Intelligent power transmission line insulator detection system and method

Abstract

The invention discloses an intelligent power transmission line insulator detection system and method, and relates to the field of intelligent power transmission line insulator detection. Power transmission line insulator data acquisition is carried out through hardware equipment, the hardware equipment comprises an unmanned aerial vehicle surveying device carrying a laser radar, a mobile monitoring device, a double-axis tilt angle sensor and a temperature sensor, the hardware equipment compresses and encodes the acquired data, and the data are transmitted to a power transmission line insulator through a TCP / IP standard protocol. Data encryption transmission is carried out on the collected data through a 5G transmission standard, the monitoring center decodes and verifies the data after receiving the data, and the specific position of the fault insulator is determined by combining data information collected by the hardware equipment and a data analysis result. State classification is carried out on the high-voltage power transmission insulator by combining monitoring data and a preset monitoring range threshold value, fault processing is carried out according to a state classification result, and multi-aspect comprehensive monitoring on the power transmission line insulator can be completed.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transmission line insulator detection, and more specifically to an intelligent transmission line insulator detection system and method. Background Art

[0002] With the rapid development of the power industry, transmission line insulators, as a key component of the power system, are crucial for the reliable operation of the power grid. Over long-term use, insulators may be affected by natural factors such as strong winds, lightning, geological disasters, and human factors, leading to tilt and displacement. Monitoring can promptly identify these potential safety hazards, preventing the serious consequences of line breaks and tower collapses, ensuring the stable operation of the power system, and avoiding power supply interruptions caused by insulator problems.

[0003] Transmission line insulators are generally installed on brackets or towers in the wild area. Under special climatic conditions such as high cold, high altitude and heavy rain, the height of the tower may be further increased. At the same time, the terrain in the wild is complex, with undulating terrain and vegetation conditions. In urban areas, there is a large flow of people and a complex environment, which may affect the use of insulators in many ways. The existing monitoring of the installation of transmission line insulators is mostly insulator leakage monitoring. Accurate measurement of the insulator leakage angle can be achieved through a variety of different methods, and the collapse of the insulator can be prevented in time. However, comprehensive monitoring of the insulators in many aspects is not considered. Summary of the Invention

[0004] In response to the shortcomings of the above-mentioned technologies, the present invention discloses an intelligent transmission line insulator detection method, which can use an unmanned aerial vehicle survey device equipped with a laser radar and a mobile monitoring device, a dual-axis tilt sensor and a temperature sensor to complete comprehensive monitoring of transmission line insulators from multiple aspects.

[0005] In order to achieve the above technical effects, the present invention adopts the following technical solutions: An intelligent transmission line insulator detection method, comprising the following steps: Step (1), starting the hardware equipment to collect data on transmission line insulators; The hardware equipment includes an unmanned aerial vehicle survey device and a mobile monitoring device equipped with a laser radar, a dual-axis tilt sensor, and a temperature sensor. The unmanned aerial vehicle survey device equipped with a laser radar includes an unmanned aerial vehicle body, a power system, a laser radar system, a positioning and navigation module, a camera module, a data transmission module, a power management module, and a control module. The control module is connected to the positioning and navigation module, the camera module, and the power management module via a data bus. The output ends of the camera module and the positioning and navigation module are connected to the data transmission module, and the output end of the laser radar system is connected to the input end of the data transmission module. Step (2), transmitting the data collected by the hardware device to the monitoring center; The hardware device compresses and encodes the collected data, uses the TCP / IP standard protocol, and encrypts and transmits the collected data via the 5G transmission standard; Step (3): data processing and analysis; After receiving the data, the monitoring center decodes and verifies the data, calculates the insulator leakage angle through the insulator leakage angle calculation model, and extracts the data features transmitted by the drone survey device equipped with a laser radar through a point cloud algorithm model to obtain detailed three-dimensional information of the insulator and its surrounding environment. The point cloud algorithm model includes a data receiving module, a data processing module, a point cloud segmentation module, a point cloud feature extraction module and a point cloud registration module. The output end of the data receiving module is connected to the input end of the data processing module, the output end of the data processing module is connected to the input end of the point cloud segmentation module, the output end of the point cloud segmentation module is connected to the input end of the point cloud feature extraction module, and the output end of the point cloud feature extraction module is connected to the input end of the point cloud feature extraction module. Step (4), locating and identifying the faulty transmission line insulator; Combining the data information collected by the hardware equipment with the results of data analysis, the specific location of the faulty insulator is determined, and the fault type, including insulator leakage, deformation, breakage, and overheating, is identified by comparing historical data with real-time data; Step (5): Classification after monitoring; The status of high-voltage transmission insulators is classified based on the monitoring data and the pre-set monitoring range threshold, and fault processing is performed according to the status classification results.

[0006] As a further embodiment of the present invention, the unmanned aerial vehicle survey device equipped with a laser radar is used for high-altitude surveys of insulators and their surroundings. The laser radar acquires three-dimensional data of the insulator and its surroundings by emitting high-frequency light pulses and receiving reflected echoes. The laser radar measures the distance and orientation of the insulator by combining position and time information provided by an inertial management unit and a global navigation satellite system. The mobile monitoring device includes a video monitoring system that captures real-time video data of the insulator and its surroundings, compresses and encodes it, and transmits it to a monitoring center. The monitoring center analyzes the received video data using a CNN image recognition algorithm model and a video analysis model to monitor the structural state of the insulator. The dual-axis tilt sensor is mounted on the insulator and utilizes gravity sensing and accelerometer principles to measure the insulator's horizontal and vertical tilt angles for real-time monitoring. The temperature sensor is mounted at the insulator's pole-tower connection to monitor temperature changes. The monitoring center analyzes the insulator for overheating or abnormal temperature rise based on the temperature change data.

[0007] As a further embodiment of the present invention, the drone body provides flight capability and a platform for carrying systems and modules. The power system includes a motor, a propeller and a battery to provide the drone with the power required for flight. The lidar system includes a laser scanner, a laser rangefinder and a data processing unit. The positioning and navigation module obtains the real-time position information of the drone through a GPS receiver, measures the acceleration and angular velocity information of the drone through an inertial measurement unit, provides attitude and speed data for the drone, and combines the data of GPS and inertial measurement units to achieve positioning and navigation of the drone. The power management module is used to monitor the battery power, voltage and temperature parameters to ensure power supply during flight. The control module is used to remotely control the takeoff, landing, hovering and flight path of the drone, and can achieve autonomous flight and data collection according to preset flight missions and paths through autonomous flight algorithms. As a further embodiment of the present invention, the dual-axis tilt sensor transmits the collected tilt angle data to a monitoring center via a built-in communication module. The monitoring center parses the tilt angle data according to the data format of the dual-axis tilt sensor. The insulator leakage angle calculation model converts the angles of the X-axis and Y-axis into vectors on a plane based on the tilt angles of the X-axis and Y-axis. The composite angle of the vectors is calculated using trigonometric functions to calculate the tilt angle and direction of the insulator on the plane. The tilt angle calculation formula is: In formula (1), is the tilt angle in the X-axis direction, is the tilt angle in the Y-axis direction, is a dual-axis tilt sensor parameter. As a further embodiment of the present invention, the data receiving module in the point cloud algorithm model is used to receive point cloud data sent by an unmanned aerial vehicle survey device equipped with a lidar, the data processing module is used to perform denoising, filtering, outlier removal and coordinate conversion processing on the received raw point cloud data, the point cloud feature extraction module extracts surface features through normal estimation, and calculates the feature vector of each point in the point cloud to describe the distribution and geometric structure of the surrounding points to achieve local feature extraction, the point cloud segmentation module divides the point cloud data into different categories, including buildings, vegetation, wires and insulators, using the geometric features, color and texture information of the point cloud data, and segments the point cloud data into independent target objects according to the classification results, and the point cloud registration module uses the ICP registration algorithm to achieve point cloud data alignment, ensuring the consistency between different data to form a three-dimensional model.

[0008] As a further embodiment of the present invention, the point cloud data acquired by the drone survey device equipped with a laser radar is: In formula (2), Indicates the actual location information of the UAV survey device equipped with LiDAR within the survey area. The x-axis, y-axis, and z-axis values represent the actual position, It represents the point cloud data collection results of the UAV survey device equipped with LiDAR. They represent the collected values of the x-axis, y-axis, and z-axis respectively, and d represents the distance from the laser radar to the data collection point. They represent the pitch angle, yaw angle, roll angle and scanning angle of the UAV survey device equipped with laser radar, b is the angular parameter coefficient, K is the reflection intensity, Represent the random error values of the x-axis, y-axis, and z-axis, respectively, and represent the category factors of the collection points; The calculation formula for the distance d from the laser radar to the data collection point is: In formula (3), v is the transmission speed of the survey signal in the UAV survey device equipped with lidar, and t is the lidar transmission time. In the absence of error, the point cloud coarse registration calculation formula is: In formula (4), is the point in the transformed point cloud Q, is the point in the point cloud P before transformation, R is the rotation matrix, and T is the translation matrix; the calculation formula for fine point cloud registration is: In formula (5), is the number of corresponding point pairs, are the corresponding points of the source point cloud and the target point cloud, R is the rotation matrix, T is the translation matrix, is the weight of the matching point in the cloud point, is the curvature of the matching point in the cloud point. As a further embodiment of the present invention, the post-monitoring classification includes normal state, mild abnormality, moderate abnormality and emergency state; The normal state is when the hardware device monitors the insulator data within the preset normal range and no abnormality is found. In this case, the normal monitoring frequency is maintained and no operation is required. When vegetation grows around the insulator, the insulator leakage, deformation, settlement, and uplift are between 0.5% and 2%, the insulator temperature exceeds the normal temperature threshold by 3%, or the pole peeling or corrosion area accounts for between 1% and 5% of the total pole area, a minor warning will be issued to remind attention and increase the monitoring frequency; When monitoring detects insulator leakage, deformation, settlement, and uplift between 2% and 10%, insulator temperature reaches 3% to 10% of the normal temperature threshold, pole coating peeling or corrosion area accounts for between 5% and 10% of the total pole area, and conductors and ground wires are rusted or broken, a moderate warning is issued and personnel are arranged for on-site inspection to determine whether to take temporary reinforcement measures or adjust the monitoring strategy; When it is monitored that the insulator leakage, deformation, settlement and pull-out exceed 10%, the insulator temperature exceeds the normal temperature threshold by 10%, and the pole coating peeling or corrosion area accounts for more than 10% of the total pole area, an emergency warning will be issued, the police will be dispatched immediately and the emergency response plan will be activated, and the relevant departments and personnel will be notified to repair or replace the insulator.

[0009] As a further embodiment of the present invention, an intelligent transmission line insulator detection system includes a hardware device module, a data transmission module, a monitoring center module, and a fault processing module; the modules work together to achieve efficient monitoring and fault processing of high-voltage transmission insulators; The hardware device module integrates a variety of advanced equipment, including an unmanned aerial vehicle survey device equipped with a laser radar, a mobile monitoring device, a dual-axis tilt sensor, and a temperature sensor. The unmanned aerial vehicle survey device equipped with a laser radar consists of a drone body, a power system, a laser radar system, a positioning and navigation module, a camera module, a data transmission module, a power management module, and a control module. The control module is interconnected with the positioning and navigation module, the camera module, and the power management module via a data bus to achieve coordinated control among the modules. The camera module and the positioning and navigation module transmit the collected information to the data transmission module, and the data collected by the laser radar system is also input into the data transmission module. All data transmission modules are based on the TCP / IP standard protocol and adopt 5G encrypted transmission technology; the data transmission module transmits the data collected by the hardware device through compression coding processing to the monitoring center module safely and quickly; The monitoring center module includes a data processing module, a data analysis module, a fault location and identification module and a post-monitoring classification module; the data processing module is responsible for decoding and verifying the received data to ensure the accuracy and integrity of the data; the data analysis module integrates the insulator leakage angle calculation model and the point cloud algorithm model, wherein the point cloud algorithm model is composed of a data receiving module, a data processing module, a point cloud segmentation module, a point cloud feature extraction module and a point cloud registration module, and each module is connected in sequence to realize in-depth analysis of the collected data; the fault location and identification module accurately locates the position of the faulty insulator and identifies the fault type based on the data collected by the hardware equipment and the data analysis results; the post-monitoring classification module classifies and evaluates the status of the high-voltage transmission insulator according to the monitoring data and the preset monitoring range threshold; the output end of the data processing module is connected to the input end of the data analysis module; the output end of the data analysis module is connected to the input end of the fault location and identification module; the output end of the fault location and identification module is connected to the input end of the post-monitoring classification module; The fault processing module quickly starts the corresponding fault processing process according to the status classification result of the insulator to ensure the safe and stable operation of the high-voltage transmission line.

[0010] Positive beneficial effects The present invention discloses an intelligent transmission line insulator detection method, which collects transmission line insulator data through hardware equipment. The hardware equipment includes an unmanned aerial vehicle survey device and a mobile monitoring device equipped with a laser radar, a dual-axis tilt sensor, and a temperature sensor. The hardware equipment compresses and encodes the collected data, uses the TCP / IP standard protocol, and encrypts and transmits the collected data through the 5G transmission standard. After receiving the data, the monitoring center decodes and verifies the data, and determines the specific location of the faulty insulator based on the data information collected by the hardware equipment and the results of data analysis. The status of the high-voltage transmission insulator is classified based on the monitoring data and a pre-set monitoring range threshold. The fault is processed according to the status classification result, which can complete comprehensive monitoring of transmission line insulators in all aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive work, among which: Figure 1 This is a flow chart of an intelligent transmission line insulator detection method according to the present invention; Figure 2 This is a structural diagram of an unmanned aerial vehicle survey device equipped with a laser radar in an intelligent transmission line insulator detection method of the present invention; Figure 3 This is a structural diagram of a point cloud algorithm model in an intelligent transmission line insulator detection method of the present invention; Figure 4 This is a workflow diagram of a point cloud algorithm model in an intelligent transmission line insulator detection method of the present invention; Figure 5 This is a structural diagram of a laser radar system in an intelligent transmission line insulator detection method of the present invention; Figure 6 This is a flow chart of an intelligent transmission line insulator detection system according to the present invention. DETAILED DESCRIPTION

[0012] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0013] like Figures 1-6 As shown, an intelligent transmission line insulator detection method includes the following steps: Step (1), starting the hardware equipment to collect data on transmission line insulators; The hardware equipment includes an unmanned aerial vehicle survey device and a mobile monitoring device equipped with a laser radar, a dual-axis tilt sensor, and a temperature sensor. The unmanned aerial vehicle survey device equipped with a laser radar includes an unmanned aerial vehicle body, a power system, a laser radar system, a positioning and navigation module, a camera module, a data transmission module, a power management module, and a control module. The control module is connected to the positioning and navigation module, the camera module, and the power management module via a data bus. The output ends of the camera module and the positioning and navigation module are connected to the data transmission module, and the output end of the laser radar system is connected to the input end of the data transmission module. Step (2), transmitting the data collected by the hardware device to the monitoring center; The hardware device compresses and encodes the collected data, uses the TCP / IP standard protocol, and encrypts and transmits the collected data via the 5G transmission standard; Step (3): data processing and analysis; After receiving the data, the monitoring center decodes and verifies the data, calculates the insulator leakage angle through the insulator leakage angle calculation model, and extracts the data features transmitted by the drone survey device equipped with a laser radar through a point cloud algorithm model to obtain detailed three-dimensional information of the insulator and its surrounding environment. The point cloud algorithm model includes a data receiving module, a data processing module, a point cloud segmentation module, a point cloud feature extraction module and a point cloud registration module. The output end of the data receiving module is connected to the input end of the data processing module, the output end of the data processing module is connected to the input end of the point cloud segmentation module, the output end of the point cloud segmentation module is connected to the input end of the point cloud feature extraction module, and the output end of the point cloud feature extraction module is connected to the input end of the point cloud feature extraction module. Step (4), locating and identifying the faulty transmission line insulator; Combining the data information collected by the hardware equipment with the results of data analysis, the specific location of the faulty insulator is determined, and the fault type, including insulator leakage, deformation, breakage, and overheating, is identified by comparing historical data with real-time data; Step (5): Classification after monitoring; The status of high-voltage transmission insulators is classified based on the monitoring data and the pre-set monitoring range threshold, and fault processing is performed according to the status classification results.

[0014] In the above-mentioned embodiment, the unmanned aerial vehicle survey device equipped with a laser radar is used for high-altitude surveys of insulators and their surroundings. The laser radar acquires three-dimensional data of the insulator and its surroundings by emitting high-frequency light pulses and receiving reflected echoes. It measures the distance and orientation of the insulator by combining position and time information provided by an inertial management unit and a global navigation satellite system. The mobile monitoring device includes a video monitoring system that captures real-time video data of the insulator and its surroundings, compresses and encodes it, and transmits it to a monitoring center. The monitoring center analyzes the received video data using a CNN image recognition algorithm model and a video analysis model to monitor the structural state of the insulator. The dual-axis tilt sensor, mounted on the insulator, utilizes gravity sensing and accelerometer principles to measure the insulator's horizontal and vertical tilt angles for real-time monitoring. The temperature sensor, mounted at the insulator's pole-tower connection, monitors the insulator's temperature changes. The monitoring center analyzes the temperature change data to determine whether the insulator is overheating or experiencing abnormal temperature rise.

[0015] In the above embodiment, the drone body provides flight capability and a platform for carrying systems and modules. The power system includes a motor, a propeller and a battery to provide the drone with the power required for flight. The lidar system includes a laser scanner, a laser rangefinder and a data processing unit. The positioning and navigation module obtains the real-time position information of the drone through a GPS receiver, measures the acceleration and angular velocity information of the drone through an inertial measurement unit, provides attitude and speed data for the drone, and combines the data of GPS and inertial measurement units to achieve positioning and navigation of the drone. The power management module is used to monitor the battery power, voltage and temperature parameters to ensure power supply during flight. The control module is used to remotely control the takeoff, landing, hovering and flight path of the drone, and can achieve autonomous flight and data collection according to preset flight missions and paths through autonomous flight algorithms.

[0016] In a specific embodiment, the positioning and navigation module of the UAV survey device equipped with a lidar is connected to the base station before taking off, and the lidar is turned on and the device initialization is completed. The UAV survey device equipped with the lidar can take off according to the planned route. The positioning and navigation module can complete autonomous obstacle avoidance in combination with the signal received by the lidar. At the same time, the UAV survey device equipped with the lidar can be manually controlled for monitoring. The camera module is used to take real-time photos or videos of insulators and the surrounding environment, and provide comprehensive three-dimensional environmental information in combination with the lidar data. The power management module manages the power supply of the UAV to ensure that each module and system can work stably during the monitoring process. The power management module uses a power sensor to accurately measure the battery power status and monitor the remaining power of the UAV in real time. According to the current status of the UAV The battery level, flight speed, flight altitude and load conditions are used to calculate the flight time estimate based on historical data and the drone performance model. The control module optimizes the monitoring task and selects the optimal flight path based on the flight time estimate and monitoring task requirements. The battery threshold is set. When the battery level is lower than 15%, the drone survey device equipped with a lidar automatically returns home or goes to a predetermined charging point for charging. When the battery level is lower than 30%, the control module adjusts the flight speed and altitude parameters to reduce power consumption. The monitoring center staff can monitor the drone's battery status in real time and remotely guide the drone survey device equipped with a lidar to perform power management and task adjustment. The drone survey device equipped with a lidar can fly and hover quickly, conveniently detect insulators and the surrounding environment, and transmit monitoring data back to the monitoring center in real time.

[0017] In a specific embodiment, a laser radar (LiDAR) transmits laser beams and receives reflected signals to achieve three-dimensional measurement of insulators, wires, and the surrounding environment of the insulators, and can measure the position and status of the insulators. Furthermore, the LiDAR can penetrate clouds, rain, and fog to obtain insulator data information under adverse climatic conditions. The LiDAR transmits laser beams and receives reflected signals to obtain distance and speed information of target objects. In rainy and foggy environments, the LiDAR uses multiple echo technology to process multiple reflected laser signals to penetrate the rain and fog and obtain information about the target insulator. The LiDAR system includes a laser scanner, a laser rangefinder, and a data processing unit. The laser scanner emits laser pulses to the ground to scan and measure the three-dimensional coordinate information of the insulator and its surrounding environment. The laser rangefinder measures the distance between the drone and the insulator by emitting and receiving laser beams, providing data support for the height, position, and offset of the insulator. The data processing unit is responsible for receiving data from the laser scanner and laser rangefinder, and performing real-time processing and analysis. By comparing historical data with real-time data, it determines whether the position, height, and offset of the insulator have changed, and whether there are any safety hazards.

[0018] In the above embodiment, the dual-axis tilt sensor transmits the collected tilt angle data to the monitoring center via a built-in communication module. The monitoring center parses the tilt angle data according to the data format of the dual-axis tilt sensor. The insulator leakage angle calculation model converts the angles of the X-axis and Y-axis into vectors on a plane based on the tilt angles of the X-axis and Y-axis. The trigonometric function is used to calculate the composite angle of the vectors to calculate the tilt angle and direction of the insulator on the plane. The tilt angle calculation formula is: In formula (1), is the tilt angle in the X-axis direction, is the tilt angle in the Y-axis direction, Parameters of the dual-axis tilt sensor. In a specific embodiment, the dual-axis tilt sensor requires initialization after installation, including zero point calibration, measurement range setting, and communication parameter configuration. The dual-axis tilt sensor uses accelerometers installed on the X and Y axes to measure the insulator's tilt angle in two directions. When the insulator tilts, the two accelerometers detect different gravity components and convert them into electrical signals for output. The signal processing circuit within the dual-axis tilt sensor digitizes the electrical signals to generate a digital signal related to the tilt angle.

[0019] In the above embodiment, the data receiving module in the point cloud algorithm model is used to receive point cloud data sent by an unmanned aerial vehicle survey device equipped with a lidar, the data processing module is used to denoise, filter, remove outliers and perform coordinate conversion on the received original point cloud data, the point cloud feature extraction module extracts surface features through normal estimation, and describes the distribution and geometric structure of surrounding points by calculating the feature vector of each point in the point cloud to achieve local feature extraction, the point cloud segmentation module uses the geometric features and color and texture information of the point cloud data to divide the point cloud data into different categories, including buildings, vegetation, wires and insulators, and segments the point cloud data into independent target objects according to the classification results, and the point cloud registration module uses the ICP registration algorithm to achieve point cloud data alignment to ensure the consistency between different data to form a three-dimensional model.

[0020] In the above embodiment, the point cloud data acquired by the drone survey device equipped with a laser radar is: In formula (2), Indicates the actual location information of the UAV survey device equipped with LiDAR within the survey area. The x-axis, y-axis, and z-axis values represent the actual position, It represents the point cloud data collection results of the UAV survey device equipped with LiDAR. They represent the collected values of the x-axis, y-axis, and z-axis respectively, and d represents the distance from the laser radar to the data collection point. They represent the pitch angle, yaw angle, roll angle and scanning angle of the UAV survey device equipped with laser radar, b is the angular parameter coefficient, K is the reflection intensity, Represent the random error values of the x-axis, y-axis, and z-axis, respectively, and represent the category factors of the collection points; The calculation formula for the distance d from the laser radar to the data collection point is: In formula (3), v is the transmission speed of the survey signal in the UAV survey device equipped with lidar, and t is the lidar transmission time. In the absence of error, the point cloud coarse registration calculation formula is: In formula (4), is the point in the transformed point cloud Q, is the point in the point cloud P before transformation, R is the rotation matrix, and T is the translation matrix; the calculation formula for fine point cloud registration is: In formula (5), is the number of corresponding point pairs, are the corresponding points of the source point cloud and the target point cloud, R is the rotation matrix, T is the translation matrix, is the weight of the matching point in the cloud point, is the curvature of the matching point in the cloud point. In a specific embodiment, an unmanned aerial vehicle survey device equipped with a laser radar was controlled to collect point cloud data, and a total of 600 sets of surveying and mapping point cloud data were prepared. The effectiveness of the point cloud algorithm model was verified by comparing the role of the point cloud algorithm model, image processing and analysis algorithm, and 3D reconstruction algorithm in extracting the data features transmitted by the unmanned aerial vehicle survey device equipped with a laser radar and obtaining detailed 3D information about the insulator and its surrounding environment. The comparison results are shown in Table 1: The comparison results in Table 1 show that the point cloud algorithm model can more accurately extract the data features transmitted by the UAV survey device equipped with lidar, the three-dimensional modeling results are more accurate, the processing speed of point cloud data is higher, and it has stronger anti-interference ability.

[0021] In a specific embodiment, the point cloud algorithm model working steps are: Step (1) receiving point cloud data sent by a UAV survey device equipped with a laser radar through a data receiving module, wherein data reception includes online reception and offline acquisition, so as to receive transmitted data in real time and read backup data; Step (2), data processing, processing the received point cloud data through the data processing module, including denoising, filtering, outlier removal and coordinate conversion. At the same time, the data processing module can perform data parsing and storage, parsing the received real-time point cloud data or backup files to obtain the original three-dimensional point cloud data, and supporting data storage in multiple formats; Step (3), the point cloud data is divided into different parts or objects, including insulators, wires, vegetation, ground and buildings, for subsequent analysis and processing through the point cloud segmentation module. The point cloud segmentation module applies the region growing and K-Means segmentation algorithms to extract the boundary information in the point cloud and divide the point cloud data into different regions; Step (4) extracting feature information from the segmented point cloud data by extracting local surface normals and feature points. The feature information includes the shape and texture of the insulator. The SHOT and FPFH feature description algorithms are used to generate feature descriptors, which are then used for subsequent classification and recognition tasks. Step (5) aligns the point cloud data collected at different times and angles to obtain a complete three-dimensional model of the insulator and its surrounding environment. The point cloud registration module extracts feature points or feature descriptors from the point cloud data features, uses the ICP registration algorithm to calculate the translation and rotation transformation relationship between the point clouds, and aligns different point cloud data sets through the transformation relationship to obtain a consistent point cloud model. In a specific embodiment, the hardware equipment performs data collection to obtain point cloud data, height information, tilt angle information and temperature changes of the insulator and its surrounding environment, and the temperature changes of the insulator and its related components. The data collected by all hardware devices are transmitted to the monitoring center by wired or wireless means. The collected data is analyzed in combination with the point cloud algorithm model and big data analysis to identify abnormal data or potential fault points. The tilt degree and possible position offset of the insulator are estimated by combining the laser lightning point cloud data and the dual-axis tilt sensor data. The temperature sensor data is used to determine whether there is a fault caused by overheating. The position of the abnormal insulator is obtained by combining the correlation and difference between the data collected by the mobile monitoring device and the dual-axis tilt sensor and the temperature sensor. At the same time, the position selection image is collected by the drone survey device equipped with a laser radar to determine the survey point at a height higher than the insulator, and the position coordinates of the abnormal insulator are settled using a triangulated positioning structure.

[0022] In the above embodiment, the classification after monitoring includes normal state, mild abnormality, moderate abnormality and emergency state; The normal state is when the hardware device monitors the insulator data within the preset normal range and no abnormality is found. In this case, the normal monitoring frequency is maintained and no operation is required. When vegetation grows around the insulator, the insulator leakage, deformation, settlement, and uplift are between 0.5% and 2%, the insulator temperature exceeds the normal temperature threshold by 3%, or the pole peeling or corrosion area accounts for between 1% and 5% of the total pole area, a minor warning will be issued to remind attention and increase the monitoring frequency; When monitoring detects insulator leakage, deformation, settlement, and uplift between 2% and 10%, insulator temperature reaches 3% to 10% of the normal temperature threshold, pole coating peeling or corrosion area accounts for between 5% and 10% of the total pole area, and conductors and ground wires are rusted or broken, a moderate warning is issued and personnel are arranged for on-site inspection to determine whether to take temporary reinforcement measures or adjust the monitoring strategy; When it is monitored that the insulator leakage, deformation, settlement and pull-out exceed 10%, the insulator temperature exceeds the normal temperature threshold by 10%, and the pole coating peeling or corrosion area accounts for more than 10% of the total pole area, an emergency warning will be issued, the police will be dispatched immediately and the emergency response plan will be activated, and the relevant departments and personnel will be notified to repair or replace the insulator.

[0023] In a specific embodiment, 150 insulators within the monitoring range are monitored, and the monitoring results within three months are shown in Table 2: Furthermore, an intelligent transmission line insulator detection system includes a hardware device module, a data transmission module, a monitoring center module, and a fault processing module; each module works in coordination to achieve efficient monitoring and fault processing of high-voltage transmission insulators; The hardware device module integrates a variety of advanced equipment, including an unmanned aerial vehicle survey device equipped with a laser radar, a mobile monitoring device, a dual-axis tilt sensor, and a temperature sensor. The unmanned aerial vehicle survey device equipped with a laser radar consists of a drone body, a power system, a laser radar system, a positioning and navigation module, a camera module, a data transmission module, a power management module, and a control module. The control module is interconnected with the positioning and navigation module, the camera module, and the power management module via a data bus to achieve coordinated control among the modules. The camera module and the positioning and navigation module transmit the collected information to the data transmission module, and the data collected by the laser radar system is also input into the data transmission module. All data transmission modules are based on the TCP / IP standard protocol and adopt 5G encrypted transmission technology; the data transmission module transmits the data collected by the hardware device through compression coding processing to the monitoring center module safely and quickly; The monitoring center module includes a data processing module, a data analysis module, a fault location and identification module and a post-monitoring classification module; the data processing module is responsible for decoding and verifying the received data to ensure the accuracy and integrity of the data; the data analysis module integrates the insulator leakage angle calculation model and the point cloud algorithm model, wherein the point cloud algorithm model is composed of a data receiving module, a data processing module, a point cloud segmentation module, a point cloud feature extraction module and a point cloud registration module, and each module is connected in sequence to realize in-depth analysis of the collected data; the fault location and identification module accurately locates the position of the faulty insulator and identifies the fault type based on the data collected by the hardware equipment and the data analysis results; the post-monitoring classification module classifies and evaluates the status of the high-voltage transmission insulator according to the monitoring data and the preset monitoring range threshold; the output end of the data processing module is connected to the input end of the data analysis module; the output end of the data analysis module is connected to the input end of the fault location and identification module; the output end of the fault location and identification module is connected to the input end of the post-monitoring classification module; The fault handling module quickly starts the corresponding fault handling process based on the status classification result of the insulator to ensure the safe and stable operation of the high-voltage transmission line. Although the specific embodiments of the present invention are described above, it should be understood by those skilled in the art that these specific embodiments are only examples, and those skilled in the art can make various omissions, substitutions and changes to the details of the above methods and systems without departing from the principles and essence of the present invention. For example, merging the above method steps so as to perform substantially the same functions in accordance with substantially the same methods to achieve substantially the same results falls within the scope of the present invention. Therefore, the scope of the present invention is limited only by the appended claims.

Claims

1. An intelligent transmission line insulator detection method, characterized by: The following steps are involved: Step (1), starting the hardware equipment to collect data on transmission line insulators; The hardware equipment includes an unmanned aerial vehicle survey device and a mobile monitoring device equipped with a laser radar, a dual-axis tilt sensor, and a temperature sensor. The unmanned aerial vehicle survey device equipped with a laser radar includes an unmanned aerial vehicle body, a power system, a laser radar system, a positioning and navigation module, a camera module, a data transmission module, a power management module, and a control module. The control module is connected to the positioning and navigation module, the camera module, and the power management module via a data bus. The output ends of the camera module and the positioning and navigation module are connected to the data transmission module, and the output end of the laser radar system is connected to the input end of the data transmission module. Step (2), transmitting the data collected by the hardware device to the monitoring center; The hardware device compresses and encodes the collected data, uses the TCP / IP standard protocol, and encrypts and transmits the collected data via the 5G transmission standard; Step (3): data processing and analysis; After receiving the data, the monitoring center decodes and verifies the data, calculates the insulator leakage angle through the insulator leakage angle calculation model, and extracts the data features transmitted by the drone survey device equipped with a laser radar through a point cloud algorithm model to obtain detailed three-dimensional information of the insulator and its surrounding environment. The point cloud algorithm model includes a data receiving module, a data processing module, a point cloud segmentation module, a point cloud feature extraction module and a point cloud registration module. The output end of the data receiving module is connected to the input end of the data processing module, the output end of the data processing module is connected to the input end of the point cloud segmentation module, the output end of the point cloud segmentation module is connected to the input end of the point cloud feature extraction module, and the output end of the point cloud feature extraction module is connected to the input end of the point cloud feature extraction module. Step (4), locating and identifying the faulty transmission line insulator; Combining the data information collected by the hardware equipment and the results of data analysis, the specific location of the faulty insulator is determined, and the fault type, including insulator leakage, deformation, breakage, and overheating, is identified by comparing historical data with real-time data; Step (5): Classification after monitoring; The status of high-voltage transmission insulators is classified based on the monitoring data and the pre-set monitoring range threshold, and fault processing is performed according to the status classification results.

2. The intelligent transmission line insulator detection method according to claim 1, characterized in that: The unmanned aerial vehicle survey device equipped with a lidar is used for high-altitude surveys of insulators and their surroundings. The lidar acquires three-dimensional data of the insulator and its surroundings by emitting high-frequency light pulses and receiving reflected echoes. It then measures the distance and orientation of the insulator by combining position and time information provided by an inertial management unit and a global navigation satellite system. The mobile monitoring device includes a video monitoring system that captures real-time video data of the insulator and its surroundings, compresses and encodes it, and transmits it to a monitoring center. The monitoring center analyzes the received video data using a CNN image recognition algorithm model and a video analysis model to monitor the structural condition of the insulator. The dual-axis tilt sensor, installed on the insulator, utilizes gravity sensing and accelerometer principles to measure the horizontal and vertical tilt angles of the insulator to monitor its tilt in real time. The temperature sensor, installed at the insulator's pole-tower connection, monitors temperature changes in the insulator. The monitoring center analyzes the temperature change data to determine whether the insulator is overheating or experiencing abnormal temperature rise.

3. The intelligent transmission line insulator detection method according to claim 1, characterized in that: The drone body provides flight capabilities and a platform for carrying systems and modules. The power system includes a motor, a propeller and a battery, which provide the drone with the power required for flight. The lidar system includes a laser scanner, a laser rangefinder and a data processing unit. The positioning and navigation module obtains the real-time position information of the drone through a GPS receiver, measures the acceleration and angular velocity information of the drone through an inertial measurement unit, provides the drone with attitude and speed data, and combines the data of GPS and inertial measurement units to achieve the positioning and navigation of the drone. The power management module is used to monitor the battery power, voltage and temperature parameters to ensure power supply during flight. The control module is used to remotely control the takeoff, landing, hovering and flight path of the drone, and can achieve autonomous flight and data collection according to preset flight missions and paths through autonomous flight algorithms.

4. The intelligent transmission line insulator detection method according to claim 1, characterized in that: The dual-axis tilt sensor transmits the collected tilt angle data to the monitoring center through a built-in communication module. The monitoring center parses the tilt angle data according to the data format of the dual-axis tilt sensor. The insulator leakage angle calculation model converts the angles of the X-axis and Y-axis into vectors on the plane based on the tilt angles of the X-axis and Y-axis. The composite angle of the vectors is calculated using trigonometric functions to calculate the tilt angle and direction of the insulator on the plane. The tilt angle calculation formula is: In formula (1), is the tilt angle in the X-axis direction, is the tilt angle in the Y-axis direction, are the parameters of the dual-axis tilt sensor.

5. The intelligent transmission line insulator detection method according to claim 1, characterized in that: The data receiving module in the point cloud algorithm model is used to receive point cloud data sent by an unmanned aerial vehicle survey device equipped with a lidar. The data processing module is used to denoise, filter, remove outliers and perform coordinate conversion on the received raw point cloud data. The point cloud feature extraction module extracts surface features through normal estimation and calculates the feature vector of each point in the point cloud to describe the distribution and geometric structure of surrounding points to achieve local feature extraction. The point cloud segmentation module uses the geometric features and color and texture information of the point cloud data to divide the point cloud data into different categories, including buildings, vegetation, wires and insulators, and segments the point cloud data into independent target objects based on the classification results. The point cloud registration module uses the ICP registration algorithm to align the point cloud data to ensure the consistency between different data to form a three-dimensional model.

6. The intelligent transmission line insulator detection method according to claim 5, characterized in that: The point cloud data obtained by the UAV survey device equipped with laser radar is: In formula (2), Indicates the actual location information of the UAV survey device equipped with LiDAR within the survey area. The x-axis, y-axis, and z-axis values represent the actual position, It represents the point cloud data collection results of the UAV survey device equipped with LiDAR. They represent the collected values of the x-axis, y-axis, and z-axis respectively, and d represents the distance from the laser radar to the data collection point. They represent the pitch angle, yaw angle, roll angle and scanning angle of the UAV survey device equipped with laser radar, b is the angular parameter coefficient, K is the reflection intensity, Represent the random error values of the x-axis, y-axis, and z-axis, respectively, and represent the category factors of the collection points; The calculation formula for the distance d from the laser radar to the data collection point is: In formula (3), v is the transmission speed of the survey signal in the UAV survey device equipped with lidar, and t is the lidar transmission time. In the absence of error, the point cloud coarse registration calculation formula is: In formula (4), is the point in the transformed point cloud Q, is the point in the point cloud P before transformation, R is the rotation matrix, and T is the translation matrix; the calculation formula for fine point cloud registration is: In formula (5), is the number of corresponding point pairs, are the corresponding points of the source point cloud and the target point cloud, R is the rotation matrix, T is the translation matrix, is the weight of the matching point in the cloud point, is the curvature of the matching point in the cloud point.

7. The intelligent transmission line insulator detection method according to claim 1, characterized in that: The classification after monitoring includes normal state, mild abnormality, moderate abnormality and emergency state; The normal state is when the hardware device monitors the insulator data within the preset normal range and no abnormality is found. In this case, the normal monitoring frequency is maintained and no operation is required. When vegetation grows around the insulator, the insulator leakage, deformation, settlement, and uplift are between 0.5% and 2%, the insulator temperature exceeds the normal temperature threshold by 3%, or the pole peeling or corrosion area accounts for between 1% and 5% of the total pole area, a minor warning will be issued to remind attention and increase the monitoring frequency; When monitoring detects insulator leakage, deformation, settlement, and uplift between 2% and 10%, insulator temperature reaches 3% to 10% of the normal temperature threshold, pole coating peeling or corrosion area accounts for between 5% and 10% of the total pole area, and conductors and ground wires are rusted or broken, a moderate warning is issued and personnel are arranged for on-site inspection to determine whether to take temporary reinforcement measures or adjust the monitoring strategy; When it is monitored that the insulator leakage, deformation, settlement and pull-out exceed 10%, the insulator temperature exceeds the normal temperature threshold by 10%, and the pole coating peeling or corrosion area accounts for more than 10% of the total pole area, an emergency warning will be issued, the police will be dispatched immediately and the emergency response plan will be activated, and the relevant departments and personnel will be notified to repair or replace the insulator.

8. An intelligent transmission line insulator detection system, characterized by: include: Including hardware equipment module, data transmission module, monitoring center module and fault handling module; The modules work together to achieve efficient monitoring and fault handling of high-voltage transmission insulators; The hardware device module integrates a variety of advanced equipment, including an unmanned aerial vehicle survey device equipped with a laser radar, a mobile monitoring device, a dual-axis tilt sensor, and a temperature sensor. The unmanned aerial vehicle survey device equipped with a laser radar consists of a drone body, a power system, a laser radar system, a positioning and navigation module, a camera module, a data transmission module, a power management module, and a control module. The control module is interconnected with the positioning and navigation module, the camera module, and the power management module via a data bus to achieve coordinated control among the modules. The camera module and the positioning and navigation module transmit the collected information to the data transmission module, and the data collected by the laser radar system is also input into the data transmission module. All data transmission modules are based on the TCP / IP standard protocol and adopt 5G encrypted transmission technology; the data transmission module transmits the data collected by the hardware device through compression coding processing to the monitoring center module safely and quickly; The monitoring center module includes a data processing module, a data analysis module, a fault location and identification module and a post-monitoring classification module; the data processing module is responsible for decoding and verifying the received data to ensure the accuracy and integrity of the data; the data analysis module integrates the insulator leakage angle calculation model and the point cloud algorithm model, wherein the point cloud algorithm model is composed of a data receiving module, a data processing module, a point cloud segmentation module, a point cloud feature extraction module and a point cloud registration module, and each module is connected in sequence to realize in-depth analysis of the collected data; the fault location and identification module accurately locates the position of the faulty insulator and identifies the fault type based on the data collected by the hardware equipment and the data analysis results; the post-monitoring classification module classifies and evaluates the status of the high-voltage transmission insulator according to the monitoring data and the preset monitoring range threshold; the output end of the data processing module is connected to the input end of the data analysis module; the output end of the data analysis module is connected to the input end of the fault location and identification module; the output end of the fault location and identification module is connected to the input end of the post-monitoring classification module; The fault processing module quickly starts the corresponding fault processing process according to the status classification result of the insulator to ensure the safe and stable operation of the high-voltage transmission line.

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