Hover throttle estimation method and system for power inspection unmanned aerial vehicle, device, and medium

Abstract

The application discloses a hovering throttle estimation method and system, equipment and a medium for a power inspection unmanned aerial vehicle, which takes the hovering throttle of the unmanned aerial vehicle as an estimation quantity and takes the vertical acceleration as a measurement quantity, and estimates the hovering throttle of the unmanned aerial vehicle in real time through an extended Kalman filtering algorithm. The extended Kalman filtering algorithm is first introduced into the hovering control of the unmanned aerial vehicle. Compared with the existing PID control algorithm, the hovering control precision and dynamic response capability of the unmanned aerial vehicle are improved, the robustness is better, high-precision and real-time dynamic stable hovering control can be realized, and the hovering control can be applied to the unmanned aerial vehicle with real-time weight change and has better generalization capability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle control, in particular, to a hovering throttle estimation method and system for power inspection unmanned aerial vehicle, an electronic device and a computer readable storage medium. BACKGROUND

[0002] With the advantages of high efficiency, safety, precision and intelligence, unmanned aerial vehicles have fully empowered power inspection and opened a new era of unmanned aerial vehicle power inspection. During the inspection process, the unmanned aerial vehicle not only needs to find problems, but also needs to locate them. Therefore, the unmanned aerial vehicle needs to hover at the fault point to realize omnidirectional and high-precision data acquisition through high-definition cameras, infrared thermal imagers, laser radars and other advanced devices, which has a high requirement for the unmanned aerial vehicle's fixed-point height control. The difficulty of the unmanned aerial vehicle's height control lies in that, from the vertical plane of the unmanned aerial vehicle's height, there is always a gravity effect. Therefore, in the height control loop, the gravity must be balanced first, which requires a hovering throttle that balances the gravity.

[0003] Currently, the height control of the unmanned aerial vehicle usually adopts a traditional PID controller. The PID controller balances the gravity effect through the continuous accumulation of the integral element, and the hovering throttle is reflected in the integral element. However, for the unmanned aerial vehicle, a small integral coefficient will lead to slow height control response in the take-off stage and unstable height control in the maneuvering stage, while a large integral coefficient may cause low-frequency oscillation of the height control response. The traditional PID controller cannot realize accurate and stable control of the height. In addition, during the use of the unmanned aerial vehicle, the gravity or mass cannot be accurately valued due to different loads, driving methods and use scenarios, and the hovering throttle corresponding to the gravity cannot be accurately valued due to factors such as motor type and battery voltage. Therefore, it is impossible to set an accurate hovering throttle in the control loop in advance to balance the gravity. Only an approximate hovering throttle can be added, and the error between the approximate hovering throttle and the real hovering throttle is regarded as the uncertainty of the system, and then the PID controller is used for control. The robustness of the controller is required to be high, but the robustness of the PID controller is poor. Moreover, this method is not applicable to unmanned aerial vehicles with large real-time weight changes (such as oil-burning unmanned aerial vehicles). SUMMARY

[0004] The present application provides a hovering throttle estimation method and system for power inspection unmanned aerial vehicle, an electronic device and a computer readable storage medium, which improves the control accuracy and dynamic response capability of the unmanned aerial vehicle's fixed-point hovering, has better robustness, can realize high-precision and real-time dynamic stable hovering control, and is applicable to unmanned aerial vehicles with large real-time weight changes and has better generalization capability.

[0005] According to one aspect of the present application, a hovering throttle estimation method for a power inspection unmanned aerial vehicle is provided, comprising the following contents:

[0006] A hovering throttle of the unmanned aerial vehicle is taken as an estimation quantity, a vertical acceleration of the unmanned aerial vehicle is taken as a measurement quantity, a state model is constructed, and a prior estimation value of the hovering throttle at a current time is obtained;

[0007] A prior estimation state covariance at the current time is calculated;

[0008] A Kalman gain is calculated based on the prior estimation state covariance at the current time;

[0009] Based on the Kalman gain and the prior estimation value of the hovering throttle at the current time, a posterior estimation value of the hovering throttle at the current time is updated;

[0010] Based on the Kalman gain and the prior estimation state covariance at the current time, a posterior estimation state covariance at the current time is updated.

[0011] Further, the Kalman gain is calculated based on the following formula:

[0012] ;

[0013] Wherein, represents the Kalman gain, represents a measurement noise, represents the prior estimation state covariance at the current time, represents a measurement matrix, which is obtained by taking a partial derivative of a measurement model with respect to the hovering throttle, and the measurement model is: , , represents a measurement value of the vertical acceleration, represents a gravitational acceleration, represents a current throttle command, represents a hovering throttle command.

[0014] Further, the measurement noise is calculated based on the following formula:

[0015] ;

[0016] Wherein, represents a sensor self-noise, represents a motion amplification coefficient, , represents a current speed, represents a speed threshold.

[0017] Further, after the measurement noise is calculated, the reliability of the estimation value is evaluated based on the following formula:

[0018] ;

[0019] in, Indicates credibility index, Represents the residual of the measured value. , The measured value representing vertical acceleration. This represents the predicted value of vertical acceleration. This indicates the leniency level of the credibility index judgment. If the credibility index is less than or equal to the preset threshold, the estimated value is considered credible and subsequent calculations continue. If the credibility index is greater than the preset threshold, the estimation ends.

[0020] Furthermore, if the confidence index is greater than a preset threshold, the posterior estimated state covariance is updated based on the following formula:

[0021] ;

[0022] in, This represents the posterior estimate of the state covariance. Indicates system process noise. This represents the time difference between two estimates.

[0023] Furthermore, the posterior estimate of the hovering throttle at the current moment is calculated based on the following formula:

[0024] ;

[0025] in, Indicates the current time k The posterior estimate of the hover throttle. Indicates the current time k Prior estimate of hover throttle. Indicates Kalman gain, The measured value representing vertical acceleration. This represents the predicted value of vertical acceleration.

[0026] Furthermore, the state model is as follows:

[0027] ;

[0028] in, This represents the prior estimate of the hover throttle. This represents the posterior estimate of the hover throttle. k Indicates the current moment. k -1 represents the previous time step. This represents the prior estimate of the hovering throttle at the current moment. This represents the posterior estimate of the throttle position at the previous hovering moment.

[0029] In addition, the present invention also provides a hovering throttle estimation system for a power line inspection drone, comprising:

[0030] The prior estimation module is used to construct a state model by taking the hover throttle of the UAV as the estimator and the vertical acceleration of the UAV as the measurement, and obtain the prior estimate of the hover throttle at the current moment.

[0031] The prior estimated state covariance calculation module is used to calculate the prior estimated state covariance at the current time.

[0032] The Kalman gain calculation module is used to calculate the Kalman gain based on the prior estimated state covariance at the current time.

[0033] The posterior estimation module is used to update the posterior estimate of the hover throttle at the current moment based on the Kalman gain and the prior estimate of the hover throttle at the current moment.

[0034] The posterior estimated state covariance calculation module is used to update the posterior estimated state covariance at the current time based on the Kalman gain and the prior estimated state covariance at the current time.

[0035] In addition, the present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method described above by calling the computer program stored in the memory.

[0036] In addition, the present invention provides a computer-readable storage medium for storing a computer program for estimating the hovering throttle of a power line inspection drone, wherein the computer program, when run on a computer, performs the steps of the method described above.

[0037] The present invention has the following beneficial effects:

[0038] The hovering throttle estimation method for power line inspection UAVs of the present invention uses the UAV's hovering throttle as an estimate and vertical acceleration as a measurement. The extended Kalman filter algorithm is used to estimate the UAV's hovering throttle in real time. This is the first time that the extended Kalman filter algorithm has been integrated into the hovering control of UAVs. Compared with the existing PID control algorithm, it improves the control accuracy and dynamic response capability of UAVs in fixed-point hovering, has better robustness, can achieve high-precision, real-time dynamic stable hovering control, and can be applied to UAVs with real-time weight changes, thus having better generalization ability.

[0039] In addition, the hovering throttle estimation system for the power line inspection drone of the present invention also has the above-mentioned advantages.

[0040] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0041] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0042] Figure 1 This is a flowchart illustrating the hovering throttle estimation method for a power line inspection drone according to a preferred embodiment of this application.

[0043] Figure 2 This is a schematic diagram of the hovering throttle estimation curve obtained during experimental verification in a preferred embodiment of this application;

[0044] Figure 3 This is a schematic diagram of the height control response curve obtained during experimental verification in a preferred embodiment of this application;

[0045] Figure 4 This is a schematic diagram of the module structure of a hovering throttle estimation system for a power line inspection drone according to another embodiment of this application. Detailed Implementation

[0046] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0047] Reference Figure 1 A preferred embodiment of this application provides a hovering throttle estimation method for a power line inspection drone, including the following:

[0048] Step S1: Using the hover throttle of the drone as an estimate and the vertical acceleration of the drone as a measurement, construct a state model to obtain the prior estimate of the hover throttle at the current moment.

[0049] Step S2: Calculate the prior estimated state covariance at the current time.

[0050] Step S3: Calculate the Kalman gain based on the prior estimated state covariance at the current time.

[0051] Step S4: Update the posterior estimate of the hover throttle at the current moment based on the Kalman gain and the prior estimate of the hover throttle at the current moment;

[0052] Step S5: Update the posterior estimated state covariance at the current time based on the Kalman gain and the prior estimated state covariance at the current time.

[0053] It is understood that the hovering throttle estimation method of the power inspection UAV in this embodiment uses the UAV's hovering throttle as the estimated quantity and vertical acceleration as the measured quantity. The extended Kalman filter algorithm is used to estimate the UAV's hovering throttle in real time. This is the first time that the extended Kalman filter algorithm has been integrated into the hovering control of the UAV. Compared with the existing PID control algorithm, it improves the control accuracy and dynamic response capability of the UAV in fixed-point hovering, has better robustness, can achieve high-precision, real-time dynamic stable hovering control, and can be applied to UAVs with real-time weight changes, thus having better generalization ability.

[0054] This application employs an Extended Kalman Filter (EKF) to estimate the hover throttle of a power line inspection drone in real time. The EKF algorithm is commonly used for position and attitude control, and this application is the first to integrate the EKF algorithm into drone hover control (altitude control). In step S1, considering that the hover throttle change during drone flight is a very slow process, the hover throttle can be considered essentially constant between two consecutive calculation cycles. The state model of the Extended Kalman Filter can be considered a zero-order system, i.e., a constant system. Therefore, this application uses the drone's hover throttle as an estimator and the drone's vertical acceleration as a measurement to construct a state model to obtain the prior estimate of the hover throttle at the current moment. The state model is as follows:

[0055] ;

[0056] in, This represents the prior estimate of the hover throttle. This represents the posterior estimate of the hover throttle, i.e., the final estimate of the extended Kalman filter. k Indicates the current moment. k -1 represents the previous time step. This represents the prior estimate of the hovering throttle at the current moment. This represents the posterior estimate of the hover throttle at the previous moment. As can be seen from the above formula, the prior estimate at the current moment is equal to the posterior estimate at the previous moment, meaning the hover throttle itself does not change actively.

[0057] In addition, in step S2, the prior estimated state covariance at the current time is calculated based on the following formula:

[0058] ;

[0059] in, This represents the prior estimate of the state covariance at the current moment. This represents the posterior estimated state covariance from the previous time step. Indicates system process noise. This represents the time difference between the two estimates. Additionally, since the state model is a zero-order system, the system process noise... It is also considered a constant value, and a very small one at that, typically set to 10. -3 Order of magnitude. As can be seen from the above formula, if the time interval... If the variance is longer, the prior estimate state covariance will increase, indicating that the uncertainty of the prior estimate increases. At this point, the prediction part of EKF has been completed.

[0060] Additionally, in step S3, the Kalman gain is calculated based on the following formula:

[0061] ;

[0062] in, Indicates Kalman gain, Indicates measurement noise. This represents the prior estimate of the state covariance at the current moment. The measurement matrix is ​​a 1×1 matrix, essentially a numerical value, obtained by taking the partial derivative of the measurement model with respect to the hovering throttle. The measurement model is as follows: , , The measured value representing vertical acceleration. Represents gravitational acceleration. Indicates the current throttle command. Indicates the hover throttle command. This sets the current throttle command to... Gravitational acceleration and the prior estimate of the hovering throttle at the current moment Substituting the values ​​into the partial derivative formula, the measurement matrix can be obtained. .

[0063] Optionally, the measurement noise can be calculated based on the following formula:

[0064] ;

[0065] in, The sensor's own noise level can be obtained from the sensor noise index. This represents the motion amplification factor, characterizing the impact of motion on measurement noise. The faster the motion speed, the greater its impact on measurement noise. , Indicates the current speed. This indicates the speed threshold.

[0066] It is understandable that in existing technologies, measurement noise is usually a constant value, generally only considering the sensor's own noise and not the impact of the UAV's speed on the measurement noise, which affects the accuracy and robustness of the estimation. In this application, when the UAV's speed is less than a preset speed threshold (set empirically or calibrated experimentally), the movement is considered to have no impact on the measurement noise. If the speed is greater than or equal to the preset speed threshold, the impact of speed on the measurement noise is considered to increase linearly, with the faster the speed, the greater its impact on the measurement noise, thereby further improving the accuracy and robustness of hover throttle estimation.

[0067] Preferably, considering that horizontal and vertical motions have different speed thresholds for affecting measurement noise, a motion amplification factor can be calculated on the horizontal and vertical planes respectively using the horizontal and vertical velocities. Then, the larger of the two factors can be taken, which helps to further improve the estimation accuracy.

[0068] Additionally, in step S4, the posterior estimate of the hovering throttle at the current moment is calculated based on the following formula:

[0069] ;

[0070] in, Indicates the current time k The posterior estimate of the hover throttle, i.e., the output value of the EKF estimator. Indicates the current time k Prior estimate of hover throttle. Indicates Kalman gain, The measured value representing vertical acceleration. This represents the predicted value of vertical acceleration. Additionally, the predicted value of vertical acceleration... By changing the current throttle command Gravitational acceleration and the prior estimate of the hovering throttle at the current moment The result was obtained by substituting the values ​​into the measurement model.

[0071] Furthermore, the posterior estimate obtained in step S4 has already been output as an estimate of the state variables, i.e., the final estimate of the hovering throttle at the current moment has been obtained. However, before outputting it, the posterior estimated state covariance at the current moment needs to be calculated to facilitate estimation at the next moment. Specifically, in step S5, the posterior estimated state covariance at the current moment is updated based on the following formula:

[0072] ;

[0073] in, This represents the posterior estimated state covariance at the current moment.

[0074] Optionally, after calculating the measurement noise, the confidence level of the estimate is also evaluated based on the following formula:

[0075] ;

[0076] in, Indicates credibility index, Represents the residual of the measured value. , The measured value representing vertical acceleration. This represents the predicted value of vertical acceleration. The confidence index represents the leniency of the judgment. It is a preset fixed value. The larger the value, the more lenient the judgment. If the confidence index is less than or equal to the preset threshold, the estimated value is considered reliable and the subsequent calculation continues. If the confidence index is greater than the preset threshold, the current estimation ends. That is, the posterior estimate of this period is not updated and the value of the previous period is used.

[0077] In Kalman filtering, if the system model is accurate and the noise is Gaussian, the residuals are assumed to follow a normal distribution (0, S), where S = HPH + R, and S is the theoretical variance of the residuals. Therefore, z = y / sqrt(S) should follow a normal distribution (0, 1). This represents the threshold, a preset value. Referring to the standard normal distribution table, z < 0.05 can be found. The pass rate (similar to the 3sigma criterion of normal distribution), so z is then divided by... This can be used as an indicator of credibility. The square is used here mainly because the standard normal distribution corresponds to positive and negative values. The probability between them is simpler to calculate by directly squaring.

[0078] It is understandable that this application assesses the reliability of the estimated value. When the estimated value is deemed unreliable, it is considered that the hover throttle estimate obtained by EKF will be abnormal, and the current estimation will end, and the estimated value of the hover throttle at the current moment will no longer be output, thereby further improving the accuracy of hover throttle estimation.

[0079] Optionally, if the confidence index is greater than a preset threshold, the posterior estimated state covariance is updated based on the following formula:

[0080] ;

[0081] in, This represents the posterior estimate of the state covariance. Indicates system process noise. This represents the time difference between two estimates.

[0082] It is understandable that when the estimate at the current moment is unreliable, it means that the estimate at the previous moment will also become unreliable in the next estimation. This application directly adds a large value to the prior estimated state covariance. This makes the posterior estimated state covariance at the current moment very large, thus making the measured value more trusted in the next estimation, which is beneficial to improving the accuracy of the next estimation.

[0083] In addition, to verify the effectiveness of the hovering throttle estimation method for the power line inspection drone proposed in this application, experimental verification was also conducted. The experimental conditions were as follows: 1) Experimental platform: DJI F450 quadcopter drone platform; 2) Experimental site: open outdoor area; 3) Experimental climate: cloudy weather, wind force 3-4. The experimental results are as follows. Figure 2 and Figure 3 As shown, where, Figure 2 This is a schematic diagram of the hovering throttle estimation curve. Figure 3 The diagram shows the altitude control response curve. It can be seen that the hover throttle estimation remains relatively constant for a short period of time. The fluctuations in the estimated value are mainly due to the wind, and the fluctuation range is about 1%-2%, which is within a reasonable estimation error range. In addition, the altitude control error after increasing the hover throttle estimation can be kept within 5cm. Even when the wind is strong, the altitude control error can be kept within 8cm. The control accuracy is very high and can fully meet the fixed-point hovering accuracy requirements required for UAV power line inspection.

[0084] In addition, such as Figure 4 As shown, another embodiment of the present invention also provides a hovering throttle estimation system for a power line inspection drone, preferably employing the hovering throttle estimation method for a power line inspection drone as described above, including:

[0085] The prior estimation module is used to construct a state model by taking the hover throttle of the UAV as the estimator and the vertical acceleration of the UAV as the measurement, and obtain the prior estimate of the hover throttle at the current moment.

[0086] The prior estimated state covariance calculation module is used to calculate the prior estimated state covariance at the current time.

[0087] The Kalman gain calculation module is used to calculate the Kalman gain based on the prior estimated state covariance at the current time.

[0088] The posterior estimation module is used to update the posterior estimate of the hover throttle at the current moment based on the Kalman gain and the prior estimate of the hover throttle at the current moment.

[0089] The posterior estimated state covariance calculation module is used to update the posterior estimated state covariance at the current time based on the Kalman gain and the prior estimated state covariance at the current time.

[0090] It is understood that the hovering throttle estimation system of the power inspection UAV in this embodiment uses the UAV's hovering throttle as the estimated quantity and vertical acceleration as the measured quantity. The extended Kalman filter algorithm is used to estimate the UAV's hovering throttle in real time. This is the first time that the extended Kalman filter algorithm has been integrated into the hovering control of the UAV. Compared with the existing PID control system, it improves the control accuracy and dynamic response capability of the UAV's fixed-point hovering, has better robustness, can achieve high-precision, real-time dynamic stable hovering control, and can be applied to UAVs with real-time weight changes, thus having better generalization ability.

[0091] In addition, another embodiment of the present invention provides an electronic device including a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method described above by calling the computer program stored in the memory.

[0092] In addition, another embodiment of the present invention provides a computer-readable storage medium for storing a computer program for estimating the hovering throttle of a power line inspection drone, the computer program executing the steps of the method described above when run on a computer.

[0093] Common computer-readable storage media include: floppy disks, flexible disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, any other optical media, punch cards, paper tape, any other physical media with perforated patterns, random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), flash erasable programmable read-only memory (FLASH-EPROM), any other memory chips or cartridges, or any other media readable by a computer. Instructions may further be transmitted or received by a transmission medium. The term transmission medium can include any tangible or intangible medium used to store, encode, or carry instructions for execution by a machine, and includes digital or analog carrier communication signals or intangible media that facilitate communication of such instructions. Transmission media include coaxial cables, copper wires, and optical fibers, which contain conductors for transmitting a bus of computer data signals.

[0094] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0095] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0096] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0098] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0099] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

[0100] 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 method for estimating the hovering throttle of a power line inspection drone, characterized in that, Includes the following: Using the drone's hover throttle as an estimator and the drone's vertical acceleration as a measurement, a state model is constructed to obtain the prior estimate of the hover throttle at the current moment. Calculate the prior estimated state covariance at the current time. Calculate the Kalman gain based on the prior estimated state covariance at the current moment; Based on the Kalman gain and the prior estimate of the hover throttle at the current moment, update the posterior estimate of the hover throttle at the current moment; Based on the Kalman gain and the prior estimated state covariance at the current time, update the posterior estimated state covariance at the current time. The state model is as follows: ； in, This represents the prior estimate of the hover throttle. This represents the posterior estimate of the hover throttle. k Indicates the current moment. k -1 represents the previous time step. This represents the prior estimate of the hovering throttle at the current moment. This represents the posterior estimate of the throttle position at the previous hovering moment.

2. The hovering throttle estimation method for power line inspection drones as described in claim 1, characterized in that, The Kalman gain is calculated based on the following formula: ； in, Indicates Kalman gain, Indicates measurement noise. This represents the prior estimate of the state covariance at the current moment. The measurement matrix is ​​obtained by taking the partial derivative of the measurement model with respect to the hovering throttle. The measurement model is as follows: , , The measured value representing vertical acceleration. Represents gravitational acceleration. Indicates the current throttle command. This indicates a hovering throttle command.

3. The hovering throttle estimation method for power line inspection drones as described in claim 2, characterized in that, The measurement noise is calculated based on the following formula: ； in, This indicates the sensor's own noise. Indicates the motion amplification factor. , This indicates the current speed of the drone. This indicates the speed threshold.

4. The hovering throttle estimation method for power line inspection drones as described in claim 3, characterized in that, After calculating the measurement noise, the reliability of the estimate is also evaluated based on the following formula: ； in, Indicates credibility index, Represents the residual of the measured value. , The measured value representing vertical acceleration. This represents the predicted value of vertical acceleration. This indicates the leniency level of the credibility index judgment. If the credibility index is less than or equal to the preset threshold, the estimated value is considered credible and subsequent calculations continue. If the credibility index is greater than the preset threshold, the estimation ends.

5. The hovering throttle estimation method for power line inspection drones as described in claim 4, characterized in that, If the confidence index is greater than the preset threshold, the posterior estimated state covariance is updated based on the following formula: ； in, This represents the posterior estimate of the state covariance. Indicates system process noise. This represents the time difference between two estimates.

6. The hovering throttle estimation method for power line inspection drones as described in claim 1, characterized in that, The posterior estimate of the hovering throttle at the current moment is calculated based on the following formula: ； in, Indicates the current time k The posterior estimate of the hover throttle. Indicates the current time k Prior estimate of hover throttle. Indicates Kalman gain, The measured value representing vertical acceleration. This represents the predicted value of vertical acceleration.

7. A hovering throttle estimation system for a power line inspection drone, employing the hovering throttle estimation method for a power line inspection drone as described in any one of claims 1 to 6, characterized in that, include: The prior estimation module is used to construct a state model by taking the hover throttle of the UAV as the estimator and the vertical acceleration of the UAV as the measurement, and obtain the prior estimate of the hover throttle at the current moment. The prior estimated state covariance calculation module is used to calculate the prior estimated state covariance at the current time. The Kalman gain calculation module is used to calculate the Kalman gain based on the prior estimated state covariance at the current time. The posterior estimation module is used to update the posterior estimate of the hover throttle at the current moment based on the Kalman gain and the prior estimate of the hover throttle at the current moment. The posterior estimated state covariance calculation module is used to update the posterior estimated state covariance at the current time based on the Kalman gain and the prior estimated state covariance at the current time.

8. An electronic device, characterized in that, The method includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the steps of the method as described in any one of claims 1 to 6 by calling the computer program stored in the memory.

9. A computer-readable storage medium for storing a computer program for estimating the hovering throttle of a power line inspection drone, characterized in that, The computer program, when run on a computer, performs the steps of the method as described in any one of claims 1 to 6.

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