An unmanned aerial vehicle-based power distribution line inspection method, device and medium

Abstract

The application provides a power distribution line inspection method and device based on a UAV, and a medium. The method comprises the following steps: acquiring a starting point and an ending point of a UAV to be inspected; receiving input terrain, obstacle information and environmental parameters, and determining an inspection path according to a preset flight path planning algorithm; controlling the UAV to inspect along the inspection path; receiving real-time data obtained by the UAV during flight, and updating the inspection path in real time according to a preset dynamic path adjustment algorithm; adjusting the pitch angle and speed of the UAV through a preset state estimation algorithm and real-time flight state data; identifying key components from power distribution line images, and performing state evaluation on the key components through a preset algorithm. The application can automatically plan an inspection route, has high detection efficiency, can more accurately identify potential problems of a power distribution line, helps to discover and handle hidden dangers in time, and has higher safety and detection accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of distributed energy storage, in particular to a power distribution line inspection method and device based on a UAV and a medium. BACKGROUND

[0002] Power distribution lines are prone to line faults caused by factors such as strong winds, forest fires, rain and snow. In order to avoid the impact of line faults on power distribution, special maintenance personnel are usually required to conduct regular inspections of power transmission lines, which is difficult and inefficient. Although some areas have begun to test the use of UAVs for power distribution line inspection, the existing UAV visual navigation system has limited detection capability in complex environments, resulting in high risk of inspection tasks and incomplete and inaccurate detection results. Therefore, how to improve the detection capability of UAVs in complex environments and improve the accuracy of power distribution line inspection is a technical problem that needs to be solved. SUMMARY

[0003] The embodiments of the present application provide a power distribution line inspection method, device and medium based on a UAV to solve the technical problem of low accuracy of current UAV power distribution line inspection.

[0004] The specific technical solutions provided by the embodiments of the present application are as follows:

[0005] In a first aspect, the embodiments of the present application provide a power distribution line inspection method based on a UAV, comprising:

[0006] Obtaining the starting point and the ending point of the UAV to be inspected;

[0007] Receiving input terrain, obstacle information and environmental parameters;

[0008] Based on the starting point and the ending point of the UAV to be inspected, and the terrain, obstacle information and environmental parameters, determining the inspection path of the UAV according to a preset flight path planning algorithm;

[0009] Controlling the UAV to inspect the power distribution line along the inspection path;

[0010] Receiving real-time data obtained by the UAV during flight, the real-time data including real-time flight state data, real-time environmental data, real-time obstacle information and power distribution line images, and updating the inspection path in real time according to a preset dynamic path adjustment algorithm;

[0011] Adjusting the pitch angle and speed of the UAV through a preset state estimation algorithm and the real-time flight state data;

[0012] Identify key components from the power distribution line image, and perform state evaluation on the key components through a preset algorithm, the key components including one or more of lines, insulators, and connectors.

[0013] In a possible embodiment, the determining the inspection path of the UAV according to the preset flight route planning algorithm specifically includes:

[0014] Let P = {p1, p2,..., pn} be a set of path points of the UAV, where p1 is a starting point to be inspected by the UAV, pn is a terminal point to be inspected by the UAV, pi represents the i-th point on the path, and i = 1, 2,..., n;

[0015] Calculate the distance between the path points pi and pi+1;

[0016] Minimize the total flight distance D as the objective function through a preset expression Determine the inspection path of the UAV;

[0017] Where D represents the total flight distance, pi represents the i-th point on the path, pi+1 represents the i+1-th point on the path, and d(pi, pi+1) represents the distance between the path points pi and pi+1.

[0018] In a possible embodiment, the power distribution line inspection method based on the UAV further includes adjusting the flight distance of the UAV through a preset dynamic adjustment formula, the preset dynamic adjustment formula being:

[0019]

[0020] Where D adj represents the adjusted flight distance, D represents the original flight distance, δ(pi, pi+1) represents a dynamic obstacle factor between adjacent path points, and a is a dynamic adjustment coefficient.

[0021] In a possible embodiment, the power distribution line inspection method based on the UAV further includes constructing a constraint condition of the flight route planning algorithm.

[0022] In a possible embodiment, the constraint condition of the flight route planning algorithm includes:

[0023] An obstacle avoidance constraint, ensuring that the path point pi is not in an obstacle;

[0024] A flight performance constraint, ensuring that the UAV meets preset performance parameters in the flight process, the preset performance parameters including a maximum flight speed, a maximum range, and a maximum load;

[0025] An environmental constraint, considering the influence of wind speed and wind direction environmental factors on the flight path.

[0026] Legal constraints are ensured to ensure that the path point pi does not violate any legal and regulatory restrictions.

[0027] In a possible embodiment, the adjusting the pitch angle and the speed of the unmanned aerial vehicle through the preset state estimation algorithm and the real-time flight state data specifically comprises:

[0028] The real-time flight state data comprises a current pitch angle and a current speed of the unmanned aerial vehicle;

[0029] Obtaining a target pitch angle and a target speed of the unmanned aerial vehicle;

[0030] Calculating a first error between the current pitch angle and the target pitch angle, and adjusting the pitch angle PID controller parameters of the unmanned aerial vehicle according to the first error;

[0031] Calculating a second error between the current speed and the target speed, and adjusting the speed PID controller parameters of the unmanned aerial vehicle according to the second error.

[0032] In a possible embodiment, before the identifying the key components from the power distribution line image and the state evaluation of the key components through the preset algorithm, further comprising:

[0033] Preprocessing the power distribution line image obtained by the unmanned aerial vehicle in real time, the preprocessing comprising image denoising and contrast enhancement.

[0034] In a possible embodiment, the identifying the key components from the power distribution line image and the state evaluation of the key components through the preset algorithm specifically comprises:

[0035] Identifying the line from the preprocessed power distribution line image;

[0036] Evaluating the abnormality of the line through the line evaluation model based on deep learning pre-constructed and trained, the abnormality of the line comprising wear and / or fracture.

[0037] In a possible embodiment, the identifying the key components from the power distribution line image and the state evaluation of the key components through the preset algorithm specifically comprises:

[0038] Identifying the insulator from the preprocessed power distribution line image;

[0039] Identifying the surface defects and / or the degree of contamination of the insulator through the insulator evaluation model based on the multi-channel feature fusion network pre-constructed and trained.

[0040] In a possible embodiment, the identifying the key components from the power distribution line image and performing state evaluation on the key components by using a preset algorithm specifically includes:

[0041] identifying the connectors from the pre-processed power distribution line image;

[0042] evaluating the looseness and / or corrosion of the connectors by using a pre-constructed and trained connector evaluation model based on a convolutional neural network.

[0043] In a possible embodiment, after the state evaluation on the key components by using the preset algorithm, an evaluation result is output.

[0044] In a possible embodiment, a real-time line state report is generated according to the evaluation result.

[0045] In a possible embodiment, the power distribution line inspection method based on the unmanned aerial vehicle further includes obtaining terrain and obstacle information of a location where the power distribution line is located by using a high-precision map and a sensor, the sensor including one or more of a gyroscope, a GPS, a GLONASS, a Beidou navigation system, an accelerometer, and a barometer, and the obstacle information including an obstacle position and / or an obstacle height.

[0046] In a possible embodiment, the unmanned aerial vehicle is further integrated with a visual odometer, and the power distribution line inspection method based on the unmanned aerial vehicle further includes estimating a motion state of the unmanned aerial vehicle by analyzing a continuous image sequence.

[0047] In a second aspect, another embodiment of the present application further provides a power distribution line inspection unmanned aerial vehicle inspection device, including:

[0048] a memory configured to store program instructions;

[0049] a processor configured to invoke the program instructions stored in the memory to implement the power distribution line inspection method based on the unmanned aerial vehicle according to any one of the first aspect.

[0050] In a third aspect, another embodiment of the present application further provides a computer readable storage medium having program codes stored thereon, the program codes being configured to implement the power distribution line inspection method based on the unmanned aerial vehicle according to any one of the first aspect.

[0051] The present application has at least the following beneficial effects:

[0052] The unmanned aerial vehicle-based power distribution line inspection method, device and medium provided by the application automatically plan an inspection route for the power distribution line to be inspected, control the unmanned aerial vehicle to inspect the power distribution line along the inspection route, have high detection efficiency, can be beneficial to more accurately identify potential problems of the power distribution line by obtaining real-time images of the power distribution line in the flight range of the unmanned aerial vehicle and analyzing and evaluating key components, discover and handle hidden dangers in time, avoid power failures caused by inspection omissions, and have higher detection accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 A flowchart of an unmanned aerial vehicle-based power distribution line inspection method is provided for the embodiments of the application.

[0054] Figure 2 A module diagram of a power distribution line inspection unmanned aerial vehicle inspection device is provided for another embodiment of the application. DETAILED DESCRIPTION

[0055] In order to more clearly understand the application purpose, features and advantages of the application, the application will be further described in detail below in combination with the drawings and specific embodiments. In the following description, a large number of specific details are set forth in order to facilitate a full understanding of the application, but the application can also be implemented in other ways different from those described herein, and therefore, the application is not limited to the specific embodiments disclosed below.

[0056] The embodiments of the application will be further described in detail below in combination with the drawings and specific embodiments of the application described in the specification, and it should be understood that the embodiments described herein are only used to illustrate and explain the application, and are not used to limit the application, and the features of the embodiments of the application and the embodiments can be combined with each other without conflict.

[0057] In an embodiment, referring to Figure 1 A flowchart of an unmanned aerial vehicle-based power distribution line inspection method is provided for the embodiments of the application, and the method comprises the following steps:

[0058] Step S1, obtaining a starting point and an ending point of an unmanned aerial vehicle to be inspected;

[0059] Step S2, receiving input terrain, obstacle information and environmental parameters;

[0060] Step S3, determining an inspection path of the unmanned aerial vehicle based on the starting point and the ending point of the unmanned aerial vehicle to be inspected, and the terrain, obstacle information and environmental parameters, according to a preset flight path planning algorithm;

[0061] Step S4, controlling the unmanned aerial vehicle to patrol the power distribution line along the patrol path, receiving real-time data obtained by the unmanned aerial vehicle during flight, the real-time data including real-time flight state data, real-time environment data, real-time obstacle information and power distribution line images, and updating the patrol path in real time according to a preset dynamic path adjustment algorithm;

[0062] Step S5, adjusting the pitch angle and speed of the unmanned aerial vehicle through a preset state estimation algorithm and the real-time flight state data;

[0063] Step S6, identifying key components from the power distribution line images, and performing state evaluation on the key components through a preset algorithm, the key components including one or more of lines, insulators and connectors.

[0064] The unmanned aerial vehicle-based power distribution line patrol method, device and medium can automatically plan a patrol route for the power distribution line to be patrolled, detect the power distribution line by controlling the unmanned aerial vehicle to patrol the power distribution line along the patrol route, obtain real-time images of the power distribution line during the flight of the unmanned aerial vehicle, and analyze and evaluate key components, which can help to more accurately identify potential problems of the power distribution line, discover and handle hidden dangers in time, avoid power failures caused by missed patrol, and improve detection accuracy.

[0065] In a possible embodiment, the patrol path of the unmanned aerial vehicle is determined according to a preset flight path planning algorithm, specifically including:

[0066] Let P={p1,p2,...,pn} be a path point set of the unmanned aerial vehicle, wherein p1 is a starting point to be inspected by the unmanned aerial vehicle, pn is a terminal point to be inspected by the unmanned aerial vehicle, pi represents the i-th point on the path, and i=1,2,...,n;

[0067] The distance between the path points pi and pi+1 is calculated.

[0068] The distance calculation can use the Euclidean distance formula, that is:

[0069] d(pi,pi+1)=(xi+1-xi)2+(yi+1-yi)2+(zi+1-zi)2

[0070] Wherein, (xi,yi,zi) and (xi+1,yi+1,zi+1) represent the coordinates of the points pi and pi+1, respectively.

[0071] Minimize the total flight distance D as the objective function, and determine the patrol path of the unmanned aerial vehicle through a preset expression

[0072] ​where D represents the total flight distance, pi represents the ith point on the path, pi+1 represents the i+1th point on the path, and d(pi, pi+1) represents the distance between path points pi and pi+1.

[0073] To cope with sudden obstacles or environmental changes and ensure the adaptability of the UAV in complex environments, in one possible embodiment, the UAV-based power line inspection method further comprises adjusting the flight distance of the UAV through a preset dynamic adjustment formula, which is:

[0074]

[0075] where D adj represents the adjusted flight distance, D represents the original flight distance, δ(pi, pi+1) represents the dynamic obstacle factor between adjacent path points, and α is a dynamic adjustment coefficient, which is set in advance according to actual needs.

[0076] When the UAV encounters an obstacle, Dadj is adjusted according to the position, size and dynamics of the obstacle. Specifically, the UAV calculates the dynamic obstacle factor (such as repulsive field or pheromone concentration) between path points by sensing the existence of the obstacle, and then adds it to the original path distance D after weighting by the adjustment coefficient α. This adjustment allows the UAV to dynamically avoid obstacles while minimizing the increase in path length.

[0077] This adjustment mechanism is used to optimize the flight path of the UAV, ensuring that it can avoid obstacles in complex environments while trying to maintain the optimality of the path. By optimizing the objective function in real time and dynamically updating the path point set, the UAV can maximize the inspection efficiency while ensuring safety. At the same time, by combining historical data and real-time monitoring information, the route planning algorithm is constantly improved, and the intelligent level of the UAV in power line inspection is improved.

[0078] In some specific implementations, to improve the flight stability and comfort of the UAV, the UAV-based power line inspection method further comprises smoothing the planned path. Path smoothing algorithms such as cubic spline interpolation, Bezier curve, etc. can be used to smooth the path, making the flight trajectory of the UAV smoother and more natural.

[0079] In one possible embodiment, the UAV-based power line inspection method further comprises constructing the constraint conditions of the route planning algorithm.

[0080] In one possible embodiment, the constraint conditions of the route planning algorithm include:

[0081] Obstacle avoidance constraint, ensuring that path point pi is not inside an obstacle.

[0082] flight performance constraints, ensuring that the UAV meets preset performance parameters during flight, including maximum flight speed, maximum range, and maximum payload;

[0083] environmental constraints, considering the impact of wind speed and direction on the flight path;

[0084] legal and regulatory constraints, ensuring that the path point pi does not violate any legal and regulatory restrictions.

[0085] In power distribution line inspection, the route planning and trajectory state estimation method of the UAV is crucial. In order to ensure that the UAV can efficiently and safely complete the inspection task, the route planning algorithm provided by the present application comprehensively considers multiple factors, including terrain and obstacle information, flight performance parameters of the UAV, environmental factors, and legal and regulatory restrictions. First, terrain and obstacle information is the basis for route planning. By using high-precision maps and advanced sensor technology, detailed terrain data and obstacle locations can be obtained. These data will be used to generate an optimal route that avoids obstacles, ensuring that the UAV does not collide with any obstacles during flight. Second, the flight performance parameters of the UAV, such as maximum flight speed, maximum range, and maximum payload, are crucial to route planning. These parameters determine the performance limitations of the UAV when performing tasks and need to be fully considered when planning the route to ensure that the UAV can successfully complete the inspection task. Environmental factors, such as wind speed, wind direction, and weather conditions, can also affect the flight trajectory of the UAV. When planning the route, the impact of these factors on the flight stability of the UAV needs to be considered and the route needs to be adjusted accordingly to ensure that the UAV can maintain stable flight in various environmental conditions. Legal and regulatory restrictions, such as no-fly zones and flight height restrictions, are rules that must be followed in route planning. When planning the route, it is necessary to ensure that the flight path of the UAV does not violate any laws and regulations to avoid possible legal risks and safety problems.

[0086] In one possible embodiment, the adjusting the pitch angle and speed of the UAV through the preset state estimation algorithm and the real-time flight state data specifically includes:

[0087] The real-time flight state data includes the current pitch angle and current speed of the UAV;

[0088] Obtaining the target pitch angle and target speed of the UAV;

[0089] Calculating the first error between the current pitch angle and the target pitch angle, and adjusting the pitch angle PID controller parameters of the UAV according to the first error;

[0090] A second error between the current velocity and the target velocity is calculated, and a velocity PID controller parameter of the UAV is adjusted according to the second error.

[0091] Specifically, the control target and the feedback signal are defined as follows:

[0092] Control target: Set the target pitch angle (pitch_ref) and the target velocity (velocity_ref) of the UAV.

[0093] Feedback signal: Obtain the current pitch angle (pitch_feedback) and the current velocity (velocity_feedback) of the UAV through a sensor.

[0094] The error is calculated in this embodiment as follows:

[0095] Pitch angle error: pitch_error = pitch_ref - pitch_feedback

[0096] Velocity error: velocity_error = velocity_ref - velocity_feedback

[0097] Pitch angle PID controller parameters in this embodiment:

[0098] Proportional gain: Kp_pitch

[0099] Integral gain: Ki_pitch

[0100] Derivative gain: Kd_pitch

[0101] Velocity PID controller parameters in this embodiment:

[0102] Proportional gain: Kp_velocity

[0103] Integral gain: Ki_velocity

[0104] Derivative gain: Kd_velocity

[0105] In one embodiment, the output of the PID controller is calculated as follows:

[0106] Pitch angle control:

[0107] Proportional term: P_pitch = Kp_pitch * pitch_error

[0108] Integral term: I_pitch = Ki_pitch * ∫pitch_error dt

[0109] Derivative term: D_pitch = Kd_pitch * (d(pitch_error) / dt)

[0110] PID output: output_pitch = P_pitch + I_pitch + D_pitch

[0111] Velocity control:

[0112] Proportional term: P_velocity = Kp_velocity * velocity_error

[0113] Integral term: I_velocity = Ki_velocity * ∫velocity_error dt

[0114] Derivative term: D_velocity = Kd_velocity * (d(velocity_error) / dt)

[0115] PID output: output_velocity = P_velocity + I_velocity + D_velocity.

[0116] After the flight path is planned, the flight status of the UAV is monitored and evaluated in real time. By using sensor data and advanced estimation algorithms, the position, velocity, attitude and other key parameters of the UAV can be calculated in real time. These parameters will be used to evaluate whether the UAV is flying according to the planned flight path, and whether adjustments need to be made to respond to unexpected situations.

[0117] By considering factors such as terrain and obstacle information, UAV flight performance parameters, environmental factors, and legal and regulatory restrictions, accurate flight path planning and real-time trajectory state estimation can be achieved, ensuring that the UAV can efficiently and safely complete the power line inspection task and provide strong support for the stable operation of the power system.

[0118] In one possible embodiment, before identifying the key components from the power line image and evaluating the state of the key components through a preset algorithm, the method further comprises:

[0119] The power line image obtained by the UAV in real time is preprocessed, and the preprocessing includes image denoising and contrast enhancement to improve image quality.

[0120] In one possible embodiment, the identifying the key components from the power line image and evaluating the state of the key components through a preset algorithm specifically comprises:

[0121] Image recognition technologies such as edge detection and feature extraction are used to identify power distribution lines from preprocessed power distribution line images;

[0122] Anomalies in the circuit, including wear and / or breakage, are assessed by a pre-built and trained deep learning-based circuit evaluation model.

[0123] Deep learning-based line assessment models, once trained, can accurately identify abnormal conditions such as wear and breakage in power lines, thus providing a scientific basis for maintenance decisions. Furthermore, by combining historical assessment data, potential faults can be predicted, enabling preventative maintenance, reducing unexpected risks, and ensuring the stability of power supply.

[0124] In one possible embodiment, identifying key components from the power distribution line image and assessing the condition of the key components using a preset algorithm specifically includes:

[0125] Image recognition technologies such as edge detection and feature extraction are used to identify insulators from preprocessed power distribution line images;

[0126] The surface defects and / or degree of contamination of insulators are identified by a pre-built and trained insulator evaluation model based on a multi-channel feature fusion network.

[0127] By identifying surface defects and the degree of contamination on insulators, the health status of insulators can be effectively determined. Furthermore, by combining meteorological data and line operation information, comprehensive analysis of the detection data provides strong data support for real-time monitoring and long-term management of power distribution lines.

[0128] In one possible embodiment, identifying key components from the power distribution line image and assessing the condition of the key components using a preset algorithm specifically includes:

[0129] Identify connectors from pre-processed power distribution line images;

[0130] Connector loosening and / or corrosion are assessed using a pre-built and trained connector evaluation model based on convolutional neural networks.

[0131] The connector evaluation model can accurately identify common faults in connectors such as loosening and corrosion, and analyze their degradation trends by comparing them with historical data. The application of this technology not only improves detection efficiency but also helps to promptly identify and address potential hazards, ensuring the stable operation of the power system and the safety of power supply. Based on this, the system will continue to optimize its algorithms to adapt to complex and ever-changing field environments, continuously improving the intelligent monitoring level of power distribution lines.

[0132] In a possible embodiment, after the state evaluation of the key components by the preset algorithm, an evaluation result is output.

[0133] In a possible embodiment, a real-time line state report is generated according to the evaluation result.

[0134] In a possible embodiment, the unmanned aerial vehicle-based power distribution line inspection method further comprises obtaining terrain and obstacle information of a location where the power distribution line is located by using a high-precision map and a sensor, the sensor comprising one or more of a gyroscope, a GPS, a GLONASS, a Beidou navigation system, an accelerometer, and a barometer, and the obstacle information comprising an obstacle position and / or an obstacle height.

[0135] In a possible embodiment, the unmanned aerial vehicle is further integrated with a visual odometry (VO) technology, and the unmanned aerial vehicle-based power distribution line inspection method further comprises estimating a motion state of the unmanned aerial vehicle by analyzing a continuous image sequence.

[0136] For ease of understanding, the specific process of the unmanned aerial vehicle-based power distribution line inspection method is described in detail below.

[0137] In power distribution line inspection, the route planning and trajectory state estimation method of the unmanned aerial vehicle is crucial. In order to ensure that the unmanned aerial vehicle can efficiently and safely complete the inspection task, the route planning algorithm needs to consider multiple factors, including terrain and obstacle information, flight performance parameters of the unmanned aerial vehicle, environmental factors, and legal and regulatory restrictions.

[0138] Firstly, terrain and obstacle information is the basis for route planning. By using high-precision maps and advanced sensor technology, detailed terrain data and obstacle positions can be obtained. These data will be used to generate an optimal route that avoids obstacles, ensuring that the unmanned aerial vehicle does not collide with any obstacles during flight.

[0139] Secondly, the flight performance parameters of the unmanned aerial vehicle, such as maximum flight speed, maximum range, and maximum load, are crucial for route planning. These parameters determine the performance limitations of the unmanned aerial vehicle when performing tasks and need to be fully considered when planning the route to ensure that the unmanned aerial vehicle can successfully complete the inspection task.

[0140] Environmental factors, such as wind speed, wind direction, weather conditions, etc., also have an impact on the flight trajectory of the unmanned aerial vehicle. When planning the route, these factors need to be considered to affect the flight stability of the unmanned aerial vehicle, and the route needs to be adjusted accordingly to ensure that the unmanned aerial vehicle can maintain a stable flight state under various environmental conditions.

[0141] Legal and regulatory restrictions, such as no-fly zones, flight height restrictions, etc., are rules that must be followed in route planning. When planning a route, it is necessary to ensure that the flight path of the UAV does not violate any laws and regulations, in order to avoid possible legal risks and safety problems.

[0142] After the route planning is completed, the trajectory state estimation method of the UAV will be used to monitor and evaluate the flight state of the UAV in real time. By using sensor data and advanced estimation algorithms, the position, speed, attitude and other key parameters of the UAV can be calculated in real time. These parameters will be used to evaluate whether the UAV is flying according to the planned route, and whether adjustments need to be made to respond to unexpected situations.

[0143] In summary, the route planning and trajectory state estimation method of the UAV for power distribution line inspection needs to consider multiple factors such as terrain and obstacle information, flight performance parameters of the UAV, environmental factors and legal and regulatory restrictions. Through accurate route planning and real-time trajectory state estimation, the UAV can efficiently and safely complete the power distribution line inspection task, providing strong support for the stable operation of the power system.

[0144] In this embodiment, during the execution of the inspection task, the UAV will make full use of the geographical information of the power distribution line and historical inspection data, and with the help of advanced optimization algorithms, it will carefully plan an optimal route. This route planning process not only considers the dynamics of the aircraft, but also fully considers the influence of battery endurance and various weather conditions. After the route planning is completed, the UAV will strictly follow the planned route and collect image data of the power distribution line in real time.

[0145] During flight, the UAV is equipped with advanced GPS and inertial navigation system (INS), which are used for precise positioning and heading control. In addition, the UAV also integrates visual odometry, which can accurately estimate the motion state of the UAV by analyzing consecutive image sequences. The application of this technology greatly improves the positioning accuracy and stability of the UAV during flight.

[0146] When the UAV completes the inspection task, the image data and flight state data collected by it will be transmitted back to the ground control center in real time. The ground control center uses advanced image processing techniques and machine learning algorithms to analyze these data in depth. Through these analyses, the ground control center can identify potential problems in the power distribution line, such as line aging, short circuit, broken line, etc., thereby providing strong data support for the maintenance and repair of the power system.

[0147] In one embodiment, during the execution of the inspection task, the unmanned aerial vehicle can monitor and record various environmental parameters in real time, such as temperature, humidity, wind speed, etc., and transmit these data together with the image data of the power distribution line back to the ground control center. Through the accumulation of inspection data and historical maintenance records, the ground control center can optimize the flight path planning algorithm, thereby improving the accuracy of fault prediction.

[0148] In practical applications, this method can support multiple unmanned aerial vehicles working together. Through unified scheduling by the ground control center, efficient inspection of large-scale power distribution networks can be achieved. After completing its own inspection task, each unmanned aerial vehicle can automatically return to the charging station for charging, ensuring the continuity and reliability of the inspection work. In this way, the unmanned aerial vehicle not only improves the inspection efficiency, but also reduces the danger and cost of manual inspection. In this way, the power company can better manage and maintain the power distribution network, ensuring the stability and reliability of power supply.

[0149] In one embodiment, an electronic device is provided, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the memory is coupled with the processor, and when the processor executes the computer program, the unmanned aerial vehicle flight path planning and trajectory state estimation method for power distribution line inspection described in the above embodiments is implemented.

[0150] In summary, the present application improves the inspection efficiency, and through the advanced sensors and image acquisition equipment carried by the unmanned aerial vehicle, detailed image data of the power distribution line can be quickly obtained, greatly shortening the time required for traditional manual inspection and improving the efficiency of the inspection work.

[0151] The present application reduces the safety risk, and the unmanned aerial vehicle inspection avoids the risk of direct contact with high-voltage lines by manual work, reducing the danger of workers working in bad weather or complex terrain, thereby ensuring the safety of the inspection personnel.

[0152] The present application improves the data accuracy, and uses image processing and machine learning technology to analyze the collected data, which can more accurately identify potential problems of the line, discover and handle hidden dangers in time, and avoid power failures caused by missed inspection.

[0153] The present application realizes intelligent flight path planning, and through the self-optimizing flight path planning algorithm of the ground control center, the unmanned aerial vehicle can automatically adjust the flight route according to the real-time environmental parameters and historical data, ensuring the efficiency and accuracy of the inspection task.

[0154] The present application supports large-scale network inspection, and multiple unmanned aerial vehicles working together can cover a larger range of power distribution networks. Through unified scheduling and efficient cooperation, comprehensive inspection of large-scale power distribution networks can be achieved, ensuring the stable operation of the power system.

[0155] Referring to Figure 2 A module diagram of a power distribution line inspection unmanned aerial vehicle inspection device 200 is provided for another embodiment of the present application, and the device comprises:

[0156] A memory 201 is configured to store program instructions.

[0157] A processor 202 is configured to invoke the program instructions stored in the memory to implement the unmanned aerial vehicle-based power distribution line inspection method described in any of the above embodiments.

[0158] Another embodiment of the present application also provides a computer-readable storage medium having program codes stored thereon, the program codes being used to implement the unmanned aerial vehicle-based power distribution line inspection method described in any of the above embodiments.

[0159] It should be noted that the computer-readable medium of the present disclosure described above can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus. In the present disclosure, the computer-readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer-readable program codes. Such a propagated data signal can take various forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or apparatus. The program codes contained in the computer-readable medium can be transmitted by any suitable medium, including but not limited to an electrical wire, an optical cable, an RF (radio frequency) or the like, or any suitable combination of the above.

[0160] The above computer-readable medium can be contained in the above electronic device; or can exist separately and not be assembled into the electronic device.

[0161] Computer program code for carrying out operations of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0162] The computer program instructions can also be loaded onto a computer or other programmable information processing apparatus to cause a series of operations to be performed on the computer or other programmable information processing apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable information processing apparatus implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0163] The units described in the embodiments of the present disclosure can be implemented by software, or by hardware, or by a combination of software and hardware. In some cases, the names of the units do not constitute a limitation on the units themselves.

[0164] The above introduces the preferred embodiments of the present application, which aims to make the spirit of the present application more clear and convenient to understand, and is not intended to limit the present application. Any modification, replacement, improvement made within the spirit and principle of the present application shall be included in the protection scope of the appended claims of the present application.

[0165] The above merely provides the preferred embodiments of the present application, and is not used to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the principles and technical scope of the present application shall fall into the scope of the present application.

Claims

1. A method for inspecting power distribution lines based on unmanned aerial vehicles (UAVs), characterized in that, include: Obtain the starting and ending points of the drone to be inspected; Receive input terrain, obstacle information, and environmental parameters; Based on the starting and ending points of the drone to be inspected, as well as terrain, obstacle information and environmental parameters, the inspection path of the drone is determined according to a preset route planning algorithm. Control the drone to inspect the power distribution lines along the inspection path; The system receives real-time data obtained during the flight of the UAV, including real-time flight status data, real-time environmental data, real-time obstacle information, and power line images, and updates the inspection path in real time according to a preset dynamic path adjustment algorithm. The pitch angle and speed of the UAV are adjusted by a preset state estimation algorithm and the real-time flight state data; Key components are identified from the power distribution line image, and their condition is assessed using a preset algorithm. These key components include one or more of the following: lines, insulators, and connectors. Specifically, determining the UAV's inspection path based on a preset route planning algorithm includes: Let P = {p1, p2, ..., pn} be the set of path points of the UAV, where p1 is the starting point of the UAV to be inspected, pn is the ending point of the UAV to be inspected, and pi represents the i-th point on the path, i = 1, 2, ..., n; Calculate the distance between path points pi and pi+1; Minimizing the total flight distance D as the objective function, through a predefined expression Determine the patrol path of the drone; Where D represents the total flight distance, pi represents the i-th point on the path, pi+1 represents the (i+1)-th point on the path, and d(pi,pi+1) represents the distance between points pi and pi+1 on the path. The method further includes: adjusting the flight distance of the drone using a preset dynamic adjustment formula, wherein the preset dynamic adjustment formula is: ； Among them, D adj δ(pi,pi+1) represents the adjusted flight distance, D represents the original flight distance, δ(pi,pi+1) represents the dynamic obstacle factor between adjacent path points, and α is the dynamic adjustment coefficient. When the drone encounters an obstacle, Dadj will adjust according to the obstacle's position, size, and dynamics. Specifically, the drone calculates the dynamic obstacle factor between path points by sensing the existence of the obstacle. The dynamic obstacle factor includes a repulsive field or pheromone concentration, and is added to the original flight path distance D after being weighted by an adjustment coefficient α.

2. The method for inspecting power distribution lines based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, Also includes: Construct the constraints for the route planning algorithm.

3. The method for inspecting power distribution lines based on unmanned aerial vehicles (UAVs) according to claim 2, characterized in that, The constraints of the route planning algorithm include: Obstacle avoidance constraints ensure that path point pi is not inside an obstacle; Flight performance constraints ensure that the UAV meets preset performance parameters during flight, including maximum flight speed, maximum range, and maximum payload; Environmental constraints, taking into account the impact of wind speed and wind direction on the flight path; Legal and regulatory constraints ensure that path point pi does not violate any legal or regulatory restrictions.

4. The method for inspecting power distribution lines based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The adjustment of the UAV's pitch angle and speed using a preset state estimation algorithm and the real-time flight state data specifically includes: The real-time flight status data includes the UAV's current pitch angle and current speed; Obtain the target pitch angle and target velocity of the UAV; Calculate the first error between the current pitch angle and the target pitch angle, and adjust the pitch angle PID controller parameters of the UAV based on the first error; Calculate a second error between the current speed and the target speed, and adjust the speed PID controller parameters of the UAV based on the second error.

5. The method for inspecting power distribution lines based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, Before identifying key components from the power distribution line image and assessing the condition of the key components using a preset algorithm, the method further includes: The images of power distribution lines acquired in real time by the UAV are preprocessed, including image denoising and contrast enhancement.

6. The method for inspecting power distribution lines based on unmanned aerial vehicles (UAVs) according to claim 5, characterized in that, The step of identifying key components from the power distribution line image and assessing the condition of the key components using a preset algorithm specifically includes: Identify the power distribution lines from the preprocessed images; Anomalies in the circuit, including wear and / or breakage, are assessed by a pre-built and trained deep learning-based circuit evaluation model.

7. The method for inspecting power distribution lines based on unmanned aerial vehicles (UAVs) according to claim 5, characterized in that, The step of identifying key components from the power distribution line image and assessing the condition of the key components using a preset algorithm specifically includes: Identify insulators from pre-processed images of power distribution lines; The surface defects and / or degree of contamination of insulators are identified by a pre-built and trained insulator evaluation model based on a multi-channel feature fusion network.

8. The method for inspecting power distribution lines based on unmanned aerial vehicles (UAVs) according to claim 5, characterized in that, The step of identifying key components from the power distribution line image and assessing the condition of the key components using a preset algorithm specifically includes: Identify connectors from pre-processed power distribution line images; Connector loosening and / or corrosion are assessed using a pre-built and trained connector evaluation model based on convolutional neural networks.

9. The method for inspecting power distribution lines based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, After the key components are evaluated using a preset algorithm, the evaluation results are output.

10. The method for inspecting power distribution lines based on unmanned aerial vehicles (UAVs) according to claim 9, characterized in that, A real-time line status report is generated based on the evaluation results.

11. The method for inspecting power distribution lines based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, It also includes acquiring terrain and obstacle information of the location of the power distribution line by using high-precision maps and sensors, wherein the sensors include one or more of gyroscopes, GPS, GLONASS, Beidou navigation system, accelerometers and barometers, and the obstacle information includes obstacle location and / or obstacle height.

12. The method for inspecting power distribution lines based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The drone also integrates a visual odometry system, which estimates the drone's motion state by analyzing a series of consecutive images.

13. A power distribution line inspection device based on unmanned aerial vehicles (UAVs), characterized in that, include: Memory, used to store program instructions; A processor is configured to invoke the program instructions stored in the memory to implement the UAV-based power distribution line inspection method as described in any one of claims 1 to 12.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code for implementing the UAV-based power line inspection method as described in any one of claims 1 to 12.

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