CN117498225B - Unmanned aerial vehicle intelligent power line inspection system - Google Patents
Unmanned aerial vehicle intelligent power line inspection system Download PDFInfo
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- 238000007781 pre-processing Methods 0.000 claims abstract description 6
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
- B64U20/00—Constructional aspects of UAVs
- B64U20/80—Arrangement of on-board electronics, e.g. avionics systems or wiring
- B64U20/87—Mounting of imaging devices, e.g. mounting of gimbals
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02G—INSTALLATION OF ELECTRIC CABLES OR LINES, OR OF COMBINED OPTICAL AND ELECTRIC CABLES OR LINES
- H02G1/00—Methods or apparatus specially adapted for installing, maintaining, repairing or dismantling electric cables or lines
- H02G1/02—Methods or apparatus specially adapted for installing, maintaining, repairing or dismantling electric cables or lines for overhead lines or cables
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- B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
- B64U2101/00—UAVs specially adapted for particular uses or applications
- B64U2101/25—UAVs specially adapted for particular uses or applications for manufacturing or servicing
- B64U2101/26—UAVs specially adapted for particular uses or applications for manufacturing or servicing for manufacturing, inspections or repairs
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
- B64U2101/00—UAVs specially adapted for particular uses or applications
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Abstract
The invention discloses an unmanned aerial vehicle intelligent power line inspection system, which comprises a line information acquisition unit, a device acquisition analysis unit, an image preprocessing unit, a line analysis processing unit and a line fault display unit. This unmanned aerial vehicle intelligent power line inspection system can make unmanned aerial vehicle accurate effectual image data on the acquisition target detection cable through equipment collection analysis unit, when avoiding unmanned aerial vehicle to acquire many image data one by one through manual operation, there is the repeated shooting condition of a certain cable position on many images, lead to data redundancy, through carrying out synthetic processing to a plurality of image data, avoid image data because a lot of, result of judgement data processing chaotic problem produces, this system can also improve the work efficiency of inspection personnel, reduce human cost and time cost, bring bigger economic benefits for the electric power industry.
Description
Technical Field
The invention relates to the technical field of power line inspection, in particular to an unmanned aerial vehicle intelligent power line inspection system.
Background
With the continuous expansion of power grid interconnection and power grid scale, the problem of safety and stability of power grid operation is attracting a great deal of attention. The traditional power line inspection mode mainly adopts manual inspection, but due to complex geographical conditions, the manual inspection has low working efficiency, high labor intensity and dangerous working environment.
At present, aiming at the defects of the traditional power line inspection mode, a technology for utilizing an unmanned aerial vehicle to inspect the power line is designed. The system can automatically and finely patrol the power grid facilities and the environment through the unmanned aerial vehicle, can rapidly locate faults and timely remove the faults, greatly reduces the working error rate and risk, and greatly improves the power grid patrol efficiency.
However, in the prior art, the defects of the power line are identified by mostly using an unmanned plane to acquire a target image of the power line and then end a deep learning model at a PC, or performing different processing methods on different targets by using a traditional method to identify the situation, but the inspection system has the following defects: if multiple target images on a power line are acquired through an unmanned aerial vehicle, repeated shooting conditions of a certain cable position exist on the multiple images, so that data redundancy is caused, and when the target images are analyzed, further cleaning processing is needed to be carried out on the image data, so that the failure judging efficiency is reduced.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an intelligent power line inspection system of an unmanned aerial vehicle, which solves the problems in the background art.
In order to achieve the above purpose, the invention is realized by the following technical scheme: an unmanned aerial vehicle intelligent power line inspection system, comprising:
the line information acquisition unit is used for acquiring a plurality of image data in a specified routing inspection line and acquiring position information of the image data; the method comprises the steps that the acquisition position information of image data is determined according to tower numbers preset on all towers in a specified inspection route;
the device information acquisition unit is used for acquiring distance information corresponding to the image data, wherein the distance information comprises two groups of distance parameters between the unmanned aerial vehicle and a specified inspection route and an included angle parameter formed by the two groups of distance parameters by taking the unmanned aerial vehicle as a circle center;
the equipment acquisition and analysis unit is used for acquiring one image data of a corresponding target detection cable by taking a tower as a starting point, and sequentially acquiring other image data on the target detection cable by combining the distance parameter and the included angle parameter;
the line analysis processing unit is used for respectively comparing the similarity between a plurality of fault image samples in the pre-trained cable fault recognition model and the image data to obtain corresponding similarity values, then adding a preset fault signal corresponding to the fault image samples according to the image data corresponding to the similarity values higher than a preset similarity threshold value, and then displaying the fault signal to operation and maintenance personnel.
Preferably, the image data comprises a high definition image and/or a thermal imaging image;
the method comprises the following steps: and shooting and/or monitoring the power line through a high-definition camera and/or a thermal infrared imager carried by the unmanned aerial vehicle, and obtaining a high-definition image and/or a thermal imaging image.
Preferably, the determination manner of acquiring the position information is as follows:
in the appointed routing inspection route, the power line takes adjacent towers as nodes and is divided into a plurality of target detection cables;
then, marking the position of a target detection cable between two adjacent towers according to the tower numbers in the two adjacent towers;
obtaining tower numbers in two adjacent towers corresponding to the target detection cables through a high-definition camera carried by the unmanned aerial vehicle, and obtaining corresponding position marks;
according to the position mark, according to the travel route of the unmanned aerial vehicle when flying from one tower to the other tower in the adjacent towers, sequentially marking the digital sequence of the image data obtained by the unmanned aerial vehicle to obtain corresponding serial numbers of the image data;
and combining the position marks with the serial numbers to form the acquired position information corresponding to each image data.
Preferably, the distance parameter is the distance between the unmanned aerial vehicle and the target detection cable, which is measured by two groups of infrared ranging sensors carried on the unmanned aerial vehicle, and specifically is the distance parameter between two ends of the target detection cable, which are positioned at the edges of two ends of the image, and the unmanned aerial vehicle in the image data shot by the unmanned aerial vehicle.
Preferably, the specific collection and analysis mode of the device collection and analysis unit is as follows:
the method comprises the steps that a tower is taken as a starting point, when an unmanned aerial vehicle acquires image data of a target detection cable, the unmanned aerial vehicle acquires the image data containing the tower and the local target detection cable as first image data and corresponding distance information, and then acquiring position information is added to the first image data;
step two, calculating the length of a target detection cable in the first image data by combining two distance parameters and an included angle parameter through a trigonometric function calculation formula;
thirdly, translating the unmanned aerial vehicle along the direction of the target detection cable and according to the length of the target detection cable in the first image data, wherein when translating according to the length of the target detection cable in the first image data, the translation length is a result value obtained by subtracting an error compensation value from the length of the target detection cable in the first image data, and the error compensation value is a preset value;
fourthly, after the unmanned aerial vehicle translates to a designated position according to the length of the target detection cable in the first image data, the unmanned aerial vehicle acquires the image data containing the local target detection cable at the position, takes the image data as second image data and corresponding distance information, and then adds acquired position information on the second image data;
and by analogy, acquiring the third image data, the fourth image data … … and corresponding acquisition position information one by one until the image data acquired by the unmanned aerial vehicle contains another tower adjacent to the tower, namely, acquiring all the image data on the target detection cable.
Preferably, the system further comprises:
the image preprocessing unit is used for carrying out merging processing on all image data of each target detection cable, obtaining a target image, and then transmitting the target image to the line analysis processing unit to replace the image data for fault analysis;
preferably, the merging processing mode of the image preprocessing unit is as follows:
selecting a target detection cable, and sorting the image data according to the position information of all the image data on the target detection cable;
combining the corresponding plurality of image data into one image, marking the image as a target image, and marking the position mark corresponding to the target detection cable as the image name of the target image;
meanwhile, in the process of synthesizing the target image, correspondingly marking the serial number in the position information corresponding to each image data at the connection position of the target image in the process of synthesizing the target image;
preferably, the line analysis processing unit is further configured to perform similarity comparison with the target image according to a pre-training cable fault recognition model according to a plurality of pre-led fault image samples and normal image samples respectively:
if the similarity value between the local position in the target image and the normal image sample is higher than a preset similarity threshold value, the corresponding local position in the target image is normal;
if the similarity value between the local position in the target image and one of the fault image samples is higher than a preset similarity threshold value, the corresponding local position in the target image is indicated to be abnormal, and a corresponding fault signal is generated;
meanwhile, adding a mark picture frame on the target image at the position generating the fault signal;
the marking frame is used for marking the fault position of the target detection cable, and is a program designed by a manager in the line analysis processing unit;
and then, displaying the target image with the mark picture frame correspondingly added to the operation and maintenance personnel according to the fault signal.
Preferably, the system further comprises:
the line fault display unit is used for displaying the target image and/or image data generating the fault signal to operation and maintenance personnel, and the operation and maintenance personnel can determine the corresponding faulty target detection cable in the field according to the image name of the target image and determine the fault position of the target detection cable according to the position of the mark frame in the target image marked by the corresponding serial number.
The invention provides an intelligent power line inspection system of an unmanned aerial vehicle. Compared with the prior art, the method has the following beneficial effects:
according to the invention, the unmanned aerial vehicle can accurately and effectively acquire the image data on the target detection cable through the equipment acquisition and analysis unit, so that the situation that the unmanned aerial vehicle acquires a plurality of pieces of image data one by one through manual operation is avoided, the repeated shooting condition of a certain cable position exists on the plurality of pieces of images, the data redundancy is caused, the image data is required to be further cleaned, the failure judgment efficiency is low, and the cleaning treatment is carried out on the image data during unmanned aerial vehicle acquisition, so that the corresponding cleaning step is reduced, and the power line inspection efficiency is greatly improved;
according to the invention, by arranging the line analysis processing unit, the electric power worker can be helped to quickly locate the fault and remove the fault in time, so that the working error rate and risk are greatly reduced, and the power grid inspection efficiency is greatly improved;
according to the invention, the synthesis processing is carried out on the plurality of image data, so that the disorder of the processing of the judging result data caused by the large number of the image data is avoided, and the analysis efficiency is reduced when the line analysis processing unit is used for analyzing the image data by importing the image data one by one, so that the extraction and the check of the related data after storage are inconvenient;
in general, through the application of the system, electric power workers can more efficiently carry out power grid inspection, discover and remove potential faults in time, and ensure the stable operation of the electric power system. Meanwhile, the system can also improve the working efficiency of the inspection personnel, reduce the labor cost and the time cost and bring greater economic benefit to the power industry.
Drawings
Fig. 1 is a system block diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made more apparent and fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As an embodiment of the invention
Referring to fig. 1, the present invention provides a technical solution: an unmanned aerial vehicle intelligent power line inspection system, comprising:
the line information acquisition unit is used for acquiring a plurality of image data in a specified routing inspection line and acquiring position information of the image data;
the image data is a high-definition image obtained by shooting the power line through a high-definition camera carried by the unmanned aerial vehicle;
the method comprises the steps that the acquisition position information of image data is determined according to preset tower numbers on all towers in a specified inspection route, and the tower numbers are coding parameters set in advance by related management staff, and specifically comprise the following steps: in the appointed routing inspection route, the power line takes adjacent towers as nodes and is divided into a plurality of target detection cables;
then, marking the position of a target detection cable between two adjacent towers according to the tower numbers in the two adjacent towers;
obtaining tower numbers in two adjacent towers corresponding to the target detection cables through a high-definition camera carried by the unmanned aerial vehicle, and obtaining corresponding position marks;
according to the position mark, according to the travel route of the unmanned aerial vehicle when flying from one tower to the other tower in the adjacent towers, sequentially marking the digital sequence of the image data obtained by the unmanned aerial vehicle to obtain corresponding serial numbers of the image data;
in this embodiment, the digital sequence marking performs the sequence marking according to a plurality of preset serial numbers, for example, if the plurality of preset serial numbers are 001, 002, … …, the serial number of the first acquired high-definition image and/or thermal imaging image is recorded as 001;
combining the position marks with the serial numbers to form acquired position information corresponding to each image data;
in this embodiment, if the number of the towers in the two adjacent towers corresponding to the target detection cable is G0045 and G0046, the corresponding position is marked as G0045G0046, and if the serial number of the high-definition image and/or the thermal imaging image is 003, the acquired position information is G0045G0046003;
the device information acquisition unit is used for acquiring distance information corresponding to the image data, wherein the distance information comprises distance parameters from the unmanned aerial vehicle to the target detection cables, which are respectively measured by two groups of infrared ranging sensors carried on the unmanned aerial vehicle, and the distance parameters from the two ends of the target detection cables at the edges of the two ends of the image to the unmanned aerial vehicle are specifically obtained in the image data shot by the unmanned aerial vehicle according to the included angle parameters between the groups of infrared ranging sensors;
the equipment acquisition and analysis unit is used for flying the unmanned aerial vehicle above the target detection cables, and acquiring image data of the target detection cables one by taking one tower as a starting point until the image data acquired by the unmanned aerial vehicle contains another tower adjacent to the tower;
the specific collection and analysis modes are as follows:
the method comprises the steps that a tower is taken as a starting point, when an unmanned aerial vehicle acquires image data of a target detection cable, the unmanned aerial vehicle acquires the image data containing the tower and the local target detection cable as first image data and corresponding distance information, and then acquiring position information is added to the first image data;
step two, calculating the length of a target detection cable in the first image data by combining two distance parameters and an included angle parameter through a trigonometric function calculation formula;
thirdly, translating the unmanned aerial vehicle along the direction of the target detection cable and according to the length of the target detection cable in the first image data, wherein when translating according to the length of the target detection cable in the first image data, the translation length is a result value obtained by subtracting an error compensation value from the length of the target detection cable in the first image data, and the error compensation value is a preset value;
fourthly, after the unmanned aerial vehicle translates to a designated position according to the length of the target detection cable in the first image data, the unmanned aerial vehicle acquires the image data containing the local target detection cable at the position, takes the image data as second image data and corresponding distance information, and then adds acquired position information on the second image data;
and so on, acquiring the third image data, the fourth image data … … and the corresponding acquired position information one by one until the image data acquired by the unmanned aerial vehicle contains another tower adjacent to the tower, namely, acquiring all the image data on the target detection cable;
according to the embodiment, the unmanned aerial vehicle can accurately and effectively acquire the image data on the target detection cable through the equipment acquisition and analysis unit, so that the situation that the unmanned aerial vehicle acquires a plurality of pieces of image data one by one through manual operation is avoided, the repeated shooting condition of a certain cable position exists on the plurality of pieces of images, data redundancy is caused, further cleaning treatment is needed to be carried out on the image data, and failure judgment efficiency is low;
the line analysis processing unit is used for carrying out fault analysis on the image data according to the pre-trained cable fault recognition model;
in the embodiment, the cable fault identification model performs similarity comparison and determination with image data according to a plurality of fault image samples and normal image samples which are pre-introduced in a plurality of ways;
in the embodiment, a fault image sample and a normal image sample respectively represent a high-definition image obtained by shooting a fault power line through a high-definition camera carried by the unmanned aerial vehicle and a high-definition image obtained by shooting a normal power line, and are led into a cable fault recognition model for comparison sample use;
wherein, a fault signal which indicates that the fault reasons of the target detection cables are different is respectively and correspondingly preset in a plurality of fault image samples;
if the similarity value of the target detection cable position in the image data and the normal image sample is higher than a preset similarity threshold value, the corresponding target detection cable position in the image data is indicated to be normal;
if the similarity value between the target detection cable position in the image data and one of the fault image samples is higher than a preset similarity threshold value, the position of the corresponding target detection cable in the image data is abnormal, and a corresponding fault signal is generated;
then, according to the fault signal, sending the image data to a line fault display unit;
according to the embodiment, the line analysis processing unit is arranged, so that electric workers can be helped to quickly locate faults and remove the faults in time, the working error rate and risk are greatly reduced, and the power grid inspection efficiency is greatly improved;
the line fault display unit is used for displaying the image data generating the fault signal to operation and maintenance personnel, and the operation and maintenance personnel determine the fault position of the corresponding target detection cable according to the acquired position information of the image data;
the embodiment is displayed to operators in an easy-to-understand mode, so that the operators are helped to know the line state and take corresponding maintenance measures, the visual display mode can improve the working efficiency of the inspection staff, and the possibility of misjudgment and missed inspection is reduced;
example two
As a second embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the technical solution of the present embodiment is different from the first embodiment only in that:
in this embodiment, the image data is a thermal imaging image obtained by monitoring the power line through a thermal infrared imager carried by the unmanned aerial vehicle;
in this embodiment, the failure image sample and the normal image sample represent a thermal imaging image obtained by photographing a failed power line by the thermal infrared imager mounted on the unmanned aerial vehicle and a thermal imaging image obtained by photographing a normal power line, respectively, and are imported into the cable failure recognition model for comparison sample use.
Example III
As an embodiment three of the present invention, in the implementation of the present application, compared with the first embodiment and the second embodiment, the technical solution of the present embodiment is that the solutions of the first embodiment and the second embodiment are implemented in combination, and the technical solution of the present embodiment is different from the solutions of the first embodiment and the second embodiment only in that:
the embodiment further comprises:
the image preprocessing unit is used for carrying out synthesis processing on all image data of each target detection cable, and the synthesis processing mode is as follows:
taking a target detection cable as an example, sorting the image data according to the position information of all the image data on the target detection cable;
combining the corresponding plurality of image data into one image, marking the image as a target image, and marking the position mark corresponding to the target detection cable as the image name of the target image;
meanwhile, in the process of synthesizing the target image, correspondingly marking the serial number in the position information corresponding to each image data at the connection position of the target image in the process of synthesizing the target image;
in the embodiment, the synthesis processing is performed on the plurality of image data, so that the disorder of the processing of the judging result data caused by the large number of the image data is avoided, and when the line analysis processing unit analyzes the image data, the image data are led in one by one for analysis, so that the analysis efficiency is reduced, and the extraction and the check of the related data after the storage are inconvenient;
the line analysis processing unit is used for carrying out fault analysis on the target image according to the pre-trained cable fault recognition model;
in the embodiment, the cable fault recognition model performs similarity comparison with the target image according to a plurality of fault image samples and normal image samples which are pre-introduced;
if the similarity value between the local position in the target image and the normal image sample is higher than a preset similarity threshold value, the corresponding local position in the target image is normal;
if the similarity value between the local position in the target image and one of the fault image samples is higher than a preset similarity threshold value, the corresponding local position in the target image is indicated to be abnormal, and a corresponding fault signal is generated;
meanwhile, adding a mark picture frame on the target image at the position generating the fault signal;
the marking frame is used for marking the fault position of the target detection cable, and is a program designed by a manager in the line analysis processing unit;
then, according to the fault signal, the target image with the mark picture frame is sent to a line fault display unit;
the line fault display unit is used for displaying the target image generating the fault signal to an operation and maintenance person, and the operation and maintenance person determines the corresponding faulty target detection cable in the field according to the image name of the target image and determines the fault position of the target detection cable according to the position of the mark frame in the target image marked by the corresponding serial number.
Example IV
As an embodiment four of the present invention, in the present application, compared with the first, second and third embodiments, the technical solution of the present embodiment is to combine and implement the solutions of the first, second and third embodiments
And all that is not described in detail in this specification is well known to those skilled in the art.
The foregoing describes one embodiment of the present invention in detail, but the disclosure is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (7)
1. Unmanned aerial vehicle intelligent power line inspection system, characterized by comprising:
the line information acquisition unit is used for acquiring a plurality of image data in a specified routing inspection line and acquiring position information of the image data; the method comprises the steps of determining the acquisition position information of image data according to preset tower numbers on all towers in a specified inspection route, wherein the determination mode of the acquisition position information is as follows:
in the appointed routing inspection route, the power line takes adjacent towers as nodes and is divided into a plurality of target detection cables;
then, marking the position of a target detection cable between two adjacent towers according to the tower numbers in the two adjacent towers;
obtaining tower numbers in two adjacent towers corresponding to the target detection cables through a high-definition camera carried by the unmanned aerial vehicle, and obtaining corresponding position marks;
according to the position mark, according to the travel route of the unmanned aerial vehicle when flying from one tower to the other tower in the adjacent towers, sequentially marking the digital sequence of the image data obtained by the unmanned aerial vehicle to obtain corresponding serial numbers of the image data;
combining the position marks with the serial numbers to form acquired position information corresponding to each image data;
the device information acquisition unit is used for acquiring distance information corresponding to the image data, wherein the distance information comprises two groups of distance parameters between the unmanned aerial vehicle and a specified inspection route and an included angle parameter formed by the two groups of distance parameters by taking the unmanned aerial vehicle as a circle center;
the equipment acquisition and analysis unit is used for acquiring one image data of a corresponding target detection cable by taking a tower as a starting point, and sequentially acquiring other image data on the target detection cable by combining the distance parameter and the included angle parameter; the specific collection and analysis modes of the equipment collection and analysis unit are as follows:
the method comprises the steps that a tower is taken as a starting point, when an unmanned aerial vehicle acquires image data of a target detection cable, the unmanned aerial vehicle acquires the image data containing the tower and the local target detection cable as first image data and corresponding distance information, and then acquiring position information is added to the first image data;
step two, calculating the length of a target detection cable in the first image data by combining two distance parameters and an included angle parameter through a trigonometric function calculation formula;
thirdly, translating the unmanned aerial vehicle along the direction of the target detection cable and according to the length of the target detection cable in the first image data, wherein when translating according to the length of the target detection cable in the first image data, the translation length is a result value obtained by subtracting an error compensation value from the length of the target detection cable in the first image data, and the error compensation value is a preset value;
fourthly, after the unmanned aerial vehicle translates to a designated position according to the length of the target detection cable in the first image data, the unmanned aerial vehicle acquires the image data containing the local target detection cable at the position, takes the image data as second image data and corresponding distance information, and then adds acquired position information on the second image data;
and so on, acquiring the third image data, the fourth image data … … and the corresponding acquired position information one by one until the image data acquired by the unmanned aerial vehicle contains another tower adjacent to the tower, namely, acquiring all the image data on the target detection cable;
the line analysis processing unit is used for respectively comparing the similarity between a plurality of fault image samples in the pre-trained cable fault recognition model and the image data to obtain corresponding similarity values, then adding a preset fault signal corresponding to the fault image samples according to the image data corresponding to the similarity values higher than a preset similarity threshold value, and then displaying the fault signal to operation and maintenance personnel.
2. The unmanned aerial vehicle intelligent power line inspection system according to claim 1, wherein: the image data includes a high definition image and/or a thermal imaging image;
the method comprises the following steps: and shooting and/or monitoring the power line through a high-definition camera and/or a thermal infrared imager carried by the unmanned aerial vehicle, and obtaining a high-definition image and/or a thermal imaging image.
3. The unmanned aerial vehicle intelligent power line inspection system according to claim 1, wherein: the distance parameter is the distance between the unmanned aerial vehicle and the target detection cable, which is measured by two groups of infrared distance measuring sensors carried on the unmanned aerial vehicle, and specifically is the distance parameter between two ends of the target detection cable, which are positioned at the edges of two ends of an image, and the unmanned aerial vehicle in the image data shot by the unmanned aerial vehicle.
4. The unmanned aerial vehicle intelligent power line inspection system according to claim 1, wherein: the system further comprises:
and the image preprocessing unit is used for carrying out merging processing on all the image data of each target detection cable, obtaining a target image, and then transmitting the target image to the line analysis processing unit to replace the image data for fault analysis.
5. The unmanned aerial vehicle intelligent power line inspection system of claim 4, wherein: the merging processing mode of the image preprocessing unit is as follows:
selecting a target detection cable, and sorting the image data according to the position information of all the image data on the target detection cable;
combining the corresponding plurality of image data into one image, marking the image as a target image, and marking the position mark corresponding to the target detection cable as the image name of the target image;
and meanwhile, in the process of synthesizing the target image, correspondingly marking the serial number in the position information corresponding to each image data at the connection position of the target image during synthesizing.
6. The unmanned aerial vehicle intelligent power line inspection system of claim 4, wherein: the line analysis processing unit is further used for respectively comparing the similarity between the plurality of fault image samples and the normal image sample which are pre-introduced according to the pre-training cable fault recognition model and the target image according to the plurality of pre-introduced fault image samples and the normal image sample:
if the similarity value between the local position in the target image and the normal image sample is higher than a preset similarity threshold value, the corresponding local position in the target image is normal;
if the similarity value between the local position in the target image and one of the fault image samples is higher than a preset similarity threshold value, the corresponding local position in the target image is indicated to be abnormal, and a corresponding fault signal is generated;
meanwhile, adding a mark picture frame on the target image at the position generating the fault signal;
the marking frame is used for marking the fault position of the target detection cable, and is a program designed by a manager in the line analysis processing unit;
and then, displaying the target image with the mark picture frame correspondingly added to the operation and maintenance personnel according to the fault signal.
7. The unmanned aerial vehicle intelligent power line inspection system of claim 6, wherein: the system further comprises:
the line fault display unit is used for displaying the target image and/or image data generating the fault signal to operation and maintenance personnel, and the operation and maintenance personnel can determine the corresponding faulty target detection cable in the field according to the image name of the target image and determine the fault position of the target detection cable according to the position of the mark frame in the target image marked by the corresponding serial number.
Priority Applications (1)
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110580717A (en) * | 2019-08-15 | 2019-12-17 | 成都优艾维智能科技有限责任公司 | Unmanned aerial vehicle autonomous inspection route generation method for electric power tower |
| CN112053461A (en) * | 2020-09-16 | 2020-12-08 | 江苏伊卡洛斯电力无人机通用技术有限公司 | Unmanned aerial vehicle-based power inspection method |
| CN112230680A (en) * | 2020-10-29 | 2021-01-15 | 国网河南省电力公司济源供电公司 | Unmanned aerial vehicle power line inspection control method |
| CN212484161U (en) * | 2020-04-03 | 2021-02-05 | 山东黄金电力有限公司 | Novel unmanned aerial vehicle automatic positioning returns to voyage device |
| CN112511808A (en) * | 2020-12-17 | 2021-03-16 | 国网辽宁省电力有限公司本溪供电公司 | Intelligent inspection system and method for power transmission line |
| CN113291440A (en) * | 2021-06-04 | 2021-08-24 | 大连海事大学 | Water surface rescue method and device for unmanned ship capable of flying |
| CN113601536A (en) * | 2021-10-11 | 2021-11-05 | 国网智能科技股份有限公司 | Distribution network vehicle-mounted intelligent inspection robot system and method |
| KR102351536B1 (en) * | 2020-09-15 | 2022-01-14 | 한전케이디엔주식회사 | Upward opgw checking for drone |
| CN115690581A (en) * | 2022-10-27 | 2023-02-03 | 国网河南省电力公司濮阳供电公司 | Power transmission line channel hidden danger identification airborne intelligent terminal application method and system |
| CN115686056A (en) * | 2022-07-20 | 2023-02-03 | 国网浙江省电力有限公司嘉兴供电公司 | System and method for generating flight routes of unmanned aerial vehicles in real time aiming at power distribution network |
| CN115826408A (en) * | 2022-11-29 | 2023-03-21 | 国能宁夏鸳鸯湖第一发电有限公司 | Self-adaptive control system for inspection unmanned aerial vehicle |
| CN116301046A (en) * | 2023-03-24 | 2023-06-23 | 安徽送变电工程有限公司 | Unmanned aerial vehicle aerial photographing safety distance automatic positioning method based on electric power pole tower identification |
| CN116627178A (en) * | 2023-06-30 | 2023-08-22 | 苏州物图数字科技有限公司 | Electric power inspection method based on intelligent unmanned aerial vehicle image analysis |
| CN116740833A (en) * | 2023-06-26 | 2023-09-12 | 中邮建技术有限公司 | Line inspection and card punching method based on unmanned aerial vehicle |
-
2024
- 2024-01-03 CN CN202410004535.XA patent/CN117498225B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110580717A (en) * | 2019-08-15 | 2019-12-17 | 成都优艾维智能科技有限责任公司 | Unmanned aerial vehicle autonomous inspection route generation method for electric power tower |
| CN212484161U (en) * | 2020-04-03 | 2021-02-05 | 山东黄金电力有限公司 | Novel unmanned aerial vehicle automatic positioning returns to voyage device |
| KR102351536B1 (en) * | 2020-09-15 | 2022-01-14 | 한전케이디엔주식회사 | Upward opgw checking for drone |
| CN112053461A (en) * | 2020-09-16 | 2020-12-08 | 江苏伊卡洛斯电力无人机通用技术有限公司 | Unmanned aerial vehicle-based power inspection method |
| CN112230680A (en) * | 2020-10-29 | 2021-01-15 | 国网河南省电力公司济源供电公司 | Unmanned aerial vehicle power line inspection control method |
| CN112511808A (en) * | 2020-12-17 | 2021-03-16 | 国网辽宁省电力有限公司本溪供电公司 | Intelligent inspection system and method for power transmission line |
| CN113291440A (en) * | 2021-06-04 | 2021-08-24 | 大连海事大学 | Water surface rescue method and device for unmanned ship capable of flying |
| CN113601536A (en) * | 2021-10-11 | 2021-11-05 | 国网智能科技股份有限公司 | Distribution network vehicle-mounted intelligent inspection robot system and method |
| CN115686056A (en) * | 2022-07-20 | 2023-02-03 | 国网浙江省电力有限公司嘉兴供电公司 | System and method for generating flight routes of unmanned aerial vehicles in real time aiming at power distribution network |
| CN115690581A (en) * | 2022-10-27 | 2023-02-03 | 国网河南省电力公司濮阳供电公司 | Power transmission line channel hidden danger identification airborne intelligent terminal application method and system |
| CN115826408A (en) * | 2022-11-29 | 2023-03-21 | 国能宁夏鸳鸯湖第一发电有限公司 | Self-adaptive control system for inspection unmanned aerial vehicle |
| CN116301046A (en) * | 2023-03-24 | 2023-06-23 | 安徽送变电工程有限公司 | Unmanned aerial vehicle aerial photographing safety distance automatic positioning method based on electric power pole tower identification |
| CN116740833A (en) * | 2023-06-26 | 2023-09-12 | 中邮建技术有限公司 | Line inspection and card punching method based on unmanned aerial vehicle |
| CN116627178A (en) * | 2023-06-30 | 2023-08-22 | 苏州物图数字科技有限公司 | Electric power inspection method based on intelligent unmanned aerial vehicle image analysis |
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|---|---|
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