A high-voltage transmission line inspection unmanned aerial vehicle system and method
Patent Information
- Application Number
- CN202611089005.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-22
- Publication Date
- 2026-09-25
AI Technical Summary
当目标被导线、金具或植被遮挡，或者受到逆光、运动模糊和拍摄角度影响时，容易产生误报或漏报
[0017]经由上述的技术方案可知，与现有技术相比，本发明公开提供了一种高压输电线路巡检无人机系统及方法，通过风险驱动任务编排模块，融合运行重要度、历史缺陷度、巡检超期度和实时缺陷可信度四个维度计算综合风险，使有限续航优先分配至高风险部件和疑似缺陷复核，避免固定航线在无异常部件上消耗电量，显著提高单位航时内的有效巡检覆盖率。进一步，本发明通过动态安全走廊规划模块，将实时风场危险度、组合定位误差和导线摆动危险度共同纳入安全间隔计算，使安全距离随环境变化自适应调整，克服了预设固定安全距离在阵风、定位漂移等场景下保护不足的问题，有效降低无人机与导线、杆塔及周边障碍物碰撞的风险。通过观测质量评价与多视角证据一致性计算，综合多幅图像或多种模态数据给出缺陷可信度，低质量观测不直接参与缺陷确认，有效抑制遮挡、逆光、运动模糊和单一视角造成的误报与漏报，使诊断结论更加可靠。更进一步，本发明通过主动复飞决策模块，在证据不足时根据具体原因自主选择改变方位、距离、高度、云台角度或检测模态等复飞策略，无需人工遥控干预，大幅缩短疑似缺陷从发现到确认的时间窗口，提升巡检作业的连续性和自动化水平。
Smart Images
Figure CN122816248A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent power line inspection technology, and more specifically to a high-voltage transmission line inspection drone system and method. Background Technology
[0002] Traditional manual inspections suffer from problems such as large operating areas, high labor intensity, low inspection efficiency, and high risks associated with high-altitude operations. Existing drone inspection solutions can collect visible light, infrared, or laser point cloud data according to preset flight paths and use recognition models to detect line defects. However, fixed flight paths make it difficult to adjust inspection priorities based on historical component defects, weather changes, and suspected anomalies detected in real time; preset safety distances are also difficult to adapt to gusts of wind, conductor galloping, positioning drift, and changes in the electromagnetic environment.
[0003] Existing defect detection methods typically output results directly from single or single-view images. False alarms or missed alarms are prone to occur when the target is obscured by wires, hardware, or vegetation, or when affected by backlighting, motion blur, or shooting angle. After a suspected defect is detected, the system usually relies on manual remote re-shooting, lacking a closed-loop mechanism to autonomously select the re-flying position, shooting angle, distance, and detection load based on insufficient evidence.
[0004] In addition, power line inspections may encounter situations such as degraded GNSS positioning quality, communication interruptions, sudden gusts of wind, and insufficient remaining power. If the drone continues to follow its original route, it may collide with power lines, towers, or surrounding obstacles; if it returns directly, it may lose important evidence of defects.
[0005] Therefore, how to provide a high-voltage transmission line inspection drone system and method that balances inspection efficiency, defect confirmation quality, and flight safety is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention
[0006] In view of this, the present invention provides a high-voltage transmission line inspection drone system and method, which forms a risk profile of line components by using digital models of the line, equipment ledgers, historical defects and real-time observation results, and dynamically arranges inspection tasks accordingly; forms a dynamic safety corridor based on wind field, conductor sway, positioning error, obstacles and remaining power; calculates the credibility of defects by combining multi-view observation quality and evidence consistency, and autonomously generates a re-flight task for suspected defects with insufficient evidence; and performs hovering, retreating, ascending, returning to base or landing at a safe point when positioning, communication or environmental conditions are abnormal.
[0007] To achieve the above objectives, the present invention adopts the following technical solution: On one hand, the present invention provides a high-voltage transmission line inspection drone system, comprising: The ground mission and monitoring terminal is used to issue inspection tasks and receive inspection data. The line digital model and equipment archive platform is used to store target component information, historical defect archives, and standard observation pose sets; Inspection drones are used to conduct inspections according to inspection tasks and collect inspection data to send to the ground task and monitoring terminal. The inspection drone has built-in edge computing and evidence storage components, which are used to perform defect diagnosis, proactive re-flight decision-making and safety degradation according to the inspection task; Edge computing and evidence storage components include: The risk-driven task orchestration module is used to calculate the comprehensive risk based on the operational importance of the target component, historical defect rate, inspection overdue rate, and real-time defect reliability, and dynamically arrange inspection tasks according to flight costs, remaining battery power, and no-fly restrictions. The dynamic safety corridor planning module is used to calculate the dynamic safety interval based on the inspection data and adjust the inspection route according to the dynamic safety interval. The flight control and integrated positioning module is used to control the UAV's autonomous flight along the line and maintain the target's attitude based on the ranging information in the inspection data; Multi-view, multi-modal acquisition module for collecting inspection data; The observation quality and defect diagnosis module is used to evaluate the observation quality of inspection data and calculate the consistency of evidence and the credibility of defects based on defect results from different perspectives or modalities. The proactive go-around decision module is used to generate go-around missions based on observation quality, evidence consistency, defect credibility, and flight safety constraints. The local security degradation and evidence storage module is used to perform security degradation actions when communication is interrupted, positioning quality deteriorates, gusts exceed the threshold, or remaining power is lower than the preset value, and to save the original inspection data, observation quality, decision-making process, and flight logs.
[0008] Preferably, the target component information includes at least one or more of the following: towers, insulator strings, conductors, hardware, passage obstacles and their coordinates, inspection cycle, risk level, and standard observation pose.
[0009] Preferably, the formula for calculating the comprehensive risk based on the operational importance of the target component, historical defect rate, inspection overdue rate, and real-time defect reliability is as follows:
[0010] in, For target components The importance of operation; For target components Historical defects; For target components i The degree of overdue inspections; For target componentsi The credibility of the defects; , , , These are weights for operational importance, historical defect rate, inspection overdue rate, and defect reliability, respectively. .
[0011] Preferably, the dynamic safety interval is calculated based on the inspection data, specifically as follows:
[0012] in, The basic safety interval corresponding to the target component; Real-time wind field hazard level; This is the normalized value of the combined positioning error; The degree of danger of conductor swaying or target movement; , , These are the safety interval adjustment coefficients for wind field, positioning error, and conductor sway, respectively.
[0013] Preferably, the formula for calculating defect reliability is:
[0014] Where, q i,k For the mass of the observed component i, p i,k The defect probability given by observation k; The quality-weighted defect probability of target component i; c i To ensure consistency of evidence from multiple perspectives; ε is used to prevent positive numbers with a denominator of zero; ρ represents the defect confidence level of target component i; ρ is the minimum retention coefficient for consistency.
[0015] Preferably, the security degradation action is performed according to the following priority: Cease close-range observation or proactive go-around missions; Retreat away from the target component to a safe hovering point that meets the dynamic safety interval, and reassess the communication status, positioning quality, wind field hazard level, and remaining battery power; If the abnormal state is not resolved and the ascent channel meets the dynamic safety interval, ascend to the preset safe return route; The return trip will commence when the remaining battery power is sufficient to complete the return journey and the return route meets safety requirements. When conditions for a safe return to base are not met, a controlled landing will be carried out at a pre-set safe point or a safe point determined in real time. Among them, if the previous priority action does not meet the conditions for safe execution or cannot reduce the flight risk, the next priority action will be executed; when the remaining power is insufficient to complete the return, the positioning quality deteriorates to the point that the return safety cannot be guaranteed, or the flight status continues to deteriorate, the controlled landing at the safe point will be executed directly.
[0016] On the other hand, the present invention provides a method for inspecting high-voltage transmission lines using a drone, comprising: Read the line equipment files, historical inspection records and meteorological data, calculate the comprehensive risk of each target component, and generate an inspection task queue; Based on real-time wind field data, positioning error, and conductor sway status, calculate dynamic safety intervals and construct dynamic safety corridors; Control the UAV to fly along the dynamic safety corridor to the observation pose of the target component, and collect observation data from multiple perspectives and multiple modes; Evaluate the observation quality of each observation data, and calculate the quality-weighted defect probability, evidence consistency, and defect credibility. When the credibility of the defect is within the preset range to be reviewed, the system will automatically generate a go-around mission until the defect is confirmed, the suspected defect is eliminated, or the safe exit conditions are met. Save the inspection results and update the target component files.
[0017] As can be seen from the above technical solutions, compared with the prior art, this invention discloses a high-voltage transmission line inspection drone system and method. Through a risk-driven task scheduling module, it integrates four dimensions—operational importance, historical defect rate, inspection overdue rate, and real-time defect reliability—to calculate comprehensive risk. This prioritizes limited flight time for high-risk components and suspected defect verification, avoiding power consumption on fixed routes on components without abnormalities, and significantly improving the effective inspection coverage per unit flight time. Furthermore, this invention, through a dynamic safety corridor planning module, incorporates real-time wind field hazard, combined positioning error, and conductor sway hazard into the safety distance calculation. This allows the safety distance to adaptively adjust with environmental changes, overcoming the problem of insufficient protection from preset fixed safety distances in scenarios such as gusts and positioning drift, effectively reducing the risk of collisions between the drone and conductors, towers, and surrounding obstacles. Through observation quality evaluation and multi-view evidence consistency calculation, it comprehensively provides defect reliability based on multiple images or multiple modal data. Low-quality observations do not directly participate in defect confirmation, effectively suppressing false alarms and missed alarms caused by occlusion, backlighting, motion blur, and single-viewpoint errors, making diagnostic conclusions more reliable. Furthermore, this invention, through an active go-around decision module, can autonomously select go-around strategies such as changing orientation, distance, altitude, gimbal angle, or detection mode based on specific reasons when evidence is insufficient, without the need for manual remote control intervention. This significantly shortens the time window from discovery to confirmation of suspected defects and improves the continuity and automation level of inspection operations. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0019] Figure 1 This is a structural framework diagram of the present invention.
[0020] Figure 2 The connection diagram of the high-voltage transmission line inspection drone system provided by this invention.
[0021] Figure 3 A schematic diagram of the main body structure of the high-voltage transmission line inspection drone provided by the present invention.
[0022] Figure 4 The flowchart for risk-driven inspection and proactive go-around provided by this invention is as follows. Detailed Implementation
[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0024] This invention discloses a high-voltage transmission line inspection drone system, such as... Figure 1 and Figure 2 As shown, it includes: The ground mission and monitoring terminal is used to issue inspection tasks and receive inspection data. The line digital model and equipment archive platform is used to store target component information, historical defect archives, and standard observation pose sets; Inspection drones, such as Figure 3 As shown, it includes a flight platform, flight controller, integrated positioning and obstacle avoidance components, visible light and infrared inspection payloads, ranging components, edge computing and evidence storage components, communication components, and power supply components. It is used to perform inspections according to inspection tasks and collect inspection data to send to the ground task and monitoring terminal; the inspection payload gimbal is used to change the inspection orientation, the integrated positioning and obstacle avoidance components are used to maintain dynamic safety intervals, and the edge computing and evidence storage components are used to locally perform defect diagnosis, proactive go-around decisions, and safety degradation.
[0025] The inspection drone has built-in edge computing and evidence storage components, which are used to perform defect diagnosis, proactive re-flight decision-making, and safety degradation based on the inspection task.
[0026] The system of this invention divides towers, insulator strings, conductors, hardware and line channels into target components that can be inspected independently, and establishes a set of standard observation poses, risk profiles and historical observation archives for each target component.
[0027] Furthermore, the edge computing and evidence storage components include: The risk-driven task orchestration module is used to calculate the comprehensive risk based on the operational importance of the target component, historical defect rate, inspection overdue rate, and real-time defect reliability, and dynamically arrange inspection tasks according to flight costs, remaining battery power, and no-fly restrictions.
[0028] The dynamic safety corridor planning module is used to calculate dynamic safety intervals based on inspection data and adjust the inspection route accordingly. Specifically, based on the route digital model and terrain obstacle model, the module calculates dynamic safety intervals according to real-time wind speed, wind direction, guide sway amplitude, combined positioning error, flight speed, and target shooting distance. When the safety interval increases, the module automatically adjusts the flight path or cancels close-range shooting actions.
[0029] The flight control and integrated positioning module is used to control the UAV to fly autonomously along the line and maintain the target attitude based on the ranging information in the inspection data. Specifically, the flight control and integrated positioning module achieves autonomous flight along the line and maintains the target attitude by fusing GNSS, inertial measurement, vision, laser or other ranging information. When the positioning reliability decreases, the UAV is restricted from approaching the line and performs hovering, retreating, ascending or returning to home according to the safety level.
[0030] The multi-view, multi-modal acquisition module controls visible light, infrared, and ranging payloads to observe the target component from different azimuths, distances, and pitch angles. Each observation is associated with and saved along with the target component identifier, UAV pose, timestamp, and environmental conditions.
[0031] The observation quality and defect diagnosis module is used to evaluate the observation quality of inspection data and calculate the consistency of evidence and the credibility of defects based on defect results from different perspectives or modalities. Specifically, data quality evaluation includes image sharpness, target integrity, occlusion rate, illumination, shooting angle, ranging stability, and infrared thermometry effectiveness. Low-quality observations do not directly output confirmed defects.
[0032] The proactive go-around decision module selects go-around actions such as changing azimuth, distance, altitude, gimbal angle, exposure parameters, or detection modes based on observation quality, evidence consistency, defect credibility, and flight safety constraints. After the go-around is completed, the evidence is re-evaluated until the defect is confirmed, suspected defects are ruled out, or safe exit conditions are met.
[0033] The local safety degradation and evidence storage module stops actions that do not meet safety conditions when communication is interrupted, positioning quality deteriorates, gusts exceed the threshold, or the remaining power is insufficient, and performs hovering, retreating, ascent, return to home, or landing at a safe point; at the same time, it saves the original observations, quality assessments, decision-making processes, and flight logs, and uploads them according to event priority after communication is restored.
[0034] like Figure 2 As shown, the digital model of the route and the equipment archive platform, along with the meteorological and terrain data interface, provide route component, historical defect, terrain, and environmental data to the risk-driven task orchestration module and the dynamic safety corridor planning module, respectively. The flight control and integrated positioning module controls the inspection UAV to execute its flight path; the multi-view, multi-modal acquisition module collects target component data; the observation quality and defect diagnosis module evaluates the evidence; and the proactive go-around decision module feeds back the go-around requirement to the dynamic safety corridor planning module, thus forming a go-around closed loop.
[0035] The security degradation actions are performed according to the following priority: Cease close-range observation or proactive go-around missions; Retreat away from the target component to a safe hovering point that meets the dynamic safety interval, and reassess the communication status, positioning quality, wind field hazard level, and remaining battery power; If the abnormal state is not resolved and the ascent channel meets the dynamic safety interval, ascend to the preset safe return route; The return trip will commence when the remaining battery power is sufficient to complete the return journey and the return route meets safety requirements. When conditions for a safe return to base are not met, a controlled landing will be carried out at a pre-set safe point or a safe point determined in real time. Among them, if the previous priority action does not meet the conditions for safe execution or cannot reduce the flight risk, the next priority action will be executed; when the remaining power is insufficient to complete the return, the positioning quality deteriorates to the point that the return safety cannot be guaranteed, or the flight status continues to deteriorate, the controlled landing at the safe point will be executed directly.
[0036] Furthermore, the formula for calculating the comprehensive risk based on the operational importance of the target component, historical defect rate, inspection overdue rate, and real-time defect reliability is as follows:
[0037] in, For target components The importance of operation; For target components Historical defects; For target components i The degree of overdue inspections; For target components i The credibility of the defects; , , , These are weights for operational importance, historical defect rate, inspection overdue rate, and defect reliability, respectively. .
[0038] The higher the risk of the target component, the higher the priority of its inspection or return-to-flight mission.
[0039] In another embodiment, the dynamic safety interval is calculated based on the inspection data, specifically as follows:
[0040] in, The basic safety interval corresponding to the target component; Real-time wind field hazard level; This is the normalized value of the combined positioning error; The degree of danger of conductor swaying or target movement; , , These are the safety interval adjustment coefficients for wind field, positioning error, and conductor sway, respectively.
[0041] When the real-time wind field hazard level, positioning error, or guide wire sway hazard level increases, the dynamic safety interval increases; if the current track cannot meet the requirements... If so, adjust the flight path or cancel close-range observation.
[0042] Furthermore, the formula for calculating defect credibility is:
[0043] Where, q i,k For the mass of the observed component i, p i,k The defect probability given by observation k; The quality-weighted defect probability of target component i; c i To ensure consistency of evidence from multiple perspectives; ε is used to prevent positive numbers with a denominator of zero; ρ represents the defect confidence level of target component i; ρ is the minimum retention coefficient for consistency.
[0044] When the quality of observations is insufficient, the consistency of evidence is low, or the credibility of defects is in the range to be verified, the active go-around decision module generates a go-around mission.
[0045] Specifically, the key technical parameters used in the embodiments of this invention are shown in Table 1.
[0046] Table 1 Key Technical Parameters
[0047] Furthermore, the inspection drone can employ a multi-rotor, compound-wing, or other flight platform suitable for line inspection; the combined positioning can employ GNSS, inertial, vision, laser, line relative positioning, or a combination thereof; the inspection payload can further include ultraviolet, acoustic, electric field, or other detection components. The formula weights and thresholds can be configured based on voltage levels, line structure, defect types, and on-site safety regulations. The above embodiments do not constitute a limitation on the scope of protection.
[0048] On the other hand, the present invention provides a method for inspecting high-voltage transmission lines using a drone, such as... Figure 4 As shown, it includes: The system reads line equipment files, historical inspection records, and meteorological data to calculate the comprehensive risk of each target component and generate an inspection task queue. Specifically, the system reads line equipment files, the last valid inspection time, and meteorological data to calculate the comprehensive risk of towers and their target components. UAVs prioritize inspecting components with historical defects or those exceeding their inspection cycle, and take images at observation positions that meet dynamic safety intervals. If the observation quality meets requirements and no anomalies are found, the system updates the target component file and continues with the next task.
[0049] Based on real-time wind field data, positioning error, and conductor sway status, a dynamic safety interval is calculated, and a dynamic safety corridor is constructed. During the inspection, if real-time gusts or combined positioning errors increase, the dynamic safety interval will increase. If the current observation attitude does not meet the safety interval, the UAV will cancel its close-range go-around and retreat to a safe hovering point. If the positioning quality continues to deteriorate, it will ascend to the preset return route and return home. Unfinished tasks, environmental data, and action logs are saved throughout the process for uploading after communication is restored.
[0050] Control the UAV to fly along the dynamic safety corridor to the observation pose of the target component, and collect observation data from multiple perspectives and multiple modes; Evaluate the observation quality of each observation data, and calculate the quality-weighted defect probability, evidence consistency, and defect credibility. When the defect confidence level falls within the preset verification range, the system autonomously generates a re-flight mission until the defect is confirmed, suspected defects are eliminated, or safe exit conditions are met. Specifically, the UAV initially acquires images of the insulator string from the side of the line. The defect identification result suggests suspected damage, but the area is partially obscured by hardware, resulting in low observation quality. The system generates a re-flight pose on the other side based on the target digital model, and changes the azimuth and gimbal angle for re-shooting within the dynamic safety corridor. If the infrared and visible light results are inconsistent, the system switches modes or increases the observation angle. After the re-flight observation and the initial observation mutually support each other, if the defect confidence level exceeds the confirmation threshold, the system saves multi-view evidence and sets the component as a high-risk target.
[0051] Save the inspection results and update the target component files.
[0052] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.
[0053] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A high-voltage transmission line inspection drone system, characterized in that, include: The ground mission and monitoring terminal is used to issue inspection tasks and receive inspection data. The line digital model and equipment archive platform is used to store target component information, historical defect archives, and standard observation pose sets; Inspection drones are used to conduct inspections according to inspection tasks and collect inspection data to send to the ground task and monitoring terminal. The inspection drone has built-in edge computing and evidence storage components, which are used to perform defect diagnosis, proactive re-flight decision-making and safety degradation according to the inspection task; Edge computing and evidence storage components include: The risk-driven task orchestration module is used to calculate the comprehensive risk based on the operational importance of the target component, historical defect rate, inspection overdue rate, and real-time defect reliability, and dynamically arrange inspection tasks according to flight costs, remaining battery power, and no-fly restrictions. The dynamic safety corridor planning module is used to calculate the dynamic safety interval based on the inspection data and adjust the inspection route according to the dynamic safety interval. The flight control and integrated positioning module is used to control the UAV's autonomous flight along the line and maintain the target's attitude based on the ranging information in the inspection data; Multi-view, multi-modal acquisition module for collecting inspection data; The observation quality and defect diagnosis module is used to evaluate the observation quality of inspection data and calculate the consistency of evidence and the credibility of defects based on defect results from different perspectives or modalities. The proactive go-around decision module is used to generate go-around missions based on observation quality, evidence consistency, defect credibility, and flight safety constraints. The local security degradation and evidence storage module is used to perform security degradation actions when communication is interrupted, positioning quality deteriorates, gusts exceed the threshold, or remaining power is lower than the preset value, and to save the original inspection data, observation quality, decision-making process, and flight logs.
2. The high-voltage transmission line inspection drone system according to claim 1, characterized in that, The target component information includes at least one or more of the following: towers, insulator strings, conductors, hardware, passage obstacles and their coordinates, inspection cycles, risk levels, and standard observation poses.
3. The high-voltage transmission line inspection drone system according to claim 1, characterized in that, The formula for calculating the comprehensive risk based on the operational importance of the target component, historical defect rate, inspection overdue rate, and real-time defect reliability is as follows: in, For target components The importance of operation; For target components Historical defects; For target components i The degree of overdue inspections; For target components i The credibility of the defects; , , , These are weights for operational importance, historical defect rate, inspection overdue rate, and defect reliability, respectively. .
4. The high-voltage transmission line inspection drone system according to claim 1, characterized in that, The dynamic safety interval is calculated based on the inspection data, specifically as follows: in, The basic safety interval corresponding to the target component; Real-time wind field hazard level; This is the normalized value of the combined positioning error; The degree of danger of conductor swaying or target movement; , , These are the safety interval adjustment coefficients for wind field, positioning error, and conductor sway, respectively.
5. The high-voltage transmission line inspection drone system according to claim 1, characterized in that, The formula for calculating defect credibility is: Where, q i,k For the mass of the observed component i, p i,k The defect probability given by observation k; The quality-weighted defect probability of target component i; c i To ensure consistency of evidence from multiple perspectives; ε is used to prevent positive numbers with a denominator of zero; ρ represents the defect confidence level of target component i; ρ is the minimum retention coefficient for consistency.
6. The high-voltage transmission line inspection drone system according to claim 1, characterized in that, The security degradation actions are performed according to the following priority: Cease close-range observation or proactive go-around missions; Retreat away from the target component to a safe hovering point that meets the dynamic safety interval, and reassess the communication status, positioning quality, wind field hazard level, and remaining battery power; If the abnormal state is not resolved and the ascent channel meets the dynamic safety interval, ascend to the preset safe return route; The return trip will commence when the remaining battery power is sufficient to complete the return journey and the return route meets safety requirements. When conditions for a safe return to base are not met, a controlled landing will be carried out at a pre-set safe point or a safe point determined in real time. Among them, if the previous priority action does not meet the conditions for safe execution or cannot reduce the flight risk, the next priority action will be executed; when the remaining power is insufficient to complete the return, the positioning quality deteriorates to the point that the return safety cannot be guaranteed, or the flight status continues to deteriorate, the controlled landing at the safe point will be executed directly.
7. A method for inspecting high-voltage transmission lines using unmanned aerial vehicles (UAVs), characterized in that, include: Read the line equipment files, historical inspection records and meteorological data, calculate the comprehensive risk of each target component, and generate an inspection task queue; Based on real-time wind field data, positioning error, and conductor sway status, calculate dynamic safety intervals and construct dynamic safety corridors; Control the UAV to fly along the dynamic safety corridor to the observation pose of the target component, and collect observation data from multiple perspectives and multiple modes; Evaluate the observation quality of each observation data, and calculate the quality-weighted defect probability, evidence consistency, and defect credibility. When the credibility of the defect is within the preset range to be reviewed, the system will automatically generate a go-around mission until the defect is confirmed, the suspected defect is eliminated, or the safe exit conditions are met. Save the inspection results and update the target component files.