CN112346481A - Method and system for unmanned aerial vehicle power inspection operation - Google Patents
Method and system for unmanned aerial vehicle power inspection operation Download PDFInfo
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Abstract
The application discloses a method and a system for unmanned aerial vehicle power inspection operation, wherein the method comprises the following steps: carrying out unmanned aerial vehicle aerial photography on the power inspection operation area of the to-be-determined area to generate flight record information; extracting a key route and routing inspection actions in the power routing inspection operation based on the flight record information; generating an automatic inspection operation script based on the key route and the inspection action; writing the automatic inspection operation script into a flight control system of the unmanned aerial vehicle, and verifying the automatic inspection operation script; when the verification is met, the unmanned aerial vehicle executes power patrol operation based on the verified automatic patrol operation script; in the process of power inspection operation, acquiring inspection data on a power inspection route; and analyzing and processing the routing inspection data. According to the embodiment of the invention, the cost of manual inspection is reduced through the inspection of the unmanned aerial vehicle, and the informatization accuracy degree of the inspection of the unmanned aerial vehicle is improved.
Description
Technical Field
The application relates to the technical field of unmanned aerial vehicle inspection, in particular to a method and a system for unmanned aerial vehicle power inspection operation.
Background
The maintenance of the power grid is mainly completed in a line patrol mode by electric workers at first, the labor intensity is high, the line patrol maintenance efficiency is low, the terrain of most power transmission lines is complex, and the power transmission lines are difficult to patrol and dangerous due to severe climate factors. With the development of automation equipment and technology, transmission line operation and maintenance units realize that the maintenance of a power grid by adopting an automatic scientific and technological means is more economical and reliable, and begin to research a comprehensive monitoring system of the transmission line, try to monitor the operation of the power grid by using various sensors installed on towers and power equipment, and replace manual inspection. However, the high-voltage transmission line crosses mountains and mountains, the natural environment is severe, the temperature is changed drastically, animal wastes and electromagnetic interference are caused, the monitoring system has power supply problems, and the like, so that the system is difficult to operate and maintain effectively, and finally 70% of the conventional comprehensive monitoring systems of the transmission line stop operating. At present, the transmission line of the domestic power grid is mainly manually inspected.
Disclosure of Invention
The utility model provides a technical problem who exists among the prior art is solved at least to the aim at of this application, provides a method and system of unmanned aerial vehicle electric power inspection operation, patrols and examines through unmanned aerial vehicle and reduces the cost that the manual work was patrolled and examined to the informationization degree of accuracy that unmanned aerial vehicle patrolled and examined has been promoted.
The invention provides a method for unmanned aerial vehicle power inspection operation, which comprises the following steps:
carrying out unmanned aerial vehicle aerial photography on the power inspection operation area of the to-be-determined area to generate flight record information;
extracting a key route and routing inspection actions in the power routing inspection operation based on the flight record information;
generating an automatic inspection operation script based on the key route and the inspection action;
writing the automatic inspection operation script into a flight control system of the unmanned aerial vehicle, and verifying the automatic inspection operation script;
when the verification is met, the unmanned aerial vehicle executes power patrol operation based on the verified automatic patrol operation script;
in the process of power inspection operation, acquiring inspection data on a power inspection route;
and analyzing and processing the routing inspection data.
Carry out unmanned aerial vehicle aerial photography to the electric power inspection operation area of undetermined area and produce flight record information and include:
the method comprises the steps of obtaining image information of an undetermined area based on a camera and extracting position information corresponding to the image information.
The method for extracting the key route and the patrol action in the power patrol operation based on the flight record information comprises the following steps of:
extracting key routing points in the power routing inspection operation and routing inspection objects of the power routing inspection operation based on the image information;
and generating a corresponding key route and routing inspection action based on the key routing inspection point and the routing inspection object.
The automatic inspection operation script generated based on the key route and the inspection action comprises the following steps:
generating a simulation calculation result of the route planning into a kml file through a Matlab open source toolbox and a custom programming, and displaying points, lines and surface elements in the three-dimensional terrain of Google Earth through the kml file description.
Unmanned aerial vehicle patrols and examines operation script execution electric power based on the automation after the check-up and patrols and examines the operation and includes:
and avoiding the obstacle in the flight path based on the autonomous obstacle avoidance system.
The obstacle avoidance system based on the self-help obstacle avoidance system for avoiding the obstacle in the flight path comprises:
the unmanned aerial vehicle obtains a depth image of the obstacle, accurately senses the specific contour of the obstacle based on the depth image, and achieves independent bypassing of the obstacle; and/or
The unmanned aerial vehicle establishes a map model for planning reasonable routes for the flight area and automatically bypasses the obstacles based on the reasonable routes.
The analyzing and processing the routing inspection data comprises the following steps:
and acquiring insulator fault information based on the deep convolutional neural network DCNN.
The obtaining of the insulator fault information based on the deep convolutional neural network DCNN includes:
and binarizing the positioned insulator image, scanning the insulator image in lines, counting the number of pixel points of each line of insulator image, and dividing the insulator image into a plurality of insulator umbrella disc images by taking the wave troughs of the number of the pixel points as dividing lines.
Correspondingly, the invention also provides a system for the unmanned aerial vehicle power inspection operation, which is characterized in that the system is used for executing the method.
Compared with the prior art, the unmanned aerial vehicle power inspection operation method and system can improve the efficiency of power inspection operation, save labor cost and reduce inspection risk of personnel. By using the DCNN as a feature extractor, the fault classification accuracy of the insulator fault information is improved to more than 95%.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for unmanned aerial vehicle power inspection operation in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention relates to a method for unmanned aerial vehicle power inspection operation, which comprises the following steps: carrying out unmanned aerial vehicle aerial photography on the power inspection operation area of the to-be-determined area to generate flight record information; extracting a key route and routing inspection actions in the power routing inspection operation based on the flight record information; generating an automatic inspection operation script based on the key route and the inspection action; writing the automatic inspection operation script into a flight control system of the unmanned aerial vehicle, and verifying the automatic inspection operation script; when the verification is met, the unmanned aerial vehicle executes power patrol operation based on the verified automatic patrol operation script; in the process of power inspection operation, acquiring inspection data on a power inspection route; and analyzing and processing the routing inspection data.
Specifically, fig. 1 shows a flowchart of a method for unmanned aerial vehicle power inspection operation in the embodiment of the present invention, including the following steps:
s101, carrying out unmanned aerial vehicle aerial photography on a power inspection operation area of an area to be determined to generate flight record information;
in the embodiment of the invention, the image information of the undetermined area is acquired based on the camera and the position information corresponding to the image information is extracted, the flight record information in the embodiment of the invention can select a bin file generated by flight control or a tlog file recorded on ground station software as the original material of the flight record, but considering that the flight log based on the M300 RTK unmanned aerial vehicle is not in a common bin file format, the tlog file recorded on the ground station is adopted as the main material.
The's of tlog' file is all data frame raw data recorded by the ground station and sent to the ground station by the unmanned aerial vehicle flight control, and the data contains key information such as real-time GPS coordinates, height, unmanned aerial vehicle attitude, input channel value, output channel value, azimuth angle and pitch angle of the load pod, but the data in the file needs to be extracted and simplified. For example, it takes 60s for the unmanned aerial vehicle to fly from point a to point B, and it is set that the attitude data of the unmanned aerial vehicle is issued to the ground station at a frequency of 2Hz, then 120 frames of data are available in the ". tlog" file to describe the process from point a to point B, and the unmanned aerial vehicle only needs two instructions to fly from point a to point B, so that a log data analysis and extraction tool software needs to be designed to automatically extract "key waypoints" and "key actions" from a large amount of redundant data, where the "key actions" refer to pod attitude angles at the moment of photographing the loads.
S102, extracting a key route and routing inspection actions in the power routing inspection operation based on the flight record information;
extracting a key routing point in the power routing inspection operation and a routing inspection object of the power routing inspection operation based on the image information; and generating a corresponding key route and routing inspection action based on the key routing inspection point and the routing inspection object.
In the embodiment of the invention, a star-tlog file recorded by a ground station is used as a main material, the star-tlog file is original data of all data frames which are recorded by the ground station and sent to the ground station by unmanned aerial vehicle flight control, the data comprises key information such as real-time GPS coordinates, height, unmanned aerial vehicle attitude, input channel values, output channel values, azimuth angles and pitch angles of a load pod and the like of the unmanned aerial vehicle, and key navigation points and key actions are extracted from the information. The "key waypoints" include the unmanned aerial vehicle's GPS coordinates, altitude, unmanned aerial vehicle attitude, input channel values, output channel values, and the like. The "key action" refers to the nacelle attitude angle at the moment the load is photographed. The critical route relates to the setting of a critical navigation point, and the patrol action relates to the setting of a critical action.
S103, generating an automatic inspection operation script based on the key route and the inspection action;
the route planning simulation calculation result is generated into a kml file through a Matlab open source tool box and custom programming, and points, lines and surface elements are displayed in the three-dimensional terrain of Google Earth through the kml file description.
The flight of the unmanned aerial vehicle is mainly controlled by the flight behavior, and the pilot is assisted by the data link on the ground, so that the safety check of route planning before the unmanned aerial vehicle flies is particularly important. In a common plain airport, the peripheral clearance condition is good, and the flight route does not need to consider the interference of peripheral terrain. When planning flight paths in a complex terrain environment, the flight paths are required to be ensured not to collide with or be dangerously close to the surrounding rugged terrain. In addition, when the multiple unmanned aerial vehicles perform cluster control operation, besides avoiding obstacles in the environment (static obstacle avoidance), the unmanned aerial vehicles in the formation need to avoid collision of tracks with each other (dynamic obstacle avoidance), so as to prevent collision of routes and damage.
The Google Earth terrain tool software is used as a base, Google Earth provides free global satellite images and elevation data, and terrain data can be updated in a designated area on the Internet at any time according to needs. The route planning simulation calculation result is generated into a kml file through a Matlab open source toolbox and custom programming, the Google Earth supports the kml file, and elements such as points, lines and surfaces can be displayed in the three-dimensional terrain of the Google Earth through the description of the kml file. And then opening the kml file in GoogleEarth, so that the mutual relation between the flight path and the three-dimensional terrain can be visually seen, and whether dangerous approaching or collision with the terrain occurs or not can be intuitively seen. Therefore, the design of the air route can be optimized, and the unmanned aerial vehicle is prevented from being dangerously close to and colliding with the terrain in flight. In addition, a plurality of kml files can be imported, and the interrelation of a plurality of flight paths can be visually seen, so that whether the approach or the collision with the danger occurs or not can be intuitively seen.
S104, writing the automatic inspection operation script into a flight control system of the unmanned aerial vehicle, and verifying the automatic inspection operation script;
s105, when the verification is met, the unmanned aerial vehicle executes power inspection operation based on the verified automatic inspection operation script;
the obstacle avoidance system is based here on avoiding obstacles in the flight path. Avoiding obstacles in the flight path based on the autonomous obstacle avoidance system comprises the following steps: the unmanned aerial vehicle obtains a depth image of the obstacle, accurately senses the specific contour of the obstacle based on the depth image, and achieves independent bypassing of the obstacle; and/or the unmanned aerial vehicle establishes a map model for the flight area to plan a reasonable route, and automatically bypasses the barrier based on the reasonable route.
The unmanned aerial vehicle autonomous obstacle avoidance system can avoid obstacles in a flight path in time, and the perfect autonomous obstacle avoidance system can reduce various losses caused by misoperation to a great extent. The unmanned plane obstacle avoidance technology can be divided into three stages, namely a stage of sensing obstacles; second, bypassing the obstacle; and thirdly, a scene modeling and path searching phase. The unmanned aerial vehicle finds the barrier, and can automatically bypass the barrier, and then reach the process of planning the path by oneself.
In the first phase, the drone can only simply sense the obstacle. When the unmanned aerial vehicle encounters an obstacle, the unmanned aerial vehicle can be quickly identified and hovered off, and the unmanned aerial vehicle waits for the next instruction of the driver of the unmanned aerial vehicle and is relatively dependent on the attention and operation of the flyer.
In the second stage, the unmanned aerial vehicle can acquire the depth image of the obstacle, accurately sense the specific contour of the obstacle and then automatically bypass the obstacle. The stage is a stage of getting rid of the operation of the flying hand, improving the intellectualization and realizing the autonomous driving of the unmanned aerial vehicle.
In the third stage, the unmanned aerial vehicle can establish a map model for a flight area and then plan a reasonable route, the map cannot be only a mechanical plane model, but also a three-dimensional map which can be updated in real time, and the map is the highest stage of the existing unmanned aerial vehicle obstacle avoidance technology.
The autonomous obstacle avoidance technology needs to be capable of sensing the surrounding environment in real time in the flight process of the unmanned aerial vehicle, and automatically avoiding collision according to the distance of environmental obstacles. To avoid the barrier, must detect the barrier at first, common unmanned aerial vehicle range finding sensor has ultrasonic wave, infrared ray, laser etc. on the market.
S106, acquiring routing inspection data on the power routing inspection line in the power routing inspection operation process;
and S107, analyzing and processing the routing inspection data.
Here, the analyzing and processing the patrol data includes: and acquiring insulator fault information based on the deep convolutional neural network DCNN.
The obtaining of the insulator fault information based on the deep convolutional neural network DCNN includes: and binarizing the positioned insulator image, scanning the insulator image in lines, counting the number of pixel points of each line of insulator image, and dividing the insulator image into a plurality of insulator umbrella disc images by taking the wave troughs of the number of the pixel points as dividing lines.
Because the breadth of our country is broad, the climate difference between the south and the north is large, the types of insulators adopted in different environments and different voltage grades are different, and various insulators have different materials and shapes, the prior art needs to design diagnosis algorithms for the insulators respectively, the prior insulator state diagnosis method based on manual characteristics has poor robustness, complex calculation and single type of insulator fault to be processed. In the embodiment of the invention, the DCNN is firstly applied to the fault diagnosis of the insulator of the power transmission line, and the corresponding expression of the insulator fault is discovered through a deep network.
And binarizing the positioned insulator image, scanning the insulator image in lines, counting the number of pixel points of each line of insulator image, and dividing the insulator image into a plurality of insulator umbrella disc images by taking the wave troughs of the number of the pixel points as dividing lines. And establishing an aerial photography insulator umbrella disk image library by using various insulator umbrella disk images such as normal, damaged, cracked and dirty images. The method comprises the steps of positioning a target, then carrying out depth feature extraction, compared with the method of directly carrying out forward calculation by using one picture, randomly generating 10 patches on an original image, carrying out forward calculation on each patch, and carrying out averaging operation on obtained features. For the resulting multi-patch features, an SVM classifier is trained. Only the DCNN is used as a feature extractor, the test result far exceeds the manual features of BoF and the like, and the fault classification accuracy is over 95 percent.
The embodiment of the invention is based on a Deep Convolutional Neural Network (DCNN), can effectively extract the high-level semantic features of data through a hierarchical structure, and has been successfully applied to a plurality of computer vision fields. The DCNN is constructed by adopting new model structural elements such as a modified Linear Unit (ReLU), a Packed Convolutional Layer (PCL) and a Local Response Normalization (LRN) and is applied to the problem of insulator fault location. The embodiment of the invention adopts a simple, convenient and effective multi-model averaging method based on the same data enhancement program. And training each DCNN by adopting a direct regression mode. And combining a plurality of DCNNs in a multilayer cascade connection mode to improve the positioning precision of the insulator fault. The method solves the problem that the existing DCNN cascade algorithm is time-consuming in training, and improves the training speed by five times compared with a method of the same scale on the premise of not reducing the prediction precision. In the embodiment of the invention, an algorithm frame for migrating DCNN characteristics is adopted, DCNN trained on an insulator recognition task is migrated to the problem of insulator fault recognition and positioning, and DCNN is used as a characteristic extractor to be embedded into a local regularized cascade regression frame, so that the problem that DCNN cannot be directly trained due to insulator faults is solved. By solving the problems of complex structure and high computation complexity of a cascaded DCNN frame, the DCNN is constructed by Batch Normalization (BN) to inhibit the change of numerical value ranges input by each layer of the DCNN during training, so that the network is trained more quickly; and a multi-task learning mode is adopted, and the characteristics with stronger learning expression capability are learned under the constraint of multi-task supervision signals. And then migrating the DCNN to the insulator fault location problem. Finally, a single network can be adopted to directly position the insulator fault reference point, so that an algorithm frame is greatly simplified, a higher prediction speed is obtained, positioning accuracy similar to that of a cascaded DCNN method is obtained, and the extraction process of insulator fault information in inspection data is greatly improved.
Correspondingly, the embodiment of the invention also provides a system for unmanned aerial vehicle power inspection operation, and the system is used for executing the method shown in the figure 1.
The above embodiments of the present invention are described in detail, and the principle and the implementation manner of the present invention should be described herein by using specific embodiments, and the above description of the embodiments is only used to help understanding the method of the present invention and the core idea thereof; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (9)
1. A method for unmanned aerial vehicle power inspection operation is characterized by comprising the following steps:
carrying out unmanned aerial vehicle aerial photography on the power inspection operation area of the to-be-determined area to generate flight record information;
extracting a key route and routing inspection actions in the power routing inspection operation based on the flight record information;
generating an automatic inspection operation script based on the key route and the inspection action;
writing the automatic inspection operation script into a flight control system of the unmanned aerial vehicle, and verifying the automatic inspection operation script;
when the verification is met, the unmanned aerial vehicle executes power patrol operation based on the verified automatic patrol operation script;
in the process of power inspection operation, acquiring inspection data on a power inspection route;
and analyzing and processing the routing inspection data.
2. The method for unmanned aerial vehicle power inspection work according to claim 1, wherein the unmanned aerial vehicle aerial photography of the power inspection work area of the area to be determined to generate flight record information comprises:
the method comprises the steps of obtaining image information of an undetermined area based on a camera and extracting position information corresponding to the image information.
3. The method of unmanned aerial vehicle power inspection work according to claim 2, wherein extracting critical routes and inspection actions in the power inspection work based on flight record information includes:
extracting key routing points in the power routing inspection operation and routing inspection objects of the power routing inspection operation based on the image information;
and generating a corresponding key route and routing inspection action based on the key routing inspection point and the routing inspection object.
4. The method of unmanned aerial vehicle power inspection work according to claim 3, wherein generating an automatic inspection work script based on the critical route and the inspection action includes:
generating a simulation calculation result of the route planning into a kml file through a Matlab open source toolbox and a custom programming, and displaying points, lines and surface elements in the three-dimensional terrain of Google Earth through the kml file description.
5. The method of unmanned aerial vehicle power inspection task of claim 4, wherein the unmanned aerial vehicle performing the power inspection task based on the verified automatic inspection task script includes:
and avoiding the obstacle in the flight path based on the autonomous obstacle avoidance system.
6. The unmanned aerial vehicle power inspection operation method of claim 5, wherein avoiding obstacles in the flight path based on the autonomous obstacle avoidance system comprises:
the unmanned aerial vehicle obtains a depth image of the obstacle, accurately senses the specific contour of the obstacle based on the depth image, and achieves independent bypassing of the obstacle; and/or
The unmanned aerial vehicle establishes a map model for planning reasonable routes for the flight area and automatically bypasses the obstacles based on the reasonable routes.
7. The method of unmanned aerial vehicle power inspection work according to claim 6, wherein the analyzing the inspection data includes:
and acquiring insulator fault information based on the deep convolutional neural network DCNN.
8. The unmanned aerial vehicle power inspection operation method according to claim 7, wherein the obtaining insulator fault information based on the deep convolutional neural network DCNN comprises:
and binarizing the positioned insulator image, scanning the insulator image in lines, counting the number of pixel points of each line of insulator image, and dividing the insulator image into a plurality of insulator umbrella disc images by taking the wave troughs of the number of the pixel points as dividing lines.
9. A system for power patrol operation of a drone, the system being adapted to perform the method of any one of claims 1 to 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113359857A (en) * | 2021-07-14 | 2021-09-07 | 广西电网有限责任公司电力科学研究院 | Unmanned aerial vehicle power equipment autonomous inspection method and device |
| CN113910223A (en) * | 2021-09-29 | 2022-01-11 | 广东核电合营有限公司 | Nuclear power station aerial inspection system and method, robot and storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103324930A (en) * | 2013-06-28 | 2013-09-25 | 浙江大学苏州工业技术研究院 | License plate character segmentation method based on grey level histogram binaryzation |
| CN107656545A (en) * | 2017-09-12 | 2018-02-02 | 武汉大学 | A kind of automatic obstacle avoiding searched and rescued towards unmanned plane field and air navigation aid |
| CN108154072A (en) * | 2016-12-02 | 2018-06-12 | 天津工业大学 | Insulator breakdown of taking photo by plane based on depth convolutional neural networks detects automatically |
| CN108614907A (en) * | 2016-12-12 | 2018-10-02 | 中国航空工业集团公司成都飞机设计研究所 | A kind of unmanned plane during flying course line and landform collision detection method |
| CN109901624A (en) * | 2019-04-11 | 2019-06-18 | 株洲时代电子技术有限公司 | A kind of bridge method for inspecting |
| CN110134143A (en) * | 2019-05-30 | 2019-08-16 | 广东电网有限责任公司 | A kind of electric inspection process method, system and electronic equipment and storage medium |
| CN110398982A (en) * | 2019-06-21 | 2019-11-01 | 万翼科技有限公司 | A kind of method for inspecting and system of unmanned plane |
| CN110703800A (en) * | 2019-10-29 | 2020-01-17 | 国网江苏省电力有限公司泰州供电分公司 | Unmanned aerial vehicle-based intelligent identification method and system for electric power facilities |
| CN211668520U (en) * | 2020-04-09 | 2020-10-13 | 天津基点科技有限公司 | Automatic aerial photography system of transmission tower equipment based on unmanned aerial vehicle |
-
2020
- 2020-11-24 CN CN202011326996.7A patent/CN112346481A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103324930A (en) * | 2013-06-28 | 2013-09-25 | 浙江大学苏州工业技术研究院 | License plate character segmentation method based on grey level histogram binaryzation |
| CN108154072A (en) * | 2016-12-02 | 2018-06-12 | 天津工业大学 | Insulator breakdown of taking photo by plane based on depth convolutional neural networks detects automatically |
| CN108614907A (en) * | 2016-12-12 | 2018-10-02 | 中国航空工业集团公司成都飞机设计研究所 | A kind of unmanned plane during flying course line and landform collision detection method |
| CN107656545A (en) * | 2017-09-12 | 2018-02-02 | 武汉大学 | A kind of automatic obstacle avoiding searched and rescued towards unmanned plane field and air navigation aid |
| CN109901624A (en) * | 2019-04-11 | 2019-06-18 | 株洲时代电子技术有限公司 | A kind of bridge method for inspecting |
| CN110134143A (en) * | 2019-05-30 | 2019-08-16 | 广东电网有限责任公司 | A kind of electric inspection process method, system and electronic equipment and storage medium |
| CN110398982A (en) * | 2019-06-21 | 2019-11-01 | 万翼科技有限公司 | A kind of method for inspecting and system of unmanned plane |
| CN110703800A (en) * | 2019-10-29 | 2020-01-17 | 国网江苏省电力有限公司泰州供电分公司 | Unmanned aerial vehicle-based intelligent identification method and system for electric power facilities |
| CN211668520U (en) * | 2020-04-09 | 2020-10-13 | 天津基点科技有限公司 | Automatic aerial photography system of transmission tower equipment based on unmanned aerial vehicle |
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