Power transmission line unmanned aerial vehicle inspection control method and device and electronic equipment

Abstract

The invention provides a power transmission line unmanned aerial vehicle inspection control method and device and electronic equipment, and relates to the field of data processing. In the method, flight environment data sent by a sensor device are acquired in real time, the flight environment data comprise wind speed and wind direction changes and airflow disturbance, and the sensor device is located on an unmanned aerial vehicle; processing the flight environment data by adopting a self-adaptive flight control model to obtain flight path data; and controlling the unmanned aerial vehicle to inspect the power transmission line according to the flight path data. By implementing the technical scheme provided by the invention, the flight safety of unmanned aerial vehicle inspection of the power transmission line can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and specifically to a transmission line drone inspection control method, device and electronic equipment. Background Art

[0002] In terms of transmission line inspection, drones have become an important tool for power inspection due to their flexibility and high efficiency.

[0003] At present, most drone inspection systems still use fixed, preset flight models for mission planning. However, this unchanging flight mode fails to fully consider the complex changes in wind speed, wind direction and airflow disturbances in high-altitude environments. When flying at high altitudes, especially where power transmission lines are often located in mountains, hills or open areas, the wind field conditions in these places are often complex, and airflow changes rapidly and unpredictably. If the drone inspection system fails to respond and adjust flight parameters in real time, it may face risks such as reduced flight stability, trajectory deviation, or even uncontrolled fall, which greatly affects the flight safety of the drone.

[0004] Therefore, there is an urgent need for a transmission line drone inspection control method, device and electronic equipment. Summary of the invention

[0005] The present application provides a transmission line drone inspection control method, device and electronic equipment to improve the flight safety of transmission line drone inspection.

[0006] In a first aspect of the present application, a transmission line UAV inspection control method is provided, the method comprising: acquiring flight environment data sent by a sensor device in real time, the flight environment data including wind speed and wind direction changes and airflow disturbances, the sensor device being located on the UAV; processing the flight environment data using an adaptive flight control model to obtain flight trajectory data; and controlling the UAV to inspect the transmission line according to the flight trajectory data.

[0007] By adopting the above technical solutions, the real-time acquisition of wind speed, wind direction and air flow disturbance data enables the drone to monitor and evaluate the flight environment in real time. This capability enables the drone to respond quickly to changes in sudden climate conditions and ensure the safety of flight. The drone can adjust the flight trajectory based on real-time data, thereby reducing the probability of accidents. The adaptive flight control model can generate the best flight trajectory by processing environmental data, thereby reducing the time required for inspection tasks. This intelligent planning enables the drone to choose the optimal route, avoid adverse airflows, and improve work efficiency. Through real-time perception of environmental changes, the drone can avoid unnecessary multiple adjustments to the flight path, thereby effectively reducing energy consumption and time waste and improving inspection efficiency. The drone can adjust the speed and flight altitude according to real-time wind speed and airflow changes to reduce energy consumption. This not only extends the drone's endurance, but also reduces the operating cost of the entire inspection mission. Automated flight control and real-time adjustment mechanisms reduce the need for manual operation, reduce labor costs and the risk of human errors, and make the inspection process more efficient and reliable. In complex terrain or severe weather conditions, fixed flight modes may not be applicable, and the adaptive flight control model can evaluate changes in environmental factors in real time and automatically adjust the flight strategy. This capability enables drones to respond flexibly in different environments, such as power transmission line inspection missions in mountainous areas, high-voltage lines, or in severe weather conditions. Since drones can adapt to complex climate conditions and terrain changes in real time, the probability of successfully completing inspection missions is significantly improved, ensuring the accuracy and integrity of data collection. Therefore, it is convenient to improve the flight safety of transmission line drone inspections.

[0008] Optionally, the adaptive flight control model is used to process the flight environment data to obtain flight trajectory data, specifically using the following calculation formula: ; in, is the UAV flight speed vector corresponding to the flight trajectory data, is the speed adjustment factor, is the basic flight speed vector, used to represent the speed vector of the drone under ideal conditions. is the disturbance amplitude coefficient corresponding to the first direction, is the perturbation wavelength parameter corresponding to the first direction, is the phase offset term corresponding to the first direction, is the disturbance amplitude coefficient corresponding to the second direction, is the perturbation wavelength parameter corresponding to the second direction, is the phase offset term corresponding to the second direction, is the Gaussian perturbation coefficient, is the diffusion parameter of the Gaussian distribution, is the nonlinear coupling coefficient, k is the adjustment term, and x and y are used to represent the two-dimensional spatial position of the UAV during flight.

[0009] By adopting the above technical solutions, the impact of environmental changes on the flight trajectory of the UAV can be captured more accurately by introducing disturbance amplitude coefficients, wavelength parameters and phase offset terms. This refinement enables the model to simulate the specific impact of different airflows and wind speeds on the flight state and improve the model's true reflection ability. The complex model can handle multiple interference factors at the same time, so that the UAV can respond effectively under various climatic conditions and improve its applicability in practical applications. The introduction of speed adjustment coefficients and Gaussian perturbation coefficients enables the UAV to adjust the flight speed and direction in real time to maintain flight stability. In complex environments, flight stability is the key to ensuring the smooth completion of inspection tasks. The nonlinear coupling coefficient enables the model to handle complex airflow interactions, ensuring that the system can quickly adapt to nonlinear disturbances and reduce flight deviations and risks. The representation of two-dimensional spatial position combined with multiple disturbance terms enables the model to comprehensively consider multiple factors for optimal planning of flight trajectories, ensuring that the UAV can fly efficiently and safely around complex transmission lines. By dynamically calculating flight trajectory data, it is possible to better offset external influences, ensure the accuracy and consistency of the UAV's heading, and reduce heading deviations caused by wind field changes. Using real-time environmental data, the model can automatically adjust parameters and dynamically optimize the flight path, thereby improving the drone's ability to respond to emergencies and ensuring the smooth progress of inspection tasks.

[0010] Optionally, the real-time acquisition of flight environment data sent by sensor equipment specifically includes: receiving wind speed and wind direction data sent by wind speed and wind direction sensors, and receiving airflow disturbance data sent by a lidar, and the sensor equipment includes the wind speed and wind direction sensors and the lidar; performing data processing on the wind speed, wind direction data and the airflow disturbance data to obtain the flight environment data, and the data processing includes denoising, filtering, feature engineering and normalization.

[0011] By adopting the above technical solutions, more comprehensive and accurate environmental information can be obtained by combining the data provided by wind speed, wind direction sensors and lidar. This multi-source data fusion can effectively compensate for the limitations of a single sensor and ensure that the drone obtains more realistic flight data in complex environments. The airflow disturbance data provided by the lidar can capture tiny environmental changes, improve the measurement accuracy of complex airflows, and ensure that the flight model of the drone can be calculated and adjusted based on more accurate environmental data. Data is often subject to various interferences during the acquisition process. De-noising technology can effectively remove irrelevant noise, making subsequent analysis and decision-making more accurate. This process ensures that the input data of the model is cleaner and improves the reliability of flight control. Through the filtering algorithm, the data can be smoothed and instantaneous fluctuations can be eliminated, ensuring that the flight control system of the drone will not be affected by short-term outliers, thereby achieving more stable flight performance. Through feature engineering, key features can be extracted from the original data. These features are crucial for the training and optimization of the flight control model, and can improve the model's predictive ability and decision-making intelligence. The data after feature extraction can better reflect the dynamic changes of the environment, so that the adaptive flight control model of the drone can adjust the flight strategy more quickly and effectively. Standardizing data of different dimensions and ranges helps improve the convergence speed and stability of the model. Normalization allows the input data to be compared on the same scale, reduces the deviation in data processing, and improves the performance of the model. Normalized data can be better understood and processed by the machine learning model, improves the training efficiency of the model, reduces overfitting, and enhances the generalization ability of the model. By acquiring and processing environmental data in real time, drones can adapt to changes in the external environment in a timely manner and enhance their flight stability and safety in dynamic environments. This is especially important for tasks with high environmental requirements, such as power inspections. Enhanced data processing capabilities help identify potential environmental hazards, such as sudden strong winds or air vortices, and adjust flight plans in a timely manner to reduce the risk of drone failures during inspections.

[0012] Optionally, the method also includes: if it is determined that multiple drones perform collaborative inspection on the transmission line, obtaining a position vector corresponding to a first drone and a position vector corresponding to a second drone, the first drone and the second drone being any two adjacent drones among the multiple drones; generating a collaborative trajectory based on the position vector corresponding to the first drone and the position vector corresponding to the second drone, the collaborative trajectory being used to control the first drone and the second drone to perform collaborative inspection on the transmission line according to the collaborative trajectory.

[0013] By adopting the above technical solution, multiple drones can inspect the transmission lines at the same time, which can significantly reduce the time required for inspection. Adjacent drones work together to cover a wider area and quickly complete the inspection task, which is particularly suitable for monitoring long-distance transmission lines. When problems are found, collaborative inspection can quickly mobilize other drones for inspection, improve the response capability to sudden failures, and reduce potential risks. Through the generation of collaborative trajectories between adjacent drones, it is possible to ensure full coverage of the transmission lines, reduce blind spots, and improve the integrity and accuracy of data collection. Multiple drones can shoot and monitor the transmission lines from different angles, so that more dimensional data can be obtained, which helps to find potential problems. The generation of collaborative trajectories enables drones to fly in an orderly manner according to preset trajectories, avoiding repeated inspections or omissions that may occur during single-machine inspections, and ensuring high quality and reliability of data. In collaborative inspections, data collected by multiple drones can be processed under the same standard to improve data consistency and facilitate subsequent analysis and processing. By calculating the position vectors of adjacent drones and generating collaborative trajectories, drones can effectively avoid collisions during inspections. The system can monitor the relative positions of drones in real time and adjust the flight path in time to ensure flight safety. During the flight, if there are environmental changes or emergencies, the drones can make dynamic adjustments based on real-time data to ensure the stability and safety of the entire coordinated flight.

[0014] Optionally, the generating of the collaborative trajectory according to the position vector corresponding to the first UAV and the position vector corresponding to the second UAV is specifically performed by the following calculation formula: ; in, is the center position vector of the first UAV and the second UAV corresponding to the collaborative trajectory, m is the number of adjacent UAVs, is the alignment coefficient, is the cohesion coefficient, is the separation force coefficient, is the position vector corresponding to the first UAV, is the flight speed vector corresponding to the second UAV, is the position vector corresponding to the second UAV.

[0015] By adopting the above technical solution, the above method allows the drone to dynamically adjust its flight path according to the surrounding conditions and the behavior of other drones when facing a complex environment, thereby enhancing the adaptability of the overall system. By calculating the center position vector of the first drone and the second drone, a reasonable flight route can be formed during the inspection process to ensure that the relative positions between the drones remain coordinated and avoid collisions and path overlaps. The introduction of the alignment coefficient and the cohesion coefficient enables the drone to automatically adjust its position and direction during flight, so that the flight direction of the entire team is consistent, thereby improving the inspection efficiency. The application of the separation force coefficient ensures that the drone can adjust its flight path in real time during the inspection process to avoid collisions with each other. This obstacle avoidance mechanism is based on cluster intelligence and can respond and make adjustments quickly to ensure flight safety. When facing sudden obstacles, the formula allows the drone to quickly adjust according to the surrounding flight environment to ensure the flight safety of the entire collaborative team. In the case of multiple drones inspecting at the same time, if a drone fails, other drones can quickly adjust their positions and trajectories, continue to perform the inspection task, and maintain the stability and continuity of the system. Using the above method, drones can flexibly respond to various situations in a complex power transmission line environment to ensure the effective completion of the task. By dynamically adjusting the position vector and velocity vector, the drone can make smarter decisions based on the current flight status and environmental conditions, forming a data-driven autonomous inspection system. Multiple drones can optimize each other's flight strategies by sharing real-time data, thus improving the overall inspection intelligence level.

[0016] Optionally, before the adaptive flight control model is used to process the flight environment data, the adaptive flight control model is trained; the training of the adaptive flight control model specifically includes: acquiring training data, the training data including historical transmission lines and terrain, airflow and wind field corresponding to the historical transmission lines; inputting the training data into an adaptive neural network for training to obtain a first training result; superimposing and standardizing the first training result and the training data to obtain a second training result; inputting the second training result into the adaptive neural network for processing to obtain a third training result; superimposing and standardizing the third training result and the second training result until the training data similarity matrix is ​​output, and the training data similarity matrix satisfies a preset logistic regression condition.

[0017] By adopting the above technical solution, the richness and diversity of training data can be ensured by obtaining historical transmission lines and their corresponding terrain, airflow and wind field data. Historical data reflects real environmental changes. The model can be trained with these data to better understand and adapt to actual flight conditions. The adaptive neural network has a strong learning ability and can automatically extract features from complex input data, thereby improving the performance of the model in a dynamic environment. This enables the drone to quickly adapt to changes in the flight environment and improve flight safety and efficiency. The neural network can effectively handle nonlinear relationships, adapt to the impact of complex environmental factors such as wind fields and airflows on flight, and improve the prediction ability of the model. The first training result is superimposed and standardized with the training data, and then repeatedly input into the neural network for processing. This iterative process helps the model to optimize on the continuously updated training data and improve its performance. Through superposition and standardization, the model can avoid overfitting during the training process, while improving the convergence and stability of the model. The output training data similarity matrix provides the relationship and similarity analysis between different training data, which helps the model better understand the connection between data. By setting preset logistic regression conditions, the rationality and availability of the training results are ensured, and the interpretation and decision-making capabilities of the model are improved. By continuously updating training data and models, drones can quickly adjust their flight strategies and enhance their adaptive capabilities when facing different environmental changes. The trained models can better predict and respond to various flight environmental factors, improve control accuracy during inspections, and ensure drone flight stability.

[0018] Optionally, the method further includes: receiving an inspection start instruction and / or an inspection end instruction for the transmission line sent by a user device, wherein the user device is used to control the drone to perform an inspection; and controlling the drone to execute the inspection start instruction and / or the inspection end instruction.

[0019] By adopting the above technical solutions, users can control the inspection tasks of drones in real time through the device at any time point, with higher flexibility. When a situation that requires immediate inspection is found, a start command can be quickly issued. During the inspection process, if an abnormal situation is found, the user can quickly send an end command to ensure that the drone stops flying to avoid possible risks and losses. The user's device can dynamically adjust the inspection plan according to the real-time situation, issue new instructions according to environmental changes or task requirements, and optimize the scheduling and arrangement of inspection tasks. Through effective scheduling, the idle time of drones can be reduced, the efficiency of equipment use can be improved, and inspection resources can be maximized.

[0020] In a second aspect of the present application, a transmission line UAV inspection control device is provided, and the transmission line UAV inspection control device includes an acquisition module and a processing module, wherein the acquisition module is used to acquire flight environment data sent by a sensor device in real time, the flight environment data including wind speed and wind direction changes and airflow disturbances, and the sensor device is located on the UAV; the processing module is used to process the flight environment data using an adaptive flight control model to obtain flight trajectory data; the processing module is also used to control the UAV to inspect the transmission line according to the flight trajectory data.

[0021] In the third aspect of the present application, an electronic device is provided, which includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs the method described above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, the method described above is executed.

[0023] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: Real-time data on wind speed, wind direction, and air disturbances enable drones to monitor and assess the flight environment in real time. This capability enables drones to respond quickly to changes in sudden climate conditions and ensure flight safety. Drones can adjust their flight trajectories based on real-time data, thereby reducing the probability of accidents. The adaptive flight control model can generate the best flight trajectory by processing environmental data, thereby reducing the time required for inspection tasks. This intelligent planning enables drones to choose the best route, avoid adverse airflows, and improve work efficiency. Through real-time perception of environmental changes, drones can avoid unnecessary multiple adjustments to the flight path, thereby effectively reducing energy consumption and time waste and improving inspection efficiency. Drones can adjust speed and flight altitude based on real-time wind speed and airflow changes to reduce energy consumption. This not only extends the drone's endurance, but also reduces the operating cost of the entire inspection mission. Automated flight control and real-time adjustment mechanisms reduce the need for manual operation, reduce labor costs and the risk of human error, and make the inspection process more efficient and reliable. In complex terrain or severe weather conditions, fixed flight modes may not be applicable, and the adaptive flight control model can evaluate changes in environmental factors in real time and automatically adjust flight strategies. This capability enables drones to respond flexibly in different environments, such as power transmission line inspection missions in mountainous areas, high-voltage lines, or in severe weather conditions. Since drones can adapt to complex climate conditions and terrain changes in real time, the probability of successfully completing inspection missions is significantly improved, ensuring the accuracy and integrity of data collection. Therefore, it is convenient to improve the flight safety of transmission line drone inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flowchart of a transmission line drone inspection control method provided in an embodiment of the present application.

[0025] Figure 2 Another flowchart of a transmission line drone inspection control method provided in an embodiment of the present application.

[0026] Figure 3 A module schematic diagram of a transmission line drone inspection control device provided in an embodiment of the present application.

[0027] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0028] Explanation of the reference numerals: 31, acquisition module; 32, processing module; 41, processor; 42, communication bus; 43, user interface; 44, network interface; 45, memory. DETAILED DESCRIPTION

[0029] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0030] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0031] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0032] In terms of power line inspection, drones have rapidly developed into one of the important tools for power line inspection due to their excellent flexibility and high efficiency. These drone systems can not only cover large areas, but also effectively obtain high-quality inspection data, thereby improving the maintenance efficiency of power facilities. However, most drone inspection systems on the market still rely on fixed, preset flight models for mission planning. This unchanging flight mode fails to fully consider factors such as complex and changeable wind speed, wind direction, and airflow disturbances in high-altitude environments.

[0033] When flying at high altitudes, especially when power transmission lines are often located in mountains, hills or open areas, the wind field conditions in these specific terrains are usually very complex, and airflow changes are often rapid and difficult to predict. If the drone inspection system fails to respond and adjust flight parameters in real time, it will face many risks, such as significantly reduced flight stability, flight trajectory deviation, and even in extreme cases, the drone may lose control and fall. Therefore, the above method is not conducive to the flight safety of drones.

[0034] In order to solve the above technical problems, the present application provides a transmission line drone inspection control method, referring to Figure 1 , Figure 1A schematic flow chart of a transmission line drone inspection control method provided in an embodiment of the present application, the transmission line drone inspection control method is applied to a drone controller, including steps S110 to S130, the above steps are as follows: S110, acquiring flight environment data sent by a sensor device in real time, the flight environment data including wind speed and direction changes and airflow disturbances, the sensor device being located on the drone.

[0035] Specifically, the drone controller is the core part of the drone, which is installed inside the drone and is responsible for processing the data obtained from various sensors and formulating flight strategies based on these data. In the embodiment of the present application, the main functions of the drone controller include real-time monitoring of the flight status of the drone, processing sensor input, controlling the flight path of the drone, and executing flight instructions. Real-time acquisition means that the controller can continuously monitor changes in the flight environment and respond quickly. Real-time data acquisition is an important prerequisite for the safe flight of drones, because changes in the flight environment may directly affect the flight stability and safety of drones. Sensor equipment may include wind speed sensors, wind direction sensors, airflow sensors, etc., which are specially designed to capture dynamic changes in the surrounding environment. Transmission lines are generally located in remote and high places, and wind speed and wind direction are important factors affecting drone inspections. Wind speed refers to the speed of air flow, while wind direction refers to the direction from which the wind blows. When a drone is flying at high altitude, wind speed and wind direction may change significantly due to factors such as terrain and climate. For example, if a drone is inspecting power lines and suddenly encounters a strong wind, the wind speed increases from 10 kilometers per hour to 30 kilometers per hour, and the wind direction changes, the controller needs to immediately adjust the drone's flight altitude and heading to maintain flight stability. Airflow disturbances refer to irregularities in air flow, such as eddies and turbulence, which may cause the drone to jitter or deviate from its trajectory during flight. For example, when flying over a hilly area, a drone may encounter airflow disturbances. The controller can detect these disturbances in real time through sensors and adjust the drone's flight attitude and speed accordingly to ensure its smooth flight.

[0036] In a possible implementation, the flight environment data sent by the sensor device is acquired in real time, specifically including: receiving wind speed and wind direction data sent by wind speed and wind direction sensors, and receiving airflow disturbance data sent by a laser radar, the sensor device including a wind speed and wind direction sensor and a laser radar; processing the wind speed, wind direction data and airflow disturbance data to obtain the flight environment data, the data processing including denoising, filtering, feature engineering and normalization.

[0037] Specifically, the sensor equipment includes: Wind speed sensor: measures the speed of air flow and helps identify changes in wind speed. Wind direction sensor: determines the direction of wind blowing and helps analyze the impact of wind on flight. LiDAR: measures distance through laser beams and can provide information about airflow disturbances. Wind speed and wind direction data are important indicators of the flight environment and can affect the flight trajectory and stability of the drone. For example: Assume that the wind speed sensor detects that the current wind speed is 20km / h, and the wind direction sensor feedbacks that the wind comes from the southwest direction. These data will be used to adjust the flight strategy of the drone. LiDAR can detect changes in the surrounding airflow, especially in complex terrain, such as mountains or near buildings, where airflow disturbances may be more obvious. For example: If the LiDAR detects strong airflow disturbances ahead, it may affect the flight stability of the drone, and the control system needs to adjust the flight attitude in real time. By processing the raw sensor data, removing noise and unnecessary information, and extracting effective features, reliable flight environment data can be obtained. Among them, average filtering and Kalman filtering are used to reduce random noise in the measurement data, thereby improving the accuracy of the data. Assuming that the wind speed sensor is disturbed during the detection process, the reading may fluctuate instantaneously. Through denoising, a more stable and reliable wind speed value can be obtained. Apply low-pass filters and high-pass filters to eliminate unnecessary frequency components to make the signal clearer. For example: For wind speed and wind direction data, it may be necessary to remove high-frequency noise and retain the actual wind speed changes at low frequencies. By analyzing the data, the most important features for flight control are extracted, such as the rate of change of wind speed, the intensity of airflow disturbances, etc. Example: Calculating the comprehensive torque of wind from wind speed and wind direction data is crucial for adjusting the flight attitude of the drone. Normalization converts data to a uniform scale, usually between 0 and 1, to facilitate subsequent algorithm processing and model training. For example: If the wind speed data ranges from 0 to 50km / h, normalizing it allows the model to better understand and utilize this data.

[0038] S120: Process the flight environment data using an adaptive flight control model to obtain flight trajectory data.

[0039] Specifically, the adaptive flight control model is a control strategy that can dynamically adjust the flight parameters of the UAV based on the real-time acquired flight environment data, such as wind speed, wind direction, and airflow disturbance. Compared with the traditional fixed flight control model, this model can better adapt to changing environmental conditions. The model can continuously adjust its control parameters based on new data to achieve the best flight performance. In addition, it can be trained based on machine learning algorithms or other intelligent algorithms to better understand and adapt to environmental changes. Flight environment data includes information from wind speed sensors, wind direction sensors, and airflow disturbance sensors, which together affect the flight state of the UAV. These data provide the controller with the necessary background information, enabling it to make timely adjustments during flight to ensure flight stability and safety. Flight trajectory data refers to the planned path and corresponding flight parameters of the UAV during flight, such as altitude, speed, heading, etc. After the controller processes the environmental data using the adaptive flight control model, it calculates the optimal flight trajectory.

[0040] In a possible implementation, an adaptive flight control model is used to process the flight environment data to obtain flight trajectory data, specifically using the following calculation formula: ; in, is the UAV flight speed vector corresponding to the flight trajectory data, is the speed adjustment factor, is the basic flight speed vector, used to represent the speed vector of the drone under ideal conditions. is the disturbance amplitude coefficient corresponding to the first direction, is the perturbation wavelength parameter corresponding to the first direction, is the phase offset term corresponding to the first direction, is the disturbance amplitude coefficient corresponding to the second direction, is the perturbation wavelength parameter corresponding to the second direction, is the phase offset term corresponding to the second direction, is the Gaussian perturbation coefficient, is the diffusion parameter of the Gaussian distribution, is the nonlinear coupling coefficient, k is the adjustment term, and x and y are used to represent the two-dimensional spatial position of the UAV during flight.

[0041] Specifically, the flight trajectory data represents a collection of information such as the position and speed of the drone during flight, and is the path that the drone needs to follow. The flight speed vector is the actual flight speed of the drone under current conditions, which is determined by multiple factors, such as wind speed, wind direction, and the power setting of the drone. The speed adjustment coefficient is used to adjust the flight speed of the drone to adapt to the current flight environment. For example, an increase in wind speed may require the drone to reduce its speed to maintain stability. The basic flight speed vector represents the speed of the drone under ideal flight conditions, and this value is measured under stable weather conditions. The disturbance amplitude coefficient represents the intensity of disturbances in the airflow or wind in the environment, which can affect the flight stability of the drone. For example, strong winds can cause larger disturbance amplitudes. The disturbance wavelength parameter indicates the spatial frequency of the disturbance, reflecting the periodic characteristics of airflow changes. A shorter wavelength may mean that the airflow changes rapidly. The phase offset term is used to describe the phase of the disturbance, affecting the temporal and spatial distribution of the disturbance. For example, the phase of the airflow may affect its intensity at a particular moment. The Gaussian disturbance coefficient is used to simulate the Gaussian distribution characteristics of airflow disturbances, and is used to represent the impact of random noise or small amplitude changes. The diffusion parameter of the Gaussian distribution describes the degree of expansion of the Gaussian distribution, affecting the range and intensity of the disturbance. A higher diffusion parameter may indicate a wider range of airflow effects. The nonlinear coupling coefficient reflects the interaction between different disturbances, such as the coupling effect between airflow disturbances, which may lead to more complex flight dynamics. The adjustment item is used to fine-tune the overall flight trajectory to ensure that the drone can fly smoothly under specific environmental conditions. The two-dimensional spatial position is the real-time position coordinate of the drone during flight, which is closely related to the calculation of the flight trajectory. In an embodiment of the present application, the above parameters can all be statistically obtained in advance. Through the above-mentioned complex adaptive flight control model formula, the drone can dynamically adjust its flight speed according to environmental data, so that it can still maintain a safe and stable flight state under complex and unstable flight conditions. The flexibility and adaptability of this model enable the drone to better cope with various external interferences when performing tasks, ensuring the efficiency and safety of its inspection tasks.

[0042] S130: Control the UAV to inspect the power transmission lines according to the flight trajectory data.

[0043] Specifically, the inspection mission refers to the drone inspecting and photographing the transmission line according to the established flight trajectory to find potential faults, damage or other areas that need maintenance. The drone controller issues specific flight instructions based on the flight trajectory data, such as changing altitude, adjusting speed, turning, etc., to ensure that the drone flies according to the planned path.

[0044] Among them, the wind speed, wind direction and airflow disturbance data obtained by the drone controller in real time enable the drone to monitor and evaluate the flight environment in real time. This capability enables the drone to respond quickly to changes in sudden climate conditions and ensure the safety of flight. In addition, the drone can adjust the flight trajectory according to real-time data, thereby reducing the probability of accidents. The adaptive flight control model can generate the best flight trajectory by processing environmental data, thereby reducing the time required for inspection tasks. This intelligent planning enables the drone to choose the optimal route, avoid adverse airflows, and improve work efficiency during the inspection process. The drone controller can avoid unnecessary multiple adjustments to the flight path through real-time perception of environmental changes, thereby effectively reducing energy consumption and time waste, and greatly improving inspection efficiency. The drone can adjust the speed and flight altitude according to real-time wind speed and airflow changes to reduce energy consumption. This not only extends the drone's endurance, but also reduces the operating cost of the entire inspection mission. Automated flight control and real-time adjustment mechanisms reduce the need for manual operation, reduce labor costs and the risk of human error, and make the inspection process more efficient and reliable. In complex terrain or bad weather conditions, fixed flight modes may not be applicable, and the adaptive flight control model can evaluate changes in environmental factors in real time and automatically adjust the flight strategy. This capability enables the UAV to respond flexibly in different environments, such as power transmission line inspection tasks in mountainous areas, high-voltage lines or bad weather conditions. Since the UAV can adapt to complex climatic conditions and terrain changes in real time, its probability of successfully completing the inspection task is significantly improved, ensuring the accuracy and completeness of data collection. Therefore, it is convenient to improve the flight safety of UAV inspections of power transmission lines.

[0045] In a possible implementation, if it is determined that multiple drones are to conduct a collaborative inspection of a transmission line, the position vector corresponding to the first drone and the position vector corresponding to the second drone are obtained, and the first drone and the second drone are any two adjacent drones among the multiple drones; based on the position vector corresponding to the first drone and the position vector corresponding to the second drone, a collaborative trajectory is generated, and the collaborative trajectory is used to control the first drone and the second drone to conduct a collaborative inspection of the transmission line according to the collaborative trajectory.

[0046] Specifically, collaborative inspection refers to the joint completion of the inspection task of the same target area, such as a power transmission line, through the coordination and cooperation between multiple drones. This method can improve the efficiency and coverage of the inspection while reducing the burden on a single drone. The position of each drone in space can be represented by a position vector, which can be an array containing its two-dimensional (x, y) or three-dimensional coordinates (x, y, z). In the embodiment of the present application, the use of two-dimensional coordinates can reduce the amount of calculation and improve the processing efficiency of the drone. Among multiple drones, any two adjacent drones will be selected to work together, which can help form a more optimized inspection path and reduce mutual interference. According to the position information of the first drone and the second drone, the control system generates a collaborative trajectory, which not only takes into account their respective positions, but also optimizes the flight path between the two drones, so that they can effectively cooperate in the inspection task. The trajectory will guide the drones on how to coordinate with each other during the flight to ensure that they can effectively cover the inspection area, avoid intersections and omissions, and maximize the inspection effect.

[0047] In a possible implementation, the collaborative trajectory is generated according to the position vector corresponding to the first drone and the position vector corresponding to the second drone, specifically using the following calculation formula: ; in, is the center position vector of the first UAV and the second UAV corresponding to the collaborative trajectory, m is the number of adjacent UAVs, is the alignment coefficient, is the cohesion coefficient, is the separation force coefficient, is the position vector corresponding to the first UAV, is the flight speed vector corresponding to the second UAV, is the position vector corresponding to the second UAV.

[0048] Specifically, the center position vector represents the center position of the first drone and the second drone, and is used to calculate the overall position and direction of the drone cluster. The number of adjacent drones indicates how many drones work together in the cluster. The alignment coefficient is used to adjust the coordination of speed and direction between drones so that they move in the same direction. The cohesion coefficient determines the strength of the drones to maintain a certain distance and move toward the center of the group, thereby maintaining the integrity of the group. The separation coefficient prevents collisions between drones and ensures that they maintain a sufficient safety distance. The position vector corresponding to the first drone represents the specific position of the first drone in space. The position vector corresponding to the second drone represents the specific position of the second drone in space. The flight speed vector corresponding to the second drone represents the movement speed and direction of the second drone. Therefore, the collaborative operation of multiple drones can be achieved through this formula. Through precise calculation and adjustment, drones can effectively perform complex inspection tasks and improve the efficiency and safety of inspections.

[0049] In a possible implementation, before an adaptive flight control model is used to process flight environment data, an adaptive flight control model is trained; training the adaptive flight control model specifically includes: obtaining training data, the training data including historical transmission lines and terrain, airflow, and wind field corresponding to the historical transmission lines; inputting the training data into an adaptive neural network for training to obtain a first training result; superimposing and standardizing the first training result and the training data to obtain a second training result; inputting the second training result into an adaptive neural network for processing to obtain a third training result; superimposing and standardizing the third training result and the second training result until a training data similarity matrix is ​​output, and the training data similarity matrix satisfies a preset logistic regression condition.

[0050] Specifically, first, data related to training the adaptive flight control model is collected, including historical transmission lines, terrain information, airflow data, and wind field information. Assuming that there is a transmission line in a specific area, the corresponding terrain may be a hilly area, and the historical airflow data may show that the wind speed reaches 30km / h in a specific season, and the wind direction is variable. The collected training data is input into the adaptive neural network for initial training to generate the first training result. After the initial training, the neural network may generate a model output, such as the optimal flight altitude and angle of the drone at a specific wind speed. The first training result is superimposed and normalized with the original training data to eliminate data deviation and dimensionality problems, thereby generating a second training result. The first training result, such as flight altitude, is combined with historical data and normalized to a range between 0 and 1 to obtain more consistent data. The second training result is input into the adaptive neural network again for processing to obtain a more accurate third training result. At this stage, the model may learn more detailed flight control strategies through additional data processing, such as how to adjust flight parameters in a high wind speed environment. After multiple iterations, a training data similarity matrix is ​​finally output. This matrix should meet the preset logistic regression conditions to ensure that the model has good generalization ability. The similarity matrix shows the performance similarity under different flight conditions, ensuring that the model can also show good predictive ability in new data.

[0051] In one possible implementation, refer to Figure 2 , Figure 2 Another flow chart of a transmission line drone inspection control method provided in an embodiment of the present application includes steps S210 to S220, and the above steps are as follows: S210, receiving an inspection start instruction and / or an inspection end instruction for the transmission line sent by a user device, and the user device is used to control the drone to perform an inspection; S220, controlling the drone to execute the inspection start instruction and / or the inspection end instruction.

[0052] Specifically, the drone controller receives instructions sent by user devices, such as mobile phones, tablets or computers, through communication interfaces, such as Wi-Fi, Bluetooth or cellular networks. The inspection start instruction instructs the drone to start the inspection task of the transmission line. The inspection end instruction instructs the drone to stop the inspection task and return to the base or execute other instructions. The drone controller adjusts the flight mode, path planning and task execution of the drone according to the received instructions to ensure that it can carry out the inspection smoothly. After receiving the instructions, the controller will start or end the corresponding flight control program to ensure that the drone flies according to the preset trajectory or logic.

[0053] For example, suppose a user is using a mobile app called "Power Inspection Assistant", which is designed for power transmission line inspection. The user opens the "Power Inspection Assistant" app. On the interface, the user selects a specific transmission line to be inspected, for example, Line A, and clicks the "Start Inspection" button. The app sends an inspection start command to the drone through the network. After receiving the command, the drone controller starts the inspection mode, automatically plans the flight path, and starts flying to the target transmission line to collect monitoring data, such as video, images, or sensor data. During the inspection process, the user observes that the data has been fully collected and decides to end the inspection. The user clicks the "End Inspection" button on the app. The app sends an inspection end command to the drone. After receiving the end command, the drone controller stops data collection, executes the return flight mode, and safely returns to the base or docks at a designated location.

[0054] The present application also provides a transmission line drone inspection control device, referring to Figure 3 , Figure 3 A module schematic diagram of a transmission line UAV inspection control device provided in an embodiment of the present application, wherein the transmission line UAV inspection control device is a UAV controller, and the UAV controller includes an acquisition module 31 and a processing module 32, wherein the acquisition module 31 acquires flight environment data sent by a sensor device in real time, the flight environment data including changes in wind speed and direction and airflow disturbances, and the sensor device is located on the UAV; the processing module 32 uses an adaptive flight control model to process the flight environment data to obtain flight trajectory data; the processing module 32 controls the UAV to inspect the transmission line according to the flight trajectory data.

[0055] In a possible implementation, the processing module 32 uses an adaptive flight control model to process the flight environment data to obtain flight trajectory data, specifically using the following calculation formula: ; in, is the UAV flight speed vector corresponding to the flight trajectory data, is the speed adjustment factor, is the basic flight speed vector, used to represent the speed vector of the drone under ideal conditions. is the disturbance amplitude coefficient corresponding to the first direction, is the perturbation wavelength parameter corresponding to the first direction, is the phase offset term corresponding to the first direction, is the disturbance amplitude coefficient corresponding to the second direction, is the perturbation wavelength parameter corresponding to the second direction, is the phase offset term corresponding to the second direction, is the Gaussian perturbation coefficient, is the diffusion parameter of the Gaussian distribution, is the nonlinear coupling coefficient, k is the adjustment term, and x and y are used to represent the two-dimensional spatial position of the UAV during flight.

[0056] In a possible implementation, the acquisition module 31 acquires the flight environment data sent by the sensor device in real time, specifically including: the acquisition module 31 receives the wind speed and wind direction data sent by the wind speed and wind direction sensors, and receives the airflow disturbance data sent by the laser radar, and the sensor device includes a wind speed and wind direction sensor and a laser radar; the processing module 32 processes the wind speed, wind direction data and the airflow disturbance data to obtain the flight environment data, and the data processing includes denoising, filtering, feature engineering and normalization processing.

[0057] In a possible implementation, if the processing module 32 determines that multiple drones are conducting a collaborative inspection of a transmission line, the position vector corresponding to the first drone and the position vector corresponding to the second drone are obtained, where the first drone and the second drone are any two adjacent drones among the multiple drones; the processing module 32 generates a collaborative trajectory based on the position vector corresponding to the first drone and the position vector corresponding to the second drone, and the collaborative trajectory is used to control the first drone and the second drone to conduct a collaborative inspection of the transmission line according to the collaborative trajectory.

[0058] In a possible implementation, the processing module 32 generates a collaborative trajectory according to the position vector corresponding to the first drone and the position vector corresponding to the second drone, specifically using the following calculation formula: ; in, is the center position vector of the first UAV and the second UAV corresponding to the collaborative trajectory, m is the number of adjacent UAVs, is the alignment coefficient, is the cohesion coefficient, is the separation force coefficient, is the position vector corresponding to the first UAV, is the flight speed vector corresponding to the second UAV, is the position vector corresponding to the second UAV.

[0059] In a possible implementation, the processing module 32 trains an adaptive flight control model before using the adaptive flight control model to process the flight environment data; the training of the adaptive flight control model specifically includes: the acquisition module 31 acquires training data, and the training data includes historical transmission lines and terrain, airflow and wind field corresponding to the historical transmission lines; the processing module 32 inputs the training data into the adaptive neural network for training to obtain a first training result; the processing module 32 superimposes and standardizes the first training result and the training data to obtain a second training result; the processing module 32 inputs the second training result into the adaptive neural network for processing to obtain a third training result; the processing module 32 superimposes and standardizes the third training result and the second training result until a training data similarity matrix is ​​output, and the training data similarity matrix meets a preset logistic regression condition.

[0060] In a possible implementation, the acquisition module 31 receives a patrol start instruction and / or patrol end instruction for a transmission line sent by a user device, and the user device is used to control a drone to perform patrols; the processing module 32 controls the drone to execute the patrol start instruction and / or patrol end instruction.

[0061] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0062] The present application also provides an electronic device, referring to Figure 4 , Figure 4 The electronic device may include: at least one processor 41 , at least one network interface 44 , a user interface 43 , a memory 45 , and at least one communication bus 42 .

[0063] The communication bus 42 is used to realize the connection and communication between these components.

[0064] The user interface 43 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 43 may also include a standard wired interface and a wireless interface.

[0065] The network interface 44 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0066] Among them, the processor 41 may include one or more processing cores. The processor 41 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 45, and calling data stored in the memory 45. Optionally, the processor 41 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 41 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 41, and it can be implemented separately through a chip.

[0067] Among them, the memory 45 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 45 includes a non-transitory computer-readable storage medium. The memory 45 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 45 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 45 may also be optionally at least one storage device located away from the aforementioned processor 41. As Figure 4 As shown, the memory 45 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a transmission line drone inspection control method.

[0068] exist Figure 4In the electronic device shown, the user interface 43 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 41 can be used to call an application program for a transmission line drone inspection control method stored in the memory 45. When executed by one or more processors, the electronic device executes one or more methods such as those in the above-mentioned embodiments.

[0069] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0070] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

[0071] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0072] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0073] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0074] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0075] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0076] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A transmission line drone inspection control method, characterized in that: The method comprises: Acquire flight environment data sent by a sensor device in real time, the flight environment data including wind speed and direction changes and airflow disturbances, the sensor device being located on the drone; Processing the flight environment data using an adaptive flight control model to obtain flight trajectory data; According to the flight trajectory data, the UAV is controlled to inspect the power transmission lines.

2. The transmission line drone inspection control method according to claim 1 is characterized in that: The adaptive flight control model is used to process the flight environment data to obtain flight trajectory data, and the following calculation formula is specifically used: ； in, is the UAV flight speed vector corresponding to the flight trajectory data, is the speed adjustment factor, is the basic flight speed vector, used to represent the speed vector of the drone under ideal conditions. is the disturbance amplitude coefficient corresponding to the first direction, is the perturbation wavelength parameter corresponding to the first direction, is the phase offset term corresponding to the first direction, is the disturbance amplitude coefficient corresponding to the second direction, is the perturbation wavelength parameter corresponding to the second direction, is the phase offset term corresponding to the second direction, is the Gaussian perturbation coefficient, is the diffusion parameter of the Gaussian distribution, is the nonlinear coupling coefficient, k is the adjustment term, and x and y are used to represent the two-dimensional spatial position of the UAV during flight.

3. The transmission line drone inspection control method according to claim 1 is characterized in that: The real-time acquisition of flight environment data sent by the sensor device specifically includes: Receiving wind speed and wind direction data sent by a wind speed and wind direction sensor, and receiving airflow disturbance data sent by a laser radar, wherein the sensor device includes the wind speed and wind direction sensor and the laser radar; The wind speed, wind direction data and the airflow disturbance data are processed to obtain the flight environment data, wherein the data processing includes denoising, filtering, feature engineering and normalization processing.

4. The transmission line drone inspection control method according to claim 1 is characterized in that: The method further comprises: If it is determined that a plurality of the drones are to perform a coordinated inspection on the power transmission line, a position vector corresponding to a first drone and a position vector corresponding to a second drone are obtained, where the first drone and the second drone are any two adjacent drones among the plurality of drones; A collaborative trajectory is generated according to the position vector corresponding to the first UAV and the position vector corresponding to the second UAV. The collaborative trajectory is used to control the first UAV and the second UAV to perform collaborative inspection on the power transmission line according to the collaborative trajectory.

5. The transmission line drone inspection control method according to claim 4 is characterized in that: The collaborative trajectory is generated according to the position vector corresponding to the first UAV and the position vector corresponding to the second UAV, specifically using the following calculation formula: ； in, is the center position vector of the first UAV and the second UAV corresponding to the collaborative trajectory, m is the number of adjacent UAVs, is the alignment coefficient, is the cohesion coefficient, is the separation force coefficient, is the position vector corresponding to the first UAV, is the flight speed vector corresponding to the second UAV, is the position vector corresponding to the second UAV.

6. The transmission line drone inspection control method according to claim 1 is characterized in that: Before the adaptive flight control model is used to process the flight environment data, the adaptive flight control model is trained; the training of the adaptive flight control model specifically includes: Acquire training data, the training data including historical power transmission lines and terrain, airflow, and wind field corresponding to the historical power transmission lines; Inputting the training data into an adaptive neural network for training to obtain a first training result; After superimposing and normalizing the first training result and the training data, a second training result is obtained; Inputting the second training result into the adaptive neural network for processing to obtain a third training result; The third training result and the second training result are superimposed and standardized until the training data similarity matrix is ​​output, and the training data similarity matrix satisfies a preset logistic regression condition.

7. The transmission line drone inspection control method according to claim 1 is characterized in that: The method further comprises: receiving an inspection start instruction and / or an inspection end instruction for the transmission line sent by a user device, wherein the user device is used to control the drone to perform inspection; Control the drone to execute the inspection start instruction and / or the inspection end instruction.

8. A transmission line drone inspection control device, characterized in that: The transmission line drone inspection control device comprises an acquisition module (31) and a processing module (32), wherein: The acquisition module (31) is used to acquire in real time the flight environment data sent by the sensor device, the flight environment data including wind speed and direction changes and airflow disturbances, and the sensor device is located on the drone; The processing module (32) is used to process the flight environment data using an adaptive flight control model to obtain flight trajectory data; The processing module (32) is also used to control the drone to inspect the power transmission line according to the flight trajectory data.

9. An electronic device, characterized in that: The electronic device comprises a processor (41), a memory (45), a user interface (43) and a network interface (44), wherein the memory (45) is used to store instructions, the user interface (43) and the network interface (44) are both used to communicate with other devices, and the processor (41) is used to execute the instructions stored in the memory (45) so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

Images