A method and system for inspecting and monitoring power transmission line towers based on unmanned aerial vehicles (UAVs).

Abstract

This invention provides a method and system for monitoring and inspecting transmission line towers based on unmanned aerial vehicles (UAVs). The method includes acquiring transmission line tower data, operating condition data, and initial state data of the UAV to divide the tower into multiple inspection units and determine the dynamic risk coefficient data of each component; constructing a differentiated constraint model based on the inspection units, preset risk thresholds, operating condition data, and dynamic risk coefficient data to determine the inspection constraint parameters of each component; generating a theoretical inspection path based on the inspection constraint parameters and adjusting the theoretical inspection path based on the dynamic risk coefficient data; controlling the UAV to conduct inspections according to the theoretical inspection path and monitoring data in real time to optimize the inspection path within the unit based on the monitoring data and preset risk thresholds. This invention solves the problem of the lack of a UAV-based method for monitoring and inspecting transmission line towers with good adaptability, high inspection accuracy, and high efficiency in the prior art.

Description

Technical Field

[0001] This invention relates to the field of power transmission line inspection technology, and in particular to a method and system for monitoring and inspecting power transmission line towers based on unmanned aerial vehicles (UAVs). Background Technology

[0002] Transmission lines are the core infrastructure for power transmission, and their safe and stable operation is directly related to national economic development and social order. With the continuous expansion of the power grid, the number of transmission line towers is increasing year by year, and many towers are distributed in complex geographical environments such as mountainous and hilly areas. Traditional manual inspection methods suffer from low efficiency, high risk, and incomplete coverage, making them insufficient to meet the needs of large-scale, high-precision inspections. Unmanned aerial vehicle (UAV) inspection, with its advantages of high flexibility, high efficiency, and low operational risk, has become the mainstream technology for transmission line tower inspection. UAV inspection path planning, as a core technical aspect, directly determines the safety, efficiency, and defect detection rate of the inspection; its optimization and upgrading are of great significance for improving the overall operation and maintenance level of transmission lines.

[0003] In the field of drone inspection path planning, existing technologies mainly revolve around the goals of safety compliance and comprehensive coverage. They achieve inspection coverage of tower components by pre-setting fixed flight trajectories or adjusting paths based on single-condition data. Specifically, existing solutions typically first collect tower structural data, then plan flight paths based on preset safety distances and shooting parameters. Some solutions incorporate simple risk assessments, adding shooting nodes for high-risk components.

[0004] However, current technologies still have shortcomings and are difficult to adapt to the actual needs of complex inspection scenarios for transmission line towers. First, path planning is mostly based on the scattered coordinates of components to generate trajectories, which easily leads to problems such as path overlap, omission of areas, and cross-regional backtracking, resulting in chaotic inspection order and low work efficiency. Second, the constraint parameters are mostly set as static fixed values, which cannot be dynamically adapted to the constraint strategy in combination with the risk level of components and real-time operating conditions. This results in insufficient inspection accuracy for high-risk components and resource redundancy for low-risk components, making it difficult to balance inspection accuracy and efficiency. In addition, there is a conflict between global path stability and local dynamic optimization, which can easily lead to path rigidity that cannot cope with real-time risks, or local adjustments that disrupt the global order, making it impossible to achieve a balance between global controllability and local precision. Summary of the Invention

[0005] Based on this, the purpose of this invention is to provide a method and system for inspecting and monitoring power transmission line towers based on unmanned aerial vehicles (UAVs), aiming to solve the problem that there is a lack of a UAV-based method for inspecting and monitoring power transmission line towers with good adaptability, high inspection accuracy and efficiency in the existing technology.

[0006] A method for inspecting and monitoring power transmission line towers based on unmanned aerial vehicles (UAVs) according to an embodiment of the present invention includes:

[0007] Data on power transmission line towers, operating conditions, and initial state data of drones are acquired. Based on the tower data, the towers are divided into units to determine multiple inspection units. Based on the tower data, the operating conditions, and the initial state data of drones, the dynamic risk coefficient data of each component is determined.

[0008] A differentiated constraint model is constructed based on the inspection unit, the preset risk threshold, the operating condition data, and the dynamic risk coefficient data to determine the inspection constraint parameters of each component.

[0009] Based on the inspection constraint parameters, a theoretical inspection path is generated, which includes the inspection path within the unit and the inspection path between units. At the same time, the inspection path within the unit is adjusted based on the dynamic risk coefficient data, and dynamic priority queue data is established to handle sudden high-risk components.

[0010] The drone is controlled to perform inspections according to the theoretical inspection path and data monitoring is carried out in real time. The inspection path within the current unit is optimized based on the monitoring data and the preset risk threshold. The subsequent inspection paths within the unit are adjusted based on the optimized dynamic risk coefficient data and inspection constraint parameters to monitor and inspect the tower.

[0011] In addition, the UAV-based transmission line tower inspection and monitoring method according to the above embodiments of the present invention may also have the following additional technical features:

[0012] Furthermore, the step of dividing the tower into units and determining multiple inspection units based on the tower data includes:

[0013] Clustering of components on the tower based on risk level and coordinates to determine clustering data;

[0014] The regions are divided according to their height and the main outline of the tower, and the regions are further divided into units based on clustering data.

[0015] Each unit is numbered and sorted according to preset rules and then verified to ensure that the different risk components within each unit are reasonably allocated and that the unit scope is appropriate and covers all components.

[0016] Furthermore, the step of determining the dynamic risk coefficient data of each component based on the tower data, the operating condition data, and the initial state data of the UAV includes:

[0017] The inherent risk coefficient is determined based on the tower data, and the corresponding operating condition risk coefficient of each component is determined according to the tower data and the operating condition data through a first preset formula.

[0018] The inspection status risk coefficient is determined by a second preset formula based on the tower data, the initial state data of the UAV, and the detection data.

[0019] The inherent risk coefficient, the working condition risk coefficient, and the inspection status risk coefficient are coupled and calculated to obtain dynamic risk coefficient data. The detection data is not included when calculating the initial dynamic risk coefficient data.

[0020] Furthermore, the first preset formula is:

[0021]

[0022] The second preset formula is:

[0023]

[0024] Among them, among them, For the working condition risk coefficient, For the first k The weights of the factors affecting each working condition. For the first k A function of several influencing factors For the first i Each inspection unit t For real-time, n The number of operating condition influencing factors, The risk coefficient for the inspection status. This is a weighting coefficient for the shooting distance deviation. This is the actual shooting distance. The minimum clear shooting distance for the component. These are the weighting coefficients for positioning drift. This is the location drift amount.

[0025] Furthermore, the step of constructing a differentiated constraint model based on the inspection unit, the preset risk threshold, the operating condition data, and the dynamic risk coefficient data to determine the inspection constraint parameters of each component includes:

[0026] Based on the operating condition data, the dynamic risk coefficient data, and the preset risk threshold, determine whether the dynamic adjustment mechanism of the inspection constraint parameters is triggered, so as to determine the triggering status;

[0027] Based on the component type, the working condition data, the dynamic risk coefficient data, and the triggering state in the inspection unit, the inspection constraint parameters corresponding to each component are calculated respectively. The inspection constraint parameters include at least the dynamic safety distance, the shooting distance, and the shooting angle.

[0028] The formula for calculating the dynamic safety distance is:

[0029]

[0030] The formula for calculating the shooting distance is:

[0031]

[0032] The formula for calculating the shooting angle is:

[0033]

[0034] in, For dynamic safety distance, For the corresponding voltage level U The baseline safety distance, , and For the shadow coefficient, For real-time humidity, This is the humidity baseline value. The amplitude of the conductor's movement. To implement electromagnetic interference intensity, This serves as a reference value for electromagnetic interference. This is a linkage function between risk and humidity. For dynamic risk coefficients, This is a threshold-triggered state. For the first i The dynamic safety distance of each inspection unit at the previous moment. For the previous moment, For wind safety redundancy factor, For real-time wind speed, This is the actual shooting distance. The minimum clear shooting distance for the component. This is the humidity correction factor. For the first i The dynamic safe distance of each inspection unit at the current moment. This is the safety distance ratio coefficient. For the first i The shooting distance of each inspection unit at the previous moment. For the shooting angle, Let be the optimal shooting angle for each component within the i-th inspection unit. This is the angle compensation amount for electromagnetic interference and conductor galloping. For the first i The camera angle of each inspection unit at the previous moment.

[0035] Furthermore, the steps of generating a theoretical inspection path that includes intra-unit and inter-unit inspection paths based on the inspection constraint parameters, and adjusting the intra-unit inspection path based on the dynamic risk coefficient data, include:

[0036] Based on the spatial distribution order of components in each unit and the inspection constraint parameters in the unit division results, the shooting nodes and continuous flight trajectories of the components within the unit are planned, and the spacing between shooting nodes is determined based on the type and risk level.

[0037] The inspection path within each inspection unit is iterated to minimize the sum of the inspection path within each unit and the inspection path between units, and to ensure that the inspection path between units is a straight line with no obstructions, thereby determining the theoretical inspection path.

[0038] The target components are determined by screening the components within the inspection unit based on the dynamic risk coefficient data.

[0039] Adjust the density and position of the corresponding shooting nodes for the target component to adjust the inspection path within the unit.

[0040] Furthermore, the steps to establish a dynamic priority queue for handling sudden high-risk components include:

[0041] Acquire information on components with sudden risks before or during inspections. The information on components with sudden risks includes the unit to which they belong, their three-dimensional coordinates, and the dynamic risk coefficient data.

[0042] The dynamic risk coefficient data is used to classify priority levels and sort them from high to low to form a queue. This allows the current inspection unit to complete its inspection, inspect the component with the sudden risk, and then return to the original inspection path to continue execution without changing the original unit order.

[0043] Another object of the present invention is a UAV-based transmission line tower inspection and monitoring system, the system comprising:

[0044] The data preparation module is used to acquire data on transmission line towers, operating conditions, and initial state data of drones. Based on the tower data, the towers are divided into units to determine multiple inspection units. Based on the tower data, the operating conditions, and the initial state data of drones, the dynamic risk coefficient data of each component is determined.

[0045] The constraint determination module is used to construct a differentiated constraint model based on the inspection unit, the preset risk threshold, the working condition data, and the dynamic risk coefficient data, so as to determine the inspection constraint parameters of each component.

[0046] The path determination module is used to generate a theoretical inspection path that includes the inspection path within the unit and the inspection path between units based on the inspection constraint parameters. At the same time, it adjusts the inspection path within the unit based on the dynamic risk coefficient data and establishes dynamic priority queue data to handle sudden high-risk components.

[0047] The path dynamic adjustment module is used to control the UAV to perform inspections according to the theoretical inspection path and to monitor data in real time. Based on the monitoring data and the preset risk threshold, the module optimizes the inspection path within the current unit and adjusts the subsequent inspection paths within the unit based on the optimized dynamic risk coefficient data and inspection constraint parameters.

[0048] Another objective of this invention is to provide a storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for monitoring and inspecting power transmission line towers based on unmanned aerial vehicles (UAVs).

[0049] Another objective of this invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method for monitoring and inspecting power transmission line towers based on unmanned aerial vehicles (UAVs).

[0050] This invention, by employing a systematic inspection unit division mechanism in the early stages, constructs a stable global inspection framework, significantly reducing path overlap, regional omissions, and cross-regional backtracking issues. This fundamentally solves the bottlenecks of chaotic inspection order and low operational efficiency in existing technologies. Furthermore, through differentiated constraint modeling based on dynamic risk coefficients and dynamic path optimization within units, it ensures the inspection accuracy of high-risk components and the inspection efficiency of low-risk components. The design of fixed global unit order and local adjustments within units balances global path stability with precise local adaptation. The closed-loop optimization mechanism provides powerful real-time risk response and strategy iteration capabilities. This ensures that the solution possesses both global inspection order and local risk adaptability, effectively avoiding the problems of rigid paths failing to cope with real-time risks or local adjustments disrupting the global order. Moreover, by adopting the coordinated approach of dynamic risk quantification, constraint modeling, and path planning, this process is entirely based on the risk characteristics of tower components and real-time inspection data, autonomously adjusting without the need for manual intervention to set fixed parameters. This invention can adaptively capture changes in component risk and fluctuations in operating conditions during the inspection process, accurately matching the inspection needs of components with different risk levels. Even in complex geographical environments and variable operating conditions of power transmission line tower inspection scenarios, it can still stably achieve efficient, accurate, and safe drone inspections. Therefore, this invention solves the problem in the prior art of lacking a drone-based power transmission line tower inspection and monitoring method with good adaptability, high inspection accuracy, and high efficiency. Attached Figure Description

[0051] Figure 1 This is a flowchart of the UAV-based transmission line tower inspection and monitoring method in the first embodiment of the present invention;

[0052] Figure 2This is a schematic diagram of the results of the UAV-based transmission line tower inspection and monitoring system in the second embodiment of the present invention;

[0053] Figure 3 This is a schematic diagram of the structure of the electronic device in the third embodiment of the present invention;

[0054] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0055] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0057] Example 1

[0058] Please see Figure 1 The figure shows a method for inspecting and monitoring power transmission line towers based on unmanned aerial vehicles (UAVs) in the first embodiment of the present invention. The method specifically includes steps S01-S04.

[0059] S01, acquire data on transmission line towers, operating conditions, and initial state data of drones; divide the towers into units based on the tower data to determine multiple inspection units; and determine the dynamic risk coefficient data of each component based on the tower data, the operating conditions, and the initial state data of drones.

[0060] Specifically, components on the tower are clustered based on risk level and coordinates to determine cluster data; regions are divided according to height and the main outline of the tower, and each region is further divided into units based on the cluster data; each unit is numbered and sorted according to preset rules and verified to ensure that different risk components are reasonably allocated within each unit, and that the unit scope is appropriate and covers all components. In practice, risk level and coordinate clustering first breaks the isolated distribution of components, forming component clusters with homogeneous risks and spatial aggregation, providing core data basis for unit division; then, combining the physical spatial characteristics of the tower height and main outline with the cluster data, the abstract component clusters are grounded into physically executable inspection units, ensuring that components within the unit can be easily operated by drones continuously and that similar risks are handled centrally; finally, a global inspection order is established through numbering and sorting, and verification eliminates problems such as incomplete coverage, risk imbalance, and inappropriate scope, clarifying the execution order for subsequent path planning. This creates a well-defined, risk-balanced, comprehensive, and orderly inspection unit system. It avoids path redundancy caused by overly large units and reduces frequent cross-unit switching caused by overly small units. Subsequent path planning can be executed sequentially by number, significantly reducing cross-regional backtracking and path overlap, thus improving inspection continuity and overall efficiency. Therefore, by using a dual logic of risk association and spatial aggregation, the scattered components on the tower are divided into appropriately sized, comprehensive, risk-balanced, and clearly ordered inspection units. This provides a stable and efficient framework for subsequent global path planning and differentiated inspections, avoiding the path chaos caused by the scattered components in traditional inspections.

[0061] Furthermore, an inherent risk coefficient is determined based on the tower data, and the corresponding operating condition risk coefficient for each component is determined using a first preset formula based on the tower data and the operating condition data; the inspection status risk coefficient is determined using a second preset formula based on the tower data, the initial state data of the UAV, and the detection data; the inherent risk coefficient, the operating condition risk coefficient, and the inspection status risk coefficient are then coupled and calculated to obtain dynamic risk coefficient data, wherein the detection data is not included when calculating the initial dynamic risk coefficient data. The first preset formula is:

[0062]

[0063] The second preset formula is:

[0064]

[0065] Among them, among them, For the working condition risk coefficient, For the first k The weights of the factors affecting each working condition. For the first kA function of several influencing factors For the first i Each inspection unit t For real-time, n The number of operating condition influencing factors, The risk coefficient for the inspection status. This is a weighting coefficient for the shooting distance deviation. This is the actual shooting distance. The minimum clear shooting distance for the component. These are the weighting coefficients for positioning drift. This is to determine the drift amount. Since the inspection units are divided through risk clustering, each inspection unit... All components possess corresponding risk level attributes. In practical implementation, inherent risk coefficients are determined based on tower data, clarifying the inherent risk differences of components due to structural type, service life, and other inherent attributes, laying the foundation for overall risk assessment. Then, discrete real-time operating condition data, such as humidity and conductor galloping, are transformed into quantifiable operating condition risk coefficients through a first preset formula, realizing the mapping from environmental changes to dynamic risk updates. Furthermore, operational parameters such as drone shooting distance deviation and positioning drift are transformed into inspection status risk coefficients through a second preset formula, completing the full-chain risk coverage of components, environment, and operations. Multiple risk coefficients, such as fixed risks, operating condition risks, and inspection status risks during inspections, are coupled to form dynamic risk data. Uninspected detection data is excluded during initial calculations to ensure the objectivity of the assessment, while reserving space for subsequent iterative optimization based on monitoring data. This approach establishes a dynamic risk assessment result that features difference identification, real-time operating condition matching, and risk control. The initial calculations are free from bias due to missing data, accurately depicting the true risk level of each component. It distinguishes between different components, such as the inherent risk differences between old and new components and different types of components, while also responding in real-time to fluctuations in operating conditions and changes in the drone's operational status. This provides precise data support for subsequent differentiated constraint parameter setting and path optimization for high-risk components, avoiding insufficient inspection accuracy or resource redundancy caused by one-sided risk assessments. Therefore, by constructing a multi-dimensional risk assessment system integrating inherent attributes, real-time operating conditions, and operational status, a comprehensive, dynamic, and initially unbiased component dynamic risk coefficient is generated. This provides a precise risk stratification basis for subsequent differentiated constraint modeling and path optimization, solving the problem that traditional single-dimensional or static risk assessments cannot adapt to complex inspection scenarios.

[0066] S02, construct a differentiated constraint model based on the inspection unit, the preset risk threshold, the working condition data, and the dynamic risk coefficient data to determine the inspection constraint parameters of each component.

[0067] Specifically, based on the operating condition data, the dynamic risk coefficient data, and the preset risk threshold, it is determined whether the dynamic adjustment mechanism of the inspection constraint parameters is triggered to determine the triggering state; according to the component type in the inspection unit, the operating condition data, the dynamic risk coefficient data, and the triggering state, the inspection constraint parameters corresponding to each component are calculated respectively. The inspection constraint parameters include at least the dynamic safety distance, the shooting distance, and the shooting angle; the dynamic safety distance calculation formula is:

[0068]

[0069] The formula for calculating the shooting distance is:

[0070]

[0071] The formula for calculating the shooting angle is:

[0072]

[0073] in, For dynamic safety distance, For the corresponding voltage level U The baseline safety distance, , and For the shadow coefficient, For real-time humidity, This is the humidity baseline value. The amplitude of the conductor's movement. To implement electromagnetic interference intensity, This serves as a reference value for electromagnetic interference. This is a linkage function between risk and humidity. For dynamic risk coefficients, This is a threshold-triggered state. For the first i The dynamic safety distance of each inspection unit at the previous moment. For the previous moment, For wind safety redundancy factor, For real-time wind speed, This is the actual shooting distance. The minimum clear shooting distance for the component. This is the humidity correction factor. For the first i The dynamic safe distance of each inspection unit at the current moment. This is the safety distance ratio coefficient. For the first i The shooting distance of each inspection unit at the previous moment. For the shooting angle, Let be the optimal shooting angle for each component within the i-th inspection unit. This is the angle compensation amount for electromagnetic interference and conductor galloping. For the first i The previous shooting angle of each inspection unit. In specific implementation, the dynamic risk coefficient, real-time operating conditions and preset thresholds are compared by judging the trigger status first, and different decision results are output to clarify whether the constraint parameters need to be adjusted, avoiding the inefficiency waste caused by indiscriminate optimization, and providing accurate guidance for subsequent parameter calculation. Then, based on the trigger status, the inspection unit component type, operating condition data and dynamic risk coefficient are integrated, and three types of core constraint parameters are calculated by a dedicated piecewise function: Dynamic safety distance formula: Combines voltage level benchmark value, humidity, conductor galloping, electromagnetic interference and risk linkage factor to calculate the optimal safety distance when the risk or operating condition exceeds the standard. Under stable operating conditions, it is corrected by historical data and wind redundancy coefficient to balance safety and efficiency; Shooting distance formula: When triggering optimization, the maximum value of the minimum clear shooting distance after humidity correction and the ratio correction value of safety distance is taken. Under stable operating conditions, historical data is used to balance image quality and safety boundary; Shooting angle formula: When triggering optimization, the angle compensation of electromagnetic interference and conductor galloping is superimposed on the optimal shooting angle of the component. Under stable operating conditions, historical angle is used to offset the impact of environmental interference on shooting accuracy. This approach enables risk-differentiated, condition-adaptive, and safety- and accuracy-enhancing constraint parameters. Components operating under high-risk and complex conditions face stricter constraint standards (closer shooting distance, better compensation angle, and more environmentally friendly safety distances), while parameters are more lenient under low-risk and stable conditions, avoiding resource redundancy. Dynamic safety distances effectively mitigate safety hazards from complex conditions such as high humidity and strong electromagnetic interference. Shooting distance and angle precisely match defect detection requirements, ensuring clear identification of component defects without exceeding safety boundaries. Compared to traditional fixed-parameter solutions, this improves drone operation safety and defect detection rates while avoiding frequent parameter adjustments or rigidity, balancing inspection accuracy and operational efficiency. Furthermore, by establishing a risk- and condition-driven adaptive constraint mechanism, core inspection constraint parameters such as dynamic safety distance, shooting distance, and shooting angle are precisely calculated for different component types, risk levels, and real-time conditions, replacing traditional static, one-size-fits-all parameter settings. This achieves a dynamic balance between constraint parameters and safety requirements, inspection accuracy, and operational efficiency.

[0074] S03, based on the inspection constraint parameters, generate a theoretical inspection path that includes the inspection path within the unit and the inspection path between units. At the same time, based on the dynamic risk coefficient data, adjust the inspection path within the unit and establish dynamic priority queue data to handle sudden high-risk components.

[0075] Specifically, based on the spatial distribution order of components and inspection constraint parameters in the unit division results, shooting nodes and continuous flight trajectories are planned for components within the unit, and the spacing between shooting nodes is determined based on part type and risk level. The inspection path within each inspection unit is iterated to minimize the sum of the inspection paths within each unit and the inspection paths between units, and to ensure that the inspection paths between units are straight lines with no obstructions, thereby determining the theoretical inspection path. Target components are identified by screening components within the inspection unit based on the dynamic risk coefficient data. The density and position of the corresponding shooting nodes are adjusted for the target components to adjust the inspection path within the unit. In practical implementation, based on the inspection unit division results, component spatial distribution order, and constraint parameters, continuous flight trajectories are planned for components within the unit. The spacing between shooting nodes is set differently according to component type and risk level (smaller spacing for high-risk components) to avoid path overlap and excessive shooting within the unit. The global path is optimized through an iterative algorithm to minimize the total path length within and between units, and unobstructed straight paths are used between units to reduce UAV flight time and energy consumption, laying the foundation for efficient global inspection. Based on dynamic risk coefficient data, high-risk target components are screened to accurately identify components requiring key attention, ensuring resources are allocated to core risk points. A dense node and precise positioning strategy is employed for target components, adjusting the density and position of shooting nodes to compensate for the limitations of theoretical paths, which are generally applicable but lack specificity, thus enhancing the coverage accuracy of high-risk components. This constructs a globally efficient and orderly inspection path with locally precise focus: at the global level, the total path length is optimal, with no redundant obstructions between units and continuous flight, significantly improving overall inspection efficiency compared to traditional paths; at the local level, high-risk components, through node densification and position optimization, also show an effective improvement in defect detection rate compared to paths without adjustment, and local adjustments do not disrupt the overall path order within units. This avoids the global chaos and inefficiency of traditional paths while addressing the shortcomings of insufficient focus and precision, achieving a dual improvement in inspection efficiency and defect detection accuracy. Furthermore, by constructing a two-layer path system that is globally optimal, collaborative, and locally precise, it achieves both high efficiency and no redundancy in the global inspection path, and enhances the local coverage accuracy for high-risk components. This resolves the contradiction between the rigidity and inefficiency of traditional path planning and the lack of local precision, providing UAVs with inspection trajectory data that combines efficiency and accuracy.

[0076] Furthermore, the step of establishing dynamic priority queue data to handle sudden high-risk components includes: obtaining information on sudden risk components before or during inspection, wherein the information on sudden risk components includes the unit to which they belong, three-dimensional coordinates, and the dynamic risk coefficient data; classifying priority levels according to the dynamic risk coefficient data, and sorting them in descending order of priority to form a queue, so that after the current inspection unit completes its inspection, it can inspect the sudden risk component and then return to the original inspection path to continue execution without changing the original unit order. In practical implementation, the core information of components facing sudden risks is first comprehensively acquired: data on components before inspection (e.g., early warning information) or during inspection (e.g., real-time monitoring detection) is collected, including the unit to which they belong, three-dimensional coordinates, and dynamic risk coefficients, providing complete and accurate data support for subsequent handling. Next, priorities are precisely defined and queues are formed, specifically based on dynamic risk coefficients, sorting components by risk level from high to low to establish an orderly handling queue, ensuring that high-priority sudden risks receive priority attention. Then, inspection paths are inserted in an orderly manner. By adopting a mechanism of insertion after the current unit is completed, the inspection of a sudden component immediately returns to the original inspection path without changing the overall unit inspection order, ensuring the continuity of the overall plan. This achieves the dual goals of no sudden risks being overlooked and no disruption to emergency response: the core information of high-risk components is complete and timely, ensuring timely inspection according to risk priority and preventing risk escalation; simultaneously, the overall inspection order remains undisturbed, and the original unit inspection order and path planning remain consistent. This solves the problem of delayed response to sudden risks in traditional inspections and avoids the chaos of the overall path caused by emergency adjustments, balancing emergency response efficiency with overall inspection stability. Therefore, by establishing a rapid response and orderly emergency risk management mechanism, while timely inspection of high-risk components, it is possible to avoid disrupting the overall inspection plan with emergency response, resolve the contradiction of traditional inspection being slow to respond to emergencies or disrupting the overall situation, and achieve a balance between emergency response and overall stability.

[0077] S04, the drone is controlled to perform inspections according to the theoretical inspection path and data monitoring is carried out in real time. The inspection path within the current unit is optimized based on the monitoring data and the preset risk threshold. The subsequent inspection paths within the unit are adjusted based on the optimized dynamic risk coefficient data and inspection constraint parameters to monitor the tower.

[0078] In practical implementation, the drone is controlled to perform operations according to the theoretical inspection path, collecting multi-dimensional monitoring data in real time (component defect information, real-time operating condition data, and drone operation status data). The data is compared with preset risk thresholds, and the current unit path is optimized in real time in response to changes in risk (such as adding shooting nodes, adjusting shooting angles, etc.) to make up for the scene adaptation gaps in the initial planning. Then, the optimized dynamic risk coefficient data and inspection constraint parameters of the current unit are extracted and used as the basis for updating the subsequent unit path planning. The shooting node density and path trajectory of subsequent units are adjusted in a targeted manner to achieve cross-unit reuse of optimization experience and avoid the recurrence of similar adaptation problems. As a result, the current unit path can be accurately adapted to sudden scenarios through real-time optimization, effectively avoiding inspection omissions or insufficient accuracy. Subsequent unit paths are continuously optimized without manual intervention, and the planning scheme is more in line with actual inspection needs through iterative updates. The global inspection path completely gets rid of the drawbacks of traditional rigid execution. In complex and ever-changing inspection scenarios, accuracy and efficiency are continuously improved, ensuring the dynamic adaptability and global optimization of the entire inspection process.

[0079] In summary, the UAV-based transmission line tower inspection and monitoring method in the above embodiments of the present invention, by adopting a systematic inspection unit division mechanism in the early stage, constructs a stable global inspection framework, significantly reducing path overlap, regional omissions, and cross-regional backtracking problems, thereby fundamentally solving the bottlenecks of chaotic inspection order and low operational efficiency in existing technologies. Furthermore, through differentiated constraint modeling based on dynamic risk coefficients and dynamic path optimization within units, the method ensures the inspection accuracy of high-risk components and the inspection efficiency of low-risk components. The design of fixed global unit order and local adjustments within units balances global path stability and local precise adaptation. Moreover, the closed-loop optimization mechanism provides powerful real-time risk response and strategy iteration capabilities. This ensures that the solution possesses both global inspection order and local risk adaptability, effectively avoiding the problems of rigid paths failing to cope with real-time risks or local adjustments disrupting the global order. In addition, by employing the coordinated use of dynamic risk quantification and constraint modeling, and path planning, this process is entirely based on the risk characteristics of tower components and real-time inspection data for autonomous adjustment, without the need for manual intervention to set fixed parameters. This invention can adaptively capture changes in component risk and fluctuations in operating conditions during the inspection process, accurately matching the inspection needs of components with different risk levels. Even in complex geographical environments and variable operating conditions of power transmission line tower inspection scenarios, it can still stably achieve efficient, accurate, and safe drone inspections. Therefore, this invention solves the problem in the prior art of lacking a drone-based power transmission line tower inspection and monitoring method with good adaptability, high inspection accuracy, and high efficiency.

[0080] Example 2

[0081] Please see Figure 2The diagram shown is a structural block diagram of the UAV-based transmission line tower inspection and monitoring system proposed in the second embodiment of the present invention. The UAV-based transmission line tower inspection and monitoring system 200 includes: a data preparation module 21, a constraint determination module 22, a path determination module 23, and a path dynamic adjustment module 24, wherein:

[0082] The data preparation module 21 is used to acquire data on transmission line towers, operating conditions, and initial state data of drones. Based on the tower data, the towers are divided into units to determine multiple inspection units. Based on the tower data, the operating conditions, and the initial state data of drones, the dynamic risk coefficient data of each component is determined.

[0083] The constraint determination module 22 is used to construct a differentiated constraint model based on the inspection unit, the preset risk threshold, the working condition data and the dynamic risk coefficient data, so as to determine the inspection constraint parameters of each component;

[0084] The path determination module 23 is used to generate a theoretical inspection path that includes the inspection path within the unit and the inspection path between units based on the inspection constraint parameters. At the same time, it adjusts the inspection path within the unit based on the dynamic risk coefficient data and establishes dynamic priority queue data to handle sudden high-risk components.

[0085] The path dynamic adjustment module 24 is used to control the UAV to perform inspections according to the theoretical inspection path and to monitor data in real time. Based on the monitoring data and the preset risk threshold, the inspection path within the current unit is optimized, and the subsequent inspection paths within the unit are adjusted according to the optimized dynamic risk coefficient data and inspection constraint parameters.

[0086] Example 3

[0087] In another aspect, the present invention also proposes an electronic device, please refer to [link to relevant documentation]. Figure 3 The diagram shows an electronic device according to the third embodiment of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, it implements the above-described method for inspecting and monitoring transmission line towers based on unmanned aerial vehicles.

[0088] In some embodiments, the processor 10 may be a central processing unit (CPU), controller, microcontroller, microprocessor or other data processing chip, used to run program code stored in memory 20 or process data, such as executing access restriction programs.

[0089] The memory 20 includes at least one type of readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 20 can be an internal storage unit of an electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 20 can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 20 can include both internal and external storage units of the electronic device. The memory 20 can be used not only to store application software and various types of data of the electronic device, but also to temporarily store data that has been output or will be output.

[0090] It should be pointed out that, Figure 3 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0091] This invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for monitoring and inspecting power transmission line towers based on unmanned aerial vehicles (UAVs).

[0092] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0093] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0094] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0095] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0096] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method for inspecting and monitoring transmission line towers based on unmanned aerial vehicles (UAVs), characterized in that, The method includes: Data on power transmission line towers, operating conditions, and initial state data of drones are acquired. Based on the tower data, the towers are divided into units to determine multiple inspection units. Based on the tower data, the operating conditions, and the initial state data of drones, the dynamic risk coefficient data of each component is determined. A differentiated constraint model is constructed based on the inspection unit, the preset risk threshold, the operating condition data, and the dynamic risk coefficient data to determine the inspection constraint parameters of each component. Based on the inspection constraint parameters, a theoretical inspection path is generated, which includes the inspection path within the unit and the inspection path between units. At the same time, the inspection path within the unit is adjusted based on the dynamic risk coefficient data, and dynamic priority queue data is established to handle sudden high-risk components. The drone is controlled to perform inspections according to the theoretical inspection path and data monitoring is carried out in real time. The inspection path within the current unit is optimized based on the monitoring data and the preset risk threshold. The subsequent inspection paths within the unit are adjusted based on the optimized dynamic risk coefficient data and inspection constraint parameters to monitor and inspect the tower. The steps for constructing a differentiated constraint model based on the inspection unit, the preset risk threshold, the operating condition data, and the dynamic risk coefficient data to determine the inspection constraint parameters of each component include: Based on the operating condition data, the dynamic risk coefficient data, and the preset risk threshold, determine whether the dynamic adjustment mechanism of the inspection constraint parameters is triggered, so as to determine the triggering status; Based on the component type, the working condition data, the dynamic risk coefficient data, and the triggering state in the inspection unit, the inspection constraint parameters corresponding to each component are calculated respectively. The inspection constraint parameters include at least the dynamic safety distance, the actual shooting distance, and the shooting angle. The formula for calculating the dynamic safety distance is: The formula for calculating the actual shooting distance is: The formula for calculating the shooting angle is: in, For dynamic safety distance, For the corresponding voltage level U The baseline safety distance, , and For the shadow coefficient, For real-time humidity, This is the humidity baseline value. The amplitude of the conductor's movement. To implement electromagnetic interference intensity, This serves as a reference value for electromagnetic interference. This is a linkage function between risk and humidity. For dynamic risk coefficients, This is a threshold-triggered state. For the first i The dynamic safety distance of each inspection unit at the previous moment. For the previous moment, For wind safety redundancy factor, For real-time wind speed, This is the actual shooting distance. The minimum clear shooting distance for the component. This is the humidity correction factor. For the first i The dynamic safe distance of each inspection unit at the current moment. This is the safety distance ratio coefficient. For the first i The actual shooting distance of each inspection unit at the previous moment. For the shooting angle, Let be the optimal shooting angle for each component within the i-th inspection unit. This is the angle compensation amount for electromagnetic interference and conductor galloping. For the first i The camera angle of each inspection unit at the previous moment.

2. The method for inspecting and monitoring transmission line towers based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The steps for dividing the iron tower into units and determining multiple inspection units based on the tower data include: Clustering of components on the tower based on risk level and coordinates to determine clustering data; The regions are divided according to their height and the main outline of the tower, and the regions are further divided into units based on clustering data. Each unit is numbered and sorted according to preset rules and then verified to ensure that the different risk components within each unit are reasonably allocated and that the unit scope is appropriate and covers all components.

3. The method for inspecting and monitoring transmission line towers based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The steps for determining the dynamic risk coefficient data of each component based on the tower data, the operating condition data, and the initial state data of the UAV include: The inherent risk coefficient is determined based on the tower data, and the corresponding operating condition risk coefficient of each component is determined according to the tower data and the operating condition data through a first preset formula. The inspection status risk coefficient is determined by a second preset formula based on the tower data, the initial state data of the UAV, and the detection data. Dynamic risk coefficient data is obtained by coupling the inherent risk coefficient, the working condition risk coefficient, and the inspection status risk coefficient. However, the detection data is not included when calculating the initial dynamic risk coefficient data.

4. The method for inspecting and monitoring transmission line towers based on unmanned aerial vehicles (UAVs) according to claim 3, characterized in that, The first preset formula is: The second preset formula is: in, For the working condition risk coefficient, For the first k The weights of the factors affecting each working condition. ( ) is the first k A function of several influencing factors For the first i Each inspection unit t For real-time, n The number of operating condition influencing factors, The risk coefficient for the inspection status. This is a weighting coefficient for the actual shooting distance deviation. This is the actual shooting distance. The minimum clear shooting distance for the component. These are the weighting coefficients for positioning drift. This is the location drift amount.

5. The method for inspecting and monitoring transmission line towers based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The steps of generating a theoretical inspection path that includes intra-unit and inter-unit inspection paths based on the inspection constraint parameters, and adjusting the intra-unit inspection path based on the dynamic risk coefficient data, include: Based on the spatial distribution order of components in each unit and the inspection constraint parameters in the unit division results, the shooting nodes and continuous flight trajectories of the components within the unit are planned, and the spacing between shooting nodes is determined based on the type and risk level. The inspection path within each inspection unit is iterated to minimize the sum of the inspection path within each unit and the inspection path between units, and to ensure that the inspection path between units is a straight line with no obstructions, thereby determining the theoretical inspection path. The target components are determined by screening the components within the inspection unit based on the dynamic risk coefficient data. Adjust the density and position of the corresponding shooting nodes for the target component to adjust the inspection path within the unit.

6. The method for inspecting and monitoring transmission line towers based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The steps to establish a dynamic priority queue for handling sudden high-risk components include: Acquire information on components with sudden risks before or during inspections. The information on components with sudden risks includes the unit to which they belong, their three-dimensional coordinates, and the dynamic risk coefficient data. The dynamic risk coefficient data is used to classify priority levels and sort them from high to low to form a queue. This allows the current inspection unit to complete its inspection, inspect the component with the sudden risk, and then return to the original inspection path to continue execution without changing the original unit order.

7. A UAV-based transmission line tower inspection and monitoring system, characterized in that, The system is used to implement the UAV-based transmission line tower inspection and monitoring method as described in any one of claims 1 to 6, the system comprising: The data preparation module is used to acquire data on transmission line towers, operating conditions, and initial state data of drones. Based on the tower data, the towers are divided into units to determine multiple inspection units. Based on the tower data, the operating conditions, and the initial state data of drones, the dynamic risk coefficient data of each component is determined. The constraint determination module is used to construct a differentiated constraint model based on the inspection unit, the preset risk threshold, the working condition data, and the dynamic risk coefficient data, so as to determine the inspection constraint parameters of each component. The path determination module is used to generate a theoretical inspection path that includes the inspection path within the unit and the inspection path between units based on the inspection constraint parameters. At the same time, it adjusts the inspection path within the unit based on the dynamic risk coefficient data and establishes dynamic priority queue data to handle sudden high-risk components. The path dynamic adjustment module is used to control the UAV to perform inspections according to the theoretical inspection path and to monitor data in real time. Based on the monitoring data and the preset risk threshold, the module optimizes the inspection path within the current unit and adjusts the subsequent inspection paths within the unit based on the optimized dynamic risk coefficient data and inspection constraint parameters.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the UAV-based transmission line tower inspection and monitoring method as described in any one of claims 1 to 6.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the UAV-based transmission line tower inspection and monitoring method as described in any one of claims 1-6.

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