A machine vision-based transmission line external damage monitoring system

Abstract

The application relates to the technical field of electric wire monitoring, in particular to a power transmission line external damage monitoring system based on machine vision, which comprises a data acquisition module, a region division module, which is used to divide the ring line risk region in a construction region based on the voltage grade, spatial trajectory and power transmission line height of the power transmission line; a monitoring type division module, which is used to calculate a construction region risk tendency value based on the average lateral coincidence area and average longitudinal intrusion depth of dynamic targets in the ring line risk region, and divide the monitoring type of the construction region based on the construction region risk tendency value; and a resource allocation module, which is used to determine the allocable monitoring resource allocation method of the construction region based on the monitoring type of the construction region and / or the average motion change degree of dynamic targets in the ring line risk region; the application improves the early warning efficiency and accuracy by improving the accuracy of dynamic target division in the construction region and risk region division.

Description

Technical Field

[0001] This invention relates to the field of power line monitoring technology, and in particular to a machine vision-based system for monitoring external damage to power transmission lines. Background Technology

[0002] With the continuous expansion of the power system, transmission lines, as the core carriers of energy transmission, are directly related to the stability of the power grid through their safe operation. However, transmission lines are susceptible to external construction activities, leading to external damage accidents. Existing systems often use fixed radii to delineate dangerous areas without dynamically adjusting them based on parameters such as line voltage levels and equipment height. Furthermore, the allocation of monitoring resources (such as cameras and computing power) lacks specificity, potentially resulting in monitoring delays in high-risk areas due to insufficient resources, while low-risk areas suffer from resource waste.

[0003] For example, Chinese patent application publication number CN117237363A discloses a method, system, medium, and device for identifying external damage sources in transmission lines. The method includes: acquiring images of the transmission line's operating environment; extracting deep network image features from the acquired images to determine candidate target regions; and using a preset external damage source identification model to detect and identify external damage sources in the determined candidate target regions, thus completing the identification of external damage sources in the transmission line. The preset external damage source identification model employs a deep convolutional neural network with an attention mechanism to perceive the feature weights of deep network image features within the determined candidate target regions, thereby detecting and identifying potential external damage sources in the images. This invention, combined with a machine vision-based intelligent external damage source identification model, enables real-time monitoring of images of the transmission line's operating environment, timely detection of potential external damage sources, and improved safety and reliability of transmission line operation.

[0004] However, existing technologies have shortcomings such as insufficient precision in classifying the risk of external damage to transmission lines, inadequate judgment of the stability of target motion, inability to dynamically adjust monitoring strategies based on motion characteristics, resulting in unreasonable allocation of monitoring resources and a tendency for false alarms or missed alarms. Summary of the Invention

[0005] To address this, the present invention provides a machine vision-based transmission line external damage monitoring system to overcome the problems of insufficient precision in the classification of transmission line external damage risks, inadequate judgment of target motion stability, inability to dynamically adjust monitoring strategies according to motion characteristics, resulting in unreasonable allocation of monitoring resources and easy occurrence of false alarms or missed alarms in the existing technology.

[0006] To achieve the above objectives, the present invention provides a machine vision-based transmission line external damage monitoring system, comprising:

[0007] The data acquisition module is used to acquire power transmission parameter data, distribution location data, and construction data of power transmission lines within the construction area.

[0008] The area division module, which is connected to the data acquisition module, is used to divide the ring line risk area within the construction area based on the voltage level, spatial trajectory, and height of the transmission line.

[0009] The monitoring type classification module, which is connected to the area classification module, is used to calculate the risk tendency value of the construction area based on the average lateral overlap area and average longitudinal intrusion depth of dynamic targets within the risk area of ​​the ring road, and classify the monitoring type of the construction area based on the risk tendency value of the construction area.

[0010] The resource allocation module, which is connected to the monitoring type classification module, is used to determine the allocation method of allocable monitoring resources for the construction area based on the monitoring type of the construction area and / or the average degree of motion change of dynamic targets in the ring road risk area; the allocable monitoring resource allocation method includes prioritizing the allocation of first dynamic targets in the ring road risk area and prioritizing the allocation of second dynamic targets in the non-ring road risk area.

[0011] The monitoring module, which is connected to the data acquisition module, the area division module, the monitoring type division module and the resource allocation module respectively, is used to determine whether to issue an external damage warning for the transmission line based on whether the movement trajectory of the dynamic target overlaps with the protection area of ​​the transmission line.

[0012] Furthermore, the area division module uses the spatial trajectory of the transmission line as a dynamic reference axis, extends outward along the radial direction of the dynamic reference axis to a preset safety distance threshold, and covers a preset length range along the axial direction of the reference axis to form a three-dimensional tubular area, namely the ring line risk area.

[0013] Furthermore, the monitoring type classification module calculates the risk tendency value of the construction area based on the weighted sum of the average lateral overlap area and the average longitudinal intrusion depth of dynamic targets within the risk area of ​​the ring road, and classifies the monitoring type of the construction area based on the risk tendency value of the construction area.

[0014] If the risk tendency value of the construction area is greater than or equal to the preset risk tendency value of the construction area, the monitoring type classification module determines the monitoring type of the construction area as a high-risk tendency type.

[0015] If the risk tendency value of the construction area is less than the preset risk tendency value of the construction area, the monitoring type classification module determines that the monitoring type of the construction area is a low-risk tendency type.

[0016] Furthermore, the resource allocation module determines the allocation method for allocable monitoring resources in the construction area based on the monitoring type of the construction area and / or the average degree of motion change of dynamic targets within the risk area of ​​the ring road, wherein,

[0017] If the monitoring type of the construction area is high-risk tendency type or the average movement change of dynamic targets in the ring road risk area is greater than the preset change level, the resource allocation module determines to prioritize the allocation of the first dynamic target in the ring road risk area.

[0018] If the monitoring type of the construction area is low-risk tendency and the average motion change of dynamic targets within the ring line risk area is less than or equal to a preset change level, the resource allocation module determines to prioritize the allocation of the second dynamic target within the non-ring line risk area. Further, the first dynamic target is a dynamic target within the ring line risk area, and the second dynamic target is a dynamic target within the non-ring line risk area that meets the external damage conditions of the transmission line. The external damage conditions are that the spatial envelope formed by the allowed motion trajectory of the dynamic target intersects with the spatial trajectory of the transmission line.

[0019] Furthermore, the resource allocation module determines the degree of motion change of the dynamic target based on the rate of change of the direction of the dynamic target and the amplitude of the velocity fluctuation.

[0020] Furthermore, the allocatable monitoring resources include a collection of monitoring hardware devices operating in an unsaturated state, remaining available computing power, unused communication transmission bandwidth, and idle storage capacity.

[0021] Furthermore, the resource allocation module determines the priority of the allocable monitoring resource allocation objects based on the rate of change of the motion trajectory of the first dynamic target.

[0022] Furthermore, the resource allocation module determines the priority of the allocable monitoring resource allocation objects based on the working mode change rate of the second dynamic target.

[0023] Furthermore, the monitoring module determines whether to issue an external damage warning for the transmission line based on whether the trajectory of the dynamic target overlaps with the protection zone of the transmission line.

[0024] If the trajectory of a dynamic target overlaps with the protection zone of a power transmission line, the monitoring module determines and issues an early warning of external damage to the power transmission line.

[0025] Compared with the prior art, the beneficial effects of the present invention are that the length of the transmission line in the construction area is dynamically adjusted according to the changes in the construction range. The axial preset length is set as the actual line length in the construction area, which allows the risk area of ​​the ring line to be extended along the axial direction as needed, avoiding ineffective monitoring of the line in the non-construction area, significantly improving the adaptability of the system to construction scenarios of different scales and locations, ensuring that monitoring resources are concentrated on the line sections that are truly at risk of external damage, and improving the utilization rate of monitoring resources through the above method.

[0026] Furthermore, this invention transforms the impact of dynamic targets on the risk area of ​​the ring line into quantifiable average lateral overlapping area and average longitudinal intrusion depth, and obtains a specific numerical risk tendency value through weighted calculation. This quantification method eliminates the interference of subjective factors and can more comprehensively capture the overall risk picture. At the same time, by adjusting the weights, it can focus on key risk dimensions and improve the accuracy of risk identification. Dividing the construction area into different types through the above method can efficiently use monitoring resources to improve the utilization efficiency of monitoring resources. At the same time, the rational use of monitoring resources improves the accuracy of early warning of external damage to transmission lines.

[0027] Furthermore, in this invention, when the monitoring type is high-risk or the target movement within the ring line changes drastically, all computing power, bandwidth, and high-resolution channels are tilted towards the risk area of ​​the ring line to ensure continuous tracking of the first dynamic target. In low-risk areas where the target movement changes less, the system actively transfers idle resources to the second dynamic target outside the ring line but meeting the external breach conditions. Resources are only allocated to the second dynamic target when there is a real possibility of intrusion. Meanwhile, the high-risk scenario within the ring line is always in a saturated monitoring state. The above method accurately allocates monitoring resources and improves the accuracy of early warning.

[0028] Furthermore, this invention is based on the inward contraction of the pre-defined ring line risk area, creating a clear progressive relationship between the two in terms of spatial boundaries. The ring line risk area is the area of ​​concern, and the protection area is the emergency warning area. The protection area is defined as 20% of the ring line risk area, and its size can be automatically adjusted according to the actual scale of the risk area. For high-voltage lines, the absolute range of the protection area is correspondingly expanded to ensure full coverage of the core safety area; for low-voltage lines, the absolute range of the protection area is correspondingly reduced to avoid excessive warnings. This method accurately allocates monitoring resources and improves the accuracy of warnings. Attached Figure Description

[0029] Figure 1 This is a structural block diagram of a machine vision-based transmission line external damage monitoring system according to an embodiment of the present invention;

[0030] Figure 2 This is a flowchart illustrating the determination of monitoring types for classifying construction areas according to an embodiment of the present invention.

[0031] Figure 3 This is a flowchart illustrating the determination process for allocating available monitoring resources in a construction area according to an embodiment of the present invention. Detailed Implementation

[0032] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0033] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0034] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0035] Please see Figures 1-3 As shown, Figure 1 This is a structural block diagram of a machine vision-based transmission line external damage monitoring system according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the determination of monitoring types for classifying construction areas according to an embodiment of the present invention. Figure 3 This is a flowchart illustrating the determination process for allocating available monitoring resources in a construction area according to an embodiment of the present invention.

[0036] This invention relates to a machine vision-based transmission line external damage monitoring system, comprising:

[0037] The data acquisition module is used to acquire power transmission parameter data, distribution location data, and construction data of power transmission lines within the construction area.

[0038] The area division module, which is connected to the data acquisition module, is used to divide the ring line risk area within the construction area based on the voltage level, spatial trajectory, and height of the transmission line.

[0039] The monitoring type classification module, which is connected to the area classification module, is used to calculate the risk tendency value of the construction area based on the average lateral overlap area and average longitudinal intrusion depth of dynamic targets within the risk area of ​​the ring road, and classify the monitoring type of the construction area based on the risk tendency value of the construction area.

[0040] The resource allocation module, which is connected to the monitoring type classification module, is used to determine the allocation method of allocable monitoring resources for the construction area based on the monitoring type of the construction area and / or the average degree of motion change of dynamic targets in the ring road risk area; the allocable monitoring resource allocation method includes prioritizing the allocation of first dynamic targets in the ring road risk area and prioritizing the allocation of second dynamic targets in the non-ring road risk area.

[0041] The monitoring module, which is connected to the data acquisition module, the area division module, the monitoring type division module and the resource allocation module respectively, is used to determine whether to issue an external damage warning for the transmission line based on whether the movement trajectory of the dynamic target overlaps with the protection area of ​​the transmission line.

[0042] The power transmission parameter data in this embodiment of the invention includes, but is not limited to, the rated voltage level of the power transmission line, the conductor type, and the safety distance threshold (the minimum safety distance corresponding to different voltage levels). The distribution location data includes, but is not limited to, the three-dimensional coordinate information of the power transmission line, the spatial distribution coordinates of the towers, and the location parameters of the inflection points of the power transmission line. The construction data includes, but is not limited to, dynamic target location information, the boundary coordinates of the planned area of ​​the construction area, and the parameter information of the construction equipment.

[0043] Specifically, the area division module uses the spatial trajectory of the transmission line as a dynamic reference axis, extends outward along the radial direction of the dynamic reference axis to a preset safety distance threshold, and covers a preset length range along the axial direction of the reference axis to form a three-dimensional tubular area, namely the ring line risk area.

[0044] In this embodiment of the invention, the preset safety distance threshold is the minimum safety distance corresponding to the voltage level of the transmission line, and the preset length is the length of the transmission line within the construction area.

[0045] The transmission line length within the construction area of ​​this invention is dynamically adjusted as the construction scope changes. By setting the axial preset length to the actual line length within the construction area, the risk area of ​​the ring line can be extended along the axial direction as needed, avoiding ineffective monitoring of lines outside the construction area. This significantly improves the system's adaptability to construction scenarios of different scales and locations, ensuring that monitoring resources are concentrated on line sections that are truly at risk of external damage. The above method improves the utilization rate of monitoring resources.

[0046] Specifically, the monitoring type classification module calculates the risk tendency value of the construction area based on the weighted sum of the average lateral overlap area and the average longitudinal intrusion depth of dynamic targets within the risk area of ​​the ring road, and classifies the monitoring type of the construction area based on the risk tendency value.

[0047] If the risk tendency value of the construction area is greater than or equal to the preset risk tendency value of the construction area, the monitoring type classification module determines the monitoring type of the construction area as a high-risk tendency type.

[0048] If the risk tendency value of the construction area is less than the preset risk tendency value of the construction area, the monitoring type classification module determines that the monitoring type of the construction area is a low-risk tendency type.

[0049] Understandably, the risk area of ​​the ring line has been designated as a key area of ​​concern based on parameters such as voltage level and line alignment. The activity of dynamic targets within this area is directly related to the risk of external damage to the transmission line. Using the target characteristics within this area as the basis for calculation, interference from non-risk areas can be eliminated. The average lateral overlap area reflects the extent to which the target encroaches on the risk area in a plane perpendicular to the line alignment. The larger the area, the higher the probability that the horizontal safety distance between the target and the line is compressed. The average longitudinal intrusion depth reflects the degree to which the target penetrates into the risk area along the line alignment. The greater the depth, the lower the axial safety redundancy between the target and the line.

[0050] In this embodiment of the invention, the average lateral overlap area can be obtained by acquiring continuous frame images of dynamic targets within the risk area of ​​the loop line using machine vision, and extracting the two-dimensional contour of the dynamic targets in each frame image using a target detection algorithm; calculating the overlap area between the two-dimensional contour of the dynamic targets and the lateral boundary of the risk area of ​​the loop line in the same coordinate system to obtain the lateral overlap area between the dynamic targets and the risk area of ​​the loop line in a single frame image; taking the arithmetic mean of the lateral overlap areas of all single frame images within a preset time period to obtain the average lateral overlap area; the average longitudinal intrusion depth can be obtained by acquiring three-dimensional coordinate data of the dynamic targets within the risk area of ​​the loop line using lidar or depth camera to determine the coordinates of the foremost position of the dynamic targets along the axial direction of the line; calculating the distance between the coordinates of the foremost position and the coordinates of the inner boundary of the risk area of ​​the loop line along the axial direction to obtain the longitudinal intrusion depth of the dynamic targets at a single moment (if the dynamic targets do not cross the inner boundary, the intrusion depth is 0); taking the arithmetic mean of the longitudinal intrusion depths at all moments within a preset time period to obtain the average longitudinal intrusion depth. The preset time period can be determined based on the average moving speed of the dynamic targets and a preset safety distance threshold, but the above values ​​are not limited to these, and those skilled in the art can adjust the values ​​according to actual needs.

[0051] In this embodiment of the invention, the weighting coefficients corresponding to the average lateral overlapping area and the average longitudinal intrusion depth can be based on historical external damage accident cases. The correlation between the average lateral overlapping area, the average longitudinal intrusion depth, and the severity of the accident at the time of the accident can be extracted, and the optimal weighting coefficients can be calculated using regression analysis or machine learning algorithms. For example, by training on 100 crane external damage accident data, if the correlation coefficient between the longitudinal intrusion depth and the severity of the accident is β = 0.8 and the correlation coefficient between the lateral overlapping area is α = 0.5, then β = 0.615 [0.8 / (0.8+0.5)] and α = 0.385 [0.5 / (0.8+0.5)] can be set proportionally. The preset construction area risk tendency value can be determined by extracting the construction area risk tendency value at the time of historical external damage accidents and taking the minimum critical value as the preset construction area risk tendency value. However, the above values ​​are not limited to this, and those skilled in the art can also adjust the values ​​according to actual needs.

[0052] This invention transforms the impact of dynamic targets on the risk area of ​​a ring line into quantifiable average lateral overlapping area and average longitudinal intrusion depth. A specific numerical risk tendency value is obtained through weighted calculation. This quantification method eliminates the interference of subjective factors and can more comprehensively capture the overall risk picture. Simultaneously, by adjusting the weights, it focuses on key risk dimensions, improving the accuracy of risk identification. Dividing the construction area into different types using the above method allows for efficient use of monitoring resources, improving their utilization efficiency. Furthermore, the rational use of monitoring resources enhances the accuracy of early warning of external damage to transmission lines.

[0053] Specifically, the resource allocation module determines the allocation method for allocable monitoring resources in the construction area based on the monitoring type of the construction area and / or the average degree of motion change of dynamic targets within the risk area of ​​the ring road.

[0054] If the monitoring type of the construction area is high-risk tendency type or the average movement change of dynamic targets in the ring road risk area is greater than the preset change level, the resource allocation module determines to prioritize the allocation of the first dynamic target in the ring road risk area.

[0055] If the monitoring type of the construction area is low-risk tendency type and the average motion change of dynamic targets within the ring road risk area is less than or equal to the preset change level, the resource allocation module determines to prioritize the allocation of the second dynamic target within the non-ring road risk area.

[0056] In this embodiment of the invention, the first dynamic target is a dynamic target within the risk area of ​​a ring line, and the second dynamic target is a dynamic target within the risk area of ​​a non-ring line that meets the external damage conditions of the transmission line. The external damage conditions of the transmission line are that the spatial envelope formed by the allowed movement trajectory of the dynamic target intersects with the spatial trajectory of the transmission line.

[0057] In this embodiment of the invention, a 220kV transmission line passes through a suburban area of ​​a city. Within 50 meters of the line, there is a large construction site where high-rise tower cranes are performing hoisting operations. The monitoring type classification module assesses the risk tendency value of the construction area as 85, classifying it as a high-risk type. The first dynamic target is a three-route tower crane within the risk area of ​​the ring line, whose operating range partially covers the line's protection zone. Real-time coordinates show that the crane booms frequently move within 10-15 meters of the conductor. The second dynamic target is a concrete pump truck in the non-ring line risk area. Its maximum pumping radius's spatial envelope potentially intersects with the line's trajectory, but it is not currently operating. Because the construction area is classified as a high-risk type, the resource allocation module prioritizes allocating idle UAV monitoring resources and video surveillance channels to the three tower cranes (the first dynamic target) within the ring line risk area, tracking their boom movements in real time to ensure timely warnings of collision risks.

[0058] In this embodiment of the invention, a 110kV transmission line crosses rural farmland. The area within 30 meters of the line is a ring line risk zone. There is a small road construction project in the area, which is assessed as a low-risk type. The first dynamic target is that there are two excavators in the ring line risk zone, which are carrying out earthwork excavation. Their buckets are frequently raised, lowered, and rotated, with an average motion change of 0.8m / s (the preset change threshold is 0.5m / s), which is considered high motion change. The second dynamic target is that there is an agricultural crane in the non-ring line risk zone (80 meters away from the line). The spatial envelope of the maximum extension range of its boom intersects with the line trajectory, but it is currently only carrying out low-speed translational operations.

[0059] Although the construction area is classified as low-risk, the average movement variation of excavators within the risk area of ​​the ring road exceeds the preset threshold. The resource allocation module prioritizes allocating infrared monitoring equipment and AI video analysis resources to two excavators, focusing on monitoring whether their buckets have a tendency to intrude into the protected area of ​​the line.

[0060] In this embodiment of the invention, a 500kV transmission line passes through a mountainous area. A 100-meter radius around the line is designated as a ring-line risk zone. Within this zone is a small fruit tree plantation, assessed as low-risk. The first dynamic target is three small pruning machines within the ring-line risk zone, used for pruning fruit tree branches. Their average movement speed is 0.1 m / s (below the preset threshold of 0.5 m / s), with gentle and fixed movement. The second dynamic target is a large wind turbine installation crane in a non-ring-line risk zone (150 meters from the line). Its boom can reach up to 80 meters in length, and its permissible movement trajectory overlaps clearly with the transmission line's trajectory. It is about to begin hoisting operations. Because the construction area is low-risk and the average movement speed of the dynamic targets within the ring-line risk zone is low, the resource allocation module prioritizes allocating high-precision radar monitoring resources and remote video monitoring channels to the wind turbine installation crane. This allows for real-time tracking of its boom height and rotation angle, preventing accidental intrusion into the line protection zone during hoisting.

[0061] In this embodiment of the invention, the resource allocation module determines the degree of motion change of the dynamic target based on the direction change rate and velocity fluctuation amplitude of the dynamic target. It obtains the position sequence of the dynamic target in consecutive frames through a target tracking algorithm (such as Kalman filtering or DeepSORT), calculates the angle of motion direction change in adjacent time periods based on trajectory points, calculates the velocity in adjacent time periods based on trajectory points, and calculates the velocity standard deviation. After normalizing the direction change rate and velocity fluctuation amplitude, it weights and sums them to obtain the degree of motion change of the dynamic target. The allocable monitoring resources include a set of monitoring hardware devices in an unsaturated operating state, remaining available computing power, unused communication transmission bandwidth, and idle storage capacity.

[0062] Understandably, when regional risks are high or target behavior is abnormal, priority should be given to dealing with targets that have already intruded into the risk zone. These targets have breached the outer defense line and their behavior directly threatens the security of the line, so resources need to be invested immediately to block the risk chain. When regional risks are controllable and target behavior is stable, resources should be moved forward to potential threat targets. Although these targets have not intruded into the risk zone, their movement trajectory may pose a threat in the future, and early monitoring can enable risk prediction.

[0063] In this embodiment of the invention, the maximum value of the direction change rate is the average value of the direction change rate of the dynamic target when several external damage warnings for transmission lines are issued, and the maximum value of the speed fluctuation amplitude is the average value of the speed fluctuation amplitude of the dynamic target when several external damage warnings for transmission lines are issued. The weighting coefficients can be trained to the optimal weights by analyzing the motion characteristics of objects in historical external damage events and their correlation with the accident. For example, data collection: organize 100 external damage events and extract the direction change rate and speed fluctuation amplitude data of objects within 5 seconds before the accident; correlation analysis: calculate the correlation coefficients between the two features and the occurrence of the accident (e.g., the correlation coefficient of the direction change rate is 0.6, and the correlation coefficient of the speed fluctuation amplitude is 0.4); normalize the correlation coefficients as weighting coefficients; the preset degree of change is the average degree of motion change of the dynamic target when several external damage warnings for transmission lines are issued, but the above values ​​are not limited to these, and those skilled in the art can adjust the values ​​according to actual needs.

[0064] In this invention, when the monitoring type is high-risk or the target movement within the loop changes drastically, all computing power, bandwidth, and high-resolution channels are allocated to the risk area of ​​the loop to ensure continuous tracking of the first dynamic target. In low-risk areas where the target movement changes less, the system actively transfers idle resources to the second dynamic target that is not on the loop but meets the external breach conditions. Resources are only allocated to the second dynamic target when there is a real possibility of intrusion. Meanwhile, the high-risk scenario within the loop is always under saturated monitoring. The above method accurately allocates monitoring resources and improves the accuracy of early warning.

[0065] Specifically, the resource allocation module determines the priority of the allocable monitoring resource allocation objects based on the rate of change of the motion trajectory of the first dynamic target, and the resource allocation module determines the priority of the allocable monitoring resource allocation objects based on the rate of change of the working form of the second dynamic target.

[0066] In this embodiment of the invention, the motion trajectory change rate is the change rate of the position data of the object contour within a preset time. The higher the change rate of the position data, the higher the priority. The working form change rate is the change rate of the shape parameters of the dynamic target. The shape parameters include, but are not limited to, "dynamic target tilt angle, dynamic target rotation speed, and dynamic target movement speed". The greater the working form change rate, the higher the priority.

[0067] Understandably, the first dynamic target is already in the core risk zone that may directly threaten the safety of the line. At this time, its threat to the line is mainly manifested in the instability of its motion state. The higher the trajectory change rate, the greater the probability of overlapping with the protection area of ​​the transmission line, and the more urgent the risk of sudden collision. The second dynamic target is a dynamic target that meets the external damage conditions within the risk area of ​​the non-loop line. Although it has not entered the risk zone, its core threat lies in the expansion of its working form because its allowed spatial envelope of motion trajectory intersects with the line. The higher the form change rate, the larger the potential range of its intrusion into the risk zone, and the higher the possibility of breaking through the safety boundary in the future.

[0068] Specifically, the monitoring module determines whether to issue an external damage warning for the transmission line based on whether the movement trajectory of the dynamic target overlaps with the protection area of ​​the transmission line.

[0069] If the trajectory of a dynamic target overlaps with the protection zone of a power transmission line, the monitoring module determines to issue an early warning of external damage to the power transmission line.

[0070] If the trajectory of the dynamic target does not overlap with the protection area of ​​the transmission line, the monitoring module determines that it is not necessary to issue an early warning of external damage to the transmission line.

[0071] In this embodiment of the invention, determining whether the trajectory of a dynamic target overlaps with the protection area of ​​a transmission line includes judging whether the real-time coordinate data of the dynamic target is within the protection area. If it is within the protection area, then an overlap is determined. The protection area is defined as 20% of the boundary of the risk area of ​​the ring line, narrowed towards the transmission line (this can be determined by the average value of the protection area when several transmission lines of the same type experience external damage). However, the above value is not limited to this, and those skilled in the art can adjust the value according to actual needs.

[0072] This invention is based on the inward contraction of pre-defined ring-shaped risk areas, creating a clear progressive relationship between the two in terms of spatial boundaries. The ring-shaped risk area is the area of ​​concern, and the protected area is the emergency warning area. The protected area is defined as 20% of the ring-shaped risk area, and its size can be automatically adjusted according to the actual scale of the risk area. For high-voltage lines, the absolute range of the protected area is correspondingly expanded to ensure comprehensive coverage of the core safety zone; for low-voltage lines, the absolute range of the protected area is correspondingly reduced to avoid excessive warnings. This method accurately allocates monitoring resources and improves the accuracy of early warnings.

[0073] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A machine vision-based transmission line external damage monitoring system, characterized in that, include: The data acquisition module is used to acquire power transmission parameter data, distribution location data, and construction data of power transmission lines within the construction area. The area division module, which is connected to the data acquisition module, is used to divide the ring line risk area within the construction area based on the voltage level, spatial trajectory, and height of the transmission line. The monitoring type classification module, which is connected to the area classification module, is used to calculate the risk tendency value of the construction area based on the average lateral overlap area and average longitudinal intrusion depth of dynamic targets within the risk area of ​​the ring road, and classify the monitoring type of the construction area based on the risk tendency value of the construction area. The resource allocation module, which is connected to the monitoring type classification module, is used to determine the allocation method of allocable monitoring resources for the construction area based on the monitoring type of the construction area and / or the average degree of motion change of dynamic targets in the ring road risk area; the allocable monitoring resource allocation method includes prioritizing the allocation of first dynamic targets in the ring road risk area and prioritizing the allocation of second dynamic targets in the non-ring road risk area. The monitoring module is connected to the data acquisition module, the area division module, the monitoring type division module and the resource allocation module respectively, and is used to determine whether to issue an external damage warning for the transmission line based on whether the movement trajectory of the dynamic target overlaps with the protection area of ​​the transmission line. The average lateral overlap area is based on continuous frame images of dynamic targets within the risk area of ​​the loop line acquired by machine vision, and the two-dimensional contours of the dynamic targets in each frame image are extracted by a target detection algorithm. The overlapping area between the two-dimensional contour of the dynamic target and the lateral boundary of the risk area of ​​the ring road in the same coordinate system is calculated to obtain the lateral overlapping area of ​​the dynamic target and the risk area of ​​the ring road in a single frame image. The arithmetic mean of the lateral overlapping areas of all single frames within a preset time is obtained as the average lateral overlapping area. The average longitudinal intrusion depth is obtained by determining the coordinates of the foremost position of the dynamic target along the axial direction of the ring road based on the three-dimensional coordinate data of the dynamic target within the risk area of ​​the ring road acquired by LiDAR or depth camera. The distance between the coordinates of the foremost position and the coordinates of the inner boundary of the risk area of ​​the ring road along the axial direction is calculated as the longitudinal intrusion depth of the dynamic target at a single moment. The arithmetic mean of the longitudinal intrusion depths at all moments within a preset time is obtained as the average longitudinal intrusion depth.

2. The machine vision-based transmission line external damage monitoring system according to claim 1, characterized in that, The region division module uses the spatial trajectory of the transmission line as a dynamic reference axis, extends outward along the radial direction of the dynamic reference axis to a preset safety distance threshold, and covers a preset length range along the axial direction of the reference axis to form a three-dimensional tubular region, namely the ring line risk region.

3. The machine vision-based transmission line external damage monitoring system according to claim 2, characterized in that, The monitoring type classification module calculates the risk tendency value of the construction area based on the weighted sum of the average lateral overlap area and the average longitudinal intrusion depth of dynamic targets within the risk area of ​​the ring road, and classifies the monitoring type of the construction area based on the risk tendency value. If the risk tendency value of the construction area is greater than or equal to the preset risk tendency value of the construction area, the monitoring type classification module determines the monitoring type of the construction area as a high-risk tendency type. If the risk tendency value of the construction area is less than the preset risk tendency value of the construction area, the monitoring type classification module determines that the monitoring type of the construction area is a low-risk tendency type.

4. The machine vision-based transmission line external damage monitoring system according to claim 3, characterized in that, The resource allocation module determines the allocation method for allocable monitoring resources in the construction area based on the monitoring type of the construction area and / or the average degree of motion change of dynamic targets within the risk area of ​​the ring road. If the monitoring type of the construction area is high-risk tendency type or the average movement change of dynamic targets in the ring road risk area is greater than the preset change level, the resource allocation module determines to prioritize the allocation of the first dynamic target in the ring road risk area. If the monitoring type of the construction area is low-risk tendency type and the average motion change of dynamic targets within the ring road risk area is less than or equal to the preset change level, the resource allocation module determines to prioritize the allocation of the second dynamic target within the non-ring road risk area.

5. The machine vision-based transmission line external damage monitoring system according to claim 4, characterized in that, The first dynamic target is a dynamic target within the risk area of ​​the ring line, and the second dynamic target is a dynamic target within the risk area of ​​the non-ring line that meets the conditions for external damage to the transmission line; The external damage condition of the transmission line is that the spatial envelope formed by the allowable motion trajectory of the dynamic target intersects with the spatial trajectory of the transmission line.

6. The machine vision-based transmission line external damage monitoring system according to claim 5, characterized in that, The resource allocation module determines the degree of motion change of the dynamic target based on the rate of change of the direction of the dynamic target and the amplitude of the velocity fluctuation.

7. The machine vision-based transmission line external damage monitoring system according to claim 6, characterized in that, The allocable monitoring resources include a collection of monitoring hardware devices that are not operating at full capacity, remaining available computing power, unused communication transmission bandwidth, and idle storage capacity.

8. The machine vision-based transmission line external damage monitoring system according to claim 6, characterized in that, The resource allocation module determines the priority of the objects to be allocated monitoring resources based on the rate of change of the trajectory of the first dynamic target.

9. The machine vision-based transmission line external damage monitoring system according to claim 6, characterized in that, The resource allocation module determines the priority of the objects to be allocated monitoring resources based on the rate of change of the working form of the second dynamic target.

10. The machine vision-based transmission line external damage monitoring system according to claim 9, characterized in that, The monitoring module determines whether to issue an external damage warning for the transmission line based on whether the trajectory of the dynamic target overlaps with the protection zone of the transmission line. If the trajectory of a dynamic target overlaps with the protection zone of a power transmission line, the monitoring module determines and issues an early warning of external damage to the power transmission line.

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