Power transmission line periphery monitoring system and power transmission line periphery monitoring program

Abstract

To provide a power transmission line periphery monitoring system and a power transmission line periphery monitoring program that can easily and properly monitor occurrence of an event which may affect a power transmission line and a power transmission tower.SOLUTION: A power transmission line periphery monitoring system 1 comprises: a monitoring drone 2 which photographs a periphery of a power transmission line by a camera 21 from the sky; a monitoring computer 3 which stores an image, obtained by photographing the periphery of the power transmission line L in a normal state, with the monitoring drone 2, as a normal image, and determines whether an abnormal event which may affect a power transmission line and a power transmission tower T occurs in the periphery of the power transmission line based upon an object image obtained by photographing the periphery of the power transmission line L with the monitoring drone 2 at monitoring object time and the normal image. The monitoring computer 3 may be arranged in an office C of an electric power company or in other places.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a power transmission line surroundings monitoring system and a power transmission line surroundings monitoring program for monitoring the presence or absence of abnormalities in the vicinity of a power transmission line. [Background technology]

[0002] For example, if construction machinery (heavy machinery) is used under power lines, or if land is developed and buildings are constructed, there is a risk of accidents occurring due to contact with the power lines, or the foundation strength of the transmission towers being weakened. For this reason, power companies that manage power lines and transmission towers regularly monitor areas under the lines and around the transmission lines, such as transmission towers, for any abnormalities or incidents, such as the use of construction machinery, construction of houses, cutting of trees, or nesting of birds and animals.

[0003] This monitoring involves patrolling the area around the power lines from the air using helicopters, in addition to patrolling on foot or by vehicle from the ground. Helicopter patrols include a method in which an inspector actually boards the helicopter to conduct a visual patrol (hereinafter referred to as "board patrol"), and a method in which images and videos taken from the helicopter are later reviewed (hereinafter referred to as "video review patrol").

[0004] Meanwhile, a system is known that can use aerial images to determine with high accuracy when trees in a forest were cut down (for example, see Patent Document 1). This system captures the forest from the air to obtain aerial images, and determines the cut-down dates of the trees in the aerial images by referring to reference aerial images previously captured of the forest from the air and three or more levels of correlation between the tree cut-down dates. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2022-53463 A Summary of the Invention [Problem to be solved by the invention]

[0006] However, whether it is a conventional boarding patrol or a video confirmation patrol, the patrol officer must visually perform the monitoring and confirmation work, which takes time and places a heavy burden on the patrol officer. Moreover, boarding patrol requires high concentration, which places an even greater burden on the patrol officer. In addition, there is a risk that the results of the patrol and confirmation will differ depending on the patrol officer's skills and experience, and that abnormalities and abnormalities will be overlooked. On the other hand, the system described in Patent Document 1 can use aerial images of the forest to determine when trees should be cut down, but cannot monitor and confirm whether any events that may affect the power transmission line or the transmission tower have occurred around the power transmission line.

[0007] Therefore, an object of the present invention is to provide a power transmission line surroundings monitoring system and a power transmission line surroundings monitoring program that enable easy and proper monitoring of the occurrence of events that may affect power transmission lines and transmission towers. [Means for solving the problem]

[0008] In order to solve the above problem, the invention of claim 1 is a power transmission line surroundings monitoring system comprising: an aerial photography means for photographing the area around the power transmission line from above; a normal image storage means for storing an image of the area around the power transmission line in a normal state photographed by the aerial photography means; and a determination means for determining whether or not an abnormal event has occurred around the power transmission line that may affect the power transmission line or a transmission tower, based on a target image, which is an image of the area around the power transmission line photographed by the aerial photography means at the time of monitoring, and the normal image.

[0009] The invention of claim 2 is characterized in that, in the power line surroundings monitoring system described in claim 1, the abnormal event includes one of the following: felling of trees, use of construction machinery, construction of houses, nesting of birds and animals, operation of a flashing indicator, and extinguishing of an aviation obstruction light.

[0010] The invention of claim 3 is characterized in that, in the power line surroundings monitoring system described in claim 1, it further comprises a power line information storage means for storing the line name and position information for each section of each power line as power line information, the aerial photographing means acquires position information of the target image, and the determination means outputs the line name and section of the power line in which the abnormal event is occurring based on the position information of the target image acquired by the aerial photographing means and the power line information.

[0011] The invention of claim 4 is characterized in that, in the power transmission line surroundings monitoring system described in claim 1, the determination means outputs a priority of response to the abnormal event based on the content and location of the abnormal event.

[0012] The invention of claim 5 is characterized in that, in the power line surroundings monitoring system described in claim 1, the determination means uses a monitoring learning model that has been machine-learned based on past performance data so as to output whether or not the abnormal event has occurred when the target image is input.

[0013] The invention of claim 6 is a power transmission line surroundings monitoring program characterized by causing a computer to function as a normal image storage means for storing an image of the surroundings of a power transmission line when there is no abnormality taken from above as a normal image, and a determination means for determining whether or not an abnormal event has occurred around the power transmission line that may affect the power transmission line or a transmission tower, based on a target image, which is an image of the surroundings of the power transmission line taken from above at the time of monitoring, and the normal image.

[0014] The invention of claim 7 is characterized in that, in the power line surroundings monitoring program described in claim 6, the computer is made to function as a power line information storage means that stores the line name and position information for each section of each power line as power line information, and the determination means outputs the line name and section of the power line in which the abnormal event is occurring based on the position information of the target image and the power line information.

[0015] The invention of claim 8 is characterized in that, in the power transmission line surroundings monitoring program according to claim 6, the determination means outputs a priority of response to the abnormal event based on the content and location of the abnormal event.

[0016] The invention of claim 9 is characterized in that, in the power line surroundings monitoring program described in claim 6, the determination means uses a monitoring learning model that has been machine-learned based on past performance data so as to output whether or not the abnormal event has occurred when the target image is input. Effect of the Invention

[0017] According to the inventions described in claims 1 and 6, whether or not an abnormal event that may affect the power transmission line or transmission tower has occurred around the power transmission line is automatically determined based on a normal image of the area around the power transmission line when there is no abnormality and a target image of the area around the power transmission line when it is being monitored. Therefore, it is possible to easily and appropriately monitor the occurrence of an abnormal event that may affect the power transmission line or transmission tower, regardless of the skills or experience of the patrolman or monitor. As a result, it is possible to quickly and appropriately respond to such an abnormal event and prevent damage or failure of the power transmission line or transmission tower.

[0018] According to the invention described in claim 2, felling of trees, use of construction machinery, construction of houses, nesting of birds and animals, operation of flashing indicators, and turning off of aviation obstruction lights are judged as abnormal events that may affect power transmission lines and transmission towers. Therefore, it is possible to monitor various abnormal events and prevent damage and failure of power transmission lines and transmission towers in advance. In particular, by monitoring and detecting felling of trees as an abnormal event, it is possible to prevent damage and failure of power transmission lines and transmission towers in advance, even if it is unknown what will be done after felling.

[0019] According to the inventions described in claims 3 and 7, the line name and section of the power transmission line in which an abnormal event that may affect the power transmission line or transmission tower is occurring are output, making it possible to take prompt and appropriate action in the location and section in which the abnormal event is occurring.

[0020] According to the inventions recited in claims 4 and 8, the priority of responses to abnormal events is output, making it possible to take appropriate responses according to the content and location of the abnormal event.

[0021] According to the inventions described in claims 5 and 9, a machine-learned monitoring learning model is used to output whether or not an abnormal event has occurred, making it possible to more accurately determine whether or not an abnormal event has occurred. [Brief description of the drawings]

[0022] [Figure 1] 1 is a schematic configuration diagram showing a power transmission line surroundings monitoring system according to an embodiment of the present invention; [Diagram 2] 2 is a schematic block diagram showing the configuration of a monitoring computer of the power transmission line surroundings monitoring system of FIG. 1. [Diagram 3] FIG. 3 is a diagram showing an example of a normal image under a power line stored in the image database of the monitoring computer of FIG. 2. [Figure 4] 3A is a diagram showing an example of a flashover indicator of a normal image stored in the image database of the surveillance computer of FIG. 2, and FIG. 3B is a diagram showing a state in which the flashover indicator is activated. [Diagram 5] 3 is a diagram showing an example of a target image judged by a judgment task of the monitoring computer of FIG. 2; [Figure 6] 3 is a functional block diagram showing a schematic configuration of a monitoring learning model of the monitoring computer of FIG. 2. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] Hereinafter, the present invention will be described based on the illustrated embodiment.

[0024] 1 is a schematic diagram showing a power transmission line periphery monitoring system 1 according to an embodiment of the present invention. This power transmission line periphery monitoring system 1 is a system for monitoring the presence or absence of abnormalities in the vicinity of a power transmission line L, and the periphery of the power transmission line L to be monitored includes the power transmission line L itself, the vicinity below the power transmission line L (trees F, etc.), a power transmission tower T, etc. In this embodiment, the power utility company that operates and manages the power transmission line L and the power transmission tower T monitors the periphery of the power transmission line L.

[0025] The power transmission line surroundings monitoring system 1 includes a monitoring drone (aerial photography means) 2 and a monitoring computer 3.

[0026] The monitoring drone 2 photographs the area around the power transmission line L from the sky. That is, it is an unmanned flying object that photographs the area around the power transmission line L while flying in the sky above the power transmission line L, and in this embodiment, it automatically flies a predetermined flight route and flight path from a predetermined landing site. That is, it is equipped with a GPS (Global Positioning System, position acquisition means), and flies autonomously along a flight route set by a monitoring computer 3 described later and stored in the monitoring drone 2 while confirming its own position with the GPS. At this time, it may be equipped with a function to avoid objects that become obstacles to flight depending on the flight environment, etc.

[0027] The monitoring drone 2 also includes a camera (imaging means) 21, and hovers in a predetermined position in the sky to capture images of the periphery of the power line L. That is, the camera 21 is started up while hovering at each imaging location set on the flight route, images of the periphery of the power line L (below the power line L) are captured, and the captured image (target image) is transmitted to the monitoring computer 3 together with the position information (latitude, longitude) of the imaging location. The image may be a still image or a video image.

[0028] In response to this, the flying position of the monitoring drone 2 is constantly and continuously recorded over time, while the camera 21 constantly captures images of the area around the power line L (viewed downward from the sky), and the flying position at each time and the captured moving images and videos are transmitted to the monitoring computer 3. Then, by comparing the flying position of the drone 2 and the images at the same time in the monitoring computer 3, it may be possible to determine which location the images were taken at.

[0029] Here, multiple such surveillance drones 2 may be provided and flown simultaneously along different flight routes, or may be remotely controlled by a person instead of automatically flying. Also, a camera 21 may be installed on a helicopter, and the vicinity of the power transmission line L may be photographed from the sky by the camera 21 while the helicopter is flying.

[0030] In this embodiment, such a monitoring drone 2 is started and flown by a flight command from a monitoring computer 3 described later, and transmits captured images of the target in real time to the monitoring computer 3. Alternatively, the monitoring drone 2 may be started and flown periodically, and the captured images of the target may be stored in the memory of the monitoring drone 2, and the images in the memory may be input and transferred to the monitoring computer 3 upon return.

[0031] The monitoring computer 3 is a computer that determines the presence or absence of anomalies around the power transmission line L based on the target image received from the monitoring drone 2, and in this embodiment, it is assumed to be located in the power company's office C, but may be located in other locations. As shown in Fig. 2, the monitoring computer 3 mainly includes an input unit 31, a display unit 32, a communication unit 33, a memory unit 34, a judgment task (judgment means) 35, a learning task 36, and a central processing unit 37 that controls these.

[0032] The input unit 31 is an interface for inputting various information and commands, and specifically, inputs flight commands for the monitoring drone 2 and start commands for the judgment task 35. The display unit 32 is a display for displaying various data and information, and specifically, displays a target image received from the monitoring drone 2 and displays a judgment result by the judgment task 35. The communication unit 33 is an interface for communicating with the outside via the Internet network, a telephone communication network, and the like, and specifically, receives a target image from the monitoring drone 2 and transmits flight commands to the monitoring drone 2.

[0033] The storage unit 34 mainly includes an image database (such as a normal image storage means) 341, a power transmission line information database (such as a power transmission line information storage means) 342, a monitoring learning model 343, and a monitoring performance database 344. Here, the databases 341 and 342 will be explained, and the monitoring learning model 343 and the monitoring performance database 344 will be explained later.

[0034] The image database 341 is a database that stores images of the vicinity of the power transmission line L taken by the monitoring drone 2 together with the location information (latitude, longitude) of the location where the images were taken, and includes a memory area for normal images, a memory area for target images, and a memory area for abnormal images. Here, the normal image is an image taken by the monitoring drone 2 of the vicinity of the power transmission line L in a state where there is no abnormality, and the target image is an image taken by the monitoring drone 2 of the vicinity of the power transmission line L when it is being monitored.

[0035] That is, the target image is an image taken by the monitoring drone 2 when checking whether there is an abnormality or anomaly around the power transmission line L, and in this embodiment, the latest image taken by the monitoring drone 2 (the image received from the monitoring drone 2) is stored as the target image in the memory area for target images. In addition, images taken without abnormalities and images determined or confirmed as having no abnormalities among past target images are stored as normal images in the memory area for normal images. Images determined or confirmed as having an abnormality among past target images are stored as abnormal images in the memory area for abnormal images.

[0036] Storing images in such an image database 341 may be done manually, or it may be done automatically when a target image is received from the surveillance drone 2 or after a judgment is made in the judgment task 35.

[0037] Here, the normal image may be, for example, an overall image of the power line L and the transmission tower T in a normal state (non-operating state) as shown in Fig. 3, or an image of the flashover indicator D in a normal state (non-operating state) as shown in Fig. 4(a). The flashover indicator D is an indicator that operates when electricity flows through the transmission tower T due to lightning, and is disposed on a member T1 of the transmission tower T and comprises a main body D1 and a cover D2. When electricity flows through the member T1 and the indicator operates, the cover D2 comes off the main body D1 as shown in Fig. 4(b), exposing the red tape D3 connecting the main body D1 and the cover D2, indicating that electricity has flowed through the transmission tower T.

[0038] The power transmission line information database 342 is a database that stores information on each power transmission line L as power transmission line information, and stores the line name of each power transmission line L and position information for each section of each power transmission line L. That is, for each power transmission line L, the line name is stored, and position information for each section of the power transmission line L (such as from which latitude and longitude to which latitude and longitude the section extends) is stored. This makes it possible to know which section of which line name the target image is from the position information of the target image, as will be described later.

[0039] The judgment task 35 is a task program that judges whether or not an abnormal event that may affect the power transmission line L or the power transmission tower T has occurred around the power transmission line L, based on a target image, which is an image taken by the monitoring drone 2 around the power transmission line L at the time of monitoring, and a normal image in the image database 341. Here, an abnormal event that may affect the power transmission line L or the power transmission tower T is an event that may adversely affect the power transmission line L or the power transmission tower T directly due to the abnormal event, or may adversely affect the power transmission line L or the power transmission tower T due to events before or after the abnormal event.

[0040] Specifically, this includes felling of a tree F (construction machinery, etc. may be used after felling and may come into contact with the power line L), use of construction machinery (may come into contact with the power line L), construction of a house (construction machinery, etc. used during construction may come into contact with the power line L), nesting of birds and animals (may come into contact with the power line L, etc.), operation of a flash indicator D (electricity may flow through a power transmission tower T due to lightning), turning off of an aviation obstruction light at night (aircraft, etc. may come into contact with the power line L or the power transmission tower T), land development (construction machinery, etc. used during development may come into contact with the power line L), etc. Here, since the use of construction machinery, construction of a house, and land development are performed after the tree F is felled, early monitoring and detection of the felling of the tree F leads to early response. Therefore, felling of the tree F may be determined as the main abnormal event.

[0041] This judgment task 35 is started when a start command is input from the input unit 31, and in this embodiment, the presence or absence of an abnormality in a target image (including the content and location of the abnormal event) is judged based on the target image stored in the image database 341 and a normal image taken in the same location as the target image. Here, if multiple target images are stored in the image database 341, the same judgment is made for all the target images. Also, instead of the target image stored in the image database 341, a target image specified by the input unit 31 may be used as the judgment target.

[0042] When making the judgment, the target image and the normal image are subjected to image analysis and compared with the target image to judge whether or not the target image differs from the normal image and contains the above-mentioned abnormal phenomenon, and if so, where and what type of abnormal phenomenon exists. For example, as shown in Fig. 3, in the normal image, a tree F is growing under the power line L, whereas as shown in Fig. 5, in the target image, there is a trace F0 of a cut tree F under the power line L, it is judged and output that a tree F has been cut under the power line L.

[0043] Similarly, when the red tape D3 of the flash indicator D is not visible in the normal image as shown in Fig. 4(a), but the red tape D3 of the flash indicator D is visible in the target image as shown in Fig. 4(b), it is determined and output that the flash indicator D has been activated. Here, when making such a determination, abnormal images stored in the image database 341 may be referenced. For example, the type of abnormal event may be determined by referring to an abnormal image for each abnormal event.

[0044] Then, when it is determined that an abnormal event has occurred, the line name and section of the power line L where the abnormal event has occurred are output based on the position information of the target image and the power line information stored in the power line information database 342. That is, the section of the power line L including the latitude and longitude of the target image is searched for in the power line information database 342, and the line name of the power line L in that section and the section are output.

[0045] Furthermore, based on the determined content of the abnormal event and the location of occurrence, the priority of the response to the abnormal event is determined and output. That is, based on where and what type of abnormal event is occurring, whether or not a response to the abnormal event is urgent (how urgent the response is) is output. Specifically, the content of an abnormal event that requires urgency and the location of occurrence (location of occurrence) are stored in advance, and if this applies, the priority is determined to be high and output as being high, and if not, the priority is determined to be low and output as being low. For example, if the felling site F0 of a tree F is near a power transmission tower T, or if construction machinery is being used close to directly below a power transmission line L, the priority is determined to be high and output as being high.

[0046] Here, the content and location of an abnormal event that does not require urgency may be stored in advance, and the priority may be determined and output based on this. Also, the priority may be determined and output based on the line name and section (location) of the power transmission line L where the abnormal event is occurring. For example, the line name and section of an important power transmission line L or a power transmission line L that requires time to respond may be stored in advance, and if an abnormal event occurs in this section, the priority may be determined to be high and output.

[0047] When a target image is input, such a judgment task 35 uses a monitoring learning model 343 that is machine-learned based on past performance data so that whether or not an abnormal event has occurred (including where and what type of abnormal event is occurring) is output. This monitoring learning model 343 is created by the learning task 36.

[0048] That is, the learning task 36 creates a monitoring learning model 343 by a known machine learning algorithm such as a neural network using past performance data recorded and accumulated in a monitoring performance database 344. This monitoring performance database 344 is a database in which performance data including abnormal events confirmed by experts and experienced persons in monitoring around the power transmission line L based on target images as input information is recorded and accumulated. The past performance data includes data created based on actual target images and abnormal events actually confirmed by experts and experienced persons in monitoring, as well as data created through advance training, etc.

[0049] As shown in Fig. 6, this learning task 36 uses machine learning and deep learning using a neural network to create a neural network with, for example, a target image as an input layer, an abnormal event as an output layer, and an analysis process from the input layer to the output layer as an intermediate layer based on the performance data recorded in the monitoring performance database 344. Then, the learning task 36 uses the performance data of the monitoring learning model 343 as learning data to learn various parameters in the intermediate layer. That is, the learning task 36 learns various parameters in the intermediate layer so that an abnormal event is appropriately output based on the target image.

[0050] The result of the determination thus made, that is, the abnormal event (contents and occurrence location), the line name and section of the power transmission line L where the abnormal event is occurring, and the priority of responding to the abnormal event, are displayed on the display unit 32. In addition, such a determination result may be transmitted to a mobile terminal of a previously registered monitor or the like.

[0051] Next, the action and operation of the power transmission line periphery monitoring system 1 configured as above, and the power transmission line periphery monitoring method using the power transmission line periphery monitoring system 1 will be described.

[0052] First, when checking whether there is an abnormality or anomaly around the power transmission line L, or periodically, a flight command is sent from the monitoring computer 3 to the monitoring drone 2. At that time, a flight route may be sent to the monitoring drone 2 along with the flight command, or the flight route may be stored and set in advance in the monitoring drone 2. In response to this, the monitoring drone 2 photographs the area around the power transmission line L while flying along the flight route, and transmits the captured image (target image) to the monitoring computer 3 together with the position information of the shooting location.

[0053] Next, in the monitoring computer 3, the image received from the monitoring drone 2 is stored as a target image in a memory area for target images in the image database 341. Next, when a start command is input from the input unit 31, the judgment task 35 is started, and as described above, based on the target image and normal image in the image database 341, it is judged whether or not an abnormal event that may affect the power transmission line L or the transmission tower T has occurred around the power transmission line L. As a result, if an abnormal event is found, the location and type of abnormal event, the line name and section of the power transmission line L where the abnormal event is occurring, and the priority of the response to the abnormal event are displayed on the display unit 32. In response to this, a monitor or the like goes to the site to conduct a detailed investigation and response.

[0054] Here, in this embodiment, the judgment task 35 is started after the monitoring drone 2 is started, but in the judgment task 35, a flight command may be automatically sent to the monitoring drone 2 prior to the judgment.

[0055] As described above, according to the power transmission line surroundings monitoring system 1, it is automatically determined whether or not an abnormal event that may affect the power transmission line L or the transmission tower T has occurred around the power transmission line L, based on a normal image of the periphery of the power transmission line L in an abnormal state and a target image of the periphery of the power transmission line L at the time of monitoring. Therefore, it is possible to easily and appropriately monitor the occurrence of an abnormal event that may affect the power transmission line L or the transmission tower T, regardless of the skills or experience of the patrolman or monitor. As a result, it is possible to quickly and appropriately respond to such an abnormal event and prevent damage or failure of the power transmission line L or the transmission tower T before it occurs.

[0056] In addition, felling of trees F, use of construction machinery, construction of houses, nesting of birds and animals, operation of flash indicator D, turning off of aviation obstruction lights, and land development are determined as abnormal events that may affect power transmission lines L and transmission towers T. Therefore, by monitoring a variety of abnormal events, it becomes possible to prevent damage and failure of power transmission lines L and transmission towers T before they occur. In particular, by monitoring and detecting felling of trees F as an abnormal event, it becomes possible to prevent damage and failure of power transmission lines L and transmission towers T before they occur, even if it is unknown what will be done after felling.

[0057] Furthermore, since the machine-learned monitoring learning model 343 is used to output whether or not an abnormal event has occurred, it becomes possible to more accurately determine whether or not an abnormal event has occurred.

[0058] In addition, the line name and section of the power transmission line L where an abnormal event that may affect the power transmission line L and the transmission tower T is occurring are output, making it possible to take prompt and appropriate action in the location and section where the abnormal event is occurring. Furthermore, the priority of the response to the abnormal event is output, making it possible to take appropriate action according to the content of the abnormal event and the location where it occurred.

[0059] Although the embodiment of the present invention has been described above in detail, the specific configuration is not limited to this embodiment, and even if there are design changes within the scope of the present invention, they are included in the present invention. For example, in the above embodiment, the monitoring computer 3 is configured as an integrated computer, but it may be configured as multiple computers or servers, which may be located in different locations. In addition, although the monitoring drone 2 equipped with the camera 21 constitutes the aerial photographing means, a fixed camera installed at a high place such as the top of a transmission tower T may be used as the aerial photographing means, and an image of the area around the power transmission line L photographed by this fixed camera may be transmitted to the monitoring computer 3.

[0060] On the other hand, the above-mentioned power transmission line surroundings monitoring system 1 and monitoring computer 3 may be configured by installing the following power transmission line surroundings monitoring program in a general-purpose computer.

[0061] That is, it is a power transmission line surroundings monitoring program that causes a computer to function as: a normal image storage means (image database 341) that stores an image of the surroundings of the power transmission line L when there is no abnormality taken from above as a normal image; a power transmission line information storage means (power transmission line information database 342) that stores the line name and position information for each section of each power transmission line L as power transmission line information; and a judgment means (judgment task 35) that judges whether or not an abnormal event that may affect the power transmission line L or the transmission tower T has occurred around the power transmission line L based on a target image, which is an image of the surroundings of the power transmission line L taken from above at the time of monitoring, and the normal image. The judgment means uses a monitoring learning model 343 that has been machine-learned based on past performance data so that, when a target image is input, whether or not an abnormal event has occurred is output, and based on the position information of the target image and the power transmission line information, the judgment means outputs the line name and section of the power transmission line L where the abnormal event is occurring, and further outputs a priority of response to the abnormal event based on the content and location of the abnormal event. [Explanation of symbols]

[0062] 1. Power line perimeter monitoring system 2 Surveillance drones (means of aerial photography) 21 Camera (photography means) 3. Surveillance Computer 341 Image database (normal image storage means, etc.) 342 Power transmission line information database (power transmission line information storage means) 343 Supervised Learning Model 344 Monitoring performance database 35 Judgment task (judgment means) L Power Line T transmission tower F Tree D Flash indicator

Claims

1. An aerial photographing means for photographing the vicinity of the power transmission line from above; a normal image storage means for storing an image of the vicinity of the power transmission line in a normal state photographed by the aerial photographing means as a normal image; a determination means for determining whether or not an abnormal event that may affect the power transmission line or the power transmission tower has occurred in the vicinity of the power transmission line based on a target image, which is an image of the vicinity of the power transmission line captured by the aerial photography means during monitoring, and the normal image; A power transmission line surroundings monitoring system comprising:

2. The abnormal event includes any one of the following: tree cutting, use of construction machinery, construction of a house, nesting of birds and animals, operation of a flashing indicator, and extinguishing of an aviation obstruction light; 2. The power transmission line surroundings monitoring system according to claim 1 .

3. a power transmission line information storage means for storing the line name and the location information for each section of each power transmission line as power transmission line information; The aerial photographing means acquires position information of the target image, The determination means outputs a line name and a section of the power transmission line in which the abnormal event is occurring based on position information of the target image acquired by the aerial photography means and the power transmission line information.

2. The power transmission line surroundings monitoring system according to claim 1 .

4. the determination means outputs a priority of a response to the abnormal event based on the content and the location of the abnormal event.

2. The power transmission line surroundings monitoring system according to claim 1 .

5. the determination means uses a monitoring learning model that has been machine-learned based on past performance data so as to output whether or not the abnormal event has occurred when the target image is input; 2. The power transmission line surroundings monitoring system according to claim 1 .

6. Computer, a normal image storage means for storing an image of the vicinity of the power transmission line when there is no abnormality, taken from above, as a normal image; a determination means for determining whether or not an abnormal event that may affect the power transmission line or the transmission tower has occurred in the vicinity of the power transmission line based on a target image, which is an image of the vicinity of the power transmission line taken from above during monitoring, and the normal image; A power transmission line surroundings monitoring program characterized by causing the power transmission line surroundings monitoring program to function as a power transmission line surroundings monitoring program.

7. causing the computer to function as a power transmission line information storage means for storing the line name and the location information for each section of each power transmission line as power transmission line information; the determination means outputs a line name and a section of the power transmission line in which the abnormal event is occurring based on the position information of the target image and the power transmission line information.

7. The power transmission line surroundings monitoring program according to claim 6.

8. the determination means outputs a priority of a response to the abnormal event based on the content and the location of the abnormal event.

7. The power transmission line surroundings monitoring program according to claim 6.

9. the determination means uses a monitoring learning model that has been machine-learned based on past performance data so as to output whether or not the abnormal event has occurred when the target image is input; 7. The power transmission line surroundings monitoring program according to claim 6.

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