Unmanned aerial vehicle power line autonomous inspection system and control method

Abstract

The invention belongs to the technical field of power equipment inspection, and discloses an unmanned aerial vehicle power line autonomous inspection system which is composed of a flight control module, a path planning module, an image acquisition module, an image processing module and a communication and data transmission module. In the aspects of inspection efficiency and accuracy, the path planning module automatically generates an optimal path based on geographic information system data and a line three-dimensional model, and the inspection time is greatly shortened; the system carries various image acquisition devices, a deep learning algorithm and a fault identification model are combined, multi-dimensional analysis is carried out on a power line, various potential faults are accurately positioned, and in the aspects of intelligence and reliability, a flight control module senses the environment through a high-precision sensor and automatically avoids obstacles to guarantee flight safety; the path planning module dynamically adjusts the strategy according to the line state; the image processing module quickly recognizes faults, all the modules cooperate to achieve whole-process automation, and manual intervention is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of power equipment inspection, and specifically relates to an autonomous inspection system and control method for power lines using a drone. Background Art

[0002] As my country's power industry enters a period of rapid development and its coverage continues to expand, power lines, the "blood vessels" of social production and people's lives, can severely impact industrial production and residential electricity consumption if they experience a failure.

[0003] In recent years, drone inspection technology has gradually emerged with its flexible and convenient advantages. However, during drone inspections, flight path planning and shooting angle adjustment rely on real-time manual control, making it difficult to autonomously optimize inspection strategies based on the actual conditions of the line. Inspection image processing and analysis mostly use manual interpretation, which not only consumes a lot of time but also makes it difficult to ensure the accuracy of fault detection. At the same time, these systems lack comprehensive perception and intelligent decision-making capabilities of the drone's own status, such as battery power and flight attitude, as well as environmental information such as surrounding obstacles and weather changes. This makes it difficult to smoothly advance inspection tasks, and drones often face safety hazards such as collisions and crashes. Summary of the Invention

[0004] The purpose of the present invention is to provide a UAV power line autonomous inspection system and control method to solve the problems raised in the above background technology.

[0005] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: an autonomous inspection system for power lines using a UAV, which is composed of a flight control module, a path planning module, an image acquisition module, an image processing module, and a communication and data transmission module;

[0006] Flight Control Module: This module is responsible for precisely controlling the drone's flight attitude, speed, and altitude. It uses inertial navigation, satellite positioning, and high-precision sensors to sense motion and dynamically adjust flight. Its obstacle avoidance function plans paths based on environmental information, ensuring a safe and secure inspection environment and laying the foundation for subsequent module operations.

[0007] Path Planning Module: Utilizing GIS and a 3D model of power lines, combined with drone performance and mission requirements, it generates an optimal inspection route. It considers multiple factors, including line direction, to avoid hazards and dynamically adjusts based on line and drone status, providing guidance for image acquisition.

[0008] Image acquisition module: Equipped with multiple high-definition cameras and sensors, the system captures images and videos of power lines at appropriate locations and angles along a planned path. Different types of equipment perform their respective functions to detect various faults, and the collected data is transmitted to the image processing module in real time, providing raw material for fault detection.

[0009] Image processing module: After receiving data from the image acquisition module, it uses deep learning and image processing technology to pre-process and improve image quality. It then uses a fault recognition model to automatically detect faults, annotate fault information, and store and manage it. The results provide a key basis for subsequent maintenance decisions.

[0010] Communication and Data Transmission Module: This module utilizes wireless communication technology to enable two-way data transmission between the drone and the ground control center, uploading flight information, images, and fault information, and receiving control commands. Data encryption ensures transmission security and efficient collaborative operation among all modules.

[0011] Preferably, the flight control module includes:

[0012] (1) Precise control of flight attitude: The flight control module is the "control center" for the UAV to achieve autonomous inspection. It receives instructions from the path planning module, uses inertial navigation and satellite positioning systems, and combines high-precision sensors such as gyroscopes and accelerometers to capture the UAV's motion status in real time. Through intelligent algorithms, it dynamically adjusts the flight attitude, speed, and altitude to ensure that the UAV flies stably along the predetermined inspection path, laying a solid foundation for subsequent data collection;

[0013] The dynamic adjustment expression of the UAV flight attitude is:

[0014]

[0015] Where: K is the acceleration adjustment of the UAV at time t; p is the position proportional gain coefficient; K d is the speed proportional gain coefficient; The reference position given to the path planning module; The current actual position of the drone; The reference speed given to the path planning module; is the current actual speed of the drone;

[0016] (2) Intelligent planning of obstacle avoidance paths: Safe flight is an important prerequisite for inspection tasks, and the flight control module has a powerful obstacle avoidance function. When the environmental perception module transmits obstacle information, the module responds quickly and uses advanced algorithms to automatically plan obstacle avoidance paths, guiding the drone to flexibly avoid danger. Whether it is an obstacle in complex terrain or a sudden obstacle in the air, it can ensure the safe flight of the drone and avoid collision accidents;

[0017] (3) Efficient and coordinated system operation: The flight control module does not operate independently, but closely coordinates with other system modules. It not only accurately executes the instructions of the path planning module, but also provides a stable shooting platform for the image acquisition module. Throughout the inspection process, it continuously senses the status of the drone and provides feedback, working together with other modules to build an efficient and reliable drone-based autonomous power line inspection system.

[0018] Preferably, the path planning module includes:

[0019] (1) Accurately generate the initial inspection path: The path planning module is based on the geographic information system (GIS) and the three-dimensional model data of the power line, deeply integrates the performance parameters of the drone and the mission requirements, and generates the optimal inspection path through the Dijkstra algorithm. It can accurately plan the route from the starting point to the end point, ensuring that the drone efficiently covers the inspection area and providing a scientific initial route plan for power line inspection;

[0020] The path optimization expression formula is:

[0021]

[0022] Where: C i,i+1 is the path cost from path node i to node i+1 (considering distance, terrain, and weather); n is the total number of path nodes;

[0023] (2) Comprehensive consideration to ensure path safety: When planning the route, this module fully considers multiple factors such as the direction of power lines, complex terrain and landforms, and meteorological conditions. For dangerous terrain such as mountainous areas and waters, or severe weather such as strong winds and heavy rain, risk areas are avoided in advance, effectively preventing drones from being put in danger due to environmental factors, and building a safe path defense line for inspection flights;

[0024] (3) Dynamic Adjustment for Intelligent Inspection: The path planning module has dynamic adaptability and receives real-time information about the power line status and the drone's own status. Once a line anomaly or a change in the drone's status is detected, such as a suspected fault in a certain line section, the path is quickly replanned and the drone is directed to conduct a focused inspection of the faulty area, achieving intelligent dynamic optimization of the inspection task.

[0025] Preferably, the image acquisition module includes:

[0026] (1) Collaborative configuration of multiple devices: The image acquisition module integrates a variety of advanced devices, including high-definition visible light cameras, infrared thermal imagers, and ultraviolet imagers. These devices work together to build a comprehensive power line data acquisition system, just like equipping drones with "smart eyes", providing the hardware foundation for accurate detection of power line faults and meeting diverse inspection data collection needs;

[0027] (2) Accurately capture line faults: Various types of equipment play a unique role in inspections. Visible light cameras focus on the appearance of the line, keenly capturing direct problems such as broken wires and damaged insulators; infrared thermal imagers reveal hidden heating hazards caused by poor contact and overload through temperature distribution; ultraviolet imagers accurately judge the insulation performance of insulators by detecting corona discharge, thus achieving comprehensive and in-depth detection of power line faults;

[0028] (3) Intelligent transmission of collected data: Based on the instructions of the path planning module, the image acquisition module captures high-definition images and videos at the optimal position and angle. The collected data is immediately transmitted to the image processing module in real time through an efficient transmission link, ensuring data timeliness and facilitating subsequent fault analysis and processing. This achieves a seamless transition from data collection to transmission, ensuring efficient inspection work.

[0029] Preferably, the image processing module includes:

[0030] (1) Image preprocessing to optimize quality: The image processing module is a key step in power line fault identification. After receiving data from the image acquisition module, it first uses advanced image processing technology to perform preprocessing operations such as denoising and enhancement on the image. By removing interference information in the image and improving image clarity, the image quality is effectively improved, laying a solid foundation for subsequent accurate fault identification.

[0031] The image denoising processing formula is:

[0032]

[0033] Where: I′(x,y) is the pixel value of the denoised image; I(x+i,y+j) is the pixel value of the original image; G(i,j) is the Gaussian kernel function, defined as k is half of the filter window size; σ is the Gaussian filter standard deviation;

[0034] (2) Intelligent identification and fault labeling: After pre-processing, this module uses deep learning algorithms and trained fault recognition models to conduct in-depth analysis of power line images. It can quickly and accurately automatically identify various fault types such as broken insulators and broken conductors, and mark the specific location and severity of the fault in detail, achieving efficient extraction and clear presentation of fault information.

[0035] (3) Data storage and management application: After completing fault identification and labeling, the image processing module takes on the important task of data storage and management. It stores the processed image data in an orderly manner and establishes a complete data management system, which facilitates staff to conduct rapid query and in-depth analysis of historical inspection data, providing strong data support for power line maintenance decisions.

[0036] Preferably, the communication and data transmission module includes:

[0037] (1) Building a wireless communication link: The communication and data transmission module serves as a "bridge" between the UAV and the ground control center. Relying on advanced wireless communication technologies such as 4G / 5G and digital radio, it builds a stable data transmission channel. It can transmit the UAV's flight status information such as attitude and speed, as well as key data such as image acquisition and fault detection, to the ground in real time and efficiently, providing information support for remote monitoring.

[0038] (2) Realize two-way data interaction: This module not only undertakes the task of uploading data, but also has the function of receiving instructions. The ground control center can send control instructions and mission parameters to the drone through this module, realizing remote operations such as adjusting the drone's flight path and setting equipment parameters. Two-way data interaction allows the drone's inspection tasks to be flexibly adjusted according to actual needs, ensuring the accurate implementation of inspection work;

[0039] (3) Ensure data transmission security: In terms of data transmission security, the communication and data transmission modules use advanced data encryption technology to encrypt all types of transmitted data. From flight data to fault detection results, encryption protection effectively prevents data leakage and tampering, ensuring the security and reliability of the drone inspection system during data exchange, and building a solid information security line for power line inspection.

[0040] A method for autonomous inspection and control of power lines by a UAV, the specific steps of the method are as follows:

[0041] S1. Mission Planning and Parameter Setting: At the ground control center, operators use the path planning module to set mission parameters and select image acquisition equipment and parameters based on power line conditions and inspection requirements. The path planning module combines GIS data with the 3D line model to generate an initial path, laying the foundation for drone inspections and guiding subsequent work.

[0042] S2. Autonomous Inspection and Real-Time Monitoring: Based on pre-planned procedures, the drone flies autonomously along a predetermined path under the control of the flight control module. During flight, the environmental perception module provides real-time feedback to help the drone avoid obstacles. The image acquisition module simultaneously collects data and transmits it to the ground. Operators monitor this data to ensure that inspections are carried out accurately as planned, providing data support for fault detection.

[0043] S3. Fault Detection and Processing: The image processing module receives and analyzes the collected data, automatically detecting faults using a fault recognition model, annotating relevant information, and transmitting it back to the ground. Operators can then develop maintenance plans and remotely instruct the drone to conduct a second inspection of the faulty area, refining the fault information and forming a complete closed loop from detection to processing.

[0044] Preferably, the specific steps of task planning and parameter setting in step S1 are as follows:

[0045] S11. Define basic inspection task parameters: In the ground control center, operators use the path planning module to accurately set the basic parameters of the drone inspection based on the actual conditions such as the distribution and operation status of the power lines, combined with the specific requirements of the inspection task. They define the inspection starting and end points, delineate the inspection area, and determine key indicators such as flight altitude and speed, thus establishing a basic framework for subsequent inspection work.

[0046] S12. Adapt image acquisition equipment parameters: Based on the specific requirements of different inspection tasks, operators further select appropriate image acquisition equipment and set acquisition parameters accordingly. For routine appearance inspections, the visible light camera's shooting angle and frequency are adjusted; for troubleshooting hidden dangers such as equipment overheating, the infrared thermal imager's temperature measurement range is reasonably set to ensure that all types of equipment can achieve optimal inspection efficiency.

[0047] S13. Generate and transmit the initial inspection route: The route planning module integrates all pre-set parameters, deeply integrates GIS geographic information data with the 3D power line model, and uses professional algorithms to generate a scientific and reasonable initial inspection route. This route information is then promptly transmitted to the flight control module, providing accurate guidance for the drone to conduct subsequent autonomous inspections along the planned route.

[0048] Preferably, the specific steps of autonomous inspection and real-time monitoring in step S2 are as follows:

[0049] S21. Intelligent Flight and Dynamic Obstacle Avoidance: Under the precise control of the flight control module, the drone flies autonomously, strictly following the predetermined inspection route. The environmental perception module, like a sensitive "antenna," monitors the distribution of obstacles, changes in weather conditions, and other environmental information in real time, and transmits it to the flight control module in a timely manner. Based on this information and its own status, the flight control module intelligently adjusts its flight attitude and path, efficiently completing dynamic obstacle avoidance and ensuring safety and stability throughout the inspection process.

[0050] S22. Real-time multi-source data acquisition: Based on safe flight, the image acquisition module systematically collects images and videos of power lines according to preset parameters. The various sensors on board each perform their respective functions, with visible light cameras, infrared thermal imagers, and ultraviolet imagers working together to capture comprehensive line status information. The collected data is transmitted to the ground control center in real time and stably via the communication and data transmission modules.

[0051] S23. Remote Monitoring and Management: The ground control center serves as the "brain center" of the entire inspection mission. Operators can view the drone's flight status, location information, and inspection images in real time through the monitoring interface. If an anomaly is detected, such as a suspected fault area or abnormal drone status, prompt instructions can be issued remotely to adjust the drone's inspection strategy, ensuring high-quality completion of the inspection mission.

[0052] Preferably, the specific steps of fault detection and processing in step S3 are as follows:

[0053] S31. Intelligent Detection of Power Line Faults: The image processing module, as the core unit of fault detection, immediately initiates in-depth processing upon receiving data from the image acquisition module. Leveraging advanced deep learning algorithms and a well-trained fault recognition model, it comprehensively analyzes power line images, automatically and accurately identifying various faults, such as broken insulators and broken conductors, providing a reliable basis for subsequent processing.

[0054] S32. Real-time feedback of key fault information: Once a fault is detected, the image processing module quickly annotates key information such as fault type, specific location, and severity, generating a clear and intuitive fault report. This information is then fed back to the ground control center in real time and in a stable manner through the communication and data transmission module, ensuring that the fault situation is immediately understood.

[0055] S33. Scientifically Plan Maintenance and Secondary Inspections: Ground control center operators formulate maintenance plans based on received fault information and the actual operating conditions of the power lines. Furthermore, they can issue commands to drones through the communication and data transmission module, flexibly adjust inspection tasks, and schedule drones for secondary inspections of the faulty area to collect further detailed information, helping maintenance personnel efficiently complete repairs.

[0056] The beneficial effects of the present invention are as follows:

[0057] 1. The present invention significantly improves inspection efficiency and accuracy through the use of intelligent technology. The path planning module automatically generates the optimal inspection path based on geographic information system data and line three-dimensional models, freeing itself from the constraints of manual real-time control and significantly shortening the inspection time. At the same time, the system is equipped with a variety of image acquisition devices such as visible light cameras and infrared thermal imagers, and cooperates with advanced deep learning algorithms and fault recognition models to conduct in-depth analysis of multi-dimensional data such as the appearance and temperature of power lines, accurately identify various types of faults, and promptly and accurately discover potential faults, thereby building a solid defense line for the safe and stable operation of power lines.

[0058] 2. The present invention uses high-precision sensors to perceive environmental changes in real time. When encountering obstacles, it automatically plans an obstacle avoidance path to ensure flight safety. The path planning module dynamically adjusts the inspection strategy based on geographic information system data and line status to accurately locate the fault area. The image processing module uses deep learning algorithms to quickly identify various line faults. The modules work together to automate the entire process from task planning, path execution to fault diagnosis, greatly reducing human intervention, and using intelligent decision-making and dynamic response mechanisms to improve the reliability and safety of inspection tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a flow chart of the autonomous inspection system for UAV power lines of the present invention;

[0060] Figure 2 This is a flow chart of the autonomous inspection control method of UAV power lines of the present invention. DETAILED DESCRIPTION

[0061] 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.

[0062] like Figures 1 to 2 As shown, an embodiment of the present invention provides a UAV power line autonomous inspection system, which is composed of a flight control module, a path planning module, an image acquisition module, an image processing module, and a communication and data transmission module;

[0063] Flight Control Module: This module is responsible for precisely controlling the drone's flight attitude, speed, and altitude. It uses inertial navigation, satellite positioning, and high-precision sensors to sense motion and dynamically adjust flight. Its obstacle avoidance function plans paths based on environmental information, ensuring a safe and secure inspection environment and laying the foundation for subsequent module operations.

[0064] Path Planning Module: Utilizing GIS and a 3D model of power lines, combined with drone performance and mission requirements, it generates an optimal inspection route. It considers multiple factors, including line direction, to avoid hazards and dynamically adjusts based on line and drone status, providing guidance for image acquisition.

[0065] Image acquisition module: Equipped with multiple high-definition cameras and sensors, the system captures images and videos of power lines at appropriate locations and angles along a planned path. Different types of equipment perform their respective functions to detect various faults, and the collected data is transmitted to the image processing module in real time, providing raw material for fault detection.

[0066] Image processing module: After receiving data from the image acquisition module, it uses deep learning and image processing technology to pre-process and improve image quality. It then uses a fault recognition model to automatically detect faults, annotate fault information, and store and manage it. The results provide a key basis for subsequent maintenance decisions.

[0067] Communication and Data Transmission Module: This module utilizes wireless communication technology to enable two-way data transmission between the drone and the ground control center, uploading flight information, images, and fault information, and receiving control commands. Data encryption ensures transmission security and efficient collaborative operation among all modules.

[0068] The present invention, relying on intelligent technology, comprehensively innovates the inspection mode of power lines and significantly improves the inspection efficiency, accuracy and reliability. In terms of inspection efficiency and accuracy, the path planning module automatically generates the optimal path based on geographic information system data and the three-dimensional model of the line, freeing itself from the constraints of manual control and significantly shortening the inspection time; the system is equipped with a variety of image acquisition devices, combined with deep learning algorithms and fault identification models, to conduct multi-dimensional analysis of power lines and accurately locate various potential faults. In terms of intelligence and reliability, the flight control module perceives the environment through high-precision sensors and automatically avoids obstacles to ensure flight safety; the path planning module dynamically adjusts the strategy according to the line status; and the image processing module quickly identifies faults. The modules work together to achieve full automation, reduce manual intervention, and provide solid guarantees for the safe and stable operation of power lines with intelligent decision-making and dynamic response mechanisms.

[0069] The flight control module, as the core control unit of the drone-based autonomous power line inspection system, plays a key role in the entire inspection process. It leverages inertial navigation and satellite positioning systems, combined with high-precision sensors such as gyroscopes and accelerometers, to accurately capture the drone's motion status in real time. Upon receiving instructions from the path planning module, it dynamically adjusts its flight attitude, speed, and altitude through intelligent algorithms to ensure the drone's stable flight along the planned path, providing a stable foundation for data collection.

[0070] This module has a powerful obstacle avoidance function. When the environmental perception module feedbacks obstacle information, it can quickly use advanced algorithms to plan an obstacle avoidance path, effectively avoiding complex terrain or sudden aerial obstacles to ensure flight safety.

[0071] The flight control module works efficiently with other system modules, accurately executing instructions while creating stable shooting conditions for the image acquisition module, and providing real-time feedback on the drone status, working together with other modules to build an efficient and reliable autonomous inspection system.

[0072] The path planning module, serving as the "smart guide" of the drone-based autonomous power line inspection system, plays a core planning role in inspection tasks. Based on geographic information system (GIS) and three-dimensional power line model data, it deeply integrates drone performance parameters with mission requirements and uses intelligent algorithms to accurately generate the optimal inspection path from the starting point to the end point, ensuring efficient drone coverage of the target area and providing a scientific and reliable initial plan for inspection work.

[0073] This module fully considers key factors such as the direction of power lines, complex terrain and meteorological conditions. It plans safe routes in advance and avoids risk areas in dangerous terrain such as mountains and waters, or in severe weather such as strong winds and heavy rains.

[0074] The path planning module has strong dynamic response capabilities and receives real-time information on the power line status and the drone's own status. Once a line abnormality is detected or the drone's status changes, it quickly replans the path and directs the drone to conduct key inspections of the fault area, realizing intelligent dynamic optimization of inspection tasks and greatly improving the accuracy and reliability of inspections.

[0075] The image acquisition module serves as the "visual core" of the drone's autonomous power line inspection system, providing key support for accurate inspections through the collaboration of multiple devices and intelligent data flow. The module integrates advanced equipment such as high-definition visible light cameras, infrared thermal imagers, and ultraviolet imagers to build a multi-dimensional data acquisition system, giving the drone sharp "smart eyes";

[0076] Visible light cameras focus on capturing visible problems such as line damage, infrared thermal imagers use temperature data to locate equipment overheating hazards, and ultraviolet imagers use corona discharge to detect the insulation status of insulators, achieving comprehensive detection of line faults.

[0077] The module can complete high-definition image and video acquisition at the optimal point according to the instructions of the path planning module, and transmit the data to the image processing module in real time and stably through an efficient transmission link, ensuring zero-delay data flow, laying a solid foundation for subsequent fault analysis and diagnosis, and ensuring the efficient advancement of power line inspection work.

[0078] The image processing module, as the core hub for fault identification in the autonomous drone power line inspection system, plays a key role in the entire inspection process. After receiving data from the image acquisition module, the module first uses advanced image processing technology to perform pre-processing operations such as denoising and enhancement on the image, effectively removing interference information and improving image clarity, laying a solid foundation for subsequent fault identification.

[0079] Leveraging deep learning algorithms and a well-trained fault recognition model, the system conducts in-depth analysis of power line images, enabling rapid and accurate automatic identification of various faults, such as broken insulators and broken conductors. It also details the fault location and severity, efficiently extracting fault information.

[0080] After completing fault identification and labeling, the image processing module is also responsible for storing the processed image data in an orderly manner and building a complete data management system to facilitate staff to quickly query historical inspection data and conduct in-depth analysis, providing strong data support for power line maintenance decisions and ensuring the integrity and efficiency of the inspection work.

[0081] The communication and data transmission module, as the key hub connecting the drone and the ground control center in the autonomous drone power line inspection system, undertakes the important mission of establishing a stable data link, achieving two-way interaction, and ensuring data security. Relying on advanced wireless communication technologies such as 4G / 5G and digital radio, this module builds a reliable data transmission channel, uploading the drone's flight attitude, speed, and other status information, as well as key data such as image acquisition and fault detection, to the ground control center in real time and efficiently, providing solid information support for remote monitoring.

[0082] The communication and data transmission module also supports the ground control center to send control instructions and mission parameters to the UAV, enabling remote operations such as adjusting the UAV's flight path and setting equipment parameters, ensuring that inspection tasks are carried out flexibly and accurately;

[0083] The communication and data transmission module provides comprehensive encryption protection for all types of transmitted data, effectively resisting the risks of data leakage and tampering, building a solid information security line for power line inspections, and ensuring the security and reliability of data interaction in the entire inspection system.

[0084] The present invention also provides a method for autonomous inspection and control of power lines by a UAV, the specific steps of which are as follows:

[0085] S1. Mission Planning and Parameter Setting: At the ground control center, operators use the path planning module to set mission parameters and select image acquisition equipment and parameters based on power line conditions and inspection requirements. The path planning module combines GIS data with the 3D line model to generate an initial path, laying the foundation for drone inspections and guiding subsequent work.

[0086] S2. Autonomous Inspection and Real-Time Monitoring: Based on pre-planned procedures, the drone flies autonomously along a predetermined path under the control of the flight control module. During flight, the environmental perception module provides real-time feedback to help the drone avoid obstacles. The image acquisition module simultaneously collects data and transmits it to the ground. Operators monitor this data to ensure that inspections are carried out accurately as planned, providing data support for fault detection.

[0087] S3. Fault Detection and Processing: The image processing module receives and analyzes the collected data, automatically detecting faults using a fault recognition model, annotating relevant information, and transmitting it back to the ground. Operators can then develop maintenance plans and remotely instruct the drone to conduct a second inspection of the faulty area, refining the fault information and forming a complete closed loop from detection to processing.

[0088] Among them, the task planning and parameter setting in step S1 refers to the task planning and parameter setting link of the UAV power line autonomous inspection system, in which the ground control center operator needs to complete a series of rigorous operations. First, based on the actual conditions such as the distribution direction and operating status of the power lines, combined with the inspection task requirements, with the help of the path planning module, the inspection starting point, end point, area, as well as basic parameters such as flight altitude and speed are accurately set to build the basic framework of the inspection work;

[0089] According to different inspection tasks, select appropriate image acquisition equipment, such as visible light cameras, infrared thermal imagers, etc., and adjust acquisition parameters such as shooting angle, frequency, and temperature measurement range to give full play to the detection efficiency of each device;

[0090] The path planning module deeply integrates the above parameters with GIS geographic information data and three-dimensional models of power lines, generates a scientific initial inspection path through professional algorithms, and transmits the path information to the flight control module to provide precise guidance for autonomous inspections by drones.

[0091] The autonomous inspection and real-time monitoring in step S2 refers to the close collaboration of all links during the autonomous inspection and real-time monitoring of the UAV power lines to ensure efficient and safe inspection. The UAV, under the control of the flight control module, flies autonomously along a predetermined path. The environmental perception module monitors obstacles and weather changes in real time, and the flight control module dynamically adjusts its attitude and path accordingly to achieve intelligent obstacle avoidance and ensure flight safety.

[0092] At the same time, the image acquisition module uses a variety of sensors such as visible light cameras and infrared thermal imagers based on preset parameters to collect images and video data of power lines in all directions, and transmits them back to the ground control center in real time through the communication and data transmission module;

[0093] Operators can monitor the drone's flight status, location, and inspection images in real time through the monitoring interface. Once an abnormality is detected, they can issue instructions remotely and flexibly adjust the inspection strategy, thereby achieving full control of the inspection task and ensuring high-quality completion of the inspection task.

[0094] The fault detection and processing in step S3 refers to the fault detection and processing phase of the UAV power line autonomous inspection system. Each step is closely linked to form an efficient closed loop. The image processing module, as the core unit, receives data from the image acquisition module and uses deep learning algorithms and fault recognition models to quickly and accurately detect various line faults such as broken insulators and broken conductors, providing solid data support for subsequent work.

[0095] Once a fault is detected, the module immediately marks the fault type, location, and severity, generates a clear fault report, and transmits the information back to the ground control center in real time through the communication and data transmission module to ensure timely feedback on the fault situation;

[0096] Operators at the ground control center develop scientific maintenance plans based on fault information and the actual conditions of the line. They can also use the communication module to command drones to conduct a second inspection of the fault area to obtain more detailed data, helping maintenance personnel complete repair work efficiently and accurately.

[0097] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0098] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An autonomous inspection system for power lines using drones, characterized by: The system consists of a flight control module, a path planning module, an image acquisition module, an image processing module, and a communication and data transmission module; Flight control module: responsible for controlling the UAV's flight attitude, speed, and altitude. It uses inertial navigation, satellite positioning, and high-precision sensors to sense the state of motion, dynamically adjust flight, and the obstacle avoidance function plans the path based on environmental information. Path planning module: Utilizes geographic information systems and three-dimensional models of power lines, combines drone performance with mission requirements, generates the optimal inspection path, and comprehensively considers various factors related to line direction to avoid danger; Image acquisition module: Equipped with high-definition cameras and sensors, it collects images and videos of power lines along the planned path, and transmits the collected data to the image processing module in real time; Image processing module: After receiving data from the image acquisition module, it first pre-processes the image to improve the quality, then automatically detects faults through the fault recognition model, annotates the fault information, and stores and manages it; Communication and data transmission module: uses wireless communication technology to achieve two-way data transmission between the UAV and the ground control center, upload flight, image and fault information, and receive control instructions.

2. The UAV power line autonomous inspection system according to claim 1 is characterized by: The flight control module includes: (1) Precisely control flight attitude: After receiving instructions from the path planning module, the inertial navigation and satellite positioning system, combined with high-precision sensors such as gyroscopes and accelerometers, captures the drone's motion status in real time and dynamically adjusts the flight attitude, speed, and altitude through intelligent algorithms; The dynamic adjustment expression of the UAV flight attitude is: Where: K is the acceleration adjustment of the UAV at time t; p is the position proportional gain coefficient; K d is the speed proportional gain coefficient; The reference position given to the path planning module; The current actual position of the drone; The reference speed given to the path planning module; is the current actual speed of the drone; (2) Intelligent obstacle avoidance path planning: When the environment perception module receives obstacle information, the module responds quickly and uses the Manhattan distance heuristic function to automatically plan the obstacle avoidance path to guide the drone to avoid danger; (3) Efficient collaborative system operation: The flight control module works closely with other system modules to accurately execute the instructions of the path planning module, provide a stable shooting platform for the image acquisition module, and continuously sense the status of the drone and provide feedback.

3. The UAV power line autonomous inspection system according to claim 1 is characterized by: The path planning module includes: (1) Accurately generate the initial inspection path: Based on the geographic information system and the three-dimensional model data of the power line, the performance parameters of the drone and the mission requirements are deeply integrated to generate the optimal inspection path through the Dijkstra algorithm; The path optimization expression formula is: Where: C i,i+1 is the path cost from path node i to node i+1; n is the total number of path nodes; (2) Comprehensive consideration to ensure route safety: When planning the route, fully consider the direction of power lines, complex terrain and meteorological conditions, and avoid risk areas in advance; (3) Dynamic adjustment to achieve intelligent inspection: Receive real-time information on the power line status and the drone's own status. When a line anomaly is detected or the drone's status changes, command the drone to conduct a focused inspection of the fault area.

4. The UAV power line autonomous inspection system according to claim 1 is characterized by: The image acquisition module includes: (1) Multi-device collaborative configuration: Equipped with high-definition visible light cameras, infrared thermal imagers, and ultraviolet imagers, a comprehensive power line data collection system is built; (2) Accurately capture line faults: The visible light camera focuses on the line appearance, and the infrared thermal imager detects the potential heating hazards caused by poor contact and overload through temperature distribution; the ultraviolet imager detects corona discharge and determines the insulation performance of the insulator; (3) Intelligent transmission of collected data: According to the instructions of the path planning module, high-definition images and videos are collected, and the collected data are transmitted to the image processing module in real time.

5. The UAV power line autonomous inspection system according to claim 1 is characterized by: The image processing module includes: (1) Image preprocessing to optimize quality: After receiving the data from the image acquisition module, the image denoising technology is first used to perform denoising and enhancement preprocessing operations on the image; The image denoising processing formula is: Where: I′(x,y) is the pixel value of the denoised image; I(x+i,y+j) is the pixel value of the original image; G(i,j) is the Gaussian kernel function; k is half the filter window size; σ is the standard deviation of the Gaussian filter; (2) Intelligent identification and fault labeling: After pre-processing, the fault recognition model is used to conduct in-depth analysis of the power line image, automatically identifying various fault types such as insulator damage and conductor breakage, and marking the specific location and severity of the fault in detail; (3) Data storage and management application: After completing fault identification and labeling, the processed image data will be stored in an orderly manner and a complete data management system will be established.

6. The UAV power line autonomous inspection system according to claim 1 is characterized by: The communication and data transmission module includes: (1) Build wireless communication links: Relying on 4G, 5G and digital radio wireless communication technologies, build data transmission channels to transmit the UAV’s flight attitude and speed status information, as well as image acquisition and fault detection data, to the ground in real time and efficiently; (2) Realize two-way data interaction: The ground control center sends control instructions and mission parameters to the UAV through the communication and data transmission module, realizing remote operation of the UAV flight; (3) Ensure data transmission security: Use data encryption technology to encrypt all types of transmitted data, from flight data to fault detection results, to prevent data leakage and tampering.

7. A method for autonomous inspection and control of power lines by a drone, characterized by: The specific steps of this method are as follows: S1. Task planning and parameter setting: The operator sets task parameters through the path planning module based on the power line conditions and inspection requirements. The path planning module combines geographic information system data and the line 3D model to generate the initial path; S2. Autonomous inspection and real-time monitoring: The drone flies autonomously along a predetermined path. During flight, the environmental perception module provides real-time feedback, and the image acquisition module simultaneously collects data and transmits it to the ground. S3. Fault detection and processing: After receiving the collected data, the image processing module uses the fault recognition model to automatically detect the fault. The operator formulates a maintenance plan and remotely instructs the drone to conduct a second inspection of the fault area.

8. The method for autonomous inspection of power lines by a drone according to claim 7, characterized in that: The specific steps of task planning and parameter setting in step S1 are as follows: S11. Define basic inspection task parameters: Operators should define the inspection starting and ending points, demarcate the inspection area, and determine key flight altitude and speed indicators based on the distribution and direction of power lines, actual operating conditions, and the specific requirements of the inspection task. S12. Adapt image acquisition device parameters: Set acquisition parameters in a targeted manner. For routine appearance inspections, adjust the shooting angle and frequency of the visible light camera. For troubleshooting equipment heating hazards, set the temperature measurement range of the infrared thermal imager. S13. Generate and transmit the initial inspection path: Based on the set parameters, integrate geographic information data and the three-dimensional model of the power line, use professional algorithms to generate the initial inspection path, and transmit the path information to the flight control module.

9. The method for autonomous inspection of power lines by a UAV according to claim 7, characterized in that: The specific steps of autonomous inspection and real-time monitoring in step S2 are as follows: S21, Intelligent Flight and Dynamic Obstacle Avoidance: The drone flies autonomously along a predetermined inspection path. The environmental perception module monitors obstacle distribution and weather conditions in real time. The flight control module intelligently adjusts the flight attitude and path based on its own status. S22, Real-time multi-source data acquisition: Carry out power line image and video acquisition work, visible light camera, infrared thermal imager, ultraviolet imager cooperates, and the collected data is transmitted through the communication and data transmission module; S23. Remote monitoring and management scheduling: Operators can view the UAV’s flight status, location information, and inspection images in real time through the monitoring interface. Once an abnormal situation is found, they can issue instructions in a timely manner and remotely dispatch the UAV to adjust the inspection strategy.

10. The method for autonomous inspection of power lines by a UAV according to claim 7, characterized in that: The specific steps of fault detection and processing in step S3 are as follows: S31. Intelligent detection of power line faults: After receiving data from the image acquisition module, the image processing module initiates a deep processing process. Leveraging advanced deep learning algorithms and a well-trained fault recognition model, it comprehensively analyzes power line images and identifies various faults, including broken insulators and broken conductors. S32. Real-time feedback of key fault information: When a fault is detected, the image processing module quickly marks the key fault information and feeds the fault information back to the ground control center; S33. Scientifically plan maintenance and secondary inspections: Based on the received fault information and the actual operating conditions of the power lines, a scientific maintenance plan is formulated. At the same time, drones are arranged to conduct secondary inspections of the fault area.

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