Power transmission line image inspection system based on machine learning

Abstract

The invention relates to the technical field of power transmission line monitoring, in particular to a power transmission line image inspection system based on machine learning, which comprises an acquisition layer, a transmission layer, a receiving layer, a scheduling layer, an inspection layer and an analysis layer. According to the invention, when the division of the inspection precision grades of the expected sites of the to-be-inspected areas is completed, whether the operation parameters of the acquisition layer are qualified is determined based on the number statistical result of the inspection precision grades, and when the operation parameters of the acquisition layer are determined to be abnormal, the operation parameters of the acquisition layer are automatically adjusted, so that the inspection efficiency of the power transmission line is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission line monitoring, and in particular to a power transmission line image inspection system based on machine learning. Background Art

[0002] With the development of science and technology, electricity has become an indispensable energy source for people's lives and work. The construction of power systems has also become an important part of urban planning. Transmission lines are an important part of the power system. Whether there are defects in the transmission lines greatly affects the normal operation of the power system. Therefore, the requirements for defect analysis of transmission lines are becoming higher and higher.

[0003] The pole tower is an important part of the transmission line. Its function is to support the overhead line conductors and overhead ground wires. Defect analysis of various parts of the pole tower has also become an important task in the defect analysis of the transmission line. With the development of drone technology, the power industry has also introduced drone technology to conduct daily inspections of transmission lines, and the method of inspecting pole towers by drones is becoming more and more widespread.

[0004] A Chinese patent application with publication number CN108365557A discloses a method and system for precision inspection of power transmission lines by unmanned aerial vehicles, comprising the following steps: obtaining the type of pole tower to be inspected; obtaining information on each part to be inspected based on the type of pole tower to be inspected; obtaining image data of each part based on the information on each part to be inspected, and associating and storing the information on each part with the corresponding acquired image data; performing defect analysis on each corresponding part based on the image data of each part, and obtaining a defect analysis report; It can be seen that the above technical solution has the following problems: it does not take into account the automatic adjustment of the operating parameters of the acquisition layer according to the abnormal conditions of the transmission line actually detected, which affects the inspection efficiency of the transmission line. Summary of the invention

[0005] To this end, the present invention provides a power transmission line image inspection system based on machine learning, which is used to overcome the problem in the prior art that the operating parameters of the acquisition layer are not automatically adjusted according to the abnormal conditions of the transmission lines actually detected, thereby affecting the inspection efficiency of the transmission lines.

[0006] To achieve the above object, the present invention provides a power transmission line image inspection system based on machine learning, comprising: The collection layer includes a number of drones for periodically collecting detection image information of each area to be inspected on the transmission line; A transmission layer, which is connected to the acquisition layer and is used to transmit the detection image information collected by each drone; A receiving layer connected to the transmission layer, for receiving the detection image information, and determining whether there is an abnormality in the corresponding area to be inspected based on the detection image information, and determining the expected location when it is determined that there is an abnormality in the detection image information; A scheduling layer, which is connected to the receiving layer and is used to select a corresponding inspection device for inspection based on the determined expected location; An inspection layer, which is connected to the dispatching layer, includes a plurality of inspection devices, each of which moves along the transmission line. When the inspection device reaches the expected location, the inspection device obtains abnormal conditions at the expected location to identify appearance defects; The analysis layer is connected to the acquisition layer, the transmission layer and the inspection layer respectively, and is used to periodically determine the deviation distance based on the longitude and latitude of the location of the appearance defect and the expected location, and divide the inspection accuracy level of the expected location based on the deviation distance, wherein the inspection accuracy level includes the primary location, the secondary location and the tertiary location. Under the condition that the analysis layer is used to complete the division of the inspection accuracy level of the expected location of each area to be inspected, the operation parameters of the acquisition layer are determined based on the quantitative statistical results of each inspection accuracy level, including: Determine whether the operating parameters of the collection layer are qualified, and control the collection layer to continue to use the current operating parameters to detect the transmission line; or, determine that the operating parameters of the collection layer are abnormal, and adjust the preset similarity to the corresponding value based on the proportion of the number of third-level sites.

[0007] Furthermore, the receiving layer is used to determine whether there is an abnormality in the corresponding area to be inspected based on the detected image information, including: The receiving layer is used to obtain the characteristic contour in the detection image information, and compare the characteristic contour with each preset contour pre-stored in the database. When the similarity between the characteristic contour in the detection image information and a single preset contour pre-stored in the database is higher than the preset similarity, it is determined that there is an abnormality in the single area to be inspected, and the corresponding area in the characteristic contour with a similarity higher than the preset similarity is selected as the abnormal area; The receiving layer determines the longitude and latitude of the abnormal area based on the coordinates of the abnormal area in the detection image information and the geographical longitude and latitude of the corresponding area to be inspected, and determines the corresponding longitude and latitude as the expected location; The dispatching layer sends instructions to move the inspection device along the transmission line to the corresponding expected location for inspection; The similarity is for a single preset contour, the preset contour is compared with the characteristic contour, and the ratio of the area of ​​the obtained overlapping region to the total area of ​​the preset contour is recorded as the similarity.

[0008] Furthermore, the inspection device obtains abnormal conditions at expected locations in a manner that includes: The inspection device is used to obtain detailed image information of the expected position through the image detector, and analyze the detailed image information through the image processing algorithm to detect the appearance defects of the transmission line, which include insulator damage, wire breakage, and hardware corrosion; When the inspection device cannot detect the appearance defect based on the detailed image information, it takes the expected location as the center and the transmission line as the extension direction, and acquires a number of detailed image information on both sides until the appearance defect is identified, or the moving distance exceeds the corresponding area to be inspected; The analysis layer records the distance between the site where the appearance defect is identified and the expected site as the deviation distance; The analysis layer is used to classify the inspection accuracy level of the expected location based on the deviation distance, including: If the deviation distance is less than or equal to the first preset deviation distance, the inspection accuracy level of the expected location is classified as a first-level location; If the deviation distance is less than or equal to the second preset deviation distance and greater than the first preset deviation distance, the inspection accuracy level of the expected location is classified as a secondary location; If the deviation distance is greater than the second preset deviation distance, the inspection accuracy level of the expected location is divided into three levels of locations.

[0009] Furthermore, the analysis layer determines whether the operating parameters of the acquisition layer are qualified based on the quantitative statistics of each inspection accuracy level after completing the division of the inspection accuracy level of the expected locations in each area to be inspected, including: If the number of first-level sites among the expected sites is the largest, the operating parameters of the acquisition layer are determined to be qualified, and the acquisition layer is controlled to continue to use the current operating parameters to detect the transmission line; If the number of secondary sites divided among the expected sites is the largest, then whether the operating parameters of the acquisition layer are qualified is determined based on the time distribution parameters of each secondary site; If the number of third-level sites among the expected sites is the largest, the operating parameters of the acquisition layer are determined to be abnormal, and the preset similarity is adjusted to the corresponding value based on the proportion of the number of third-level sites.

[0010] Furthermore, the analysis layer is used to determine whether the operating parameters of the acquisition layer are qualified based on the time distribution parameters of each secondary site, including: It is used to determine the time distribution parameters of each secondary site, determine the time node for the acquisition layer to obtain the detection image information corresponding to each secondary site, calculate the interval length between each time node, solve the average value of each interval length, and obtain the time distribution parameters; If the time distribution parameter is less than or equal to the preset time distribution parameter, it is determined that the operation parameter of the acquisition layer is qualified, and the transmission bandwidth of the transmission layer is adjusted to a corresponding value based on the total number of secondary sites; If the time distribution parameter is greater than a preset time distribution parameter, the area of ​​the area to be inspected is adjusted to a corresponding value based on the time distribution parameter.

[0011] Furthermore, the analysis layer is used to adjust the area of ​​the area to be inspected to a corresponding value based on the time distribution parameter, wherein: The reduction range of the area of ​​the to-be-inspected region determined based on the time distribution parameter is inversely proportional to the time distribution parameter.

[0012] Further, the analysis layer is used to adjust the transmission bandwidth of the transmission layer to a corresponding value based on the total number of secondary sites, wherein: The increase in transmission bandwidth determined based on the total number of secondary sites is proportional to the total number of secondary sites.

[0013] Furthermore, the analysis layer is used to adjust the preset similarity to a corresponding value based on the proportion of the number of the third-level sites, wherein: The analysis layer is used to record the ratio of the number of tertiary sites to the total number of each expected site as the number ratio of tertiary sites; The increase in the preset similarity determined based on the quantity ratio is proportional to the quantity ratio.

[0014] Furthermore, the analysis layer, under the condition of completing the adjustment of the preset similarity, re-classifies the inspection accuracy level of each expected location according to the acquired detection image information; if it is still determined that the number of the third-level locations among the expected locations is the largest, the operating height of each drone is adjusted to a corresponding value based on the ratio of the re-acquired number proportion to the number proportion acquired in the previous cycle; The reduction in the operating altitude determined based on the quantity ratio is proportional to the quantity ratio.

[0015] Compared with the prior art, the beneficial effect of the present invention lies in that the inspection accuracy level of each expected location is divided based on the inspection results of the inspection device. When the inspection accuracy level of the expected locations in each area to be inspected is completed, it is determined whether the operating parameters of the collection layer are qualified based on the quantitative statistical results of each inspection accuracy level. When it is determined that the operating parameters of the collection layer are abnormal, the operating parameters of the collection layer are automatically adjusted, thereby improving the inspection efficiency of the transmission line.

[0016] Furthermore, the UAV collects images of the area to be inspected in the transmission line during flight, and compares the characteristic contour of the acquired detection image information with the pre-stored preset contour. When the similarity between the characteristic contour in the detection image information and the single preset contour pre-stored in the database is higher than the preset similarity, it is determined that the area has an expected abnormality, and the location of the abnormality is determined based on the determination result, and the location of the abnormality is determined as the expected location, and a command is sent to move the inspection device along the line to the corresponding location for inspection; the inspection device obtains the abnormality of the expected location, and when the deviation distance is less than or equal to the first preset deviation distance, the expected abnormality exists and the distance between the expected location and the inspection location is small, the inspection location is the location where the appearance defect is identified, and the image information at the location where the appearance defect is identified is collected, and the collection is performed locally, and based on the characteristic information obtained in the image information, the inspection device moves to the corresponding location along the line to inspect. A corresponding emergency repair notice or maintenance notice is issued, and the expected location in this case is recorded as a first-level location; when the deviation distance is less than or equal to the second preset deviation distance and greater than the first preset deviation distance, there is an expected abnormality and the distance between the expected location and the inspection location is large. In this case, the inspection device collects detailed image information of the transmission line with appearance defects, and the analysis layer records the corresponding expected location as a second-level location; when the deviation distance is greater than the second preset deviation distance, there is a situation in which the expected abnormality cannot be identified in the area to be inspected. In this case, the expected location is recorded as a third-level location, and the inspection accuracy level of each determined expected location is divided to determine the accuracy of the expected location, further providing a basis for adjusting the operating parameters of the collection layer, effectively improving the processing efficiency of the collected data while further improving the inspection efficiency of the transmission line.

[0017] Furthermore, after completing the inspection of a single cycle, the number of inspection accuracy levels of each level within the statistical cycle is counted. When there are many primary sites, the collection layer is determined to be qualified and continuously monitored. When there are many secondary sites, the collection layer is further judged based on the time distribution parameter. The time distribution parameter characterizes the distribution of each secondary site in the time dimension. When the time distribution parameter is less than or equal to the preset time distribution parameter, the time span of the secondary site is large and the distribution of each time interval is concentrated in a smaller range. At this time, abnormal transmission occurs randomly, and each occurrence leads to abnormal transmission of multiple data. At this time, due to the data transmission error, the data transmission error is very large. There are network fluctuations during the transmission process. At this time, the transmission bandwidth of the transmission layer is adjusted to ensure the accurate transmission of data. When the time distribution parameter is greater than the preset time distribution parameter, the secondary site distribution is scattered, and the collection site of the drone is unqualified. The collection parameters of the drone when collecting detection image information are adjusted, and the area of ​​each area to be inspected is corrected based on the time distribution parameter to ensure the accuracy of image acquisition; when the number of tertiary sites divided into each expected site is the largest, the operating parameters of the collection layer are determined to be abnormal, and the preset similarity is adjusted to the corresponding value to improve the accuracy of abnormality identification, which further effectively improves the inspection efficiency of the transmission line. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a module block diagram of a power transmission line image inspection system based on machine learning according to an embodiment of the present invention; Figure 2 This is a logical decision diagram for dividing the inspection accuracy levels of expected locations based on deviation distances at the analysis layer in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0020] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0021] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0022] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0023] See also Figure 1 as well as Figure 2 As shown, they are respectively a module block diagram of a power transmission line image inspection system based on machine learning according to an embodiment of the present invention, and a logic decision diagram of the analysis layer dividing the inspection accuracy level of the expected position based on the deviation distance; a power transmission line image inspection system based on machine learning according to an embodiment of the present invention comprises: The collection layer includes a number of drones for periodically collecting detection image information of each area to be inspected on the transmission line; A transmission layer, which is connected to the acquisition layer and is used to transmit the detection image information collected by each drone; A receiving layer connected to the transmission layer, for receiving the detection image information, and determining whether there is an abnormality in the corresponding area to be inspected based on the detection image information, and determining the expected location when it is determined that there is an abnormality in the detection image information; A scheduling layer, which is connected to the receiving layer and is used to select a corresponding inspection device for inspection based on the determined expected location; An inspection layer, which is connected to the dispatching layer, includes a plurality of inspection devices, each of which moves along the transmission line. When the inspection device reaches the expected location, the inspection device obtains abnormal conditions at the expected location to identify appearance defects; The analysis layer is connected to the acquisition layer, the transmission layer and the inspection layer respectively, and is used to periodically determine the deviation distance based on the longitude and latitude of the location of the appearance defect and the expected location, and divide the inspection accuracy level of the expected location based on the deviation distance, wherein the inspection accuracy level includes the primary location, the secondary location and the tertiary location. Under the condition that the analysis layer is used to complete the division of the inspection accuracy level of the expected location of each area to be inspected, the operation parameters of the acquisition layer are determined based on the quantitative statistical results of each inspection accuracy level, including: Determine whether the operating parameters of the collection layer are qualified, and control the collection layer to continue to use the current operating parameters to detect the transmission line; or, determine that the operating parameters of the collection layer are abnormal, and adjust the preset similarity to the corresponding value based on the proportion of the number of third-level sites.

[0024] Specifically, the inspection accuracy level of each expected location is divided based on the inspection results of the inspection device. When the inspection accuracy level of the expected locations in each area to be inspected is completed, it is determined whether the operating parameters of the collection layer are qualified based on the quantitative statistical results of each inspection accuracy level. When it is determined that the operating parameters of the collection layer are abnormal, the operating parameters of the collection layer are automatically adjusted, thereby improving the inspection efficiency of the transmission line.

[0025] Specifically, the receiving layer is used to determine whether there is an abnormality in the corresponding area to be inspected based on the detected image information, including: The receiving layer is used to obtain the characteristic contour in the detection image information, and compare the characteristic contour with each preset contour pre-stored in the database. When the similarity between the characteristic contour in the detection image information and a single preset contour pre-stored in the database is higher than the preset similarity, it is determined that there is an abnormality in the single area to be inspected, and the corresponding area in the characteristic contour with a similarity higher than the preset similarity is selected as the abnormal area; The receiving layer determines the longitude and latitude of the abnormal area based on the coordinates of the abnormal area in the detection image information and the geographical longitude and latitude of the corresponding area to be inspected, and determines the corresponding longitude and latitude as the expected location; The dispatching layer sends instructions to move the inspection device along the transmission line to the corresponding expected location for inspection; The similarity is for a single preset contour, the preset contour is compared with the characteristic contour, and the ratio of the area of ​​the obtained overlapping region to the total area of ​​the preset contour is recorded as the similarity.

[0026] Specifically, there is no limitation on the specific method of obtaining the feature contour in the detection image information. The edge information of the transmission line in the detection image information can be obtained by edge detection algorithms such as Canny edge detection, Sobel operator, Roberts operator, etc. This is a prior art and will not be described in detail.

[0027] Specifically, there is no limitation on the specific method of comparing the feature contour with each preset contour pre-stored in the database. The corresponding preset contour and the feature contour can be translated and compared one by one, or the feature contour can be input into the neural network training model according to the neural network training model, and the output completes the calibration of the suspicious area, and the suspicious area is overlapped and compared with each preset contour in turn to determine the abnormal area by calculating the similarity. This will not be repeated.

[0028] Specifically, the inspection device obtains abnormal conditions at expected locations in the following ways: The inspection device is used to obtain detailed image information of the expected position through the image detector, and analyze the detailed image information through the image processing algorithm to detect the appearance defects of the transmission line, which include insulator damage, wire breakage, and hardware corrosion; When the inspection device cannot detect the appearance defect based on the detailed image information, it takes the expected location as the center and the transmission line as the extension direction, and acquires a number of detailed image information on both sides until the appearance defect is identified, or the moving distance exceeds the corresponding area to be inspected; The analysis layer records the distance between the site where the appearance defect is identified and the expected site as the deviation distance; The analysis layer is used to classify the inspection accuracy level of the expected location based on the deviation distance, including: If the deviation distance is less than or equal to the first preset deviation distance, the inspection accuracy level of the expected location is classified as a first-level location; If the deviation distance is less than or equal to the second preset deviation distance and greater than the first preset deviation distance, the inspection accuracy level of the expected location is classified as a secondary location; If the deviation distance is greater than the second preset deviation distance, the inspection accuracy level of the expected location is divided into three levels of locations.

[0029] Specifically, the first preset deviation distance is selected within the interval [0.0277L0, 0.0312L0], and the second preset deviation distance is selected within the interval [0.05L0, 0.0833L0], where L0 is the perimeter of the area to be inspected.

[0030] Specifically, the UAV collects images of the area to be inspected in the transmission line during flight, and compares the feature contour of the acquired detection image information with the pre-stored preset contour. When the similarity between the feature contour in the detection image information and the single preset contour pre-stored in the database is higher than the preset similarity, it is determined that the area has an expected abnormality, and the location of the abnormality is determined based on the determination result. The location of the abnormality is determined as the expected location, and a command is sent to move the inspection device along the line to the corresponding location for inspection; the inspection device obtains the abnormality of the expected location, and when the deviation distance is less than or equal to the first preset deviation distance, the expected abnormality exists and the distance between the expected location and the inspection location is small, the inspection location is the location where the appearance defect is identified, and the image information at the location where the appearance defect is identified is collected, and the collection is performed locally, and based on the feature information obtained in the image information, the inspection device moves to the corresponding location along the line for inspection. A corresponding emergency repair notice or maintenance notice is issued, and the expected location in this case is recorded as a first-level location; when the deviation distance is less than or equal to the second preset deviation distance and greater than the first preset deviation distance, there is an expected abnormality and the distance between the expected location and the inspection location is large. In this case, the inspection device collects detailed image information of the transmission line with appearance defects, and the analysis layer records the corresponding expected location as a second-level location; when the deviation distance is greater than the second preset deviation distance, there is a situation in which the expected abnormality cannot be identified in the area to be inspected. In this case, the expected location is recorded as a third-level location, and the inspection accuracy level of each determined expected location is divided to determine the accuracy of the expected location, further providing a basis for adjusting the operating parameters of the collection layer, effectively improving the processing efficiency of the collected data while further improving the inspection efficiency of the transmission line.

[0031] Specifically, the analysis layer determines whether the operating parameters of the acquisition layer are qualified based on the quantitative statistics of each inspection accuracy level, under the condition that the inspection accuracy level of the expected locations of each area to be inspected is divided, including: If the number of first-level sites among the expected sites is the largest, the operating parameters of the acquisition layer are determined to be qualified, and the acquisition layer is controlled to continue to use the current operating parameters to detect the transmission line; If the number of secondary sites divided among the expected sites is the largest, then whether the operating parameters of the acquisition layer are qualified is determined based on the time distribution parameters of each secondary site; If the number of third-level sites among the expected sites is the largest, the operating parameters of the acquisition layer are determined to be abnormal, and the preset similarity is adjusted to the corresponding value based on the proportion of the number of third-level sites.

[0032] Specifically, the analysis layer is used to determine whether the operating parameters of the acquisition layer are qualified based on the time distribution parameters of each secondary site, including: It is used to determine the time distribution parameters of each secondary site, determine the time node for the acquisition layer to obtain the detection image information corresponding to each secondary site, calculate the interval length between each time node, solve the average value of each interval length, and obtain the time distribution parameters; If the time distribution parameter is less than or equal to the preset time distribution parameter, it is determined that the operation parameter of the acquisition layer is qualified, and the transmission bandwidth of the transmission layer is adjusted to a corresponding value based on the total number of secondary sites; If the time distribution parameter is greater than a preset time distribution parameter, the area of ​​the area to be inspected is adjusted to a corresponding value based on the time distribution parameter.

[0033] Specifically, the preset time distribution parameter S0 is 0.33T0, where T0 is the maximum value of each time interval.

[0034] Specifically, the analysis layer is used to adjust the area of ​​the area to be inspected to a corresponding value based on the time distribution parameter, wherein: The reduction range of the area of ​​the to-be-inspected region determined based on the time distribution parameter is inversely proportional to the time distribution parameter.

[0035] In this embodiment, optionally, Comparing the time distribution parameter with a first preset time comparison threshold and a second preset time comparison threshold; If the time distribution parameter is less than or equal to the first preset time comparison threshold, the area of ​​the area to be inspected is adjusted to 0.81 times the initial area; If the time distribution parameter is less than or equal to the second preset time comparison threshold and greater than the first preset time comparison threshold, the area of ​​the area to be inspected is adjusted to 0.87 times the initial area; If the time distribution parameter is greater than the second preset time comparison threshold, the area of ​​the area to be inspected is adjusted to 0.93 times the initial area; The first preset time comparison threshold is 0.7S0, and the second preset time comparison threshold is 0.63S0.

[0036] Specifically, after the adjustment of the area of ​​the area to be inspected is completed, the area of ​​the area to be inspected after the adjustment is compared with the preset critical area. If the area of ​​the area to be inspected after the adjustment is larger than the preset critical area, the area of ​​the area to be inspected after the adjustment is used as the operating parameter of the collection layer; if the area of ​​the area to be inspected after the adjustment is less than or equal to the preset critical area, the preset critical area is used as the operating parameter of the collection layer, and the operating height of each UAV is adjusted to 0.91 times the initial operating height.

[0037] Specifically, after completing the inspection of a single cycle, the number of inspection accuracy levels at each level within the statistical cycle is counted. When there are many primary sites, the collection layer is determined to be qualified and continuous monitoring is performed. When there are many secondary sites, the collection layer is further judged based on the time distribution parameter. The time distribution parameter characterizes the distribution of each secondary site in the time dimension. When the time distribution parameter is less than or equal to the preset time distribution parameter, the time span of the secondary site is large and the distribution of each time interval is concentrated in a smaller range. At this time, abnormal transmission occurs randomly, and each occurrence leads to abnormal transmission of multiple data. At this time, due to the data transmission error, the data transmission error is very large. There are network fluctuations during the transmission process. At this time, the transmission bandwidth of the transmission layer is adjusted to ensure the accurate transmission of data. When the time distribution parameter is greater than the preset time distribution parameter, the secondary site distribution is scattered, and the collection site of the drone is unqualified. The collection parameters of the drone when collecting detection image information are adjusted, and the area of ​​each area to be inspected is corrected based on the time distribution parameter to ensure the accuracy of image acquisition; when the number of tertiary sites divided into each expected site is the largest, the operating parameters of the collection layer are determined to be abnormal, and the preset similarity is adjusted to the corresponding value to improve the accuracy of abnormality identification, which further effectively improves the inspection efficiency of the transmission line.

[0038] Specifically, the analysis layer is used to adjust the transmission bandwidth of the transmission layer to a corresponding value based on the total number of secondary sites, wherein: The increase in transmission bandwidth determined based on the total number of secondary sites is proportional to the total number of secondary sites.

[0039] In this embodiment, optionally, Compare the total number of each secondary site in each area to be inspected obtained by the analysis layer during this detection cycle with the first preset number and the second preset number; If the total number of the secondary sites is less than or equal to the first preset number, the transmission bandwidth of the transmission layer is adjusted to 1.12 times the initial bandwidth; If the total number of the secondary sites is less than or equal to the second preset number and greater than the first preset number, adjusting the transmission bandwidth of the transmission layer to 1.22 times the initial bandwidth; If the total number of the secondary sites is greater than the second preset number, adjusting the transmission bandwidth of the transmission layer to 1.28 times the initial bandwidth; The first preset number is 0.77N0, and the second preset number is 0.92N0, where N0 is the total number of expected sites.

[0040] Specifically, the analysis layer is used to adjust the preset similarity to a corresponding value based on the proportion of the number of the third-level sites, wherein: The analysis layer is used to record the ratio of the number of tertiary sites to the total number of each expected site as the number ratio of tertiary sites; The increase in the preset similarity determined based on the quantity ratio is proportional to the quantity ratio.

[0041] In this embodiment, optionally, Comparing the quantity ratio with the first preset quantity ratio and the second preset quantity ratio; If the quantity ratio is less than or equal to the first preset quantity ratio, the preset similarity is adjusted to 1.11 times the initial preset similarity; If the quantity ratio is less than or equal to the second preset quantity ratio and greater than the first preset quantity ratio, the preset similarity is adjusted to 1.21 times the initial preset similarity; If the quantity ratio is greater than the second preset quantity ratio, the preset similarity is adjusted to 1.29 times the initial preset similarity; The first preset quantity accounts for 0.6, and the second preset quantity accounts for 0.8.

[0042] Specifically, the analysis layer, under the condition of completing the adjustment of the preset similarity, re-classifies the inspection accuracy level of each expected location according to the acquired detection image information; if it is still determined that the number of third-level locations among the expected locations is the largest, the operating height of each drone is adjusted to the corresponding value based on the ratio of the re-acquired number ratio to the number ratio acquired in the previous cycle; The reduction in the operating altitude determined based on the quantity ratio is proportional to the quantity ratio.

[0043] In this embodiment, optionally, comparing the quantity ratio with a first preset quantity ratio and a second preset quantity ratio; If the quantity ratio is less than or equal to the first preset quantity ratio, the operating height is adjusted to 0.91 times the initial operating height; If the quantity ratio is less than or equal to the second preset quantity ratio and greater than the first preset quantity ratio, the operating height is adjusted to 0.81 times the initial operating height; If the quantity ratio is greater than the second preset quantity ratio, the operating height is adjusted to 0.72 times the initial operating height; The first preset quantity ratio is 0.62, and the second preset quantity ratio is 0.8.

[0044] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0045] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A power transmission line image inspection system based on machine learning, characterized in that: include: The collection layer includes a number of drones for periodically collecting detection image information of each area to be inspected on the transmission line; A transmission layer, which is connected to the acquisition layer and is used to transmit the detection image information collected by each drone; A receiving layer connected to the transmission layer, for receiving the detection image information, and determining whether there is an abnormality in the corresponding area to be inspected based on the detection image information, and determining the expected location when it is determined that there is an abnormality in the detection image information; A scheduling layer, which is connected to the receiving layer and is used to select a corresponding inspection device for inspection based on the determined expected location; An inspection layer, which is connected to the dispatching layer, includes a plurality of inspection devices, each of which moves along the transmission line. When the inspection device reaches the expected location, the inspection device obtains abnormal conditions at the expected location to identify appearance defects; The analysis layer is connected to the acquisition layer, the transmission layer and the inspection layer respectively, and is used to periodically determine the deviation distance based on the longitude and latitude of the location of the appearance defect and the expected location, and divide the inspection accuracy level of the expected location based on the deviation distance, wherein the inspection accuracy level includes the primary location, the secondary location and the tertiary location. Under the condition that the analysis layer is used to complete the division of the inspection accuracy level of the expected location of each area to be inspected, the operation parameters of the acquisition layer are determined based on the quantitative statistical results of each inspection accuracy level, including: Determine that the operating parameters of the collection layer are qualified, and control the collection layer to continue to use the current operating parameters to detect the transmission line; or, determine that the operating parameters of the collection layer are abnormal, and adjust the preset similarity to a corresponding value based on the proportion of the number of third-level sites; The analysis layer is used to classify the inspection accuracy level of the expected location based on the deviation distance, including: If the deviation distance is less than or equal to the first preset deviation distance, the inspection accuracy level of the expected location is classified as a first-level location; If the deviation distance is less than or equal to the second preset deviation distance and greater than the first preset deviation distance, the inspection accuracy level of the expected location is classified as a secondary location; If the deviation distance is greater than the second preset deviation distance, the inspection accuracy level of the expected location is divided into three levels of locations.

2. The power transmission line image inspection system based on machine learning according to claim 1 is characterized in that: The receiving layer is used to determine whether there is an abnormality in the corresponding area to be inspected based on the detected image information, including: The receiving layer is used to obtain the characteristic contour in the detection image information, and compare the characteristic contour with each preset contour pre-stored in the database. When the similarity between the characteristic contour in the detection image information and a single preset contour pre-stored in the database is higher than the preset similarity, it is determined that there is an abnormality in the single area to be inspected, and the corresponding area in the characteristic contour with a similarity higher than the preset similarity is selected as the abnormal area; The receiving layer determines the longitude and latitude of the abnormal area based on the coordinates of the abnormal area in the detection image information and the geographical longitude and latitude of the corresponding area to be inspected, and determines the corresponding longitude and latitude as the expected location; The dispatching layer sends instructions to move the inspection device along the transmission line to the corresponding expected location for inspection; The similarity is for a single preset contour, the preset contour is compared with the characteristic contour, and the ratio of the area of ​​the obtained overlapping region to the total area of ​​the preset contour is recorded as the similarity.

3. The power transmission line image inspection system based on machine learning according to claim 2 is characterized in that: The inspection device acquires abnormal conditions at expected locations in a manner including: The inspection device is used to obtain detailed image information of the expected position through the image detector, and analyze the detailed image information through the image processing algorithm to detect the appearance defects of the transmission line, which include insulator damage, wire breakage, and hardware corrosion; When the inspection device cannot detect the appearance defect based on the detailed image information, it takes the expected location as the center and the transmission line as the extension direction, and acquires a number of detailed image information on both sides until the appearance defect is identified, or the moving distance exceeds the corresponding area to be inspected; The analysis layer records the distance between the site where the appearance defect is identified and the expected site as the deviation distance.

4. The power transmission line image inspection system based on machine learning according to claim 3 is characterized in that: The analysis layer determines whether the operating parameters of the acquisition layer are qualified based on the quantitative statistics of each inspection accuracy level, after completing the division of the inspection accuracy level of the expected locations in each area to be inspected, including: If the number of first-level sites among the expected sites is the largest, the operating parameters of the acquisition layer are determined to be qualified, and the acquisition layer is controlled to continue to use the current operating parameters to detect the transmission line; If the number of secondary sites divided among the expected sites is the largest, then whether the operating parameters of the acquisition layer are qualified is determined based on the time distribution parameters of each secondary site; If the number of third-level sites among the expected sites is the largest, the operating parameters of the acquisition layer are determined to be abnormal, and the preset similarity is adjusted to the corresponding value based on the proportion of the number of third-level sites.

5. The power transmission line image inspection system based on machine learning according to claim 4 is characterized in that: The analysis layer is used to determine whether the operating parameters of the acquisition layer are qualified based on the time distribution parameters of each secondary site, including: It is used to determine the time distribution parameters of each secondary site, determine the time node for the acquisition layer to obtain the detection image information corresponding to each secondary site, calculate the interval length between each time node, solve the average value of each interval length, and obtain the time distribution parameters; If the time distribution parameter is less than or equal to the preset time distribution parameter, it is determined that the operation parameter of the acquisition layer is qualified, and the transmission bandwidth of the transmission layer is adjusted to a corresponding value based on the total number of secondary sites; If the time distribution parameter is greater than a preset time distribution parameter, the area of ​​the area to be inspected is adjusted to a corresponding value based on the time distribution parameter.

6. The power transmission line image inspection system based on machine learning according to claim 5 is characterized in that: The analysis layer is used to adjust the area of ​​the area to be inspected to a corresponding value based on the time distribution parameter, wherein: The reduction range of the area of ​​the to-be-inspected region determined based on the time distribution parameter is inversely proportional to the time distribution parameter.

7. The power transmission line image inspection system based on machine learning according to claim 6 is characterized in that: The analysis layer is used to adjust the transmission bandwidth of the transmission layer to a corresponding value based on the total number of secondary sites, wherein: The increase in transmission bandwidth determined based on the total number of secondary sites is proportional to the total number of secondary sites.

8. The power transmission line image inspection system based on machine learning according to claim 7 is characterized in that: The analysis layer is used to adjust the preset similarity to a corresponding value based on the proportion of the number of the third-level sites, wherein: The analysis layer is used to record the ratio of the number of tertiary sites to the total number of each expected site as the number ratio of tertiary sites; The increase in the preset similarity determined based on the quantity ratio is proportional to the quantity ratio.

9. The power transmission line image inspection system based on machine learning according to claim 8, characterized in that: The analysis layer, under the condition of completing the adjustment of the preset similarity, re-classifies the inspection accuracy level of each expected location according to the acquired detection image information; if it is still determined that the number of the third-level locations among the expected locations is the largest, the operating height of each drone is adjusted to a corresponding value based on the ratio of the re-acquired number proportion to the number proportion acquired in the previous cycle; The reduction in the operating altitude determined based on the quantity ratio is proportional to the quantity ratio.

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