Unmanned aerial vehicle control method and system

Abstract

The invention discloses an unmanned aerial vehicle control method and system, relates to the technical field of flight path control, and solves the problems of insufficient image acquisition precision, inaccurate routing inspection path planning and limited real-time data analysis capability. Through an image acquisition and enhancement module, a real-time image enhancement algorithm and a multi-image comparative analysis module, the focal length and exposure parameters of a camera are dynamically adjusted, the image definition in a complex environment is improved in combination with evaluation of an image enhancement effect value Enh and an effect threshold E, and a dimensionless size value Fdz, a position value Fdw and a relative distance value Fjl of a fault point are extracted; the image acquisition quality is effectively controlled; the environment modeling module is used for generating a three-dimensional model containing cable and obstacle information, ensuring that the path coverage value Qcd reaches the standard and avoiding an inspection blind area; and an airborne data processing mechanism is combined with an electromagnetic interference prediction and collision prediction model, so that real-time fault analysis and alarm are realized, the detection efficiency is improved, and the data real-time performance is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of flight path control, and in particular to a method and system for controlling an unmanned aerial vehicle. Background Art

[0002] The origin of drone control technology can be traced back to the radio remote control system in the early 20th century, which was mainly used in the military field at that time. With the continuous advancement of technology, especially the development of global positioning system (GPS), inertial navigation system (INS) and sensor technology, drone control has been significantly improved. It has gradually evolved from the initial simple flight control to today's intelligent autonomous flight and mission execution. In recent years, with the introduction of artificial intelligence and 5G networks, drone control technology has achieved further breakthroughs, which not only improves the speed of real-time response and data transmission, but also enhances the ability of autonomous decision-making, and promotes its wide application in multiple fields.

[0003] However, existing drone control technology used in overhead cable inspection has the following three main disadvantages: 1. Insufficient image acquisition accuracy: UAVs usually rely on high-resolution cameras to perform cable appearance inspections. However, when flying at high altitudes, the clarity of image acquisition will be disturbed due to the influence of external environments such as light and weather, making it difficult to accurately locate the fault point.

[0004] 2. Inaccurate inspection path planning: The path planning of drones during the inspection process is not smart enough, which may lead to uneven coverage of cable detection, missing certain key areas, and affecting the comprehensive inspection of fault points.

[0005] 3. Limited real-time data analysis capabilities: Although drones can collect large amounts of image and video data, existing processing systems still have bottlenecks in real-time fault point detection and intelligent analysis. Data usually needs to be transmitted back to the ground for processing, which increases the delay in fault discovery. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a drone control method and system to solve the technical problems in the background technology of insufficient cable fault point positioning accuracy, poor real-time signal transmission and data processing, and poor operation continuity due to limited endurance.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A drone control method comprises the following steps: S1. Based on the geographic information of the drone mission area, environmental modeling is performed to build a three-dimensional environmental model that includes cable location, cable height, surrounding buildings and geographical obstacles. Based on the three-dimensional environmental model, the inspection area is preset, and the high-incidence points of faults are determined as key inspection targets. In addition, multiple inspection paths are generated in combination with external meteorological conditions and lighting conditions. The path coverage value Qcd is calculated for each path to ensure that the inspection range is not missed, and the image acquisition angle is adjusted in combination with the flight altitude. S2. During the inspection route planning process, based on the evaluation results of the route coverage value Qcd, combined with the environmental parameters including the flight attitude, wind speed and light of the drone, the focal length and exposure parameters of the camera are dynamically adjusted, and the image acquisition clarity of the drone in complex environments is improved through the real-time image enhancement algorithm; at the same time, a multiple image comparison and analysis module is set up to pre-process the collected cable appearance images, identify and mark possible fault points; S3. Based on the identified potential fault point information, the path planning module generates a secondary inspection path, performs high-frequency scanning of key points, and dynamically adjusts the flight speed and inspection angle of the drone to ensure detailed inspection of the fault area; for areas that may be missed, the coverage rate is recalculated in real time and a supplementary path is generated to ensure all-round inspection without omissions; S4. Real-time fault analysis is performed through the onboard data processing mechanism. The images collected during multiple inspections are intelligently compared with the standard images of the cable, and the image difference detection algorithm is used to identify potential fault characteristics. The real-time analysis results are matched with historical data, and suspected fault areas are automatically marked. The location and characteristic information of the fault point are generated and fed back to the ground control system to achieve real-time processing and alarm. S5. Real-time monitoring of the battery life and external meteorological environment information of the drone. Based on the weather monitoring system, obtain real-time meteorological data and calculate and evaluate the meteorological index Qxxs. When the battery is low, adjust the operation plan based on the evaluation content of the meteorological index Qxxs, including automatically selecting the best return route or extending the battery life through wireless charging technology.

[0008] Preferably, S1 includes: S11, collecting environmental information of the mission area by deploying multiple sensors at the bottom of the drone, including using lidar to accurately measure the cable position and surrounding obstacles, and locating and analyzing the geographical location in combination with geomagnetic signals; then through multi-source data fusion technology, after obtaining cable height information, terrain undulation information and obstacle distribution information, they are integrated into a unified data format to build a complete three-dimensional environmental model, which includes key points of cable inspection and high-prone areas of faults, and combines model data to determine key inspection targets and areas.

[0009] Preferably, S1 further includes: S12, real-time recording and collecting drone flight related data, calculating the coverage of each inspection path in combination with the following formula, and obtaining the path coverage value Qcd: ; In the formula, Lcd represents the total path length in the UAV flight related data, Fsd represents the actual height of the target cable in the UAV flight related data, Zhz represents the height of the geographical obstacle in the UAV flight related data, and Fsj represents the signal strength change value in the UAV flight related data; The preset coverage threshold Q is compared with the path coverage value Qcd for evaluation. The specific evaluation contents are as follows: When the path coverage value Qcd < the coverage standard value Qc, it means that the path coverage and image acquisition of the current path meet the preset requirements, and the current path is marked as a priority; When the path coverage value Qcd ≥ the coverage standard value Qc, it means that the path coverage and image acquisition of the current path do not meet the preset requirements; there is a detection blind area or the image acquisition angle is not ideal, and this path is excluded.

[0010] Preferably, S2 includes: S21, dynamically adjusting the environmental parameters in the initially selected priority inspection path, using the sensing system carried by the drone, including a camera, a wind speed sensor, and a light sensing device; combining the wind speed and light intensity during the inspection process, adjusting the focal length and exposure parameters of the camera in real time to ensure image acquisition clarity in complex environments; at the same time, activating the image enhancement algorithm module, clarifying the collected cable appearance image, detecting abnormal areas through multiple image comparison and analysis modules, and marking suspected fault points to enhance the accuracy and real-time performance of fault detection.

[0011] Preferably, S2 also includes: S22, extracting the fault features of the cable appearance image after image enhancement, obtaining the size of the suspected fault point, the position of the suspected fault point and the relative position from the drone, and converting them into a standard numerical format after dimensionless processing, obtaining the dimensionless position value Fdw of the fault point, the dimensionless size value Fdz of the fault point and the dimensionless value Fjl of the relative distance of the fault point; and calculating the image enhancement effect value Enh using the following formula: ; By comparing and evaluating the preset effect threshold E with the image enhancement effect value Enh, the following evaluation content is generated: When the effect threshold E ≥ the enhancement effect value Enh, it means that the image clarity and fault detection accuracy are normal, and the current inspection path is marked as a high priority path; When the effect threshold E < the enhancement effect value Enh, it means that the image clarity and fault detection accuracy are abnormal and cannot be used as a priority path. This path is re-evaluated or readjusted as an alternative path.

[0012] Preferably, S3 includes: S31. Based on the identified potential fault point information, real-time planning and generation of secondary inspection paths, high-frequency scanning of key fault points, and adjusting the accuracy of detail collection of the fault area by adjusting the flight speed and inspection angle of the drone; in this process, monitoring information related to the inspection coverage rate, and identifying inspection blind spots; when insufficient coverage is detected, automatically recalculating and generating a supplementary path, and performing supplementary inspections on the fault area to ensure comprehensive coverage and no faults are missed; when the supplementary path is generated, the drone gives priority to executing the current supplementary path.

[0013] Preferably, S4 includes: S41. During the drone inspection process, real-time fault analysis is performed through the onboard data processing mechanism, and the cable images collected during multiple inspections are intelligently compared with the cable standard images to identify potential fault features in the image in real time; when a difference is detected between the collected cable image and the cable standard image, the collected cable image is annotated as an abnormality, and the fault point is marked as a reminder through the image difference detection algorithm, and the location information and feature information of the fault point are automatically generated; if interference signals and obstacles are identified, the electromagnetic interference prediction model and the collision prediction model are used to dynamically adjust the drone's inspection path, and different obstacle avoidance paths are simulated during the adjustment process, and the flight trajectory is finally determined and adjusted, and the fault point location information and features are fed back to the ground control system for real-time reminders.

[0014] Preferably, S4 also includes: S42, after completing the dynamic adjustment of the inspection path and performing the simulation, real-time recording of the adjusted UAV flight related data, and dimensionless processing of these data, calculating the optimization coefficient Yhxs to evaluate the path optimization effect, the specific formula is as follows: ; Wherein, Ztd is the detour angle in the adjusted UAV flight-related data, Zlz is the path oscillation frequency in the adjusted UAV flight-related data, Zpy is the electromagnetic interference intensity in the adjusted UAV flight-related data, and Zph is the path smoothness in the adjusted UAV flight-related data; n is the total number of path segments, i is the path segment number, represents the detour angle of the i-th path segment, represents the path smoothness of the i-th path segment, represents the electromagnetic interference intensity of the i-th path segment, represents the wind direction deviation coefficient of the i-th path segment; The optimization coefficient Yhxs is evaluated by the preset optimization threshold E. The specific evaluation content is as follows: When the optimization threshold E ≥ optimization coefficient Yhxs, it means that the currently adjusted path meets the optimization criteria in terms of energy consumption, obstacle avoidance efficiency and signal reception stability, and is put into use; When the optimization threshold E is less than the optimization coefficient Yhxs, it means that the currently adjusted path does not meet the optimization criteria in terms of energy consumption, obstacle avoidance efficiency and signal reception stability, and a simulated dynamic adjustment is performed again.

[0015] Preferably, S5 includes: S51, during the operation of the drone, the battery life is monitored in real time through the built-in battery monitoring system to obtain the current power information; at the same time, based on the weather monitoring system carried by the drone, real-time meteorological data is obtained, including airflow turbulence intensity Tlr, air humidity gradient Hjd, surface thermal radiation intensity Rhs and electromagnetic interference intensity Emr, and the real-time meteorological data is dimensionlessly processed, and the meteorological index Qxxs is calculated by the following formula: ; S52: When the battery power of the drone is low, the preset weather threshold R is compared with the weather index Qxxs to evaluate the impact of the current weather on the drone's return or continued operation. The specific contents are as follows: When the meteorological threshold R ≥ meteorological index Qxxs, it means that the current meteorological conditions are normal and the current weather has no effect on the drone's return or continued operation; at this time, the drone continues to perform the current operation task. In the case of limited power, it will give priority to high-priority tasks, and at the same time, it will replenish power through wireless charging technology; When the meteorological threshold R is less than the meteorological index Qxxs, it means that the current meteorological conditions are abnormal and the current weather has an impact on the drone's return or continued operation; at this time, the drone adjusts its operation plan and chooses a safe return path; when selecting the return path, avoid areas with strong air turbulence and choose the shortest return path based on the battery power.

[0016] A UAV control system includes an environment modeling module, an image acquisition and enhancement module, a secondary inspection and path supplement module, a fault analysis and intelligent comparison module, and a battery monitoring and meteorological assessment module; The environmental modeling module performs environmental modeling based on the geographic information of the drone mission area, constructs a three-dimensional environmental model containing information on cable location, cable height, surrounding buildings and geographical obstacles; presets inspection areas based on the three-dimensional environmental model, determines high-prone fault points as key inspection targets, and generates multiple inspection paths in combination with external meteorological conditions and lighting conditions; calculates the path coverage value Qcd for each path to ensure that the inspection range is not missed, and adjusts the image acquisition angle in combination with the flight altitude; The image acquisition and enhancement module is used to dynamically adjust the focal length and exposure parameters of the camera in the process of inspection path planning based on the evaluation results of the path coverage value Qcd, combined with the environmental parameters including the flight attitude, wind speed and light of the drone, and improve the image acquisition clarity of the drone in complex environments through a real-time image enhancement algorithm; at the same time, a multiple image comparison and analysis module is set to pre-process the collected cable appearance images, identify and mark possible fault points; The secondary inspection and path supplement module generates a secondary inspection path through the path planning module based on the identified potential fault point information, performs high-frequency scanning of key points, and dynamically adjusts the flight speed and inspection angle of the drone to ensure detailed inspection of the fault area; for areas that may be missed, the coverage rate is recalculated in real time and a supplementary path is generated to ensure all-round inspection without omissions; The fault analysis and intelligent comparison module performs real-time fault analysis through an onboard data processing mechanism, intelligently compares images collected during multiple inspections with standard images of the cable, and uses an image difference detection algorithm to identify potential fault features; matches real-time analysis results with historical data, automatically marks suspected fault areas, and generates fault point location and feature information to feed back to the ground control system to achieve real-time processing and alarm; The battery monitoring and meteorological assessment module is used to monitor the battery life and external meteorological environment information of the UAV in real time. Based on the weather monitoring system, it obtains real-time meteorological data and calculates and evaluates the meteorological index Qxxs. When the battery is low, the operation plan is adjusted in combination with the evaluation content of the meteorological index Qxxs, including automatically selecting the best return path or extending the battery life through wireless charging technology.

[0017] The present invention provides a drone control method and system, which have the following beneficial effects: (1) A method and system for controlling a UAV, in order to solve the problem of insufficient image acquisition accuracy, improves the image clarity of the UAV in a complex environment through the real-time image enhancement algorithm of the image acquisition and enhancement module and the multiple image comparison and analysis module; in the process of path planning, dynamically adjusts the focal length and exposure parameters of the camera, and compares and evaluates the image enhancement effect value Enh with the preset effect threshold E to ensure that the image acquisition effect meets expectations, thereby improving the accuracy of fault point identification; by extracting the dimensionless size value Fdz of the fault point, the dimensionless position value Fdw of the fault point and the dimensionless relative distance value Fjl of the fault point, effective control of the image acquisition clarity is achieved, avoiding the interference of high-altitude lighting, weather and other environments on the image acquisition effect.

[0018] (2) In this UAV control method and system, in order to solve the problem of inaccurate inspection path planning, the environmental modeling module in the system constructs a three-dimensional environmental model that includes information on cable location, cable height, surrounding buildings and geographical obstacles; based on this model, multiple inspection paths are generated, and the path coverage value Qcd is calculated to ensure that no inspection area is missed; during the inspection process, according to the evaluation result of the path coverage value Qcd, a secondary inspection path is dynamically planned and generated, and the comprehensive coverage is ensured by combining the coverage standard value Qc, and the detailed observation of the environment around the fault point is ensured by supplementary path generation, so as to realize blind spot inspection of the cable and effectively avoid the omission of key areas.

[0019] (3) This UAV control method and system addresses the problem of limited real-time data analysis capabilities. The system performs real-time fault analysis through an onboard data processing mechanism and intelligently compares inspection images with standard images. It uses an image difference detection algorithm to generate fault point location and feature information in real time and feeds it back to the ground control system to achieve real-time processing and alarm. It combines the electromagnetic interference prediction model and the collision prediction model to optimize parameters such as the detour angle Ztd, path oscillation frequency Zlz, electromagnetic interference intensity Zpy, and path smoothness Zph to ensure the signal reception stability and fault detection efficiency of the path, thereby overcoming the delay problem of traditional UAVs that need to transmit data back to the ground for processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of the steps of a method for controlling a drone according to the present invention; Figure 2 This is a schematic diagram of the framework structure of a UAV control system of the present invention; Figure 3 Schematic diagram of the deployment position of the sensors of the UAV in the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example 1

[0022] See also Figure 1 , a drone control method, comprising the following steps: S1. Based on the geographic information of the drone mission area, environmental modeling is performed to build a three-dimensional environmental model that includes cable location, cable height, surrounding buildings and geographical obstacles. Based on the three-dimensional environmental model, the inspection area is preset, and the high-incidence points of faults are determined as key inspection targets. In addition, multiple inspection paths are generated in combination with external meteorological conditions and lighting conditions. The path coverage value Qcd is calculated for each path to ensure that the inspection range is not missed, and the image acquisition angle is adjusted in combination with the flight altitude. S2. During the inspection route planning process, based on the evaluation results of the route coverage value Qcd, combined with the environmental parameters including the flight attitude, wind speed and light of the drone, the focal length and exposure parameters of the camera are dynamically adjusted, and the image acquisition clarity of the drone in complex environments is improved through the real-time image enhancement algorithm; at the same time, a multiple image comparison and analysis module is set up to pre-process the collected cable appearance images, identify and mark possible fault points; S3. Based on the identified potential fault point information, the path planning module generates a secondary inspection path, performs high-frequency scanning of key points, and dynamically adjusts the flight speed and inspection angle of the drone to ensure detailed inspection of the fault area; for areas that may be missed, the coverage rate is recalculated in real time and a supplementary path is generated to ensure all-round inspection without omissions; S4. Real-time fault analysis is performed through the onboard data processing mechanism. The images collected during multiple inspections are intelligently compared with the standard images of the cable, and the image difference detection algorithm is used to identify potential fault characteristics. The real-time analysis results are matched with historical data, and suspected fault areas are automatically marked. The location and characteristic information of the fault point are generated and fed back to the ground control system to achieve real-time processing and alarm. S5. Real-time monitoring of the battery life and external meteorological environment information of the drone. Based on the weather monitoring system, obtain real-time meteorological data and calculate and evaluate the meteorological index Qxxs. When the battery is low, adjust the operation plan based on the evaluation content of the meteorological index Qxxs, including automatically selecting the best return route or extending the battery life through wireless charging technology.

[0023] In this embodiment, step S1 constructs a three-dimensional environmental model based on the geographic information of the drone mission area, including cable location, cable height, surrounding buildings and geographical obstacle information, which can effectively identify and preset the inspection area, determine the high-incidence points of faults as key inspection targets, and generate multiple inspection paths in combination with meteorological conditions and lighting conditions; by calculating the path coverage value Qcd and dynamically adjusting the image acquisition angle in combination with the flight altitude, it ensures that the inspection range is not missed, effectively improving the coverage and efficiency of the inspection; In the process of inspection path planning, step S2 dynamically adjusts the camera focal length and exposure parameters based on the evaluation results of the path coverage value Qcd and the environmental parameters such as the UAV flight attitude, wind speed, and light. The image clarity is improved through the image enhancement algorithm, and the image is pre-processed through the multiple image comparison and analysis module to mark possible fault points, thereby improving the recognition accuracy of fault points in complex environments; Step S3 generates a secondary inspection path based on the identified potential fault point information, and performs high-frequency scanning of key points. At the same time, the drone's flight speed and inspection angle are dynamically adjusted to ensure detailed inspection of the fault area. In addition, the coverage is recalculated in real time and supplementary paths are generated to ensure all-round inspection without omissions, effectively reducing the risk of missed fault detection. Step S4 uses the onboard data processing mechanism to perform real-time fault analysis, compares the inspection image with the standard image of the cable, uses the image difference detection algorithm to identify potential fault characteristics, and matches the analysis results with historical data, automatically marking the suspected fault area and generating the fault point location and characteristic information to feed back to the ground control system, realizing real-time processing and alarm, thereby improving the accuracy and timeliness of fault location; Step S5 monitors the battery life and external meteorological environment information of the drone in real time, obtains meteorological data based on the weather monitoring system, calculates and evaluates the meteorological index Qxxs. When the battery is low, it automatically selects the best return path or uses wireless charging technology to extend the battery life based on the evaluation content of the meteorological index Qxxs, ensuring the drone's safe return or battery life under low battery or severe weather conditions, effectively improving the drone's operational reliability and continuous operation capability. Example 2

[0024] S1 includes: S11, by deploying a variety of sensors at the bottom of the drone to collect environmental information of the mission area, including the use of lidar to accurately measure the cable position and surrounding obstacles, and combining geomagnetic signals to locate and analyze the geographical location; then through multi-source data fusion technology, after obtaining cable height information, terrain undulation information and obstacle distribution information, it is integrated into a unified data format to build a complete three-dimensional environmental model, which includes key points of cable inspection and high-prone areas of faults, and combines model data to determine key inspection targets and areas.

[0025] S1 also includes: S12, real-time recording and collection of drone flight related data, combining the following formula to calculate the coverage of each inspection path, and obtain the path coverage value Qcd: ; In the formula, Lcd represents the total path length in the UAV flight related data, Fsd represents the actual height of the target cable in the UAV flight related data, Zhz represents the height of the geographical obstacle in the UAV flight related data, and Fsj represents the signal strength change value in the UAV flight related data; The preset coverage threshold Q is compared with the path coverage value Qcd for evaluation. The specific evaluation contents are as follows: When the path coverage value Qcd < the coverage standard value Qc, it means that the path coverage and image acquisition of the current path meet the preset requirements, and the current path is marked as a priority; When the path coverage value Qcd ≥ the coverage standard value Qc, it means that the path coverage and image acquisition of the current path do not meet the preset requirements; there is a detection blind area or the image acquisition angle is not ideal, and this path is excluded.

[0026] In this embodiment, S1 has significant advantages in information collection and path optimization in the UAV inspection mission area; S11 obtains accurate environmental information of the mission area by deploying multiple sensors at the bottom of the UAV, including laser radar ranging information of the cable position and surrounding obstacles, and accurately locates and analyzes the geographical location in combination with geomagnetic signals; through multi-source data fusion technology, it integrates cable height information, terrain undulation information and obstacle distribution information, and unifies all information into a three-dimensional environmental model format, thereby constructing a complete inspection area environmental model; this model includes cable inspection key points and high-prone areas of faults, ensuring that the inspection target is accurately focused on the key fault location; S12 records and collects UAV flight-related data in real time, and calculates the total path length Lcd It indicates the flight path range of the drone during inspection. The ideal distance between the drone and the cable is determined by the actual height Fsd of the target cable to ensure safe inspection. The geographical obstacle height Zhz is used to evaluate the influence of the obstacle position and height, which is helpful for dynamic obstacle avoidance. The signal strength change value Fsj is used to reflect the change of signal strength during the inspection process, which is convenient for improving the signal stability of the path. The path coverage effect is evaluated by calculating the path coverage value Qcd and comparing it with the preset coverage threshold Q. When the path coverage value Qcd is less than the coverage standard value Qc, it indicates that the current path has the conditions for priority selection. If the path coverage value Qcd ≥ the coverage standard value Qc, the current path is excluded to ensure the effectiveness of the inspection path and the quality and comprehensiveness of data collection. Example 3

[0027] S2 includes: S21. Dynamically adjust the environmental parameters in the initially selected priority inspection path, using the sensing system carried by the drone, including cameras, wind speed sensors and light sensing equipment; during the inspection process, combine the wind speed and light intensity to adjust the focal length and exposure parameters of the camera in real time to ensure the clarity of image acquisition in complex environments; at the same time, activate the image enhancement algorithm module, clarify the collected cable appearance image, detect abnormal areas through multiple image comparison and analysis modules, and mark suspected fault points to enhance the accuracy and real-time performance of fault detection.

[0028] S2 also includes: S22, extracting the fault features of the cable appearance image after image enhancement, obtaining the size of the suspected fault point, the position of the suspected fault point and the relative position from the drone, and converting them into a standard numerical format after dimensionless processing, obtaining the dimensionless position value Fdw of the fault point, the dimensionless size value Fdz of the fault point and the dimensionless value Fjl of the relative distance of the fault point; and calculating and obtaining the image enhancement effect value Enh using the following formula: ; By comparing and evaluating the preset effect threshold E with the image enhancement effect value Enh, the following evaluation content is generated: When the effect threshold E ≥ the enhancement effect value Enh, it means that the image clarity and fault detection accuracy are normal, and the current inspection path is marked as a high priority path; When the effect threshold E < the enhancement effect value Enh, it means that the image clarity and fault detection accuracy are abnormal and cannot be used as a priority path. This path is re-evaluated or readjusted as an alternative path.

[0029] In this embodiment, S2 has a significant effect in optimizing the UAV inspection path and improving the image acquisition accuracy; S21 dynamically adjusts the environmental parameters in the priority inspection path, and uses the camera, wind speed sensor and light sensing equipment carried by the UAV to adjust the camera focal length and exposure parameters in real time according to the changes in wind speed and light intensity during the inspection process to ensure image clarity in complex environments; the image enhancement algorithm module further improves the image detail capture capability, detects and marks suspected fault points through multiple image comparison and analysis modules, and achieves higher accuracy and real-time fault detection; S22 extracts fault features from the image-enhanced cable appearance image, collects the size, position and relative distance of the suspected fault point, converts it into a standardized numerical format through dimensionless processing, and generates a fault point The dimensionless position value Fdw, the dimensionless size value Fdz of the fault point and the dimensionless value Fjl of the relative distance of the fault point, the standardization of these parameters makes the fault point characteristics more comparable; by calculating the image enhancement effect value Enh and comparing and evaluating it with the preset effect threshold E, when the effect threshold E is greater than or equal to the image enhancement effect value Enh, it means that the image clarity and fault detection accuracy meet the preset requirements, and this path is marked as a high priority path to ensure that the UAV patrols on the optimal path; if the effect threshold E is less than the image enhancement effect value Enh, it means that the image acquisition effect is not up to standard, and this path needs to be used as an alternative path for re-evaluation or adjustment; this method effectively improves the stability of image acquisition and the accuracy of fault detection in complex environments, and optimizes the selection of patrol paths and the operating efficiency of UAVs. Example 4

[0030] S3 includes S31, specifically: S31, based on the identified potential fault point information, real-time planning and generation of secondary inspection paths, high-frequency scanning of key fault points, and adjusting the accuracy of detail collection of the fault area by adjusting the flight speed and inspection angle of the drone; in this process, monitoring the inspection coverage-related information and identifying the inspection blind spots; when insufficient coverage is detected, automatically recalculating and generating a supplementary path, and performing supplementary inspections on the fault area to ensure comprehensive coverage and no faults are missed; when the supplementary path is generated, the drone gives priority to executing the current supplementary path.

[0031] In this embodiment, S3 has significant advantages in improving the accuracy of fault point detection and ensuring that no inspection areas are missed; S31 plans and generates secondary inspection paths in real time based on the identified potential fault point information, performs high-frequency scanning on key fault points, and optimizes the accuracy of detail acquisition in the fault area by dynamically adjusting the flight speed and inspection angle of the drone to ensure the quality of image acquisition; during the inspection process, the system monitors the inspection area through information related to the inspection coverage rate, identifies possible blind spots, and automatically recalculates and generates supplementary paths if insufficient coverage is detected, performs supplementary inspections on the fault area, and further ensures full coverage and avoids missed detections; when the supplementary path is generated, the drone prioritizes executing the path, making the inspection more efficient and comprehensive; this module ensures high coverage and reliability of the inspection process in the system by accurately identifying the fault area and intelligently generating supplementary paths, effectively improving the accuracy and timeliness of fault detection, and reducing the risk of missed detections and false detections. Example 5

[0032] S4 includes: S41. During the drone inspection process, real-time fault analysis is performed through the onboard data processing mechanism. The cable images collected during multiple inspections are intelligently compared with the cable standard images to identify potential fault features in the image in real time. When a difference is detected between the collected cable image and the cable standard image, the collected cable image is annotated as an abnormality, and the fault point is marked as a reminder through the image difference detection algorithm, and the location information and feature information of the fault point are automatically generated. If interference signals and obstacles are identified, the electromagnetic interference prediction model and collision prediction model are used to dynamically adjust the drone's inspection path, and different obstacle avoidance paths are simulated during the adjustment process. Finally, the flight trajectory is determined and adjusted, and the fault point location information and features are fed back to the ground control system for real-time reminders.

[0033] S4 also includes: S42, after completing the dynamic adjustment of the inspection path and performing simulation, real-time recording of the adjusted UAV flight related data, and dimensionless processing of these data, calculating the optimization coefficient Yhxs to evaluate the path optimization effect, the specific formula is as follows: ; Wherein, Ztd is the detour angle in the adjusted UAV flight-related data, Zlz is the path oscillation frequency in the adjusted UAV flight-related data, Zpy is the electromagnetic interference intensity in the adjusted UAV flight-related data, and Zph is the path smoothness in the adjusted UAV flight-related data; n is the total number of path segments, i is the path segment number, represents the detour angle of the i-th path segment, represents the path smoothness of the i-th path segment, represents the electromagnetic interference intensity of the i-th path segment, represents the wind direction deviation coefficient of the i-th path segment; The optimization coefficient Yhxs is evaluated by the preset optimization threshold E. The specific evaluation content is as follows: When the optimization threshold E ≥ optimization coefficient Yhxs, it means that the currently adjusted path meets the optimization criteria in terms of energy consumption, obstacle avoidance efficiency and signal reception stability, and is put into use; When the optimization threshold E is less than the optimization coefficient Yhxs, it means that the currently adjusted path does not meet the optimization criteria in terms of energy consumption, obstacle avoidance efficiency and signal reception stability, and a simulated dynamic adjustment is performed again.

[0034] In this embodiment, S4 has significant advantages in improving the reliability of the inspection path and the accuracy of fault identification; S41 performs real-time fault analysis through the onboard data processing mechanism, intelligently compares the cable images collected during multiple inspections with the standard images, identifies potential fault characteristics, and marks abnormal images; uses the image difference detection algorithm to remind and mark the fault point, generates the fault point location information and feature information, and feeds back to the ground control system to ensure real-time monitoring and rapid alarm; if an interference signal or obstacle is identified, the electromagnetic interference prediction model and the collision prediction model dynamically adjust the drone inspection path, and finally optimize the flight trajectory by simulating different obstacle avoidance paths to ensure flight safety; S42 records the adjusted drone flight data in real time after completing the dynamic adjustment, and performs dimensionless processing to standardize it as an evaluation parameter, and calculates The detour angle Ztd, path oscillation frequency Zlz, electromagnetic interference intensity Zpy and path smoothness Zph are used to generate the optimization coefficient Yhxs; the significance of each parameter is that the detour angle Ztd reflects the complexity and detour frequency of the path, the path oscillation frequency Zlz represents the path stability, the electromagnetic interference intensity Zpy is used to evaluate the signal interference level, and the path smoothness Zph represents the smoothness of the flight trajectory; by comparing and evaluating the optimization coefficient Yhxs with the preset optimization threshold E, when the optimization threshold E is greater than or equal to the optimization coefficient Yhxs, it indicates that the path optimization meets the standard and has the best stability of energy consumption, obstacle avoidance efficiency and signal reception. If the optimization threshold E is less than the optimization coefficient Yhxs, it indicates that the current path has not reached the optimal state, then dynamic adjustment will be performed again to further optimize the path and ensure the reliability and accuracy of the UAV inspection. Example 6

[0035] S5 includes: S51. During the operation of the drone, the battery life is monitored in real time through the built-in battery monitoring system to obtain the current power information; at the same time, based on the weather monitoring system carried by the drone, real-time meteorological data is obtained, including airflow turbulence intensity Tlr, air humidity gradient Hjd, surface thermal radiation intensity Rhs and electromagnetic interference intensity Emr. After dimensionless processing of the real-time meteorological data, the meteorological index Qxxs is calculated by the following formula: ; S52: When the battery power of the drone is low, the preset weather threshold R is compared with the weather index Qxxs to evaluate the impact of the current weather on the drone's return or continued operation. The specific contents are as follows: When the meteorological threshold R ≥ meteorological index Qxxs, it means that the current meteorological conditions are normal and the current weather has no effect on the drone's return or continued operation; at this time, the drone continues to perform the current operation task. In the case of limited power, it will give priority to high-priority tasks, and at the same time, it will replenish power through wireless charging technology; When the meteorological threshold R is less than the meteorological index Qxxs, it means that the current meteorological conditions are abnormal and the current weather has an impact on the drone's return or continued operation; at this time, the drone adjusts its operation plan and chooses a safe return path; when selecting the return path, avoid areas with strong air turbulence and choose the shortest return path based on the battery power.

[0036] In this embodiment, it has significant advantages in improving the endurance management and operation safety of the UAV; S51 monitors the battery life of the UAV in real time through the built-in battery monitoring system, obtains the current power information for endurance management, and combines the real-time meteorological data obtained by the weather monitoring system, including the airflow turbulence intensity Tlr, the air humidity gradient Hjd, the surface thermal radiation intensity Rhs and the electromagnetic interference intensity Emr. These parameters play an important role in the calculation of the meteorological index Qxxs; the airflow turbulence intensity Tlr reflects the stability of the flight environment, the air humidity gradient Hjd affects the detection accuracy of the UAV sensor, the surface thermal radiation intensity Rhs evaluates the impact of the ground temperature on the temperature control of the UAV, and the electromagnetic interference intensity Emr reflects the degree of signal interference in the environment; after these meteorological parameters are dimensionlessly processed, the meteorological index is calculated by Qxxs comprehensively evaluates the current meteorological conditions; when the battery power of S52 is low, the preset meteorological threshold R is compared with the meteorological index Qxxs. When the meteorological threshold R is greater than or equal to the meteorological index Qxxs, it indicates that the current meteorological conditions are good, and the UAV can continue to perform operations, giving priority to completing high-priority tasks, and replenishing power through wireless charging technology to ensure continuous operation; when the meteorological threshold R is less than the meteorological index Qxxs, it indicates that the meteorological conditions are not conducive to flight. The UAV will adjust the operation plan and choose a safe return path. When selecting the path, it avoids areas with high air turbulence intensity, and selects the shortest return path based on the battery power to ensure the safe return of the UAV; this method effectively improves the operation stability and flight safety of the UAV under poor battery and meteorological conditions, ensuring the reliable completion of the mission. Example 7

[0037] See also Figure 2 , a UAV control system, including an environment modeling module, an image acquisition and enhancement module, a secondary inspection and path supplement module, a fault analysis and intelligent comparison module, and a battery monitoring and meteorological assessment module; The environmental modeling module performs environmental modeling based on the geographic information of the drone mission area, constructs a three-dimensional environmental model containing information on cable location, cable height, surrounding buildings and geographical obstacles; presets inspection areas based on the three-dimensional environmental model, determines high-prone fault points as key inspection targets, and generates multiple inspection paths in combination with external meteorological conditions and lighting conditions; calculates the path coverage value Qcd for each path to ensure that the inspection range is not missed, and adjusts the image acquisition angle in combination with the flight altitude; The image acquisition and enhancement module is used to dynamically adjust the focal length and exposure parameters of the camera in the process of inspection path planning based on the evaluation results of the path coverage value Qcd, combined with the environmental parameters including the flight attitude, wind speed and light of the drone, and improve the image acquisition clarity of the drone in complex environments through a real-time image enhancement algorithm; at the same time, a multiple image comparison and analysis module is set to pre-process the collected cable appearance images, identify and mark possible fault points; The secondary inspection and path supplement module generates a secondary inspection path through the path planning module based on the identified potential fault point information, performs high-frequency scanning of key points, and dynamically adjusts the flight speed and inspection angle of the drone to ensure detailed inspection of the fault area; for areas that may be missed, the coverage rate is recalculated in real time and a supplementary path is generated to ensure all-round inspection without omissions; The fault analysis and intelligent comparison module performs real-time fault analysis through an onboard data processing mechanism, intelligently compares images collected during multiple inspections with standard images of the cable, and uses an image difference detection algorithm to identify potential fault features; matches real-time analysis results with historical data, automatically marks suspected fault areas, and generates fault point location and feature information to feed back to the ground control system to achieve real-time processing and alarm; The battery monitoring and meteorological assessment module is used to monitor the battery life and external meteorological environment information of the UAV in real time. Based on the weather monitoring system, it obtains real-time meteorological data and calculates and evaluates the meteorological index Qxxs. When the battery is low, the operation plan is adjusted in combination with the evaluation content of the meteorological index Qxxs, including automatically selecting the best return path or extending the battery life through wireless charging technology.

[0038] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A drone control method, characterized in that: The following steps are involved: S1. Based on the geographic information of the drone mission area, environmental modeling is performed to build a three-dimensional environmental model that includes information on cable location, cable height, surrounding buildings and geographical obstacles; based on the three-dimensional environmental model, inspection areas are preset, high-prone fault points are identified as key inspection targets, and multiple inspection routes are generated in combination with external meteorological conditions and lighting conditions; Calculate the path coverage value Qcd for each path to ensure that the inspection range is complete, and adjust the image acquisition angle based on the flight altitude; S2. During the inspection route planning process, based on the evaluation results of the route coverage value Qcd, combined with the environmental parameters including the flight attitude, wind speed and light of the drone, the focal length and exposure parameters of the camera are dynamically adjusted, and the image acquisition clarity of the drone in complex environments is improved through the real-time image enhancement algorithm; at the same time, a multiple image comparison and analysis module is set up to pre-process the collected cable appearance images, identify and mark possible fault points; S3. Based on the identified potential fault point information, the path planning module generates a secondary inspection path, performs high-frequency scanning of key points, and dynamically adjusts the flight speed and inspection angle of the drone to ensure detailed inspection of the fault area; for areas that may be missed, the coverage rate is recalculated in real time and a supplementary path is generated to ensure all-round inspection without omissions; S4. Real-time fault analysis is performed through the onboard data processing mechanism. The images collected during multiple inspections are intelligently compared with the standard images of the cable, and the image difference detection algorithm is used to identify potential fault characteristics. The real-time analysis results are matched with historical data, and suspected fault areas are automatically marked. The location and characteristic information of the fault point are generated and fed back to the ground control system to achieve real-time processing and alarm. S5. Real-time monitoring of the battery life and external meteorological environment information of the drone. Based on the weather monitoring system, obtain real-time meteorological data and calculate and evaluate the meteorological index Qxxs. When the battery is low, adjust the operation plan based on the evaluation content of the meteorological index Qxxs, including automatically selecting the best return route or extending the battery life through wireless charging technology.

2. A drone control method according to claim 1, characterized in that: S1 includes: S11. Environmental information of the mission area is collected by deploying a variety of sensors at the bottom of the drone, including the use of lidar to accurately measure the cable position and surrounding obstacles, and the use of geomagnetic signals to locate and analyze the geographic location; then, through multi-source data fusion technology, after obtaining cable height information, terrain undulation information and obstacle distribution information, it is integrated into a unified data format to build a complete three-dimensional environmental model, which includes key points of cable inspection and high-prone areas of faults, and combines model data to determine key inspection targets and areas.

3. The method for controlling a drone according to claim 1, characterized in that: S1 also includes: S12. Real-time recording and collection of drone flight-related data, calculation of the coverage of each inspection path using the following formula, and deriving the path coverage value Qcd: ; In the formula, Lcd represents the total path length in the UAV flight related data, Fsd represents the actual height of the target cable in the UAV flight related data, Zhz represents the height of the geographical obstacle in the UAV flight related data, and Fsj represents the signal strength change value in the UAV flight related data; The preset coverage threshold Q is compared with the path coverage value Qcd for evaluation. The specific evaluation contents are as follows: When the path coverage value Qcd < the coverage standard value Qc, it means that the path coverage and image acquisition of the current path meet the preset requirements, and the current path is marked as a priority; When the path coverage value Qcd ≥ the coverage standard value Qc, it means that the path coverage and image acquisition of the current path do not meet the preset requirements; there is a detection blind area or the image acquisition angle is not ideal, and this path is excluded.

4. The method for controlling a drone according to claim 1, characterized in that: S2 includes: S21. Dynamically adjust the environmental parameters in the initially selected priority inspection path, using the sensing system carried by the drone, including cameras, wind speed sensors and light sensing equipment; during the inspection process, adjust the focal length and exposure parameters of the camera in real time in combination with wind speed and light intensity to ensure image acquisition clarity in complex environments; at the same time, activate the image enhancement algorithm module, clarify the collected cable appearance image, detect abnormal areas through multiple image comparison and analysis modules, and mark suspected fault points to enhance the accuracy and real-time performance of fault detection.

5. The method for controlling a drone according to claim 1, characterized in that: S2 also includes: S22. By extracting the fault features of the cable appearance image after image enhancement, the size of the suspected fault point, the position of the suspected fault point and the relative position from the drone are obtained, and the dimensionless processing is performed and converted into a standard numerical format to obtain the dimensionless position value Fdw of the fault point, the dimensionless size value Fdz of the fault point and the dimensionless value Fjl of the relative distance of the fault point; the image enhancement effect value Enh is calculated using the following formula: ; By comparing and evaluating the preset effect threshold E with the image enhancement effect value Enh, the following evaluation content is generated: When the effect threshold E ≥ the enhancement effect value Enh, it means that the image clarity and fault detection accuracy are normal, and the current inspection path is marked as a high priority path; When the effect threshold E < the enhancement effect value Enh, it means that the image clarity and fault detection accuracy are abnormal and cannot be used as a priority path. This path is re-evaluated or readjusted as an alternative path.

6. The method for controlling a drone according to claim 1, characterized in that: S3 includes S31, specifically: S31. Based on the identified potential fault point information, a secondary inspection route is planned and generated in real time, key fault points are scanned at high frequencies, and the accuracy of detailed collection of the fault area is adjusted by adjusting the flight speed and inspection angle of the drone. In this process, information related to the inspection coverage is monitored to identify the inspection blind spots. When insufficient coverage is detected, a supplementary path is automatically recalculated and generated. By conducting additional inspections on the faulty area, the inspection is fully covered and no faults are missed. When the supplementary path is generated, the drone will prioritize executing the current supplementary path.

7. The method for controlling a drone according to claim 1, characterized in that: S4 includes: S41. During the drone inspection process, real-time fault analysis is performed through the onboard data processing mechanism. By intelligently comparing the cable images collected during multiple inspections with the cable standard images, potential fault features in the images are identified in real time. When a difference is detected between the collected cable image and the cable standard image, the collected cable image is annotated as an abnormality, and the fault point is marked as a reminder through the image difference detection algorithm, and the location information and feature information of the fault point are automatically generated. If interference signals and obstacles are identified, the electromagnetic interference prediction model and collision prediction model are used to dynamically adjust the drone's inspection path, and different obstacle avoidance paths are simulated during the adjustment process. Finally, the flight trajectory is determined and adjusted, and the fault point location information and features are fed back to the ground control system for real-time reminders.

8. The method for controlling a drone according to claim 1, characterized in that: S4 also includes: S42. After completing the dynamic adjustment of the inspection path and performing simulation, the adjusted UAV flight related data is recorded in real time, and these data are dimensionlessly processed to calculate the optimization coefficient Yhxs to evaluate the path optimization effect. The specific formula is as follows: ; Wherein, Ztd is the detour angle in the adjusted UAV flight-related data, Zlz is the path oscillation frequency in the adjusted UAV flight-related data, Zpy is the electromagnetic interference intensity in the adjusted UAV flight-related data, and Zph is the path smoothness in the adjusted UAV flight-related data; n is the total number of path segments, i is the path segment number, represents the detour angle of the i-th path segment, represents the path smoothness of the i-th path segment, represents the electromagnetic interference intensity of the i-th path segment, represents the wind direction deviation coefficient of the i-th path segment; The optimization coefficient Yhxs is evaluated by the preset optimization threshold E. The specific evaluation content is as follows: When the optimization threshold E ≥ optimization coefficient Yhxs, it means that the currently adjusted path meets the optimization criteria in terms of energy consumption, obstacle avoidance efficiency and signal reception stability, and is put into use; When the optimization threshold E is less than the optimization coefficient Yhxs, it means that the currently adjusted path does not meet the optimization criteria in terms of energy consumption, obstacle avoidance efficiency and signal reception stability, and a simulated dynamic adjustment is performed again.

9. The method for controlling a drone according to claim 1, characterized in that: S5 includes: S51. During the operation of the drone, the built-in battery monitoring system is used to monitor the battery life in real time and obtain the current power information. At the same time, based on the weather monitoring system carried by the drone, real-time meteorological data is obtained, including airflow turbulence intensity Tlr, air humidity gradient Hjd, surface thermal radiation intensity Rhs and electromagnetic interference intensity Emr. After dimensionless processing of the real-time meteorological data, the meteorological index Qxxs is calculated by the following formula: ; S52: When the battery power of the drone is low, the preset weather threshold R is compared with the weather index Qxxs to evaluate the impact of the current weather on the drone's return or continued operation. The specific contents are as follows: When the meteorological threshold R ≥ meteorological index Qxxs, it means that the current meteorological conditions are normal and the current weather has no effect on the drone's return or continued operation; at this time, the drone continues to perform the current operation task. In the case of limited power, it will give priority to high-priority tasks, and at the same time, it will replenish power through wireless charging technology; When the meteorological threshold R is less than the meteorological index Qxxs, it means that the current meteorological conditions are abnormal and the current weather has an impact on the drone's return or continued operation; at this time, the drone adjusts its operation plan and chooses a safe return path; when selecting the return path, avoid areas with strong air turbulence and choose the shortest return path based on the battery power.

10. A drone control system, according to any one of the drone control methods according to any one of claims 1 to 9, characterized in that: It includes environment modeling module, image acquisition and enhancement module, secondary inspection and path supplement module, fault analysis and intelligent comparison module, and battery monitoring and meteorological assessment module; The environmental modeling module performs environmental modeling based on the geographic information of the drone mission area, and constructs a three-dimensional environmental model containing information on cable location, cable height, surrounding buildings and geographical obstacles; based on the three-dimensional environmental model, the inspection area is preset, the high-incidence points of faults are determined as key inspection targets, and multiple inspection paths are generated in combination with external meteorological conditions and lighting conditions; Calculate the path coverage value Qcd for each path to ensure that the inspection range is complete, and adjust the image acquisition angle based on the flight altitude; The image acquisition and enhancement module is used to dynamically adjust the focal length and exposure parameters of the camera in the process of inspection path planning based on the evaluation results of the path coverage value Qcd, combined with the environmental parameters including the flight attitude, wind speed and light of the drone, and improve the image acquisition clarity of the drone in complex environments through a real-time image enhancement algorithm; at the same time, a multiple image comparison and analysis module is set to pre-process the collected cable appearance images, identify and mark possible fault points; The secondary inspection and path supplement module generates a secondary inspection path through the path planning module based on the identified potential fault point information, performs high-frequency scanning of key points, and dynamically adjusts the flight speed and inspection angle of the drone to ensure detailed inspection of the fault area; for areas that may be missed, the coverage rate is recalculated in real time and a supplementary path is generated to ensure all-round inspection without omissions; The fault analysis and intelligent comparison module performs real-time fault analysis through an onboard data processing mechanism, intelligently compares images collected during multiple inspections with standard images of the cable, and uses an image difference detection algorithm to identify potential fault features; matches real-time analysis results with historical data, automatically marks suspected fault areas, and generates fault point location and feature information to feed back to the ground control system to achieve real-time processing and alarm; The battery monitoring and meteorological assessment module is used to monitor the battery life and external meteorological environment information of the UAV in real time. Based on the weather monitoring system, it obtains real-time meteorological data and calculates and evaluates the meteorological index Qxxs. When the battery is low, the operation plan is adjusted in combination with the evaluation content of the meteorological index Qxxs, including automatically selecting the best return path or extending the battery life through wireless charging technology.

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