Attitude control method and system for overhead line galloping monitoring aiming holder

Abstract

The invention discloses an attitude control method and system for an overhead line galloping monitoring aiming holder. The method comprises the following steps: carrying out initialization setting, hardware self-inspection and operation parameter calibration on the holder; the holder comprises a motor, a harmonic reducer and a 5G module; acquiring lead motion data and holder operation parameters collected by a plurality of sensors in real time so as to obtain multi-modal data; fusing the multi-modal data, and calculating the current attitude information of the holder; formulating a dynamic adjustment strategy based on the attitude information to optimize the position and speed of the holder; according to the control strategy, an execution mechanism is driven to accurately adjust the attitude of the holder; and acquiring multi-modal data of operation after the attitude of the holder is adjusted, and optimizing the operation parameters of the holder. According to the invention, the accuracy and efficiency of overhead line galloping monitoring can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gimbal posture control, and particularly relates to a posture control method and system for an overhead line galloping monitoring aiming gimbal. BACKGROUND

[0002] Overhead power transmission lines are prone to a low-frequency large-amplitude vibration phenomenon called "galloping" under icing conditions in winter. This self-excited vibration not only accelerates the fatigue and damage of the conductor, but also can lead to tower structure damage, accessory shedding, and power transmission interruption, posing a significant threat to the safety and stability of the power grid.

[0003] Currently, monitoring the galloping phenomenon of overhead lines mainly relies on gimbal devices equipped with sensors to capture the three-dimensional motion trajectory of the conductor in real time, including but not limited to amplitude changes in the vertical and horizontal directions, elliptical tilt angles, etc. However, if the posture of the gimbal deviates, it will cause the collected data to be distorted, thereby affecting the accurate capture of key parameters such as galloping frequency or amplitude mutation, reducing the effectiveness of the early warning system. In addition, traditional fixed-view cameras have limited monitoring range, making it difficult to fully cover the dynamically changing galloping area; while adjusting the position of the gimbal through posture control technology can achieve wider field of view coverage and multi-angle monitoring, helping to improve the quality of data collection. Considering that power transmission lines are usually located in harsh environments such as high-cold, strong wind, and icing areas, these external conditions can cause mechanical vibrations, further interfering with the normal operation of the gimbal. If the posture control system of the gimbal cannot effectively respond to these disturbances, it may cause greater vibrations due to resonance effects, thereby severely affecting the reliability of the data.

[0004] Therefore, it is necessary to design a new method to improve the accuracy and efficiency of overhead line galloping monitoring, solve the problems of current overhead line galloping monitoring gimbals, such as data distortion due to insufficient posture control, limited monitoring range, and susceptibility to external disturbances in harsh environments, thereby severely reducing the accuracy and reliability of monitoring data. SUMMARY

[0005] The purpose of the present application is to overcome the defects of the prior art and provide a posture control method and system for an overhead line galloping monitoring aiming gimbal.

[0006] To achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows: a posture control method for an overhead line galloping monitoring aiming gimbal, comprising: initializing the gimbal, performing hardware self-checking, and calibrating operating parameters; the gimbal includes a motor, a harmonic reducer, and a 5G module; acquiring conductor motion data and gimbal operating parameters collected in real time by multiple sensors to obtain multi-modal data; fuse the multi-modal data and calculate the current attitude information of the gimbal; formulate a dynamic adjustment strategy based on the attitude information to optimize the gimbal position and speed; drive the actuator according to the dynamic adjustment strategy to accurately adjust the gimbal attitude; obtain the multi-modal data of the gimbal running after attitude adjustment and optimize the gimbal running parameters.

[0007] A further technical solution is that the initialization setting, hardware self-checking and running parameter calibration of the gimbal include: use a Hall sensor to read the initial position of the gimbal motor and check whether the gimbal harmonic reducer has a stuck phenomenon; evaluate the signal strength of the 5G module of the gimbal by sending a test message; use a static six-face method to fix the gimbal on a horizontal plane in six orthogonal directions, respectively collect the data of the axis accelerometer and gyroscope to compensate for zero offset and scale factor error.

[0008] A further technical solution is that the wire movement data and gimbal running parameters collected by various sensors in real time are obtained to obtain multi-modal data, including: capture and recognize the wire edge features by a global shutter CMOS camera and a target detection model, calculate the pixel displacement using the Lucas-Kanade optical flow method to obtain the wire movement data; collect the data of a three-axis accelerometer and a three-axis gyroscope, use a zero offset-temperature lookup table for temperature drift compensation, and apply a Butterworth low-pass filter for vibration filtering to obtain the gimbal running parameters.

[0009] A further technical solution is that the wire edge features are captured and recognized by a global shutter CMOS camera and a target detection model, and the pixel displacement is calculated using the Lucas-Kanade optical flow method to obtain the wire movement data, including: select and fix the global shutter CMOS camera according to the measurement requirements, and adjust the exposure time and gain parameters to ensure image quality; use the global shutter CMOS camera to capture the wire image in real time and determine the position and size of the wire region through the target detection model to obtain the wire ROI; apply a corner detection algorithm to identify key feature points in the wire ROI to obtain the feature point coordinates; select two consecutive frames of images, use the feature point coordinates in the first frame as the starting point, and continuously apply the Lucas-Kanade optical flow method to track the new position of the feature points in each subsequent frame based on the position of the feature points in the previous frame to obtain the position of the feature points between adjacent frames. By comparing the change of the position of the feature points between adjacent frames, the displacement amount of the feature points in the horizontal and vertical directions is calculated to obtain displacement data; After removing noise from the displacement data and its timestamp data by filtering technology, the actual motion parameters of the guide wire are calculated according to the camera parameters, and the motion trajectory and state thereof are analyzed to obtain guide wire motion data.

[0010] Further technical solutions thereof are as follows: after removing noise from the displacement data and its timestamp data by filtering technology, the actual motion parameters of the guide wire are calculated according to the camera parameters, and the motion trajectory and state thereof are analyzed to obtain guide wire motion data, including: The displacement data and its corresponding timestamp information are recorded and saved in a text file or a database; The displacement data is processed by using mean filtering, median filtering or Kalman filtering method to remove existing noise interference to obtain processed displacement data; The processed displacement data is used to calculate the actual motion parameters of the guide wire in the physical space in combination with the specific parameters of the camera; The motion trajectory and motion state of the guide wire are determined by analyzing the change of the processed displacement data over time, and the guide wire motion data is generated in combination with the actual motion parameters.

[0011] Further technical solutions thereof are as follows: the data of the three-axis accelerometer and the three-axis gyroscope are collected, temperature drift compensation is performed by using a zero offset-temperature lookup table, and vibration filtering is performed by using a Butterworth low-pass filter to obtain gimbal operation parameters, including: The acceleration values and angular velocity values of the three-axis accelerometer and the three-axis gyroscope in the three axial directions of the gimbal are collected in real time to obtain collected data; The temperature of the environment in which the device is located is recorded in real time to obtain the current environmental temperature; A lookup table of zero offset and temperature relationship is created by measuring and recording the zero offset values of the three-axis accelerometer and the three-axis gyroscope at different constant temperatures; According to the current environmental temperature, the corresponding zero offset compensation value in the lookup table is used to correct the collected data to obtain gimbal operation parameters.

[0012] Further technical solutions thereof are as follows: the multi-modal data is fused, and the current attitude information of the gimbal is calculated, including: Based on the multi-modal data, the improved Mahony complementary filtering algorithm is used to calculate and compensate the pitch angle, roll angle and yaw angle of the device in real time through a quaternion dynamics model to obtain the current attitude information of the gimbal; The formula of the improved Mahony complementary filtering algorithm is as follows: , q is a quaternion, omega is a gyroscope three-axis angular velocity, a measured is an accelerometer measurement value, a gravity is a gravity direction projection, beta is an adaptive filter coefficient.

[0013] Further technical solutions thereof are that the dynamic adjustment strategy is formulated based on the attitude information to optimize the position and speed of the holder, and the dynamic adjustment strategy comprises the following steps: An error between a target angle and a current angle is calculated based on the attitude information to determine an expected angular velocity; a current output is adjusted according to motor encoder feedback; PID parameters are adaptively adjusted by a fuzzy rule base according to an input angle error and a change rate; and the motor driving current is adjusted by applying the adjusted PID parameters; The dynamic control strategy adopts double closed-loop PID control combined with fuzzy logic compensation, wherein the fuzzy logic realizes accurate and adaptive control of the motor by adjusting the PID parameters.

[0014] Further technical solutions thereof are that the actuator adopts a double gear transmission system, a driving gear is driven by a stepping motor, is engaged with a driven gear to form a transmission ratio of 4:1, and outputs a torque according to a dynamic model; and the motor speed is adjusted in real time by combining a PID controller to make the horizontal rotation angle of the holder reach a target positioning accuracy.

[0015] The application further provides an attitude control system of a sighting holder for overhead line galloping monitoring, comprising: A holder initialization unit is configured to initialize the holder, perform hardware self-checking and calibrate operating parameters; the holder comprises a motor, a harmonic reducer and a 5G module; A multi-modal data acquisition unit is configured to acquire conductor motion data and holder operating parameters collected in real time by multiple sensors to obtain multi-modal data; A calculation unit is configured to fuse the multi-modal data and calculate current attitude information of the holder; A strategy formulation unit is configured to formulate a dynamic adjustment strategy based on the attitude information to optimize the position and speed of the holder; A driving unit is configured to drive an actuator according to the dynamic adjustment strategy to accurately adjust the holder attitude; An optimization unit is configured to acquire multi-modal data of the holder after attitude adjustment and optimize holder operating parameters.

[0016] Compared with the prior art, the present application has the beneficial effects that: the present application ensures the optimal initial state of the holder system through initialization setting, hardware self-checking and running parameter calibration; the comprehensive multi-modal data are obtained by using various sensors to collect the conductor motion data and holder running parameters in real time, and the current accurate attitude information of the holder is calculated through data fusion; the dynamic adjustment strategy is formulated based on the information, the holder position and speed are optimized, and the accurate attitude adjustment is performed by driving the actuator. Subsequently, the holder running parameters are optimized by obtaining the adjusted multi-modal data again to form a closed-loop feedback mechanism. This method not only solves the problems of data distortion and limited monitoring range caused by insufficient attitude control of the traditional holder, but also enhances the anti-interference ability of the system in harsh environments, thereby greatly improving the accuracy and reliability of the monitoring data, and realizing more stable and accurate monitoring of the overhead line galloping.

[0017] The present application will be further described below in combination with the drawings and specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0019] Figure 1 The application scenario schematic diagram of the attitude control method of the overhead line galloping monitoring aiming holder provided by the embodiment of the present application is shown in the figure. Figure 2 The flowchart of the attitude control method of the overhead line galloping monitoring aiming holder provided by the embodiment of the present application is shown in the figure. Figure 3 The schematic block diagram of the attitude control system of the overhead line galloping monitoring aiming holder provided by the embodiment of the present application is shown in the figure. Figure 4 The schematic block diagram of the computer device provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0021] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0022] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0023] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0024] Please see Figure 1 and Figure 2 , Figure 1 This is a schematic diagram illustrating an application scenario of the attitude control method for an aiming gimbal used for monitoring galloping of overhead power lines, as provided in an embodiment of the present invention. Figure 2 This is a schematic flowchart illustrating the attitude control method for an aiming pan-tilt unit used for monitoring overhead line galloping, provided in an embodiment of the present invention. This attitude control method is applied in a server. The server interacts with the terminal, integrating multiple sensors (such as a three-axis accelerometer, a three-axis gyroscope, and a global shutter CMOS camera) and advanced signal processing technologies (such as the Lucas-Kanade optical flow method and an improved Mahony complementary filtering algorithm) to achieve high-precision real-time acquisition and analysis of conductor motion data and pan-tilt unit operating parameters. The method utilizes multi-modal data fusion technology to calculate the pan-tilt unit's attitude information and, based on this, formulates a dynamic adjustment strategy to optimize the pan-tilt unit's position and speed, ensuring precise attitude adjustment even in harsh environments. Furthermore, the use of dual-closed-loop PID control combined with fuzzy logic compensation and a specific transmission system design further enhances the system's stability and response speed. These technologies work together to not only improve the accuracy and efficiency of overhead line galloping monitoring but also solve problems such as data distortion, limited monitoring range, and external disturbances caused by insufficient traditional attitude control, thereby significantly improving the accuracy and reliability of monitoring data.

[0025] Figure 2 This is a flowchart illustrating the attitude control method for an aiming pan-tilt unit used for monitoring galloping of overhead power lines, provided in an embodiment of the present invention. Figure 2 As shown, the method includes the following steps S110 to S160.

[0026] S110. Initialize the gimbal, perform hardware self-test and calibrate operating parameters; the gimbal includes a motor, a harmonic reducer and a 5G module.

[0027] In one embodiment, step S110 described above may include steps S111 to S113.

[0028] S111. Use a Hall sensor to read the initial position of the gimbal motor and check if there is any jamming in the gimbal harmonic reducer. S112. Evaluate the signal strength of the 5G module of the gimbal by sending a test message; S113. Using the static six-sided method, the gimbal is fixed in six orthogonal directions on the horizontal plane, and data from the accelerometers and gyroscopes of each axis are collected to compensate for zero bias and scaling factor errors.

[0029] First, the initial position of the motor is read using a Hall sensor, and the presence of any jamming in the harmonic reducer is checked to ensure the motor is functioning correctly. Second, the signal strength of the 5G module is confirmed by sending test messages (such as specific handshake signals on the CAN bus), thereby verifying the connectivity of the communication link. Finally, the gimbal is fixed sequentially in six orthogonal directions on the horizontal plane using a static six-sided method, and data from the accelerometers and gyroscopes of each axis are collected to compensate for zero bias and scaling factor errors, thus completing the calibration of the inertial sensor.

[0030] Before starting the motor, the output signal of the Hall sensor is first read to determine the initial position of the motor. The initial angle of the motor is calculated based on the changes in the Hall signal. Then, the motor is started without load, and its current value is monitored in real time. The no-load current of the motor is recorded and ensured to be below a set threshold (e.g., less than 0.5A). If the no-load current of the motor exceeds this threshold, it indicates that there may be a jamming problem in the harmonic reducer. In addition, the signal strength of the 5G module is confirmed to be above -90dBm by sending a specific test message (e.g., the 0xAA 0x55 handshake signal on the CAN bus) to ensure the smooth operation of the communication link.

[0031] When calibrating the inertial sensor, a static six-sided method is used. This involves fixing the gimbal to a horizontal platform and placing it sequentially in six orthogonal directions (+X, -X, +Y, -Y, +Z, -Z). The gimbal remains stationary in each direction while collecting output data from the accelerometer and gyroscope. To ensure data stability and reliability, a sufficient number of data samples need to be collected in each direction.

[0032] For accelerometer calibration, the operation must be performed in a stationary state, at which point the accelerometer output should equal the component of gravitational acceleration. The average accelerometer output in each direction is calculated as the gravitational acceleration component in that direction. The zero bias and scaling factor of the accelerometer are calculated using linear regression or least squares. For gyroscope calibration, the average gyroscope output in each direction is calculated as the zero bias, and the scaling factor is calculated by comparing the gyroscope outputs in different directions.

[0033] After completing the above steps, the outputs of the accelerometer and gyroscope are compensated based on the calculated zero bias and scaling factor. To verify the validity of the calibration results, the gimbal is repositioned in any direction, the compensated sensor data is collected, and these data are checked to see if they meet expectations, thus ensuring the accuracy of the calibration results.

[0034] By performing the above steps, the inertial sensor can be effectively calibrated, thereby improving the accuracy and reliability of the attitude control system.

[0035] S120: Acquire conductor motion data and gimbal operating parameters collected in real time by multiple sensors to obtain multimodal data.

[0036] In this embodiment, multimodal data refers to data collected by different types of sensors (such as global shutter CMOS cameras, three-axis accelerometers, three-axis gyroscopes, etc.), including but not limited to the motion data of the conductor and the operating parameters of the gimbal. This data can be used to accurately analyze the positional changes and dynamic behavior of the conductor, as well as the working status and performance indicators of the gimbal.

[0037] In one embodiment, step S120 described above may include steps S121 to S122.

[0038] S121. Capture and identify the edge features of the conductor using a global shutter CMOS camera and a target detection model, and calculate pixel displacement using the Lucas-Kanade optical flow method to obtain conductor motion data.

[0039] In this embodiment, conductor motion data refers to information about the changes in the position of the conductor obtained by processing the conductor image, including but not limited to the conductor's velocity, acceleration, and trajectory.

[0040] In one embodiment, step S121 described above may include steps S1211 to S1216.

[0041] S1211. Select and fix the global shutter CMOS camera according to the measurement requirements, and adjust parameters such as exposure time and gain to ensure image quality.

[0042] S1212. Use the global shutter CMOS camera to capture the image of the conductor in real time and determine the position and size of the conductor region through the target detection model to obtain the ROI of the conductor.

[0043] In this embodiment, the Region of Interest (ROI) refers to the key region in the image that contains the conductor. This region forms the basis for subsequent feature point detection and tracking.

[0044] S1213. Apply a corner detection algorithm within the ROI of the conductor to identify key feature points and obtain the coordinates of the feature points.

[0045] In this embodiment, feature point coordinates refer to the specific location information of key feature points detected within the ROI (Region of Interest). These feature points are used for subsequent tracking and displacement calculation.

[0046] S1214. Select two consecutive frames of images, use the coordinates of the feature points in the first frame as the starting point, and based on the position of the feature points in the previous frame, continuously use the Lucas-Kanade optical flow method to track the new position of the feature points in each subsequent frame to obtain the position of the feature points between adjacent frames.

[0047] In this embodiment, the position of feature points between adjacent frames refers to the change in the position of feature points in two adjacent frames of an image in a time series. This step is the basis for calculating the conductor motion data.

[0048] S1215. By comparing the changes in the position of feature points between adjacent frames, the displacement of feature points in the horizontal and vertical directions is calculated to obtain displacement data.

[0049] In this embodiment, displacement data refers to the distance that a feature point moves along the horizontal and vertical directions on the image plane. These data reflect the actual movement of the conductor.

[0050] S1216. After removing noise from the displacement data and its timestamp data using filtering technology, the actual motion parameters of the conductor are calculated based on the camera parameters, and its motion trajectory and state are analyzed to obtain the conductor motion data.

[0051] In one embodiment, step S1216 described above may include steps S12161 to S12164.

[0052] S12161. Record and save the displacement data and its corresponding timestamp information in a text file or database; S12162. Apply mean filtering, median filtering or Kalman filtering to process the displacement data to remove existing noise interference and obtain processed displacement data. S12163. Based on the specific parameters of the camera, use the processed displacement data to calculate the actual motion parameters of the conductor in physical space; S12164. Analyze the changes of the processed displacement data over time, determine the trajectory and motion state of the conductor, and generate conductor motion data in combination with the actual motion parameters.

[0053] S122. By collecting data from the three-axis accelerometer and the three-axis gyroscope, and using a zero-bias-temperature lookup table for temperature drift compensation, and applying a Butterworth low-pass filter for vibration filtering, the operating parameters of the gimbal are obtained.

[0054] In this embodiment, gimbal operating parameters refer to data about the gimbal's motion state collected by sensors (such as a three-axis accelerometer and a three-axis gyroscope), including but not limited to acceleration and angular velocity values ​​along three axes. This data, after processing, can provide important information about the gimbal's attitude, position changes, and stability.

[0055] In one embodiment, step S122 described above may include steps S1221 to S1224.

[0056] S1221. Real-time acquisition of acceleration and angular velocity values ​​of the three-axis accelerometer and three-axis gyroscope along the three axes of the gimbal to obtain the acquired data.

[0057] In this embodiment, the collected data refers to the raw measurement values ​​read in real time from the three-axis accelerometer and the three-axis gyroscope. These values ​​reflect the changes in acceleration and rotation speed experienced by the gimbal in the X, Y, and Z axes.

[0058] S1222. Synchronously record the temperature of the environment where the device is located to obtain the current ambient temperature.

[0059] This step ensures that the ambient temperature conditions during gimbal operation can be accurately measured and recorded. This is crucial for subsequent temperature drift compensation, as temperature changes can alter the zero bias of the sensor output, thus affecting measurement accuracy.

[0060] S1223. By measuring and recording the zero bias values ​​of the triaxial accelerometer and triaxial gyroscope at different constant temperatures, a lookup table is created to show the relationship between zero bias and temperature.

[0061] In this embodiment, the lookup table is a data structure containing the zero-bias values ​​of the triaxial accelerometer and triaxial gyroscope under different temperature conditions. This lookup table is built by measuring the zero bias of the sensors at a series of known temperature points, and its purpose is to quickly find the corresponding zero-bias compensation value based on the current operating temperature during actual operation.

[0062] S1224. Based on the current ambient temperature, the collected data is corrected using the corresponding zero-bias compensation value in the lookup table to obtain the gimbal operating parameters.

[0063] In this step, the corresponding zero-bias compensation value is first retrieved from the lookup table created in step S1223 based on the current ambient temperature obtained in step S1222. Then, this compensation value is used to correct the raw sensor data collected in step S1221, eliminating zero-bias errors caused by temperature variations. Finally, a Butterworth low-pass filter is applied to reduce high-frequency vibration noise in the signal, thereby obtaining more stable and accurate gimbal operating parameters. The processed data more accurately reflects the true motion state of the gimbal, improving the reliability and performance of the entire system.

[0064] In summary, the above steps not only effectively compensate for sensor bias errors caused by temperature changes, but also remove unnecessary noise interference through filtering technology, ultimately achieving accurate acquisition and analysis of gimbal operating parameters.

[0065] For step S120, the main objective is to achieve comprehensive monitoring and analysis of the gimbal's operational status by integrating multiple sensor technologies. Specifically, this process includes the collection and processing of conductor motion data and operational parameter data. To achieve this goal, multiple sensors, such as a global shutter CMOS camera, a three-axis accelerometer, and a three-axis gyroscope, are employed, combined with advanced algorithms and technologies to ensure the accuracy and reliability of the data.

[0066] First, select a global shutter CMOS camera with appropriate resolution, frame rate, and sensitivity based on the actual measurement requirements. This type of camera ensures that the entire image is captured simultaneously, avoiding motion blur caused by rapid object movement. During installation, the camera must be fixed in a stable position to ensure that its field of view completely covers the moving area of ​​the conductor. Simultaneously, the camera's exposure time and gain parameters need to be adjusted according to the specific ambient lighting conditions to obtain optimal image quality.

[0067] Real-time image acquisition of the conductor was performed using a global shutter CMOS camera, and these images were then fed into a pre-trained object detection model. This model automatically identified and located the region of interest (ROI) of the conductor in the image, outputting its position and size information. Next, within the detected ROI, a corner detection algorithm was used to extract feature points on the conductor's edges, preparing for subsequent optical flow calculations.

[0068] Based on the Lucas-Kanade optical flow method, the movement of selected feature points between consecutive frames can be tracked. First, initial feature points are selected from two adjacent image frames. Then, the optical flow method is used to calculate the corresponding positions of these points in the next frame. For each image frame, the feature points tracked in the previous frame are used as the starting points to continue tracking until all frames have been processed. Finally, by comparing the coordinate changes of feature points across different frames, the pixel displacement of each feature point is calculated, thus obtaining the actual trajectory of the conductor.

[0069] Record the pixel displacement data and their corresponding timestamps obtained in the above steps. You can choose a text file or a database as the storage medium. To improve data accuracy, the raw data usually needs to be filtered. Common methods include mean filtering, median filtering, and Kalman filtering. By denoising the pixel displacement data, physical parameters such as the velocity and acceleration of the conductor can be calculated more accurately.

[0070] For gimbal operating parameters, specialized data acquisition equipment is used to read the output signals of the three-axis accelerometer and three-axis gyroscope in real time, including acceleration and angular velocity values ​​in three directions. Simultaneously, the current ambient temperature also needs to be recorded for subsequent temperature drift compensation.

[0071] To compensate for sensor bias errors caused by temperature variations, the bias values ​​of the triaxial accelerometer and triaxial gyroscope need to be measured multiple times under different constant temperature conditions. Specifically, the equipment is placed in a constant temperature chamber, the temperature is gradually changed, and the corresponding bias data is recorded after the system stabilizes at each set temperature. This data is then compiled into a lookup table for subsequent temperature drift compensation.

[0072] To reduce the impact of vibration noise on sensor output, a Butterworth low-pass filter can be used to process the acquired data. When designing the filter, the appropriate cutoff frequency and order must be determined based on the characteristics of the accelerometer and gyroscope signals and the vibration frequency range. The filtered signal is more stable, which helps improve the working accuracy and stability of the attitude control system.

[0073] In summary, by comprehensively utilizing multiple sensors, advanced image processing technologies, and filtering algorithms, the S120 not only achieves efficient monitoring of the pan-tilt-zoom (PTZ) operating status but also significantly improves the overall system performance and reliability. This method effectively addresses many limitations of traditional monitoring methods, providing strong technical support for high-precision control in complex environments.

[0074] S130. The multimodal data is fused together, and the current attitude information of the gimbal is calculated.

[0075] In this embodiment, attitude information refers to the current pitch, roll, and yaw angles of the gimbal, calculated by fusing multimodal data (including data from gyroscopes, accelerometers, and magnetometers) and using an improved Mahony complementary filtering algorithm.

[0076] Specifically, based on the multimodal data, the pitch angle, roll angle and yaw angle of the device are calculated and compensated in real time using the improved Mahony complementary filtering algorithm through the quaternion dynamics model to obtain the current attitude information of the gimbal. The formula for the improved Mahony complementary filtering algorithm is as follows: q is a quaternion, ω is the angular velocity of the gyroscope's three axes, and a measured For accelerometer measurements, a gravity β represents the projection along the direction of gravity, and β is the adaptive filtering coefficient.

[0077] Accurate attitude estimation is crucial in modern navigation and control systems. The goal of this step is to achieve high-precision attitude calculation by fusing data from inertial measurement units (IMUs), including gyroscopes, accelerometers, and magnetometers. Specifically, this process relies on an improved Mahony complementary filtering algorithm to ensure highly accurate attitude estimation even in complex or dynamically changing environments.

[0078] Inertial Measurement Unit (IMU) and Attitude Estimation Fundamentals

[0079] An IMU typically consists of three parts: a gyroscope, an accelerometer, and a magnetometer. Their respective functions are as follows: Gyroscope: Used to measure the angular velocity of an object rotating about three orthogonal axes.

[0080] Accelerometer: It can sense the acceleration of an object along three orthogonal directions. In particular, it can be used to determine the orientation of an object relative to the ground.

[0081] Magnetometer: Similar to an electronic compass, it senses the direction of the Earth's magnetic field, thus helping to determine the yaw angle of an object.

[0082] The combination of these sensors provides a rich source of information, but each sensor has its limitations and sources of error. For example, gyroscopes are prone to drift, while accelerometers and magnetometers can be affected by external interference. Therefore, how to effectively fuse this data has become a key issue.

[0083] The improved Mahony complementary filter algorithm is a powerful tool for combining data from different types of sensors to improve the accuracy of attitude estimation. The core of this algorithm lies in using a quaternion dynamics model to update the attitude in real time and employing adaptive filter coefficients β to dynamically adjust the compensation level, reducing the impact of gyroscope drift.

[0084] Quaternions provide a concise and singular-free way to represent rotations in three-dimensional space. Compared to Euler angles, quaternions avoid the gimbal lock problem and are more suitable for numerical calculations. In this embodiment, a quaternion dynamics model is used to update pitch, roll, and yaw angles in real time.

[0085] To further improve the accuracy of attitude estimation, an improved Mahony complementary filtering algorithm was used to compensate the original data. The specific steps include: Based on the readings from the accelerometer and magnetometer, the object's attitude was initially estimated.

[0086] An adaptive filter coefficient β is introduced, and the compensation intensity is dynamically adjusted by comparing the gravity component measured by the accelerometer with the theoretical gravity direction projection, effectively suppressing gyroscope drift.

[0087] As new data arrives, the above process is repeated continuously, making attitude estimation more accurate and stable.

[0088] Here, β is used as an adaptive filtering coefficient, which is dynamically adjusted according to the actual situation to achieve the best compensation effect.

[0089] By fusing data from multiple sensors in the IMU and employing an improved Mahony complementary filtering algorithm, high-precision attitude estimation can be achieved under complex environmental conditions. This method not only improves the robustness of the system but also enhances the reliability and stability of attitude control, which is of great significance for drones, robots, and other applications requiring precise positioning and orientation.

[0090] S140. Based on the attitude information, formulate a dynamic adjustment strategy to optimize the gimbal position and speed.

[0091] In this embodiment, the dynamic adjustment strategy refers to calculating the error between the target angle and the current angle based on attitude information, determining the desired angular velocity based on this error, and simultaneously using motor encoder feedback to precisely adjust the current output to optimize the gimbal position and speed. Furthermore, by introducing a fuzzy logic compensation mechanism, the parameters (Kp, Ki, Kd) of the PID controller are adaptively adjusted according to the input angle error and its rate of change, thereby improving the system's performance in nonlinear and uncertain environments.

[0092] Specifically, the error between the target angle and the current angle is calculated based on the attitude information to determine the desired angular velocity; the current output is adjusted according to the motor encoder feedback; the PID parameters are adaptively adjusted using a fuzzy rule base by utilizing the input angle error and rate of change; and the adjusted PID parameters are applied to regulate the motor drive current. The dynamic control strategy employs dual closed-loop PID control combined with fuzzy logic compensation, wherein the fuzzy logic achieves precise and adaptive control of the motor by adjusting the PID parameters.

[0093] Specifically, the dynamic adjustment strategy includes the following steps: First, based on the attitude information, the error between the target angle and the current angle is calculated. This error is used to determine the desired angular velocity, which serves as the target value for position loop control.

[0094] For speed loop control, the feedback data from the motor encoder is used to adjust the current output of the drive motor in real time to ensure a fast response and accurate achievement of the expected speed setpoint.

[0095] By using a fuzzy logic controller, the adjustment amounts of PID parameters (Kp, Ki, Kd) are obtained by querying a predefined fuzzy rule base based on the angle error and the rate of change of error. This method enables the control system to automatically optimize its response characteristics under different operating conditions, such as speeding up response, reducing steady-state error, or reducing overshoot.

[0096] Finally, the PID parameter adjustment obtained through fuzzy logic is applied to the actual PID controller to further adjust the motor drive current, thereby achieving precise and adaptive control of the gimbal position and speed.

[0097] The entire process employs a dual-closed-loop PID control combined with fuzzy logic compensation, which not only improves the system's dynamic performance and robustness but also effectively copes with complex operating environments and changing working conditions. This integrated control strategy aims to ensure high precision and stability of the gimbal when performing precise movements.

[0098] In this embodiment, a comprehensive method combining dual-closed-loop PID control and fuzzy logic compensation is employed to generate a dynamic control strategy for the gimbal based on the attitude calculation results. This combination not only improves the dynamic response performance and robustness of the system but also effectively addresses the challenges posed by nonlinear and uncertain environments.

[0099] Firstly, in a dual-loop PID control system, the position loop calculates the desired angular velocity based on the target angle error, while the speed loop uses data from the motor encoder to adjust the current output to ensure precise speed control. Specifically, the position loop focuses on converting the difference between the current angle and the target angle into the desired angular velocity; while the speed loop monitors the actual execution and adjusts the current accordingly to achieve precise control of the motor speed.

[0100] Secondly, to further enhance system performance, a fuzzy logic compensation strategy is introduced. This strategy primarily analyzes the angle error e(t) and its rate of change de(t) to adaptively adjust the PID controller parameters (Kp, Ki, Kd). The fuzzy logic system inputs are the angle error and its rate of change, and the outputs are the PID parameter adjustments ΔKp, ΔKi, and ΔKd. Based on these input variables, a series of fuzzy rule bases are defined to guide the adjustment of the PID parameters. For example: When the angle error e(t) is large and the rate of change of the error is also large, the proportional coefficient Kp is increased to speed up the system response.

[0101] If the angle error e(t) is small and the rate of change of the error is also small, then appropriately increasing the integral coefficient KiKi can help reduce the steady-state error.

[0102] If the angle error e(t) is large but the rate of change of the error is small, increasing the differential coefficient Kd can help reduce the occurrence of overshoot.

[0103] The fuzzy inference engine calculates the corresponding fuzzy values ​​based on the input variables and the preset fuzzy rule base, and converts them into specific PID parameter adjustment quantities through a defuzzification process (such as the centroid method), thereby realizing real-time optimization and adjustment of PID parameters.

[0104] In summary, this embodiment achieves efficient and precise control of the gimbal position and speed by integrating dual closed-loop PID control and fuzzy logic compensation technology, significantly improving the overall performance and adaptability of the system.

[0105] S150. Drive the actuator according to the dynamic adjustment strategy to precisely adjust the gimbal attitude.

[0106] In this embodiment, the actuator adopts a dual-gear transmission system. The driving gear is driven by a stepper motor and meshes with the driven gear to form a 4:1 transmission ratio. According to the dynamic model, the output torque is determined. Combined with the PID controller, the motor speed is adjusted in real time so that the horizontal rotation angle of the gimbal reaches the target positioning accuracy.

[0107] Specifically, the actuator employs a dual-gear transmission system, where the driving gear is driven by a stepper motor and forms a 4:1 transmission ratio with the driven gear to output torque. A PID controller adjusts the motor speed in real time to ensure the gimbal's horizontal rotation angle achieves the target positioning accuracy. For the pitch axis, a worm gear structure (30:1 transmission ratio) paired with a 17-bit resolution, ±5 arcsecond accuracy absolute encoder is used. The azimuth axis uses crossed roller bearings (stiffness coefficient 500 N·m / rad), supporting 360° continuous rotation. Furthermore, dynamic limit protection is implemented, preventing excessive movement through soft angle limits (e.g., pitch axis -10° to +45°) and an S-curve deceleration mechanism triggered when exceeding limits. A photoelectric switch with a response time of 1ms is installed to cut off the motor power at mechanical limit positions. Additionally, rubber damping pads with a damping coefficient of 0.15 are installed on the gimbal base to absorb high-frequency vibrations. Residual vibrations are detected by an accelerometer, and a reverse acceleration command is injected.

[0108] S160. Obtain multimodal data of the gimbal after attitude adjustment and optimize the gimbal operating parameters.

[0109] In this embodiment, the gimbal's operating parameters are fed back and optimized based on real-time acquired multimodal data. This includes using sensors such as gyroscopes and accelerometers to acquire attitude information such as the gimbal's pitch, roll, and yaw angles, which directly reflect the gimbal's spatial position and orientation. For example, angular velocities in three axes are measured using gyroscopes, and angle changes are obtained using an integral algorithm. Simultaneously, parameters such as motor current, voltage, and speed are collected to reflect whether the motor load and movement speed meet expectations. Image or video information captured by cameras is also used to indirectly evaluate the effectiveness of gimbal attitude adjustment, such as analyzing the position, size, and trajectory of target objects in the images.

[0110] Set reasonable threshold ranges for various parameters and monitor the gimbal's operating parameters in real time. Once a parameter exceeds its threshold range, it is considered an anomaly, and the system will issue an alarm and record the relevant information. Machine learning algorithms, such as Isolation Forest and Support Vector Machine, are used to detect anomalies in the gimbal's operating data, identifying data that deviates from the normal pattern. Based on the anomaly detection results, a feedback signal is generated containing information such as the name, value, and occurrence time of the abnormal parameter. For example, "Motor speed abnormal, current speed is X rpm, occurrence time is YYYY-MM-DD HH:MM:SS".

[0111] This embodiment's method proceeds from initializing hardware self-tests and calibrating operating parameters, to acquiring real-time data through multiple sensors, then fusing multimodal data for attitude calculation, and finally generating a dynamic control strategy based on the calculation results. Ultimately, the gimbal actuator is adjusted according to this strategy, and real-time feedback and optimization ensure the stability and accuracy of the gimbal. This method not only reduces monitoring errors caused by attitude deviations and improves the accuracy of overhead line galloping monitoring, but also effectively copes with complex weather conditions, enhancing the system's stability and reliability.

[0112] By precisely controlling the attitude, the monitoring pan-tilt unit is kept in a stable state, reducing monitoring errors caused by attitude deviations and improving the accuracy of overhead line galloping monitoring. This invention monitors attitude data in real time and makes feedback adjustments, effectively coping with complex weather conditions and improving the system's stability and reliability.

[0113] The aforementioned attitude control method for the pan-tilt unit (PTZ) used for monitoring overhead line galloping ensures the optimal initial state of the PTG system through initialization settings, hardware self-testing, and operational parameter calibration. It utilizes multiple sensors to collect conductor motion data and PTG operating parameters in real time, obtaining comprehensive multimodal data. Data fusion is then used to calculate the PTG's precise current attitude information. Based on this information, a dynamic adjustment strategy is formulated to optimize the PTG's position and speed, driving the actuators to perform precise attitude adjustments. Subsequently, the adjusted multimodal data is acquired again to optimize the PTG operating parameters, forming a closed-loop feedback mechanism. This method not only solves the problems of data distortion and limited monitoring range caused by insufficient attitude control in traditional PTGs but also enhances the system's anti-interference capability in harsh environments, thereby significantly improving the accuracy and reliability of monitoring data and achieving more stable and accurate monitoring of overhead line galloping.

[0114] Figure 3 This is a schematic block diagram of an attitude control system 300 for an aiming pan-tilt unit used for monitoring galloping of overhead power lines, provided in an embodiment of the present invention. Figure 3 As shown, corresponding to the above-described attitude control method for an aiming pan-tilt unit used for monitoring galloping of overhead lines, the present invention also provides an attitude control system 300 for such an aiming pan-tilt unit. This attitude control system 300 includes a unit for executing the aforementioned attitude control method for the aiming pan-tilt unit used for monitoring galloping of overhead lines, and the system can be configured in a server. Specifically, please refer to... Figure 3 The attitude control system 300 for the aiming PTZ for monitoring galloping of overhead lines includes a PTZ initialization unit 301, a multimodal data acquisition unit 302, a calculation unit 303, a strategy formulation unit 304, a drive unit 305, and an optimization unit 306.

[0115] The gimbal initialization unit 301 is used to initialize the gimbal, perform hardware self-tests, and calibrate operating parameters. The gimbal includes a motor, a harmonic reducer, and a 5G module. The multimodal data acquisition unit 302 is used to acquire real-time conductor motion data and gimbal operating parameters collected by various sensors to obtain multimodal data. The calculation unit 303 is used to fuse the multimodal data and calculate the current attitude information of the gimbal. The strategy formulation unit 304 is used to formulate a dynamic adjustment strategy based on the attitude information to optimize the gimbal position and speed. The drive unit 305 is used to drive the actuator according to the dynamic adjustment strategy to precisely adjust the gimbal attitude. The optimization unit 306 is used to acquire the multimodal data of the gimbal after attitude adjustment and optimize the gimbal operating parameters.

[0116] In one embodiment, the gimbal initialization unit 301 is further configured to: use a Hall sensor to read the initial position of the gimbal motor and check whether there is any jamming in the gimbal harmonic reducer; evaluate the signal strength of the 5G module of the gimbal by sending a test message; and use a static six-sided method to fix the gimbal in six orthogonal directions on the horizontal plane and collect data from the accelerometers and gyroscopes of each axis to compensate for zero bias and scaling factor errors.

[0117] In one embodiment, the multimodal data acquisition unit 302 is further configured to capture and identify the edge features of the conductor using a global shutter CMOS camera and a target detection model, calculate pixel displacement using the Lucas-Kanade optical flow method to obtain conductor motion data, and obtain gimbal operating parameters by collecting data from a three-axis accelerometer and a three-axis gyroscope, using a zero bias-temperature lookup table for temperature drift compensation, and applying a Butterworth low-pass filter for vibration filtering.

[0118] In one embodiment, the multimodal data acquisition unit 302 is further configured to select and fix a global shutter CMOS camera according to measurement requirements, and adjust parameters such as exposure time and gain to ensure image quality; use the global shutter CMOS camera to capture conductor images in real time and determine the position and size of the conductor region through a target detection model to obtain the conductor ROI; apply a corner detection algorithm to identify key feature points within the conductor ROI to obtain feature point coordinates; select two consecutive frames of images, using the feature point coordinates in the first frame as the starting point, and based on the position of the feature points in the previous frame, continuously apply the Lucas-Kanade optical flow method to track the new position of the feature points in each subsequent frame to obtain the position of the feature points between adjacent frames; calculate the displacement of the feature points in the horizontal and vertical directions by comparing the changes in the position of the feature points between adjacent frames to obtain displacement data; after removing noise from the displacement data and its timestamp data through filtering technology, calculate the actual motion parameters of the conductor based on the camera parameters, and analyze its motion trajectory and state to obtain conductor motion data.

[0119] In one embodiment, the multimodal data acquisition unit 302 is further configured to record and save the displacement data and its corresponding timestamp information in a text file or database; apply mean filtering, median filtering or Kalman filtering methods to process the displacement data to remove existing noise interference to obtain processed displacement data; combine the specific parameters of the camera to calculate the actual motion parameters of the conductor in physical space using the processed displacement data; analyze the changes of the processed displacement data over time to determine the trajectory and motion state of the conductor, and generate conductor motion data in combination with the actual motion parameters.

[0120] In one embodiment, the multimodal data acquisition unit 302 is further configured to acquire in real time the acceleration and angular velocity values ​​of the triaxial accelerometer and triaxial gyroscope along the three axes of the gimbal to obtain the acquired data; synchronously record the temperature of the environment in which the device is located to obtain the current ambient temperature; create a lookup table of the relationship between zero bias and temperature by measuring and recording the zero bias values ​​of the triaxial accelerometer and triaxial gyroscope at different constant temperatures; and correct the acquired data using the corresponding zero bias compensation value in the lookup table according to the current ambient temperature to obtain the gimbal operating parameters.

[0121] In one embodiment, the computing unit 303 is further configured to calculate and compensate the pitch angle, roll angle and yaw angle of the device in real time using a quaternion dynamics model based on the multimodal data and an improved Mahony complementary filtering algorithm, so as to obtain the current attitude information of the gimbal. The formula for the improved Mahony complementary filtering algorithm is as follows: q is a quaternion, ω is the angular velocity of the gyroscope's three axes, and a measured For accelerometer measurements, a gravity β represents the projection along the direction of gravity, and β is the adaptive filtering coefficient.

[0122] In one embodiment, the strategy formulation unit 304 is further configured to calculate the error between the target angle and the current angle based on the attitude information to determine the desired angular velocity; adjust the current output according to the motor encoder feedback; adaptively adjust the PID parameters through a fuzzy rule base using the input angle error and rate of change; and apply the adjusted PID parameters to regulate the motor drive current. The dynamic control strategy employs dual closed-loop PID control combined with fuzzy logic compensation, wherein the fuzzy logic achieves precise and adaptive control of the motor by adjusting the PID parameters.

[0123] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the attitude control system 300 and each unit of the above-mentioned overhead line galloping monitoring aiming gimbal can be referred to the corresponding description in the foregoing method embodiments. For the sake of convenience and brevity, it will not be repeated here.

[0124] The attitude control system 300 for the aiming pan-tilt unit used for monitoring galloping of overhead lines described above can be implemented as a computer program, which can be used in, for example... Figure 4 It runs on the computer device shown.

[0125] Please see Figure 4 , Figure 4 This is a schematic block diagram of a computer device provided in an embodiment of this application. The computer device 500 can be a server, wherein the server can be a standalone server or a server cluster composed of multiple servers.

[0126] See Figure 4 The computer device 500 includes a processor 502, a memory, and a network interface 505 connected via a system bus 501. The memory may include a non-volatile storage medium 503 and internal memory 504.

[0127] The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions that, when executed, cause the processor 502 to perform an attitude control method for an overhead line galloping monitoring aiming gimbal.

[0128] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0129] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute an attitude control method for an overhead line galloping monitoring aiming gimbal.

[0130] This network interface 505 is used for network communication with other devices. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 500 to which the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0131] The processor 502 is used to run a computer program 5032 stored in the memory to perform the following steps: The process involves initializing the gimbal, performing hardware self-tests, and calibrating its operating parameters. The gimbal includes a motor, a harmonic reducer, and a 5G module. Multimodal data is obtained by acquiring real-time conductor motion data and gimbal operating parameters from various sensors. This multimodal data is then fused to calculate the gimbal's current attitude information. A dynamic adjustment strategy is developed based on this attitude information to optimize the gimbal's position and speed. The dynamic adjustment strategy drives the actuators to precisely adjust the gimbal's attitude. Finally, multimodal data of the gimbal's operation after attitude adjustment is acquired, and the gimbal's operating parameters are optimized.

[0132] The actuator adopts a dual-gear transmission system. The driving gear is driven by a stepper motor and meshes with the driven gear to form a 4:1 transmission ratio. According to the dynamic model, the output torque is determined. Combined with the PID controller, the motor speed is adjusted in real time so that the horizontal rotation angle of the gimbal reaches the target positioning accuracy.

[0133] In one embodiment, when implementing the steps of initializing the gimbal, performing hardware self-test, and calibrating operating parameters, the processor 502 specifically implements the following steps: The initial position of the gimbal motor is read using a Hall sensor, and the gimbal harmonic reducer is checked for jamming. The signal strength of the 5G module of the gimbal is evaluated by sending test messages. The static six-sided method is used to fix the gimbal in six orthogonal directions on the horizontal plane, and data from the accelerometers and gyroscopes of each axis are collected to compensate for zero bias and scaling factor errors.

[0134] In one embodiment, when the processor 502 implements the step of acquiring the conductor motion data and gimbal operating parameters collected in real time by multiple sensors to obtain multimodal data, the specific steps are as follows: The edge features of the conductor are captured and identified by a global shutter CMOS camera and a target detection model. The pixel displacement is calculated using the Lucas-Kanade optical flow method to obtain the conductor motion data. Data from a three-axis accelerometer and a three-axis gyroscope are collected, and temperature drift compensation is performed using a zero bias-temperature lookup table. At the same time, a Butterworth low-pass filter is applied for vibration filtering to obtain the gimbal operating parameters.

[0135] In one embodiment, when the processor 502 implements the step of capturing and identifying the edge features of the conductor using a global shutter CMOS camera and a target detection model, and calculating pixel displacement using the Lucas-Kanade optical flow method to obtain conductor motion data, the specific steps are as follows: A global shutter CMOS camera is selected and fixed according to measurement requirements, and parameters such as exposure time and gain are adjusted to ensure image quality. The global shutter CMOS camera is used to capture images of the conductor in real time, and the position and size of the conductor region are determined by a target detection model to obtain the conductor ROI. A corner detection algorithm is applied within the conductor ROI to identify key feature points to obtain feature point coordinates. Two consecutive frames are selected, and the feature point coordinates in the first frame are used as the starting point. Based on the position of the feature points in the previous frame, the Lucas-Kanade optical flow method is continuously used to track the new position of the feature points in each subsequent frame to obtain the position of the feature points between adjacent frames. By comparing the changes in the position of the feature points between adjacent frames, the displacement of the feature points in the horizontal and vertical directions is calculated to obtain displacement data. After removing noise from the displacement data and its timestamp data using filtering techniques, the actual motion parameters of the conductor are calculated based on the camera parameters, and its motion trajectory and state are analyzed to obtain conductor motion data.

[0136] In one embodiment, when the processor 502 performs the step of removing noise from the displacement data and its timestamp data using filtering techniques, calculating the actual motion parameters of the conductor based on the camera parameters, and analyzing its motion trajectory and state to obtain the conductor motion data, the specific implementation is as follows: The displacement data and its corresponding timestamp information are recorded and saved in a text file or database; mean filtering, median filtering, or Kalman filtering methods are applied to process the displacement data to remove existing noise interference, so as to obtain processed displacement data; combined with the specific parameters of the camera, the actual motion parameters of the conductor in physical space are calculated using the processed displacement data; the changes of the processed displacement data over time are analyzed to determine the trajectory and motion state of the conductor, and conductor motion data is generated by combining the actual motion parameters.

[0137] In one embodiment, when the processor 502 implements the step of obtaining gimbal operating parameters by collecting data from a triaxial accelerometer and a triaxial gyroscope, using a zero-bias-temperature lookup table for temperature drift compensation, and applying a Butterworth low-pass filter for vibration filtering, the specific steps are as follows: The acceleration and angular velocity values ​​of the three-axis accelerometer and three-axis gyroscope on the gimbal are acquired in real time to obtain the collected data; the temperature of the environment in which the device is located is recorded simultaneously to obtain the current ambient temperature; by measuring and recording the zero bias values ​​of the three-axis accelerometer and three-axis gyroscope at different constant temperatures, a lookup table of the relationship between zero bias and temperature is created; based on the current ambient temperature, the collected data is corrected using the corresponding zero bias compensation value in the lookup table to obtain the gimbal operating parameters.

[0138] In one embodiment, when the processor 502 implements the step of fusing the multimodal data and calculating the current attitude information of the gimbal, it specifically implements the following steps: Based on the multimodal data, the pitch angle, roll angle and yaw angle of the device are calculated and compensated in real time using the improved Mahony complementary filtering algorithm through the quaternion dynamics model to obtain the current attitude information of the gimbal. The formula for the improved Mahony complementary filtering algorithm is as follows: q is a quaternion, ω is the angular velocity of the gyroscope's three axes, and a measured For accelerometer measurements, a gravity β represents the projection along the direction of gravity, and β is the adaptive filtering coefficient.

[0139] In one embodiment, when implementing the step of formulating a dynamic adjustment strategy based on the attitude information to optimize the gimbal position and speed, the processor 502 specifically implements the following steps: Based on the attitude information, the error between the target angle and the current angle is calculated to determine the desired angular velocity; the current output is adjusted according to the motor encoder feedback; the PID parameters are adaptively adjusted using a fuzzy rule base by utilizing the input angle error and rate of change; and the adjusted PID parameters are applied to regulate the motor drive current. The dynamic control strategy employs dual closed-loop PID control combined with fuzzy logic compensation, wherein the fuzzy logic achieves precise and adaptive control of the motor by adjusting the PID parameters.

[0140] It should be understood that in the embodiments of this application, the processor 502 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0141] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0142] Therefore, the present invention also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program, wherein when executed by a processor, the computer program causes the processor to perform the following steps: The process involves initializing the gimbal, performing hardware self-tests, and calibrating its operating parameters. The gimbal includes a motor, a harmonic reducer, and a 5G module. Multimodal data is obtained by acquiring real-time conductor motion data and gimbal operating parameters from various sensors. This multimodal data is then fused to calculate the gimbal's current attitude information. A dynamic adjustment strategy is developed based on this attitude information to optimize the gimbal's position and speed. The dynamic adjustment strategy drives the actuators to precisely adjust the gimbal's attitude. Finally, multimodal data of the gimbal's operation after attitude adjustment is acquired, and the gimbal's operating parameters are optimized.

[0143] The actuator adopts a dual-gear transmission system. The driving gear is driven by a stepper motor and meshes with the driven gear to form a 4:1 transmission ratio. According to the dynamic model, the output torque is determined. Combined with the PID controller, the motor speed is adjusted in real time so that the horizontal rotation angle of the gimbal reaches the target positioning accuracy.

[0144] In one embodiment, when the processor executes the computer program to perform the steps of initializing the gimbal, performing hardware self-tests, and calibrating operating parameters, it specifically implements the following steps: The initial position of the gimbal motor is read using a Hall sensor, and the gimbal harmonic reducer is checked for jamming. The signal strength of the 5G module of the gimbal is evaluated by sending test messages. The static six-sided method is used to fix the gimbal in six orthogonal directions on the horizontal plane, and data from the accelerometers and gyroscopes of each axis are collected to compensate for zero bias and scaling factor errors.

[0145] In one embodiment, when the processor executes the computer program to acquire real-time conductor motion data and gimbal operating parameters collected by multiple sensors to obtain multimodal data, the processor specifically implements the following steps: The edge features of the conductor are captured and identified by a global shutter CMOS camera and a target detection model. The pixel displacement is calculated using the Lucas-Kanade optical flow method to obtain the conductor motion data. Data from a three-axis accelerometer and a three-axis gyroscope are collected, and temperature drift compensation is performed using a zero bias-temperature lookup table. At the same time, a Butterworth low-pass filter is applied for vibration filtering to obtain the gimbal operating parameters.

[0146] In one embodiment, when the processor executes the computer program to implement the step of capturing and identifying wire edge features using a global shutter CMOS camera and a target detection model, and calculating pixel displacement using the Lucas-Kanade optical flow method to obtain wire motion data, the processor specifically implements the following steps: A global shutter CMOS camera is selected and fixed according to measurement requirements, and parameters such as exposure time and gain are adjusted to ensure image quality. The global shutter CMOS camera is used to capture images of the conductor in real time, and the position and size of the conductor region are determined by a target detection model to obtain the conductor ROI. A corner detection algorithm is applied within the conductor ROI to identify key feature points to obtain feature point coordinates. Two consecutive frames are selected, and the feature point coordinates in the first frame are used as the starting point. Based on the position of the feature points in the previous frame, the Lucas-Kanade optical flow method is continuously used to track the new position of the feature points in each subsequent frame to obtain the position of the feature points between adjacent frames. By comparing the changes in the position of the feature points between adjacent frames, the displacement of the feature points in the horizontal and vertical directions is calculated to obtain displacement data. After removing noise from the displacement data and its timestamp data using filtering techniques, the actual motion parameters of the conductor are calculated based on the camera parameters, and its motion trajectory and state are analyzed to obtain conductor motion data.

[0147] In one embodiment, when the processor executes the computer program to perform the steps of removing noise from the displacement data and its timestamp data using filtering techniques, calculating the actual motion parameters of the conductor based on camera parameters, and analyzing its motion trajectory and state to obtain the conductor motion data, the specific implementation is as follows: The displacement data and its corresponding timestamp information are recorded and saved in a text file or database; mean filtering, median filtering, or Kalman filtering methods are applied to process the displacement data to remove existing noise interference, so as to obtain processed displacement data; combined with the specific parameters of the camera, the actual motion parameters of the conductor in physical space are calculated using the processed displacement data; the changes of the processed displacement data over time are analyzed to determine the trajectory and motion state of the conductor, and conductor motion data is generated by combining the actual motion parameters.

[0148] In one embodiment, when the processor executes the computer program to obtain gimbal operating parameters by using data from a triaxial accelerometer and a triaxial gyroscope, performing temperature drift compensation using a zero-bias-temperature lookup table, and applying a Butterworth low-pass filter for vibration filtering, the specific steps are as follows: The acceleration and angular velocity values ​​of the three-axis accelerometer and three-axis gyroscope on the gimbal are acquired in real time to obtain the collected data; the temperature of the environment in which the device is located is recorded simultaneously to obtain the current ambient temperature; by measuring and recording the zero bias values ​​of the three-axis accelerometer and three-axis gyroscope at different constant temperatures, a lookup table of the relationship between zero bias and temperature is created; based on the current ambient temperature, the collected data is corrected using the corresponding zero bias compensation value in the lookup table to obtain the gimbal operating parameters.

[0149] In one embodiment, when the processor executes the computer program to fuse the multimodal data and calculate the current attitude information of the gimbal, it specifically implements the following steps: Based on the multimodal data, the pitch angle, roll angle and yaw angle of the device are calculated and compensated in real time using the improved Mahony complementary filtering algorithm through the quaternion dynamics model to obtain the current attitude information of the gimbal. The formula for the improved Mahony complementary filtering algorithm is as follows: q is a quaternion, ω is the angular velocity of the gyroscope's three axes, and a measured For accelerometer measurements, a gravity β represents the projection along the direction of gravity, and β is the adaptive filtering coefficient.

[0150] In one embodiment, when the processor executes the computer program to implement the step of formulating a dynamic adjustment strategy based on the attitude information to optimize the gimbal position and speed, it specifically implements the following steps: Based on the attitude information, the error between the target angle and the current angle is calculated to determine the desired angular velocity; the current output is adjusted according to the motor encoder feedback; the PID parameters are adaptively adjusted using a fuzzy rule base by utilizing the input angle error and rate of change; and the adjusted PID parameters are applied to regulate the motor drive current. The dynamic control strategy employs dual closed-loop PID control combined with fuzzy logic compensation, wherein the fuzzy logic achieves precise and adaptive control of the motor by adjusting the PID parameters.

[0151] The storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0152] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. 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 implementations should not be considered beyond the scope of this invention.

[0153] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of each unit is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0154] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the system of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0155] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0156] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for attitude control of a sighting gimbal for overhead line galloping monitoring, characterized in that, The method comprises the following steps: initializing the gimbal, performing hardware self-checking, and calibrating running parameters; the gimbal comprises a motor, a harmonic reducer, and a 5G module; collecting wire movement data and gimbal running parameters in real time to obtain multi-modal data; fusing the multi-modal data and calculating the current attitude information of the gimbal; formulating a dynamic adjustment strategy based on the attitude information to optimize the position and speed of the gimbal; driving the actuator according to the dynamic adjustment strategy to accurately adjust the attitude of the gimbal; collecting multi-modal data after adjusting the attitude of the gimbal and optimizing the running parameters of the gimbal.

2. The attitude control method for an overhead line galloping monitoring sighting head according to claim 1, characterized in that, The initialization setting, hardware self-checking, and calibration of the running parameters of the gimbal comprise the following steps: reading the initial position of the gimbal motor using a Hall sensor and checking whether the harmonic reducer of the gimbal has a stuck phenomenon; evaluating the signal strength of the 5G module of the gimbal by sending test messages; using the static six-face method to fix the gimbal in six orthogonal directions on the horizontal plane to collect data of the accelerometer and gyroscope of each axis respectively to compensate for zero offset and scale factor errors.

3. The method of attitude control for an overhead line galloping monitoring sighting head according to claim 1, characterized in that, The collection of wire movement data and gimbal running parameters in real time to obtain multi-modal data comprises the following steps: capturing and recognizing the edge features of the wire using a global shutter CMOS camera and a target detection model, calculating the pixel displacement using the Lucas-Kanade optical flow method, and obtaining the wire movement data; collecting data of a three-axis accelerometer and a three-axis gyroscope, using a zero offset-temperature lookup table for temperature drift compensation, and applying a Butterworth low-pass filter for vibration filtering to obtain the gimbal running parameters.

4. The attitude control method for an overhead line galloping monitoring sighting head according to claim 3, characterized in that, The capturing and recognizing of the edge features of the wire using a global shutter CMOS camera and a target detection model, the calculation of the pixel displacement using the Lucas-Kanade optical flow method, and the obtaining of the wire movement data comprise the following steps: selecting and fixing a global shutter CMOS camera according to the measurement requirements, and adjusting the exposure time and gain parameters to ensure image quality; using the global shutter CMOS camera to capture wire images in real time and determining the position and size of the wire region through a target detection model to obtain a wire ROI; applying a corner detection algorithm to identify key feature points within the wire ROI to obtain feature point coordinates; selecting two consecutive images, using the feature point coordinates in the first image as the starting point, and continuously tracking the new position of the feature points in each subsequent frame based on the position of the feature points in the previous frame to obtain the position of the feature points between adjacent frames; calculating the displacement of the feature points in the horizontal and vertical directions by comparing the changes in the positions of the feature points between adjacent frames to obtain displacement data; after removing noise from the displacement data and its timestamp data through filtering technology, calculating the actual movement parameters of the wire according to the camera parameters, and analyzing the movement trajectory and state to obtain the wire movement data.

5. The method for attitude control of an aiming gimbal for overhead line galloping monitoring according to claim 4, characterized in that, After removing noise from the displacement data and its timestamp data through filtering technology, calculating the actual movement parameters of the wire according to the camera parameters, and analyzing the movement trajectory and state to obtain the wire movement data comprise the following steps: record the displacement data and its corresponding timestamp information in a text file or a database; apply mean filtering, median filtering or Kalman filtering method to process the displacement data to remove existing noise interference to obtain processed displacement data; use the processed displacement data to calculate the actual motion parameters of the wire in the physical space in combination with the specific parameters of the camera; analyze the change of the processed displacement data over time to determine the motion trajectory and motion state of the wire, and generate wire motion data in combination with the actual motion parameters.

6. The method of attitude control for an overhead line galloping monitoring sighting head according to claim 3, characterized in that, The data of the three-axis accelerometer and the three-axis gyroscope are combined, temperature drift compensation is performed using a zero offset-temperature lookup table, and vibration filtering is performed using a Butterworth low-pass filter to obtain gimbal operation parameters, including: Real-time acquisition of acceleration values and angular velocity values of the three-axis accelerometer and the three-axis gyroscope in the three axial directions of the gimbal to obtain collected data; Synchronously record the temperature of the environment in which the device is located to obtain the current environmental temperature; Create a zero offset-temperature relationship lookup table by measuring and recording the zero offset values of the three-axis accelerometer and the three-axis gyroscope at different constant temperatures; According to the current environmental temperature, use the corresponding zero offset compensation value in the lookup table to correct the collected data to obtain the gimbal operation parameters.

7. The method of attitude control for an overhead line galloping monitoring sighting gimbals according to claim 1, characterized in that, The multi-modal data are fused, and the current attitude information of the gimbal is calculated, including: Based on the multi-modal data, the improved Mahony complementary filtering algorithm is used to calculate and compensate the pitch angle, roll angle and yaw angle of the device in real time through a quaternion dynamics model to obtain the current attitude information of the gimbal; The formula of the improved Mahony complementary filter algorithm is: , q is a quaternion, ω is a three-axis angular velocity of a gyroscope, a measured is an accelerometer measurement value, a gravity is a gravity direction projection, and β is an adaptive filter coefficient.

8. The method for attitude control of an aiming gimbal for overhead line galloping monitoring according to claim 1, characterized in that, Based on the attitude information, a dynamic adjustment strategy is formulated to optimize the position and speed of the gimbal, including: Calculate the error between the target angle and the current angle based on the attitude information to determine the expected angular velocity; adjust the current output according to the motor encoder feedback; use the input angle error and rate of change to adaptively adjust the PID parameters through a fuzzy rule base; apply the adjusted PID parameters to adjust the motor drive current; The dynamic control strategy adopts double closed-loop PID control combined with fuzzy logic compensation, wherein the fuzzy logic realizes precise and adaptive control of the motor by adjusting the PID parameters.

9. The method of attitude control for an overhead line galloping monitoring sighting head according to claim 1, characterized in that, The actuator adopts a double gear transmission system, the driving gear is driven by a stepper motor, and the driving gear is engaged with the driven gear to form a 4:1 transmission ratio, according to the dynamics model, the output torque; combine the PID controller to adjust the motor speed in real time, so that the horizontal rotation angle of the gimbal reaches the target positioning accuracy.

10. A system for attitude control of a surveillance gimbals for overhead line galloping monitoring, characterized in that, It includes: The gimbal initialization unit is used for initializing the gimbal, performing hardware self-checking and calibrating the running parameters; The gimbal includes a motor, a harmonic reducer and a 5G module; The multi-modal data acquisition unit is used for acquiring wire motion data and gimbal operation parameters collected by multiple sensors in real time to obtain multi-modal data; The calculation unit is used for fusing the multi-modal data and calculating the current attitude information of the gimbal; The strategy formulation unit is used for formulating a dynamic adjustment strategy based on the attitude information to optimize the position and speed of the gimbal; A driving unit is configured to drive the actuator according to the dynamic adjustment strategy, so as to accurately adjust the gimbal attitude. An optimization unit is configured to acquire multi-modal data after the gimbal attitude is adjusted, and optimize the gimbal operation parameters.

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