JP7718153B2 - Nesting response work support device and nesting response work support method - Google Patents

Description

The present invention relates to a nesting response support device and a nesting response support method.

In recent years, particularly in urban areas, there has been an increase in cases of crows and other birds using wire coat hangers and other materials instead of twigs to build nests on utility poles. These nests can come into contact with power lines and other electrical equipment, potentially causing power outages. For this reason, power company distribution employees and employees of subcontracted companies patrol equipment to check for nesting sites and take measures such as removing them. However, it is extremely difficult to thoroughly inspect and understand the numerous pieces of equipment installed over a wide area with limited personnel.

As a technology for automating part of the nest inspection process, for example, Patent Document 1 discloses a nest information management server that acquires nest image information from an information provider terminal, identifies nest-related equipment based on the nest image and the original image, determines the urgency of the response, and determines whether or not countermeasures are required. If countermeasures are required, the content of the countermeasure work is determined, and the results of the urgency determination process and the countermeasure work content determination process are displayed on a display unit.

JP 2011-204012 A

However, the criteria for determining nesting in Patent Document 1 require pre-defined information, such as when a lightning arrester is buried in the nest, when nesting occurs on top of an open-type switchgear or bushing, or when a cutout is buried in the nest. Because the impact of nesting on each piece of power equipment can vary depending on, for example, the structure of the utility pole and the type of attached power equipment, applying the technology in the above patent document to a large number of utility poles installed over a wide area may be impractical due to the complicated definition of the criteria. Furthermore, even if the criteria for implementing countermeasures are established, the decision of when and what measures should actually be taken against nesting is based on the many years of experience and know-how of on-site workers, etc., and may not be amenable to standardization like that in the above patent document.

The present invention was made in light of this background, and its purpose is to provide a nesting response support device and nesting response support method that can provide information regarding the response that should be taken in response to nests built on power equipment and the timing of that response.

One aspect of the present invention that achieves the aforementioned objectives is a nest response work support device that includes an image acquisition unit having a processor and memory that acquires images, and an urgency determination unit that inputs images containing power equipment and bird nests into a trained model that outputs information on the response content and timing for the bird nests, and then outputs information on the response content and timing for the nests included in the acquired images.

In this way, the nest response work support device of the present invention inputs site images into a nest risk assessment model, which is a trained model that outputs information indicating the likelihood of bird nests approaching power equipment in the future. This outputs information on the response and timing for the nest captured in the site image. Therefore, workers and others can learn the specific response and timing to take in response to the nest simply by preparing an image of a utility pole or other object where a nest is being built. In other words, the nest response work support device of the present invention can provide information on the response and timing to take in response to nests built on power equipment.

In one aspect of the present invention to achieve the above-mentioned objective, the trained model is a model to which information on the area and time when the image was taken is further input, and the urgency determination unit further inputs information on the area and time when the acquired image was taken into the trained model, thereby outputting information on the response content and timing of the response to the nest contained in the acquired image.

In this way, the nesting response work support device of the present invention outputs information on the response content and timing for nests contained in the site images by inputting information on the area and time when the site images were taken into a trained model (nesting risk assessment model) that receives information on the area and time when the images were taken. Because bird nesting rates can vary by region or season, inputting the area and time when the site images were taken as feature quantities for the nesting risk assessment model allows workers and others to accurately know the specific response that should be taken in response to nesting.

Furthermore, one aspect of the present invention for achieving the above-mentioned objectives is a nest response work support device, in which the trained model is a model that outputs, as information on the response content and response timing for the bird's nest, any of the following: information indicating that the nest needs to be removed immediately, information indicating that the nest needs to be removed by a specified time in the future, or information indicating that removal of the nest is not necessary; and the urgency determination unit inputs the acquired image into the trained model, and outputs, as the response content and response timing information, any of the following: information indicating that the nest included in the acquired image needs to be removed immediately, information indicating that the nest needs to be removed by a specified time in the future, or information indicating that removal of the nest is not necessary.

In this way, the nesting response work support device of the present invention inputs site images into a nesting risk assessment model, and outputs either information indicating that the nest contained in the site image needs to be removed immediately, information indicating that the nest needs to be removed by a specified time in the future, or information indicating that the nest does not need to be removed. This allows workers and others to know more specific measures that should be taken in response to nesting.

Furthermore, one aspect of the present invention for achieving the aforementioned objectives is a nest response support device that includes a display unit that displays on a screen information about the response content and timing for the nest contained in the acquired image.

In this way, the nest response work support device of the present invention displays information on the response content and timing for nests captured in on-site images on the screen, allowing workers and others to quickly understand the response that should be taken in response to nests.

Another aspect of the present invention for achieving the aforementioned objectives is a nest response work support device, in which the urgency determination unit inputs an image including power equipment and bird nests into a first trained model that outputs information about the areas of the bird nests and power equipment in the image, thereby outputting information about the areas of the bird nests and power equipment in the acquired image; and inputs information about the areas of the bird nests and power equipment into a second trained model that inputs information about the areas of the bird nests and power equipment and outputs information about the response content and timing for the bird nests, thereby outputting information about the response content and timing for the nests included in the acquired image.

In this way, the nest response work support device of the present invention outputs response and timing information using a trained model (nesting risk determination model) consisting of a first trained model and a second trained model. That is, the nest response work support device 1 of the present invention inputs an image including power equipment and bird nests into the first trained model, which outputs information about the areas of the bird nests and power equipment in the image, thereby outputting information about the areas of the bird nests and power equipment in the site image. Next, the information about the areas of the bird nests and power equipment is input into the second trained model, which outputs information about the response content and timing for the bird nests, thereby outputting information about the response content and timing for the nests included in the site image. In this way, by using the second trained model with information about the areas of the nests and power equipment recognized by the first trained model as input values, it is possible to more accurately estimate the response content and timing for the bird nests.

Furthermore, one aspect of the present invention for achieving the above-mentioned objectives is a nesting response work support method, in which an information processing device executes an image acquisition process for acquiring an image, and an urgency assessment process for outputting information on the response content and timing for the nest contained in the acquired image by inputting the acquired image into a trained model that receives an image including an electric power facility and a bird's nest and outputs information on the response content and timing for the bird's nest.

The present invention can provide information on what actions should be taken and the timing of those actions in response to nests built on power equipment.

FIG. 10 is a diagram illustrating an example of a performance image. FIG. 10 is a diagram illustrating another example of the performance image. FIG. 2 is a diagram illustrating an example of functions provided in the nest response operation support device. FIG. 2 is a diagram illustrating an example of a teacher data DB. FIG. 2 is a diagram illustrating an example of hardware of a nest response operation support device. FIG. 10 is a flow diagram illustrating an example of a model learning process. FIG. 10 is a flow diagram illustrating an example of an urgency determination process. 10 is an example of an urgency determination screen.

Hereinafter, a nest response operation support device according to one embodiment of the present invention will be described with reference to the drawings.
On utility poles installed in power systems, birds such as crows and sparrows sometimes build nests using metal parts such as wires or hangers, using the cross arms or electric wires attached to the utility poles as footholds. If nests built in this way are left unattended, the nests may come into contact with surrounding power equipment and cause serious accidents such as short circuits.

The nest response work support device of this embodiment therefore performs a predetermined machine learning process using images of bird nests and nearby power equipment taken in the past by workers or the like (hereinafter also referred to as actual images) as training data to create a trained model (hereinafter referred to as a nest risk assessment model) that outputs information on measures to be taken against nests. After that, when an image (hereinafter referred to as an on-site image) taken by a worker or the like who has newly discovered nesting on a utility pole or other power equipment is input into the nest risk assessment model, the nest response work support device outputs and displays on the screen information on the measures that the worker or the like should take against the nest and the timing of such measures.

(Actual image)
1 is a diagram showing an example of an actual image 50. This actual image 50 is an image of a utility pole 51 erected from the ground (not shown) taken at a predetermined elevation angle from a position a predetermined distance away.

Specifically, utility pole 51 comprises a first cross arm 54 extending horizontally from a predetermined height on support 52 and having a switch 53 attached to its tip, and a second cross arm 56 extending horizontally from a predetermined height on support 52 above first cross arm 54. Multiple high-voltage electrical wires 55 are strung across second cross arm 56. Bird nests 59 are made using metal members (not shown) such as wire or hangers carried by birds from elsewhere, using first cross arm 54, second cross arm 56, or high-voltage electrical wires 55 as footholds. If such nests 59 grow large, they may come into contact with power equipment near first cross arm 54 or second cross arm 56, such as switch 53 or high-voltage electrical wires 55.

Figure 2 shows another example of an actual image. Similar to actual image 50 described above, actual image 70 is an image of a utility pole 71 erected from the ground (not shown) taken at a predetermined elevation angle from a position a predetermined distance away.

Specifically, a pole-mounted transformer 73 is mounted on the support 72 of the utility pole 71. The utility pole 71 also has a first cross arm 76 that extends horizontally from a predetermined height on the support 72 and has a high-voltage cutout support arm 75 with a high-voltage cutout 74 fixed to its tip, and a second cross arm 77 that extends horizontally from the support 72 above the first cross arm 76. The tip of the second cross arm 77 is equipped with multiple lightning arresters 78 and high-voltage wires 79. Bird nests 80 are made using metal members (not shown) such as wires or hangers that have been carried by the birds from elsewhere, using the first cross arm 76, second cross arm 77, or high-voltage wires 79 as footholds. If such a nest 80 grows large, it may come into contact with power equipment near the first cross arm 76 and the second cross arm 77, such as the high-voltage cutout 74, the high-voltage cutout support arm 75, the lightning arrester 78, or the high-voltage power line 79.

In this way, as nests 59 and 80 grow and the distance between them and the surrounding power equipment decreases, the likelihood that nests 59 and 80 will come into contact with the power equipment increases.

Furthermore, the growth rate of nests 59, 80 depends on the activity level of the birds, i.e., the time of year (season, month, etc.) when the nests 59, 80 are built. The growth rate of nests 59, 80 also depends on the region in which the birds live. Differences in this region include not only simple differences in location, but also differences in land use, such as whether the area is urban or mountainous.

From the above, it is believed that the urgency of the response, such as removing nests 59 and 80, is correlated not only with the relative positions of nests 59 and 80 and each power facility, but also with the time and region of nesting. Therefore, the nesting response work support device of this embodiment utilizes these correlations to generate a nesting risk assessment model based on actual images.

The nest response support device will be described in detail below.
--Connected phase determination device--
3 is a diagram illustrating an example of functions of the nesting response work support device 1 according to this embodiment. The nesting response work support device 1 is an information processing device (computer) including an image acquisition unit 11, a preprocessing unit 13, a learning unit 15, an urgency determination unit 17, and an information display unit 19.

The nest response work support device 1 also includes a training data DB 200 that stores feature values and correct label data for actual images, and an image DB 300 that stores actual images and on-site images.

The image acquisition unit 11 acquires images (actual images and on-site images) by reading them from a recording medium or receiving them via a communications network.

Based on the training data DB 200, the learning unit 15 inputs images (site images) containing power equipment and bird nests, and creates a trained model (nesting risk assessment model) that outputs information on the response content and timing of the response to the bird nest (hereinafter referred to as urgency).

The trained model may also be a model to which information about the region and time when the site images were taken is further input. The nest risk assessment model of this embodiment is a model to which information about the region and time is further input.

This trained model is constructed by machine learning the features of on-site images using deep learning. In this embodiment, this trained model is a neural network that has an input layer to which image pixel information (pixel content and coordinates) is input, one or more intermediate layers (hidden layers) that extract and output image features from the pixel information, and an output layer that outputs a level of urgency from the image features.

Note that, although it is possible to apply, for example, a CNN (Convolution Neural Network), an SVM (Support Vector Machine), a Bayesian network, or a regression tree as the neural network in the trained model of this embodiment, this embodiment assumes the use of a CNN, which is a method suitable for image recognition.

The urgency determination unit 17 inputs the on-site images acquired by the image acquisition unit 11 into the trained model, and outputs information on the response content and response timing corresponding to the nest contained in the on-site images.

The information display unit 19 displays the information output by the urgency determination unit 17 on the screen.

(Teacher data DB)
4 is a diagram illustrating an example of the teacher data DB 200. The teacher data DB 200 stores actual images and related information. The teacher data DB 200 includes the following data items: utility pole ID 201, which sets the ID of the utility pole in each image; image capture date/time 202, which sets the date and time each image was captured; image data 203, which sets information indicating the data file in which each image was recorded; image capture location 204, which sets the location where each image was captured; nest area 205, which sets area information for the nest in each image (information about the content and location of each pixel in the image); power equipment area 206, which sets area information for the power equipment in each image (information about the content and location of each pixel in the image); power equipment type 207, which indicates the type of power equipment in each image; and urgency 208 for nest removal in each image.

In the power equipment type 207, information on the type of power equipment, such as each switch, lightning arrester, transformer, high-voltage cutout, high-voltage cross arm, and high-voltage cutout support arm, is set, and in the power equipment area 206, area information in the actual image is set.

In this embodiment, the urgency level 208 is set to one of the following: information indicating that the nest needs to be removed immediately, information indicating that the nest needs to be removed within three days, information indicating that the nest needs to be removed within one week, or information indicating that the nest does not need to be removed. Note that the number of days is not intended to be limited to this value.

Each data item in the training data DB 200 is set, for example, by a worker involved in nest monitoring or removal, while referring to the actual image. The shooting date and time 202 and shooting location 204 may be extracted from the metadata of the actual image, or may be set by the worker who took the image.

Note that the data items in the training data DB 200 shown in Figure 4 are examples. In addition to these data items, information that influences the response to nesting and the timing of that response (for example, bird species) may also be set as a feature of the nesting risk assessment model.

Next, Figure 5 is a diagram showing an example of the hardware of the nesting response work support device 1. The nesting response work support device 1 includes a processor 31, a main memory device 32, an auxiliary memory device 33, an input device 34, an output device 35, and a communication device 36.

The processor 31 is configured using, for example, a CPU (Central Processing Unit), MPU (Micro Processing Unit), GPU (Graphics Processing Unit), FPGA (Field Programmable Gate Array), ASIC (Application Specific Integrated Circuit), AI (Artificial Intelligence) chip, etc.

The main memory device 32 is a device that stores programs and data, and is, for example, ROM (Read Only Memory), RAM (Random Access Memory), or non-volatile memory (NVRAM (Non Volatile RAM)).

The auxiliary storage device 33 may be, for example, an SSD (Solid State Drive), a hard disk drive, an optical storage device (CD (Compact Disc), DVD (Digital Versatile Disc), etc.), a storage system, an IC card, a read/write device for recording media such as an SD card or optical recording media, or a storage area of a cloud server. Programs and data can be read into the auxiliary storage device 33 via a recording media reader or communication device 36. Programs and data stored in the auxiliary storage device 33 are read into the main storage device 32 as needed.

The input device 34 is an interface that accepts input from outside, and may be, for example, a keyboard, mouse, touch panel, card reader, pen-input tablet, or voice input device.

The output device 35 is an interface that outputs various information such as processing progress and results. The output device 35 is, for example, a display device (liquid crystal monitor, LCD (Liquid Crystal Display), graphics card, etc.) that visualizes the various information described above, a device that converts the various information described above into audio (audio output device (speaker, etc.)), or a device that converts the various information described above into text (printer, etc.). Note that, for example, the nest response work support device 1 may be configured to input and output information to and from other devices via the communication device 36.

The input device 34 and output device 35 form a user interface that accepts and presents information.

The communication device 36 is a device that enables communication with other devices. The communication device 36 is a wired or wireless communication interface that enables communication with other devices via a communication network such as the Internet, and is, for example, a NIC (Network Interface Card), a wireless communication module, a USB module, etc.

The nesting response work support device 1 may be realized, in whole or in part, using virtual information processing resources provided using virtualization technology, process space separation technology, etc., such as a virtual server provided by a cloud system. Also, all or part of the functions provided by the nesting response work support device 1 may be realized, for example, by a service provided by the cloud system via an API (Application Programming Interface), etc. Furthermore, the nesting response work support device 1 may be configured as a system including multiple information processing devices connected to each other so that they can communicate with each other.

The above-mentioned functions of the nesting response work support device 1 are realized by the processor 31 of the nesting response work support device 1 reading and executing a program stored in the main memory device 32, or by the hardware (FPGA, ASIC, AI chip, etc.) that constitutes the nesting response work support device 1. The nesting response work support device 1 stores the above-mentioned various information (data), for example, as a database table or a file managed by a file system.
Next, the processing performed by the nest response work support device 1 will be described.

--Model learning process--
6 is a flow diagram illustrating an example of a model learning process performed by the nesting response work support device 1. The model learning process is started, for example, when a predetermined input is made to the nesting response work support device 1 by a worker or the like, or at a predetermined timing (a predetermined time or a predetermined time interval).

First, the learning unit 15 acquires information about the performance image and its corresponding correct label (s11). Specifically, the learning unit 15 acquires data for each record in the training data DB 200.

Then, the learning unit 15 performs preprocessing on the data acquired in s11 to generate features for the nest risk assessment model (s13).

Specifically, for example, the learning unit 15 generates information about the time when the actual image was captured (e.g., seasonal information) based on the capture date and time 202 in the training data DB 200.

Furthermore, for example, the learning unit 15 generates information on the area where the actual image was taken (for example, this may be an area based on an administrative district, or information on geographical characteristics that characterize the activity patterns of birds, such as residential areas or mountainous areas) based on the shooting location 204 in the teacher data DB 200 and predetermined map data.

The learning unit 15 may also enlarge or reduce each actual image so that identical objects in all actual images are composed of the same number of image elements.

The learning unit 15 may also adjust each actual image by performing a projective transformation or the like to make it appear as if it were captured from a predetermined direction, or may correct the color tone, etc., of each actual image.

The learning unit 15 creates a nest risk assessment model by performing machine learning on the actual image acquired in s11, the feature values calculated in s13, and the correct label (s15).

For example, the learning unit 15 inputs the image data 203 in the training data DB 200 acquired in s11 and the seasonal and regional information calculated in s13 into the nesting risk assessment model, thereby acquiring output values and intermediate values corresponding to the actual image. Note that when this process is executed for the first time, initial values are set in advance for the hyperparameters (described next) of the nesting risk assessment model.

The learning unit 15 optimizes the hyperparameters in the nest risk assessment model so that each acquired output value approaches the nest area 205, power equipment area 206, power equipment type 207, and urgency level 208 registered in the training data DB 200 (s17). For example, the learning unit 15 adjusts hyperparameters such as weights between units (neurons) or coefficients in activation functions using a learning method such as backpropagation. This completes the model creation process.

The output value of the nesting risk assessment model may include information on the degree of urgency as well as information on its accuracy. In this case, the nesting risk assessment model can output one or more combinations of information on the degree of urgency and information on its accuracy.

The learning unit 15 may configure the nest risk assessment model using two trained models. For example, first, the learning unit 15 generates a first trained model that receives an actual image and outputs information on the nest area, power equipment type, and power equipment area in the actual image. That is, the learning unit 15 inputs the image data 203 in the teacher data DB 200 acquired in s11 into the first trained model, thereby acquiring each output value corresponding to the actual image. The learning unit 15 optimizes the hyperparameters in the nest risk assessment model so that each acquired output value approaches the nest area 205, power equipment area 206, and power equipment type 207 in the teacher data DB 200, respectively.

Second, the learning unit 15 generates a second trained model that receives the nest area, type of power equipment, area of the power equipment, season, and region as input and outputs urgency information. That is, the learning unit 15 inputs the information on the nest area, type of power equipment, and area of the power equipment output by the first trained model into the second trained model to obtain each output value. The learning unit 15 optimizes the hyperparameters in the second trained model so that each obtained output value approaches the urgency 208 in the training data DB 200.

In this way, by constructing a nest risk assessment model using two trained models, it is possible to accurately recognize the areas of nests and each piece of power equipment and estimate the level of urgency. This makes it possible to accurately determine the response that should be taken in response to nesting and the timing of that response.

- Urgency determination process -
7 is a flow diagram illustrating an example of the urgency determination process. The urgency determination process is started, for example, when a predetermined input is made to the nest response work support device 1 by a worker or at a predetermined timing (a predetermined time or a predetermined time interval).

The image acquisition unit 11 acquires an image (site image) including the nest and utility pole for which the urgency level is to be determined (s31). For example, the image acquisition unit 11 reads an image taken by a camera or terminal held by a worker or the like via a recording medium or a communications network such as the Internet or a LAN (Local Area Network). The image acquisition unit 11 may also read an image taken by a surveillance camera installed on or near a utility pole.

The urgency determination unit 17 also receives input from the worker or the like regarding information on the season and region in which the site image was taken (s33). For example, the urgency determination unit 17 receives input from the worker or the like regarding the season and region while displaying the site image acquired in s31 on a screen. Note that the urgency determination unit 17 may generate information on the season and region in which the site image was taken from the time information or location information included in the site image data using processing similar to s13.

The urgency assessment unit 17 inputs the on-site image acquired in s31 and the seasonal and regional information input in s33 into the nesting risk assessment model, thereby acquiring urgency information output from the nesting risk assessment model (s35). The urgency assessment unit 17 then displays the urgency information on the urgency assessment screen described below (s37). This completes the urgency assessment process.

<Urgency determination screen>
8 is an example of the urgency determination screen 500. The urgency determination screen 500 includes a display field 501 for an on-site image, a display field 503 for the season in which the on-site image was taken, a display field 505 for the region in which the on-site image was taken, and a display field 507 for the urgency determined by the nest risk determination model.

By referring to this urgency assessment screen 500, users can determine what action should be taken and when in response to the nesting spot captured in the on-site image.

As described above, the nest response work support device 1 of this embodiment receives images (actual images) containing power equipment and bird nests, and outputs information on the response content and timing for the bird nests by inputting images (site images) into a trained model (nesting risk assessment model). This outputs information (urgency) on the response content and timing for the nests contained in the site images. Therefore, workers who monitor or remove nests can learn the specific response and timing to take in response to the nests simply by preparing site images of nests. In other words, the nest response work support device 1 of this embodiment can provide information on the response and timing to take in response to nests built in power equipment.

In addition, the nesting response work support device 1 of this embodiment outputs information on the response content and timing for nests contained in the site images by inputting information on the area and time when the site images were taken into a trained model (nesting risk assessment model) that receives information on the area and time when the images were taken. Since bird nesting rates can vary by region or season, by inputting the area and time when the site images were taken as feature quantities for the nesting risk assessment model, workers and others can accurately know the specific response that should be taken in response to nesting.

In addition, by inputting site images into the nest risk assessment model, the nest response work support device 1 of this embodiment outputs either information indicating that the nest contained in the site image needs to be removed immediately, information indicating that the nest needs to be removed by a specified time in the future, or information indicating that the nest does not need to be removed. This allows workers and others to know more specific measures that should be taken in response to nest formation.

In addition, the nest response work support device 1 of this embodiment displays information on the response content and timing for the nest contained in the on-site image on the screen, allowing workers and others to quickly understand the response that should be taken in response to the nest.

The nest response work support device 1 of this embodiment also outputs response and timing information using a trained model (nesting risk determination model) consisting of a first trained model and a second trained model. That is, the nest response work support device 1 of this embodiment receives an image including a power facility and a bird's nest and outputs information about the bird's nest and power facility areas in the image. By inputting a site image into the first trained model, which receives an image including the power facility and a bird's nest and outputs information about the bird's nest and power facility areas in the image, the device outputs information about the bird's nest and power facility areas in the site image. Next, the device inputs the information about the bird's nest and power facility areas output by the first trained model into the second trained model, which receives an image of the bird's nest and power facility areas and outputs information about the response content and timing for the bird's nest. In this way, by using the second trained model with the information about the nest and power facility areas recognized by the first trained model as input values, the device can more accurately estimate the response content and timing for the bird's nest.

The above description of the embodiments is intended to facilitate understanding of the present invention and is not intended to limit the present invention. The present invention may be modified or improved without departing from its spirit, and equivalents thereof are also included within the scope of the present invention.

For example, the combination of each functional unit of the nest response operation support device 1 described in this embodiment is one example, and for example, some functional units may be provided in other functional units, or multiple functional units may be combined into a single functional unit.

In addition, in this embodiment, the actual images and on-site images are assumed to be images of nests on utility poles, but they may also be images of nests on steel towers or other power equipment. Furthermore, the equipment related to nesting may also be used as one of the features of the nest risk assessment model.

In addition, in this embodiment, the urgency level, which is the output value of the nest risk assessment model, is set to the presence or absence of nest removal and the time until removal, but other types of responses and timing may also be used as the urgency level. For example, the urgency level may include the strengthening of nest patrols and their duration, specific nest removal methods, etc.

The nest response work support device 1 may also accept requests for site images of the same utility pole from multiple directions and output urgency information for each site image. Because the configuration of power equipment installed on utility poles is complex, the images captured from different directions may vary significantly. Therefore, by outputting urgency information based on site images from multiple directions, workers and others can more accurately determine the level of urgency.

1 Nest response operation support device, 11 Image acquisition unit, 13 Preprocessing unit, 15 Learning unit, 17 Urgency determination unit, 19 Information display unit

Claims (6)

a memory for storing an image including the bird's nest and the power facility; and
an image acquisition process for acquiring the image;
a processor that executes an urgency determination process that inputs an image including an area of an electric power facility where birds are using the electric power facility as a foothold, an area of the bird's nest located on the electric power facility, and an area of other electric power facility near the electric power facility, and outputs information about the action content and timing of the action to be taken with respect to the bird's nest by inputting the acquired image into a trained model that outputs information about the action content and timing of the action to be taken with respect to the bird's nest.
A nest response support device equipped with the above. The trained model is a model to which information on the area and time when the image was taken is further input,
In the urgency determination process , the processor further inputs information about the area where the acquired image was taken and the time when the image was taken into the trained model, and outputs information about the response content and response timing for the nest included in the acquired image.
The nesting response work support device according to claim 1. The trained model is a model that outputs, as information on the response content and response timing for the bird's nest, any of information indicating that the nest needs to be immediately removed, information indicating that the nest needs to be removed by a predetermined time in the future, or information indicating that the nest does not need to be removed,
In the urgency determination process , the processor inputs the acquired image into the trained model, and outputs, as information on the response content and response timing, any one of information indicating that the nest included in the acquired image needs to be immediately removed, information indicating that the nest needs to be removed by a predetermined time in the future, and information indicating that the nest does not need to be removed.
The nesting response work support device according to claim 1. The nest response work support device according to claim 1 , wherein the processor executes a display process for displaying information on the content and timing of response to the nest contained in the acquired image on a screen. In the urgency determination process , the processor
an image including an area of an electric power facility where birds are using the facility as a foothold, an area of the bird's nest located on the electric power facility, and areas of other electric power facilities in the vicinity of the electric power facility is input, and information on the area of the bird's nest and the area of each piece of electric power facility in the image is output by inputting the acquired image into a first trained model;
information on bird nest areas and areas of each piece of power equipment is input to a second trained model, which outputs information on the response content and response timing for the bird nests, and by inputting the output information on bird nest areas and areas of each piece of power equipment into the second trained model, information on the response content and response timing for the nests included in the acquired image is output.
The nesting response work support device according to claim 1. A method for supporting nest response operations using an information processing device including a memory for storing an image including a bird's nest and an electric power facility, and a processor, the processor comprising :
an image acquisition process for acquiring the image;
an urgency determination process in which an image including the area of the power equipment where the birds are using it as a foothold, the area of the bird's nest located on the power equipment, and the area of other power equipment in the vicinity of the power equipment is input, and information regarding the response content and timing of the response to the bird's nest is output by inputting the acquired image into a trained model;
A method for supporting nest response operations.