interference capabilities, which can improve the safety of the UAV.

[0077] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the UAV power kit anomaly identification method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0078] This application also provides a device for identifying abnormalities in the power kit of a drone. Please refer to [link / reference]. Figure 5 The UAV power kit anomaly identification device includes a memory 10, a processor 20, and a computer program stored in the memory 10 and executable on the processor 20. The computer program is configured to implement the steps of the UAV power kit anomaly identification method.

[0079] The UAV power sleeve anomaly identification device provided in this application, employing the UAV power sleeve anomaly identification method in the above embodiments, can improve the safety of UAVs. Compared with the prior art, the beneficial effects of the UAV power sleeve anomaly identification device provided in this application are the same as those of the UAV power sleeve anomaly identification method provided in the above embodiments, and other technical features in the UAV power sleeve anomaly identification device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0080] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the UAV power kit anomaly identification method in the above embodiments.

[0081] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0082] The aforementioned computer-readable storage medium may be included in the UAV power kit anomaly identification device; or it may exist independently and not be assembled into the UAV power kit anomaly identification device.

[0083] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the UAV power kit anomaly identification device, cause the UAV power kit anomaly identification device to implement the steps of the UAV power kit anomaly identification method.

[0084] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0085] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0086] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0087] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described UAV power kit anomaly identification method, thereby improving the safety of the UAV. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the UAV power kit anomaly identification method provided in the above embodiments, and will not be repeated here.

[0088] This application also provides a drone that uses the aforementioned drone power kit anomaly identification method or includes the aforementioned drone power kit anomaly identification device. Therefore, compared with the prior art, the beneficial effects of the drone provided in this application are the same as those of the drone power kit anomaly identification method provided in the above embodiments, and will not be elaborated further here.

[0089] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for identifying anomalies in the power kit of a UAV, characterized in that, The unmanned aerial vehicle power set comprises a three-phase motor and an electronic speed regulator, the three-phase motor and the electronic speed regulator are electrically connected, and the method comprises: Based on the electronic speed regulator, a preset time length of pulse voltage is output to the two-phase winding of the three-phase motor, the corresponding response current is obtained, and based on the preset time length, the pulse voltage and the response current, the line inductance value between the two-phase winding is obtained; The above steps are repeated to obtain three groups of line inductance values between the windings of the three-phase motor; According to three groups of the line inductance values, the average line inductance value is calculated, and then based on the average line inductance value, the stator inductance value of the three-phase motor is calculated; The stator inductance value is subtracted from the preset inductance nominal value to obtain an inductance difference value, and the size of the inductance difference value is used to determine the abnormal condition of the unmanned aerial vehicle power set; The inductance nominal value is the motor inductance value corresponding to the electronic speed regulator FOC control module, and the steps of subtracting the stator inductance value from the preset inductance nominal value and determining the abnormal condition of the unmanned aerial vehicle power set according to the size of the difference value comprise: When the inductance difference value is greater than the first percentage of the inductance nominal value, it is determined that the unmanned aerial vehicle power set has an abnormality; Otherwise, it is determined that the unmanned aerial vehicle power set has no abnormality; The step of determining that the unmanned aerial vehicle power set has an abnormality when the inductance difference value is greater than the first percentage of the inductance nominal value comprises: When the inductance difference value is greater than the first percentage of the inductance nominal value and the inductance difference value is less than the second percentage of the inductance nominal value, it is determined that the unmanned aerial vehicle power set has a water vapor entry failure; When the inductance difference value is greater than the second percentage of the inductance nominal value, it is determined that the unmanned aerial vehicle power set has a motor failure. 2.The method of claim 1, wherein, The preset time length is less than the reciprocal of the internal switching frequency of the electronic speed regulator. 3.The method of claim 1, wherein, The step of obtaining the line inductance value between the two-phase winding based on the preset time length, the pulse voltage and the response current comprises calculating according to the following formula: ； wherein L ab represents the line inductance value between the a-phase winding and the b-phase winding, U dc represents the pulse voltage, Δt represents the preset time length, I ab represents the response current. 4.The method of claim 1, wherein, The first percentage of the inductance nominal value is 10% of the inductance nominal value, and the second percentage of the inductance nominal value is 20% of the inductance nominal value. 5.The method of claim 1, wherein, The unmanned aerial vehicle power set further comprises a pre-warning module, and the method further comprises: When it is determined that the unmanned aerial vehicle power set has an abnormality, an abnormality prompt is given through the pre-warning module; the abnormality prompt comprises at least one of a sound prompt and a light prompt; When it is determined that the unmanned aerial vehicle power set has an abnormality, the water vapor entry failure or the motor failure identified is recorded by the electronic speed regulator; When it is determined that the unmanned aerial vehicle power set has an abnormality, the unmanned aerial vehicle power set is prohibited from starting to run.

6. An unmanned aerial vehicle power pack abnormality recognition device, characterized by comprising: The unmanned aerial vehicle power set abnormality identification device comprises a memory, a processor and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the unmanned aerial vehicle power set abnormality identification method according to any one of claims 1 to 5.

7. A storage medium, characterized by The storage medium is a computer readable storage medium, and the storage medium stores a computer program. The computer program is executed by a processor to implement the steps of the unmanned aerial vehicle power set anomaly identification method in any one of claims 1 to 5.

8. A drone, characterized in that, The unmanned aerial vehicle uses the unmanned aerial vehicle power set anomaly identification method in any one of claims 1 to 5 or comprises the unmanned aerial vehicle power set anomaly identification device in claim 6.

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