Method and system for controlling a power line inspection machine

Abstract

The application discloses a power transmission line inspection machine control method and system, and relates to the technical field of power transmission line operation and maintenance, including the following steps: acquiring visual and conductor internal state sensing data; positioning obstacles on the travel path based on the visual sensing data, and identifying conductor defects based on the conductor internal state sensing data; the identification of conductor defects includes collecting real-time motion state signals and real-time eddy current signals; the real-time eddy current signals are dynamically compensated, and the purified eddy current signals are divided into multiple signal base elements; the base element feature vectors of the signal base elements are extracted for dynamic matching, candidate defect types are screened, and final judgment is made according to the base element feature vector change mode; and integrated obstacle crossing and detection collaborative control instructions are generated and executed according to the obstacle recognition result. The application effectively solves the problem that the detection function of the inspection machine is interrupted during the obstacle crossing process, a data blind area is formed, and the internal health condition of the conductor cannot be completely evaluated.

Description

Technical Field

[0001] This invention relates to the field of power transmission line operation and maintenance technology, and more specifically, to a control method and system for power transmission line inspection machines. Background Technology

[0002] As the main artery of the power system, the safe and stable operation of transmission lines is of paramount importance. Regular inspections of transmission lines to promptly detect defects such as broken strands, cracks, and corrosion in the conductors are crucial for preventing safety accidents and ensuring the reliability of the power grid. Currently, the main methods for inspecting transmission lines include manual inspection, drone inspection, helicopter inspection, and automated machine inspection.

[0003] Automated inspection robot systems have relatively simple functions, with their technical focus mainly on basic functions such as movement and obstacle crossing. Detection methods are usually limited to visible light imaging, essentially making them a moving camera. Although some studies have attempted to integrate detection functions into robots, they usually just involve simply mounting the detection equipment. When the robot must move relative to the guide wire to cross obstacles, the normal operation of the detection equipment and the continuity of data cannot be guaranteed, thus creating blind spots in the data and preventing a complete assessment of the internal health condition of the guide wire. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a control method and system for a transmission line inspection machine. By acquiring, processing, and collaboratively analyzing the visual perception data and conductor internal condition perception data of the inspection machine, the system enables simultaneous identification of obstacles and conductor defects along the travel path. Based on the identification results, it generates and executes integrated obstacle-crossing and detection collaborative control commands. This addresses the problem that existing automated inspection machines experience interruptions in detection function and data blind spots during obstacle-crossing, resulting in an inability to fully assess the internal health status of the conductors.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A control method for a power transmission line inspection machine includes the following steps: acquiring visual perception data and conductor internal state perception data of the inspection machine; locating obstacles on the travel path based on the visual perception data, and identifying conductor defects based on the conductor internal state perception data; the identification of conductor defects includes acquiring real-time motion state signals of the inspection machine and real-time eddy current signals of the conductor; dynamically compensating the real-time eddy current signals based on the motion state signals to obtain and divide the purified eddy current signals into multiple signal primitives; extracting the primitive feature vectors of each signal primitive and dynamically matching them with a pre-stored defect feature pattern library, filtering candidate defect types by calculating the Fréchet distance, and making a final judgment based on the change pattern of the primitive feature vectors corresponding to the candidate defect types; generating and executing integrated obstacle crossing and detection collaborative control commands based on the obstacle identification results.

[0007] In a preferred embodiment, the inspection machine includes rollers, a through-type eddy current detector, a camera device, a parallel double-arm lifting mechanism, and a counterweight electrical box; the visual perception data is composed of two-dimensional image information acquired by a monocular camera device; the internal state perception data of the conductor is obtained by scanning the conductor with the through-type eddy current detector.

[0008] In a preferred embodiment, locating obstacles on the travel path based on visual perception data specifically involves: acquiring a two-dimensional image containing the guide wire and the obstacle; performing image processing on the two-dimensional image to identify and fit the pixel contour trajectory of the guide wire; using a fixed vertical distance between the guide wire and the optical center of the camera device as an absolute scale reference; drawing a plumb line through the projection point of the optical center in the image coordinate system of the two-dimensional image, and calculating the intersection point of the plumb line and the pixel trajectory of the guide wire; calculating the vertical pixel distance from the pixel coordinates of the intersection point to the center point of the two-dimensional image; calculating the metric conversion ratio of the current frame based on the pinhole imaging model, the fixed vertical distance, and the vertical pixel distance; identifying the bottom pixel of the obstacle in the two-dimensional image, and calculating the pixel distance between the bottom pixel and the center point of the two-dimensional image; and calculating the actual distance between the obstacle and the inspection machine based on the metric conversion ratio and the pixel distance.

[0009] In a preferred embodiment, the method for dynamically compensating for real-time eddy current signals specifically comprises: controlling an inspection machine to execute a standard action sequence on a standard conductor segment, and simultaneously acquiring standard motion signals and corresponding standard eddy current signals; establishing a mapping relationship between the standard motion signals and the standard eddy current signals through regression analysis; obtaining a motion interference prediction signal from the real-time motion signals through the mapping relationship; and performing differential processing on the real-time eddy current signals and the motion interference prediction signals to obtain purified eddy current signals.

[0010] In a preferred embodiment, dividing the purified eddy current signal into multiple signal elements specifically involves: performing time-frequency analysis on the purified eddy current signal, extracting multi-frequency band features, and constructing a two-dimensional feature matrix; based on the feature change rate of continuous sampling points in the two-dimensional feature matrix, dividing the purified eddy current signal into multiple signal elements, with each signal element corresponding to a physical segment of the conductor.

[0011] In a preferred embodiment, the extraction of the primitive feature vectors of each signal primitive specifically involves: performing wavelet decomposition and reconstruction on each signal primitive; calculating the energy entropy and singular value entropy of the reconstructed signal to form the primitive feature vectors.

[0012] In a preferred embodiment, the final judgment based on the change pattern of the primitive feature vector corresponding to the candidate defect type is specifically as follows: if the primitive feature vector sequence shows a step-like abrupt change, the defect type is determined to be a broken strand; if the primitive feature vector sequence shows a gradual decay, the defect type is determined to be corrosion; if the primitive feature vector sequence shows periodic fluctuations, the defect type is determined to be a crack.

[0013] In a preferred embodiment, the step of generating and executing integrated obstacle-crossing and detection collaborative control commands based on obstacle recognition results specifically involves: setting safe distance intervals on both sides of the obstacle along the guide wire direction; when the robot's forearm reaches the starting boundary of the safe distance interval, adjusting the counterweight so that the overall center of mass is located in the rear arm, and raising the forearm to move away from the guide wire; controlling the rear arm to drive independently until the forearm completely crosses the obstacle and reaches the ending boundary of the safe distance interval, at which point the forearm descends and re-contacts the guide wire; when the robot's rear arm reaches the starting boundary of the safe distance interval, adjusting the counterweight so that the overall center of mass is located in the forearm, and raising the rear arm to move away from the guide wire; controlling the forearm to drive independently until the rear arm completely crosses the obstacle and reaches the ending boundary of the safe distance interval, at which point the rear arm descends and re-contacts the guide wire; adjusting the robot to a horizontal posture and resuming normal walking state.

[0014] In a preferred embodiment, the cooperative control command includes: controlling the robot's forearm and rear arm to cooperate in crossing obstacles; synchronously controlling the working state of a through-type eddy current detector during obstacle crossing, the through-type eddy current detector employing a separable coil structure; controlling its coil to perform a separation operation when the through-type eddy current detector reaches the starting boundary of the safe distance interval; and controlling its coil to reclose when the through-type eddy current detector has completely crossed the obstacle and reached the ending boundary of the safe distance interval.

[0015] A system for controlling a transmission line inspection machine includes: a data acquisition module for acquiring visual perception data and conductor internal state perception data of the inspection machine; an environment and defect identification module for locating obstacles on the travel path based on visual perception data and identifying conductor defects based on conductor internal state perception data; the identification of conductor defects includes acquiring real-time motion state signals of the inspection machine and real-time eddy current signals of the conductor; dynamically compensating the real-time eddy current signals based on the motion state signals to obtain and divide the purified eddy current signals into multiple signal primitives; extracting the primitive feature vectors of each signal primitive and dynamically matching them with a pre-stored defect feature pattern library; filtering candidate defect types by calculating the Friesian distance; and making a final judgment based on the change pattern of the primitive feature vectors corresponding to the candidate defect types; and a collaborative control module for generating and executing integrated obstacle crossing and detection collaborative control commands based on the obstacle identification results.

[0016] The technical effects and advantages of the control method and system for the transmission line inspection machine of the present invention are as follows:

[0017] 1. This invention generates and executes integrated obstacle crossing and detection collaborative control commands, realizing the integrated intelligent linkage of mechanical actions and detection tasks in complex inspection tasks. This effectively solves the problem that traditional inspection equipment must interrupt detection work when crossing obstacles, ensuring the continuity and integrity of detection data, and greatly improving the accuracy and reliability of power transmission line health status assessment.

[0018] 2. By acquiring visual perception data from the inspection machine and internal conductor condition perception data, this invention can not only monitor obstacle information along the machine's path in real time, but also simultaneously obtain internal conductor defect characteristics. This enables multi-dimensional and comprehensive perception and evaluation of the transmission line's operating status, thereby significantly improving the completeness, accuracy, and reliability of subsequent health analysis of the inspection data, and providing solid data support for preventive maintenance and fault early warning of transmission lines. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the control method of the power transmission line inspection machine of the present invention.

[0020] Figure 2 This is a schematic diagram of the system structure of the control method for the transmission line inspection machine of the present invention;

[0021] Figure 3 This is a schematic diagram of the working process of the parallel double-arm lifting mechanism of the present invention. Detailed Implementation

[0022] 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0023] Example 1, Figure 1 The present invention provides a control method for the transmission line inspection machine, including:

[0024] S1, acquire visual perception data of the inspection machine and internal condition perception data of the conductor;

[0025] In this embodiment, the acquisition of visual perception data of the inspection machine and internal state perception data of the conductor specifically includes:

[0026] The inspection machine includes rollers, a through-type eddy current detector, a camera device, a parallel double-arm lifting mechanism, and a counterweight electrical box.

[0027] The monocular camera device acquires real-time images of the surface of the conductor along the inspection path, obtaining two-dimensional images including the conductor and obstacles. The visual perception data consists of the two-dimensional image information acquired by the monocular camera device.

[0028] The through-type eddy current detector performs electromagnetic scanning of the conductor's circumference as the inspection machine moves along the conductor, acquiring the electromagnetic response signal of the metal layer inside the conductor. The electromagnetic response signal is then filtered, denoised, and phase-calculated to obtain the conductor's internal state perception data.

[0029] S2, based on visual perception data, locates obstacles on the travel path and identifies wire defects based on wire internal state perception data;

[0030] In this embodiment, locating obstacles on the travel path based on visual perception data specifically involves:

[0031] A 2D image containing the conductor and obstacles is acquired. The pixel contours of the conductor are extracted using image recognition algorithms (such as Canny edge detection or deep learning semantic segmentation) to form a pixel contour point set P. The pixel contour point set P is then fitted in the image coordinate system using the least squares method to obtain the pixel contour trajectory curve of the conductor.

[0032]

[0033] in, , , The fitting coefficients are denoted as .

[0034] The pixel trajectory curve of the conductor is the pixel outline trajectory of the conductor.

[0035] Using the fixed vertical distance H between the conductor and the optical center of the camera device as a known absolute scale reference, a plumb line is drawn through the projection point of the optical center of the camera device in the two-dimensional image. Calculate the intersection point Q of the vertical line and the pixel trajectory of the conductor, specifically:

[0036]

[0037]

[0038] in, The x-coordinate of the center point of the two-dimensional image. Let Q be the ordinate of the intersection point.

[0039] Calculate the vertical pixel distance from the pixel coordinates of the intersection point to the center point of the 2D image. Specifically:

[0040]

[0041] in, The ordinate is the y-coordinate of the image center point.

[0042] Based on the pinhole imaging model, and using a fixed vertical distance and vertical pixel distance, the metric conversion ratio of the current frame is calculated in real time. :

[0043]

[0044] Obstacles are identified using an object detection model, specifically the coordinates of the bottom pixels of the obstacles in a 2D image. Calculate the pixel distance between the bottom pixel and the center point of the 2D image. :

[0045]

[0046] According to the metric conversion ratio and pixel distance Calculate the actual distance between the obstacle and the inspection machine. :

[0047]

[0048] It should be noted that the fixed vertical distance H is determined by the structure of the inspection machine and is measured and fixed after assembly.

[0049] The positioning method described in this embodiment enables the system to achieve real-time, high-precision positioning of obstacles on the line without the need for expensive stereo vision or lidar.

[0050] The method for identifying defects in the conductor is as follows:

[0051] The real-time motion status signals of the inspection machine are collected, including the roller angular velocity, forearm position, rear arm position, and the movement status of the counterweight box.

[0052] Real-time eddy current signals of the conductor are collected by a through-type eddy current detector. The real-time eddy current signals are dynamically compensated based on the motion state signals to suppress interference components introduced by the movement of the inspection robot and obtain purified eddy current signals. Based on the purified eddy current signals, defects in the conductor are identified and their types are determined.

[0053] The method for dynamically compensating for real-time eddy current signals is as follows:

[0054] The standard conductor segment refers to a conductor sample that has been confirmed by non-destructive testing to be free of any defects, with uniform material and a smooth surface. The standard action sequence covers all typical motion states that the inspection machine may generate during actual inspection, including uniform walking, acceleration and deceleration walking, swaying, and obstacle crossing posture.

[0055] On a standard conductor segment, the inspection machine is controlled to execute a standard action sequence. The standard motion signal and corresponding standard eddy current signal generated during the execution of the standard action sequence are recorded. A mapping relationship between the standard motion signal and the standard eddy current signal is established through regression analysis. Real-time motion signals and real-time eddy current signals are collected during the inspection process. The real-time motion signal is then mapped to obtain the interference prediction signal corresponding to the current motion state. The real-time eddy current signal and the interference prediction signal are differentially processed to obtain the purified eddy current signal. Specifically:

[0056]

[0057] in, The purified eddy current signal For real-time eddy current signals, This is an interference signal.

[0058] The method for identifying defects in the wire is as follows:

[0059] Time-frequency analysis was performed on the purified eddy current signal, and continuous wavelet transform was conducted to extract amplitude abrupt changes and phase shift features under different frequency sub-bands. A two-dimensional feature matrix was constructed from the amplitude abrupt changes and phase shift features.

[0060]

[0061] in, It is a two-dimensional feature matrix. For each frequency sub-band amplitude abrupt change, Phase offset for each frequency sub-band.

[0062] Calculate the rate of change of features at consecutive sampling points in a two-dimensional feature matrix. :

[0063]

[0064] in, The sampling time interval, It is the Euclidean norm.

[0065] An adaptive threshold segmentation algorithm is used to divide the purified eddy current signal into several signal primitives. The threshold is calculated as follows:

[0066]

[0067] in, , These are several past sampling points. The mean and standard deviation, This is an empirical coefficient (usually taken as 1.5~2).

[0068] When continuous sampling points When two signal element boundary points are identified, the purified eddy current signal between them constitutes a signal element. Each signal element corresponds to a physical segment of the conductor.

[0069] For each signal element Wavelet decomposition and reconstruction are performed, and the energy entropy and singular value entropy of the reconstructed signal are calculated, specifically as follows:

[0070]

[0071]

[0072]

[0073] in, Let J be the energy entropy, and J be the number of decomposition levels. Let j be the energy percentage of the j-th layer. Let i be the wavelet coefficient of the i-th signal element in the j-th layer. Let be the wavelet coefficient energy of the i-th signal element in the j-th layer. The sum of the wavelet coefficients of all layers of the i-th signal primitive is given by [the sum of the energy of the wavelet coefficients]. Let K be the singular value entropy, and K be the total number of singular values. It is the kth singular value.

[0074] From energy entropy and singular value entropy A primitive feature vector representing each signal primitive is formed. The sequence of primitive feature vectors to be detected is dynamically matched with a pre-stored defect feature pattern library. The Fréchet distance between this sequence and various defect reference sequences in the pattern library is calculated. The formula for calculating the Fréchet distance is as follows:

[0075]

[0076] in, For the Frege distance, The sequence of primitive feature vectors to be detected. Let be the reference feature sequence for the g-th type of defect, and t be the normalized time parameter, representing the matching progress of the two feature sequences on the normalized time axis. The parameterized mapping function for the sequence to be detected. The parameterized mapping function for the reference sequence.

[0077] The calculated Fraser distance is compared with a preset threshold, and defect types with Fraser distance values ​​less than the corresponding threshold are included in the candidate defect set to filter out candidate defect types.

[0078] It should be noted that the parameterized mapping function is used to characterize the nonlinear coupling relationship between the motion state of the inspection machine and the interference component of the eddy current signal. It is obtained by modeling through standard experimental datasets. The introduction of the parameterized mapping function realizes real-time compensation for the nonlinear interference component caused by the motion of the inspection machine, so that the eddy current detection results can accurately reflect the characteristics of conductor defects, and significantly improve the stability and reliability of the inspection.

[0079] The final judgment is based on the change pattern of the primitive feature vector corresponding to the candidate defect type, specifically as follows:

[0080] The system calculates the characteristic variation amplitude between adjacent signal elements. When a sudden increase in the characteristic variation amplitude within a very short distance is observed in a continuous segment, followed by a rapid return to stability, it indicates a step-like abrupt change in the electromagnetic properties of the conductor in that segment. This typically corresponds to macroscopic structural damage such as strand breakage, and the system classifies this defect type as strand breakage. When the characteristic variation amplitude shows a gradually decreasing trend in space, indicating a gradual attenuation of the local signal energy of the conductor and a slow decrease in singular entropy, it indicates a gradual weakening of the electromagnetic response of the conductor material, possibly caused by long-term oxidation or chemical reactions. The system classifies this defect type as corrosion. When the characteristic variation amplitude exhibits periodic fluctuations among multiple continuous signal elements, and the period and amplitude of the fluctuations are basically stable, it indicates the presence of a periodic disturbance source within the conductor, possibly caused by a crack extending along the conductor's axial direction, resulting in a localized repetitive reflection effect. The system classifies this defect type as a crack.

[0081] S3 generates and executes integrated obstacle crossing and detection collaborative control commands based on obstacle recognition results.

[0082] In this embodiment, the step of generating and executing integrated obstacle-crossing and detection collaborative control commands based on obstacle recognition results specifically includes:

[0083] First, it should be noted that the S1 content already describes that the inspection machine includes rollers, a through-type eddy current detector, a camera device, a parallel double-arm lifting mechanism, and a counterweight electrical box.

[0084] The parallel double-arm lifting mechanism includes a forearm and a rear arm. The forearm and rear arm are independently controlled by the lifting mechanism, which can realize single-arm lifting, tilting and independent walking. The counterweight electrical box can move along the robot body slide rail to adjust the overall center of gravity position, so that the robot can always maintain the force balance in the direction of the guide line during obstacle crossing.

[0085] After obstacle recognition is completed, the system determines the position coordinates of the obstacle in the direction of the guide line based on the recognition results. A boundary point is set on both the front and back sides of the obstacle along the direction of the guide line, and the area between the two points is defined as the safe distance interval. This safe distance interval is used to ensure that the robot has sufficient safety margin during lifting and lowering operations to prevent the detection device from colliding with the obstacle.

[0086] When the robot's forearm reaches the starting boundary of the safe distance zone, the counterweight electrical box is adjusted so that the robot's overall center of mass is located in the rear arm. The forearm is then raised so that its rollers leave the guide wire. The rear arm rollers are then driven to move independently until the forearm completely crosses the obstacle and reaches the ending boundary of the safe distance zone. The forearm then descends so that its front rollers re-contact the guide wire.

[0087] Similarly, when the robot's rear arm reaches the starting boundary of the safe distance range, adjust the counterweight electrical box so that the robot's overall center of gravity is located at the forearm. Control the rear arm to lift so that its rollers leave the guide wire. Control the forearm rollers to drive the robot to move independently until the rear arm completely crosses the obstacle and reaches the ending boundary of the safe distance range. The rear arm then lowers so that its rear rollers re-contact the guide wire. After the robot has successfully overcome the obstacle, adjust it to a horizontal posture and return the counterweight electrical box to the middle position to resume normal walking.

[0088] The coordinated control instructions include:

[0089] The robot's forearm and rear arm work together to overcome obstacles. During the obstacle-crossing process, the working state of the through-type eddy current detector is controlled synchronously. The through-type eddy current detector adopts a structure with separate upper and lower coils. When the through-type eddy current detector reaches the starting boundary of the safe distance range, its upper and lower coils are controlled to perform a separation operation. When the through-type eddy current detector completely crosses the obstacle and reaches the ending boundary of the safe distance range, its upper and lower coils are controlled to close again.

[0090] Example 2, Figure 2 The system providing the control method for the transmission line inspection machine of the present invention includes:

[0091] The data acquisition module is used to acquire visual perception data and internal condition perception data of the inspection machine.

[0092] The environment and defect identification module is used to locate obstacles on the travel path based on visual perception data and to identify conductor defects based on conductor internal state perception data. The identification of conductor defects includes collecting real-time motion state signals of the inspection machine and real-time eddy current signals of the conductor; dynamically compensating the real-time eddy current signals based on the motion state signals to obtain and divide the purified eddy current signals into multiple signal primitives; extracting the primitive feature vectors of each signal primitive and dynamically matching them with a pre-stored defect feature pattern library; filtering candidate defect types by calculating the Friesian distance; and making a final judgment based on the change pattern of the primitive feature vectors corresponding to the candidate defect types.

[0093] The collaborative control module generates and executes integrated obstacle crossing and detection collaborative control commands based on obstacle recognition results.

[0094] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0095] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0096] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0097] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0098] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0099] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A control method of a power line inspection machine, characterized by, The method comprises the following steps: acquiring visual perception data of the inspection machine and internal state perception data of the conductor; locating obstacles on the travel path based on the visual perception data and identifying conductor defects based on the internal state perception data of the conductor; the identification of the conductor defects comprises collecting real-time motion state signals of the inspection machine and real-time eddy current signals of the conductor, dynamically compensating the real-time eddy current signals based on the motion state signals to obtain purified eddy current signals, and dividing the purified eddy current signals into a plurality of signal base elements; extracting base element feature vectors of each signal base element and pre-stored defect feature mode library for dynamic matching, screening candidate defect types by calculating Fréchet distance, and finally judging based on the base element feature vector change mode corresponding to the candidate defect type; generating and executing integrated obstacle crossing and detection collaborative control instructions according to the obstacle identification result.

2. The control method of a power transmission line inspection machine according to claim 1, characterized by, The inspection machine comprises a roller, a through-type eddy current detector, a camera device, a parallel double-arm lifting mechanism and a counterweight electrical box; The visual perception data is composed of two-dimensional image information collected by a monocular camera device; The internal state perception data of the conductor is obtained by scanning the conductor with the through-type eddy current detector.

3. The control method of a power transmission line inspection machine according to claim 2, characterized by, The locating of obstacles on the travel path based on the visual perception data specifically comprises: collecting two-dimensional images containing the conductor and the obstacles; performing image processing on the two-dimensional images to identify and fit the pixel contour track of the conductor; taking the fixed vertical distance between the conductor and the optical center of the camera device as an absolute scale reference; in the image coordinate system of the two-dimensional image, drawing a plumb line through the optical center projection point, and calculating the intersection point of the plumb line and the pixel track of the conductor; calculating the vertical pixel distance from the pixel coordinates of the intersection point to the center point of the two-dimensional image; calculating the metric conversion ratio of the current frame based on the fixed vertical distance and the vertical pixel distance according to the pinhole imaging model; identifying the bottom pixel point of the obstacle in the two-dimensional image, and calculating the pixel distance from the bottom pixel point to the center point of the two-dimensional image; calculating the real distance between the obstacle and the inspection machine according to the metric conversion ratio and the pixel distance.

4. The control method of a power transmission line inspection machine according to claim 3, characterized by, The method for dynamically compensating the real-time eddy current signals specifically comprises: controlling the inspection machine to perform a standard action sequence on a standard conductor segment, and synchronously collecting standard motion signals and corresponding standard eddy current signals; establishing a mapping relationship between the standard motion signals and the standard eddy current signals through regression analysis; obtaining a motion interference prediction signal through the mapping relationship from the real-time motion signals; differentially processing the real-time eddy current signals and the motion interference prediction signal to obtain purified eddy current signals.

5. The control method of a power transmission line inspection machine according to claim 4, characterized by, The division of the purified eddy current signals into a plurality of signal base elements specifically comprises: performing time-frequency analysis on the purified eddy current signals, extracting multi-band features, and constructing a two-dimensional feature matrix; based on the feature change rate of consecutive sampling points in the two-dimensional feature matrix, dividing the purified eddy current signals into a plurality of signal base elements, each signal base element corresponding to a physical section of the conductor.

6. The control method of a power transmission line inspection machine according to claim 5, characterized by, The extraction of the base element feature vectors of each signal base element specifically comprises: wavelet decomposition and reconstruction of each signal base element; calculating the energy entropy and singular value entropy of the reconstructed signal to form a base element feature vector.

7. The control method of a power line inspection machine according to claim 6, characterized by, The final judgment is made based on the variation mode of the element feature vector corresponding to the candidate defect type, and specifically: If the element feature vector sequence presents a step mutation, it is determined that the defect type is a broken strand; If the element feature vector sequence presents a gradual attenuation, it is determined that the defect type is corrosion; If the element feature vector sequence presents a periodic fluctuation, it is determined that the defect type is a crack.

8. The control method of a power transmission line inspection machine according to claim 7, characterized by, The integrated obstacle crossing and detection collaborative control instructions are generated and executed according to the obstacle recognition result, and specifically: A safety distance interval is set on the front and back sides of the obstacle along the direction of the conductor; When the robot forearm reaches the starting boundary of the safety distance interval, the counterweight is adjusted to make the overall center of mass located on the rear arm, the forearm is lifted to leave the conductor; The rear arm is controlled to drive walking alone until the forearm completely crosses the obstacle and reaches the end boundary of the safety distance interval, and the rear arm is lowered to re-contact the conductor; When the robot rear arm reaches the starting boundary of the safety distance interval, the counterweight is adjusted to make the overall center of mass located on the front arm, the rear arm is lifted to leave the conductor; The front arm is controlled to drive walking alone until the rear arm completely crosses the obstacle and reaches the end boundary of the safety distance interval, and the rear arm is lowered to re-contact the conductor; The robot is adjusted to a horizontal posture to restore the normal walking state.

9. The control method of a power transmission line inspection machine according to claim 8, characterized by, The collaborative control instructions include: Controlling the front arm and the rear arm of the robot to cross the obstacle together; In the process of crossing the obstacle, the working state of the through-type eddy current detector is synchronously controlled, and the through-type eddy current detector adopts a separable coil structure; When the through-type eddy current detector reaches the starting boundary of the safety distance interval, the coil is controlled to perform a separation operation; When the through-type eddy current detector completely crosses the obstacle and reaches the end boundary of the safety distance interval, the coil is controlled to be re-closed.

10. A system for using a control method of a power line inspection machine according to any one of claims 1 to 9, characterized in that, It includes: A data acquisition module for acquiring visual perception data and conductor internal state perception data of the inspection robot; An environment and defect identification module for locating obstacles on the travel path based on the visual perception data and identifying conductor defects based on the conductor internal state perception data; The identification of the conductor defects includes collecting real-time motion state signals of the inspection robot and real-time eddy current signals of the conductor, performing dynamic compensation on the real-time eddy current signals based on the motion state signals to obtain and divide the purified eddy current signals into multiple signal elements, extracting the element feature vectors of each signal element, and dynamically matching the element feature vectors with a pre-stored defect feature mode library, filtering candidate defect types by calculating the Fréchet distance, and making a final judgment based on the variation mode of the element feature vector corresponding to the candidate defect type; A collaborative control module for generating and executing integrated obstacle crossing and detection collaborative control instructions according to the obstacle recognition result.

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