JP7460042B1 - Disaster occurrence prediction system and disaster occurrence prediction method - Google Patents

Abstract

To efficiently and at low cost realize a mechanism for predicting the occurrence of disasters using infrasound. The disaster occurrence prediction system includes an optical analysis unit and an information processing device, and acquires, by distributed acoustic sensing (DAS), the time change in vibration intensity for each frequency of an optical fiber at a measurement point set along an optical fiber attached to a power transmission line, monitors in real time whether or not there is a feature caused by infrasound in the time change in vibration intensity for each frequency at the measurement point, and outputs information predicting the occurrence of a disaster when the above-mentioned feature is detected.

Description

The present invention relates to a disaster occurrence prediction system and a disaster occurrence prediction method.

Patent Document 1 discloses that backward Rayleigh scattered light is acquired from an OPGW (optical fiber composite overhead ground wire), and the natural frequency of the optical fiber composite overhead ground wire is determined based on the acquired backward Rayleigh scattered light. This document describes an abnormality detection device that generates vibration information in a frequency range including the above, and detects an abnormality in power transmission equipment based on the generated vibration information.

Patent Document 2 describes a tsunami detection device that acquires information regarding tsunamis using infrasound (infrasound, micropressure waves). The tsunami detection device determines whether the earthquake detected by the earthquake sensor is an earthquake of a predetermined size or more, measures infrasound, and when an earthquake of a predetermined size or more is detected. The presence or absence of a tsunami is determined based on the magnitude of the sound pressure change of the measured infrasound.

Non-Patent Document 1 describes micropressure vibration data that has been experimentally observed in "Research on early detection of tsunamis using observation results of micropressure vibrations."

Japanese Patent Application Publication No. 2023-50257 JP 2016-8865 A

"Infrasound Monitoring Network", Japan Weather Association, [online], Internet <URL: https://micos-sc.jwa.or.jp/infrasound-net/observed/>, Reiwa 5 Searched on November 1st

Infrasound is generated by large-scale physical phenomena that cause disasters, such as earthquakes, tsunamis, typhoons, volcanic eruptions, and meteorite falls, and has the property of propagating long distances in the atmosphere. Therefore, it is expected to be used as a means of remote sensing to obtain information for disaster prevention from a remote location. Further, as described in Patent Document 2 and Non-Patent Document 1, development and research on disaster prevention systems using infrasound are progressing.

By the way, when trying to realize a disaster prevention system using infrasound using the mechanism described in Patent Document 2 and Non-Patent Document 1, it is necessary to prepare a large number of expensive tsunami detection devices equipped with seismometers as earthquake detection units. There is. It is also necessary to secure an installation location that can supply power to keep the tsunami detection device in constant operation. Furthermore, installing and maintaining tsunami detection equipment requires a great deal of labor and cost.

The present invention has been made in consideration of the above background, and aims to provide a disaster occurrence prediction system and a disaster occurrence prediction method that can efficiently and low-costly realize a disaster occurrence prediction mechanism using infrasound.

One of the means to solve the above problems is a disaster prediction system that utilizes power transmission equipment that is widely and widely maintained. The time change of the vibration intensity for each frequency of the optical fiber at measurement points set along the optical fiber attached along the optical fiber is acquired by DAS (Distributed Acoustic Sensing), and the vibration intensity for each frequency at the measurement point is obtained. The system monitors in real time whether or not there is an aspect caused by infrasound in the temporal change of , and outputs information predicting the occurrence of a disaster when the aspect is detected.

Other problems disclosed in the present application and methods for solving the problems will be made clear by the detailed description section and the drawings.

According to the present invention, a disaster occurrence prediction system using infrasound can be realized efficiently and at low cost.

FIG. 1 is a diagram showing a schematic configuration of a disaster occurrence prediction system. FIG. 2 is a diagram illustrating a mechanism for measuring a vibration state by a DAS. It is a graph showing the time change for each frequency of the vibration state of each measurement point. FIG. 1 is a diagram showing the main configuration of a disaster occurrence prediction device. FIG. 2 is a diagram showing the main functions of the disaster prediction device. FIG. 2 is a diagram showing the main configuration of an alarm device. It is a figure showing the main functions with which an alarm device is provided. 11 is a flowchart illustrating a disaster occurrence prediction process. It is a flowchart explaining disaster occurrence prediction information output processing. 13 is an example of a disaster occurrence prediction information output screen.

DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described below based on one embodiment thereof with reference to the accompanying drawings. From the description of this specification and the attached drawings, at least the following matters will become clear. In the following explanation, the letter "S" added before the reference numeral means a processing step.

FIG. 1 shows a schematic configuration of a disaster occurrence prediction system 1 described as an embodiment of the present invention. The disaster occurrence prediction system 1 includes a disaster occurrence prediction device 100 provided at a substation 6 or the like, and one or more warning devices 300.

The disaster occurrence prediction device 100 is configured using an information processing device (computer). The disaster prediction device 100 is communicably connected to the warning device 300 via the communication network 5. Examples of the communication network include a LAN (Local Area Network), a WAN (Wide Area Network), the Internet, a PLC (Power Line Communication), a public communication network, and a dedicated line.

The disaster occurrence prediction device 100 uses the optical fiber 4a of the OPGW4 (OPTICAL fiber composite overhead ground wire) installed on the power transmission line 3 as a vibration detection sensor, and acquires the vibration state at each measurement point by a technique (distributed multipoint vibration measurement method (hereinafter referred to as "DAS" (Distributed Acoustic Sensing)) that measures the vibration state (vibration intensity, vibration frequency) based on the expansion and contraction of the optical fiber 4a at each of a number of measurement positions (hereinafter referred to as "measurement points") set along the optical fiber 4a. In DAS, the vibration state at each measurement point is acquired, for example, using the principle of C-OTDR (Coherent detection Optical Time Domain Reflectometer).

Figure 2 is a diagram explaining the mechanism by which the disaster occurrence prediction device 100 measures the vibration state of each measurement point by the DAS. As shown in the figure, the disaster occurrence prediction device 100 inputs a light pulse (laser pulse, hereinafter also referred to as "incident light") from the end face of the optical fiber 4a, and measures the change speed (≒ stretching frequency) of the phase difference of the backscattered light of the light pulse at each measurement point. The phase difference is estimated from the intensity change due to the interference between the backscattered lights. Then, the disaster occurrence prediction device 100 calculates the vibration frequency (for example, vibration frequency in the range of up to 10 kHz) of the longitudinal wave and transverse wave of the optical fiber 4a at each measurement point based on the measured change speed. The disaster occurrence prediction device 100 also calculates the vibration intensity (spectral intensity, vibration amplitude) at each measurement point based on the phase difference for each vibration frequency. The disaster occurrence prediction device 100 also calculates the position of each measurement point (distance from the end face) based on the elapsed time from the time when the incident light is input to the end face to the time when the return light is received.

The above measurement points are set, for example, at predetermined intervals d (m) along the optical fiber that are shorter than the span of the transmission tower 2 (0 (m), d (m), ..., N (m), N + d (m), N + 2 d (m)). For example, as shown in Figure 1, if the predetermined interval d is 5 (m) and measurement points are set over a maximum range of 70 (km) of the transmission line 3, approximately 14,000 measurement points will be set along the optical fiber.

Note that the vibration state of the optical fiber 4a at each measurement point includes the influence of sound (sound pressure) received by the optical fiber 4a from the outside. That is, the optical fiber 4a between adjacent power transmission towers 2 (spans) is a string with fixed ends of the power transmission towers 2 at both ends, and generates natural vibration by resonating with sound of a specific frequency received from the outside.

The disaster prediction device 100 acquires information about infrasound (infrasound, micro-pressure waves) based on the time change in the vibration state at each measurement point (time change in vibration intensity for each frequency). For example, if the speed of sound is 340 (m/s), and the frequency of infrasound is 1 Hz, to observe infrasound (20 Hz or less), the length of the power line 3 needs to be at least 1/2 the wavelength (= 340 (m/s)/1 (Hz)) (= 170 m). Many existing power lines 3 meet this condition.

The disaster occurrence prediction device 100 generates information predicting the occurrence of a disaster (hereinafter referred to as "disaster occurrence prediction information") based on infrasound observed by the DAS, and transmits the generated disaster occurrence prediction information to the warning device 300.

Returning to FIG. 1, the alarm device 300 is an information processing device (computer) that can communicate with the disaster prediction device 100 via the communication network 5, and includes, for example, a personal computer, a smartphone, a tablet, various server devices, a mainframe, etc. It is. The alarm device 300 is operated by, for example, a facility of an organization that performs disaster monitoring (a disaster prevention center, a facility operated by a municipality, etc.). When the warning device 300 receives disaster prediction information sent from the disaster prediction device 100, it outputs information based on the received disaster prediction information (display on a display device, audio output from an audio output device, etc.). do.

Next, a description will be given of the relationship between infrasound and temporal changes in the vibration state (vibration intensity, vibration frequency) of each measurement point (vibration intensity, vibration frequency) acquired by the disaster occurrence prediction device 100 using DAS (time change in vibration intensity for each frequency).

FIG. 3 shows how the disaster prediction device 100 uses DAS to detect power transmission lines 3 extending from Hiroshima Prefecture to Shimane Prefecture when an earthquake (seismic intensity "4") occurred in the Hyuga-nada Sea at around 21:14 on July 22, 2023. It is a graph showing the time change of the vibration intensity for each frequency of each measurement point that was acquired. The three graphs respectively indicate three adjacent spans (identifiers (hereinafter referred to as "span IDs") of the power transmission line 3 near Hatsukaichi City, Hiroshima Prefecture, which are "K1", "K2", and "K3"). This is a time change in the vibration intensity for each frequency of the optical fiber 4a, measured at measurement points in each span of . Note that in each graph, time flows from the top to the bottom of the page. Furthermore, the shading of colors in the figure represents the vibration intensity (arbitrary unit) for each frequency (the lighter the color, the greater the vibration intensity).

As shown in the figure, vibrations with a frequency of 3 to 4 Hz due to infrasound were observed at around 21:15:30 at all measurement points. In addition, during the same time period, vibrations due to infrasound were observed over a wide frequency band at the measurement points of each span due to the shaking of the earthquake. When the observation results shown in the figure were compared with micro-pressure vibration observation data for the same time period at an observation point in the "Infrasound Monitoring Network" described in Non-Patent Document 1 (an observation point installed at the Aki City Fire and Disaster Prevention Center (Aki City, Kochi Prefecture). Hatsukaichi City, Hiroshima Prefecture and Aki City, Kochi Prefecture are located approximately equidistant from the Hyuga-Nada Sea), it was confirmed that the standard deviation of atmospheric pressure fluctuations increased for about 10 seconds during the same time period, and that the standard deviation of atmospheric pressure fluctuations peaked at 21:15:33.

Here, the transmission speed of infrasound is almost the same as the speed of sound, so when an earthquake occurs, it is observed by the DAS before the tsunami arrives. Therefore, when infrasound is observed by the DAS, it is possible to transmit this fact and information based on the observed infrasound (such as the predicted arrival of a tsunami, the predicted time of the tsunami's arrival, and the scale of the tsunami (wave height, etc.)) to the warning device 300 installed in the area where a disaster is predicted to occur in advance as disaster occurrence prediction information.

FIG. 4A is a diagram showing the main configuration of the disaster occurrence prediction device 100. As shown in the figure, the disaster occurrence prediction device 100 includes a processor 101, a main storage device 102 (memory), an auxiliary storage device 103 (external storage device), an input device 104, an output device 105, a communication device 106, and an optical analysis device. A unit 107 is provided. These are communicably connected via a bus, communication cable, or the like. Note that the disaster occurrence prediction device 100 may be realized, in whole or in part, using virtual information processing resources, such as a virtual server provided by a cloud system, for example.

The processor 101 is, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), a GPU (Graphics Processing Unit), an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), an AI (Artificial Intelligence) chip, etc. It is configured using

The main memory device 102 is a memory device used by the processor 101 when executing a program, and may be, for example, a ROM (Read Only Memory), a RAM (Random Access Memory), or a non-volatile memory (NVRAM (Non Volatile RAM)).

The auxiliary storage device 103 is a device that stores programs and data, and includes, for example, an SSD (Solid State Drive), a hard disk drive, an optical storage device (CD (Compact Disc), DVD (Digital Versatile Disc), etc.). can do. Programs and data can be read into the auxiliary storage device 103 from a non-temporary recording medium or another information processing device equipped with a non-temporary storage device via a recording medium reading device or a communication device 106. . Programs and data stored in the auxiliary storage device 103 are read into the main storage device 102 at any time.

The input device 104 is an interface that accepts input of information from outside, such as a keyboard, mouse, touch panel, voice input device, etc.

The output device 105 is an interface that outputs various information such as processing progress and processing results to the outside. The output device 105 is, for example, a display device that visualizes the above various information (liquid crystal monitor, LCD (Liquid Crystal Display), etc.), a device that converts the above various information into audio (sound output device (speaker, etc.)), It is a device (printing device, etc.) that converts various information into characters. Note that, for example, a configuration may be adopted in which the information processing device 10 inputs and outputs information to and from another device via the communication device 106.

The input device 104 and the output device 105 constitute a user interface that realizes interactive processing with the user (accepting information, providing information, etc.).

The communication device 106 is a device that realizes communication with other devices via a communication network 5 (LAN (Local Area Network), WAN (Wide Area Network), the Internet, a public communication network, a dedicated line, etc.). The communication device 106 is a wired or wireless communication interface that realizes communication with other devices via a communication medium, such as a NIC (Network Interface Card), a wireless communication module, a USB module (USB: Universal Serial Bus), etc.

The optical analysis unit 107 is a device that measures the vibration state of the measurement point using a DAS, and includes a vibration measurement device using a C-OTDR and a signal processing circuit. The optical analysis unit 107 includes a CW (continuous wave) laser light source that generates an optical pulse (laser light) to be input to the end face of the optical fiber 4a, an optical pulse generator, an optical amplifier, optical equipment (optical detector, optical interferometer), a signal processing circuit (phase calculation circuit, etc.), etc. The optical analysis unit 107 and the optical fiber 4a are connected, for example, by optically connecting the output part of the laser light source of the optical analysis unit 107 to the connection port (socket) of the core wire of the OPGW installed in the substation. Therefore, the connection does not cause any impact on the power system, such as a power outage.

For example, an operating system, a file system, a DBMS (DataBase Management System) (relational database, NoSQL, etc.), a KVS (Key-Value Store), etc. may be installed in the disaster occurrence prediction device 100.

Various functions of the disaster prediction device 100 can be implemented by the processor 101 reading and executing programs stored in the main storage device 102, or by using hardware (FPGA, ASIC, etc.) that constitutes the disaster prediction device 100. , AI chip, etc.) itself. The disaster occurrence prediction device 100 stores various information (data) as, for example, a database table or a file managed by a file system.

Figure 4B is a block diagram explaining the main functions of the disaster occurrence prediction device 100. As shown in the figure, the disaster occurrence prediction device 100 has the functions of a memory unit 110, a vibration state measurement unit 120, an infrasound detection unit 125, a disaster type identification unit 130, a disaster occurrence prediction information generation unit 132, and a disaster occurrence prediction information transmission unit 135.

Among the above functions, the storage unit 110 stores vibration state for each measurement point 111, infrasound detection information 112, disaster type information 113, disaster occurrence prediction information 114, equipment information 115, and geographic information 116. Of these, the vibration state for each measurement point 111, infrasound detection information 112, disaster type information 113, and disaster occurrence prediction information 114 will be described later. The equipment information 115 includes information regarding the power transmission equipment (correspondence between each measurement point and each span, the type of power transmission equipment, the position (latitude, longitude) of the power transmission equipment and the span, etc.). Further, the geographic information 116 includes information regarding a map and geography of the surrounding area where the power transmission equipment is deployed.

The vibration state measurement unit 120 measures the temporal change in vibration intensity for each frequency at each measurement point in each span using DAS, and manages the measured results as a vibration state 111 for each measurement point. The vibration state for each measurement point 111 includes, for example, information on the graph illustrated in FIG. 3 .

The infrasound detection unit 125 detects infrasound based on the change over time in the vibration intensity for each frequency at each measurement point included in the vibration state for each measurement point 111 during the above-mentioned period. The infrasound detection unit 125 detects infrasound by monitoring in real time whether or not there is an aspect attributable to infrasound in the change over time in the vibration intensity for each frequency at each measurement point. The infrasound detection unit 125 detects infrasound by, for example, performing data analysis (background noise removal, signal identification, etc.) on the graph shown in FIG. 3.

When the infrasound detection unit 125 detects infrasound, it collects information indicating that infrasound has been detected and information based on the aspect caused by infrasound in the temporal change in vibration intensity for each frequency of the measurement point. It is managed as information 112. For example, the infrasound detection unit 125 uses information indicating the date and time when infrasound was detected, and information used as a basis for determining that infrasound has been detected (for example, information obtained from the graph shown in FIG. 3) to It is managed as detection information 112.

For example, the infrasound detection unit 125 determines that infrasound has been detected when a vibration intensity of a predetermined magnitude or more is observed in the infrasound frequency band (20 Hz or less) at each measurement point of a predetermined number or more adjacent spans. Also, for example, the infrasound detection unit 125 determines that infrasound has been detected when vibrations of a predetermined magnitude or more are observed over a wide frequency band at each measurement point of a predetermined number or more adjacent spans. Note that the infrasound detection unit 125 may detect infrasound by applying methods or criteria other than those described above to the time change in vibration intensity for each frequency at each measurement point. Also, multiple methods and criteria may be combined to improve detection accuracy.

Based on the information (for example, information obtained from the graph shown in FIG. 3) used as the basis for determining that the infrasound has been detected when the infrasound detector 125 detects the infrasound, the disaster type identifying unit 130 The type of disaster (earthquake, tsunami, typhoon, volcanic eruption, meteorite fall, etc.) is specified, and the specified disaster type is managed as disaster type information 113.

The disaster occurrence prediction information generation unit 132 generates and manages disaster occurrence prediction information 114 based on the infrasound detection information 112 and the disaster type information 113. The disaster occurrence prediction information 114 includes, for example, information indicating the date and time when infrasound was detected and the span where infrasound was detected, which is obtained from the infrasound detection information 112. The disaster occurrence prediction information 114 also includes, for example, information obtained from the disaster type information 113, such as the type of disaster, information identifying the area where a disaster is predicted to occur, the predicted time when the disaster will occur for each identified area, the predicted scale of the disaster for each identified area, and the area where a disaster is predicted (expected) to occur.

The disaster occurrence prediction information generating unit 132, for example, identifies the position (latitude, longitude) of the span where infrasound was detected from the equipment information 115, and compares the identified position with the geographic information 116 to identify the area where a disaster is predicted to occur. For example, the disaster occurrence prediction information generating unit 132 identifies an area that is within a specified distance from the position of the span where infrasound was detected as an area where a disaster is predicted to occur. Furthermore, the disaster occurrence prediction information generating unit 132 determines the predicted time of disaster occurrence and the scale of the disaster for each area, for example, based on the distance between the identified area and the span where infrasound was detected. Furthermore, the disaster occurrence prediction information generating unit 132 determines the scale based on, for example, vibration intensity obtained from the graph shown in FIG. 3.

The disaster occurrence prediction information transmitting unit 135 shown in the figure transmits the disaster occurrence prediction information 114 generated by the disaster occurrence prediction information generation unit 132 to the warning device 300 via the communication network 5.

FIG. 5A is a diagram showing the main configuration of alarm device 300. Each element shown in the same figure is basically the same as the element with the same name of the disaster occurrence prediction device 100 shown in FIG. 4A, so the explanation will be omitted.

Figure 5B is a block diagram explaining the main functions of the warning device 300. As shown in the figure, the warning device 300 has the functions of a memory unit 310, a disaster occurrence prediction information receiving unit 320, and a disaster occurrence prediction information output unit 325.

Among the above functions, the storage unit 110 stores disaster occurrence prediction information 311.

The disaster occurrence prediction information receiving unit 320 receives the disaster occurrence prediction information 114 sent from the disaster occurrence prediction device 100, and manages the received disaster occurrence prediction information 114 as disaster occurrence prediction information 311.

The disaster occurrence prediction information output unit 325 outputs information based on the disaster occurrence prediction information 311 to an output device (display device, audio output device (speaker), etc.).

Figure 6 is a flowchart explaining the process performed by the disaster prediction device 100 (hereinafter referred to as "disaster prediction process S600"). Disaster prediction process S600 will be explained below with reference to the same figure. Note that, as a premise for the following explanation, it is assumed that the vibration state measurement unit 120 of the disaster prediction device 100 measures the time change (time change of vibration strength for each frequency) of the vibration state (vibration intensity, vibration frequency) of each measurement point of each span in real time using DAS, and manages the latest vibration state of each measurement point as the vibration state for each measurement point 111.

As shown in the figure, the infrasound detection unit 125 monitors in real time the change over time in the vibration intensity for each frequency at each measurement point based on the vibration state for each measurement point 111 (S611 to S612: No). When the infrasound detection unit 125 detects an aspect attributable to infrasound in the change over time in the vibration intensity for each frequency at each measurement point acquired from the vibration state for each measurement point 111 (S612: Yes), it generates and manages infrasound detection information 112 for the detected infrasound (S613).

Next, the disaster type identification unit 130 identifies the type of disaster based on the information used as the basis for determining that the infrasound detection unit 125 has detected infrasound, and manages the identified type of disaster as disaster type information 113 (S614).

Next, the disaster occurrence prediction information generation unit 132 generates and manages disaster occurrence prediction information 114 based on the infrasound detection information 112 and the disaster type information 113 (S615).

Next, the disaster occurrence prediction information transmission unit 135 transmits the disaster occurrence prediction information 114 generated by the disaster occurrence prediction information generation unit 132 to the warning device 300 via the communication network 5 (S616). Then, the process returns to S611.

FIG. 7 is a flowchart illustrating a process performed by the warning device 300 (hereinafter referred to as "disaster occurrence prediction information output process S700"). The disaster occurrence prediction information output process S700 will be described below with reference to the same figure.

As shown in the figure, the disaster occurrence prediction information receiving unit 320 waits in real time to receive the disaster occurrence prediction information 114 sent from the disaster occurrence prediction device 100 (S711: No). When receiving the disaster prediction information 114 (S711: Yes), the disaster prediction information receiving unit 320 manages the received disaster prediction information 114 as the disaster prediction information 311 (S712).

Subsequently, the disaster occurrence prediction information output unit 325 outputs information based on the disaster occurrence prediction information 311 to the output device (S713). After that, the process returns to S711.

Figure 8 is an example of a screen displayed when the disaster occurrence prediction information output unit 325 outputs the above information (hereinafter referred to as the "disaster occurrence prediction information output screen 800").

As shown in the figure, the exemplary disaster occurrence prediction information output screen 800 has a display field 811 for the infrasound detection date and time, a display field 812 for the detection distance, a display field 813 for the type of disaster, a display field 814 for the predicted disaster occurrence area, a display field 815 for the predicted disaster occurrence time, and a display field 816 for the scale of the disaster.

Among these, the infrasound detection date and time display column 811 displays the date and time when the infrasound detection unit 125 detected the infrasound.

In addition, the detection span display field 812 displays the span ID of the span in which infrasound was detected.

Further, the disaster type display field 813 displays the contents of the disaster type information 113 (information indicating the type of disaster).

In addition, the disaster predicted area display field 814 displays information indicating areas where disasters are predicted to occur.

In addition, the predicted time of disaster occurrence display field 815 displays the predicted time of disaster occurrence.

In addition, the disaster scale display field 816 displays the scale of the disaster that will occur.

As explained above, the disaster occurrence prediction system 1 of the present embodiment is able to detect temporal changes in the vibration state (vibration intensity, vibration frequency) of each measurement point acquired by DAS (temporal change in vibration intensity for each frequency). It monitors in real time whether or not aspects caused by infrasound exist, and outputs information that predicts the occurrence of a disaster when the above-mentioned aspects are detected. In this way, the disaster occurrence prediction system 1 uses existing power transmission equipment to quickly provide information for predicting the occurrence of disasters, so it can efficiently realize a disaster prediction system using infrasound at low cost. be able to.

In addition, the disaster occurrence prediction system 1 uses information such as the span where infrasound was observed, the type of disaster, the area where the disaster is expected to occur, the time when the disaster is expected to occur, and the scale of the disaster to predict the occurrence of a disaster. Since the information is output as information 114, useful information can be quickly provided to people in areas where a disaster is expected to occur.

The above embodiment 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 the spirit of the present invention, and equivalents thereof are also included in the present invention.

For example, when the infrasound detection unit 125 inputs as an explanatory variable the feature quantity extracted by performing image recognition processing on the graph (image) shown in FIG. This may be performed using a learned machine learning model.

Further, for example, when the disaster type identification unit 130 inputs the feature amount extracted by performing image recognition processing on the graph (image) shown in FIG. 3 as an explanatory variable, it outputs information indicating the type of disaster as an objective variable. This may be performed using a machine learning model learned as described above.

Further, for example, the disaster occurrence prediction device 100 may take into account information provided via the communication network 5 etc. from other systems (for example, the "Infrasound Monitoring Network" described in Non-Patent Document 1) to predict the occurrence of a disaster. The accuracy of prediction information may be increased.

Further, for example, the information provided by the disaster occurrence prediction system 1 may be linked with earthquake early warning information, warning information, etc. issued by public institutions, etc., and used for identifying events and preventing false alarms. For example, since an earthquake usually occurs when infrasound is observed, it is possible to verify the accuracy (credibility) of identifying events when issuing earthquake early warnings, warning information, etc.

1 Disaster prediction system 2 Power transmission tower 3 Power transmission line 4 OPGW
4a Optical fiber 100 Disaster occurrence prediction device 107 Optical analysis unit 110 Storage unit 111 Vibration state for each measurement point 112 Infrasound detection information 113 Disaster type information 114 Disaster occurrence prediction information 115 Equipment information 116 Geographical information 120 Vibration state measurement unit 125 Infrasound detection Section 130 Disaster type identification section 132 Disaster occurrence prediction information generation section 135 Disaster occurrence prediction information transmission section 300 Warning device 310 Storage section 320 Disaster occurrence prediction information reception section 325 Disaster occurrence prediction information output section S600 Disaster occurrence prediction processing S700 Disaster occurrence prediction information Output processing 800 Disaster occurrence prediction information output screen

Claims (10)

The optical analysis unit and the information processing device are included.
Obtaining a time change in vibration intensity for each frequency of an optical fiber at measurement points set along the optical fiber installed along the power transmission line by a distributed acoustic sensing (DAS);
Monitor in real time whether or not there is a change in the vibration intensity for each frequency at the measurement point over time that is attributable to infrasound;
When the above-mentioned condition is detected, information predicting the occurrence of a disaster is output.
Disaster occurrence prediction system. The disaster occurrence prediction system according to claim 1,
Identifying the type of disaster by performing data analysis on the aspect,
outputting information indicating the identified type of disaster;
Disaster prediction system. The disaster occurrence prediction system according to claim 1,
storing information indicating a correspondence between the measurement points and the spans of the power transmission line;
outputting information indicating a span having the measurement point, the information including a feature attributable to infrasound in the time change of the vibration intensity for each frequency;
Disaster occurrence prediction system. The disaster occurrence prediction system according to claim 1,
storing information indicating a correspondence between the measurement point and a span of the power transmission line, information indicating a position where the span exists, and geographic information around the power transmission line;
identifying a span having the measurement point that includes an aspect caused by infrasound in the temporal change in vibration intensity for each frequency;
outputting information that predicts the occurrence of a disaster in the area surrounding the specified span;
Disaster prediction system. The disaster occurrence prediction system according to claim 4,
determining a time at which the disaster is predicted to occur in the surrounding area based on the specified distance from the span, and outputting information indicating the determined time;
Disaster prediction system. The disaster occurrence prediction system according to claim 4,
determining a scale of the disaster in the surrounding area based on the distance from the identified span, and outputting information indicating the determined scale.
Disaster occurrence prediction system. In a disaster prediction system that includes an optical analysis unit and an information processing device,
The information processing device
A step of acquiring, by DAS (Distributed Acoustic Sensing), a temporal change in vibration intensity for each frequency of the optical fiber at measurement points set along the optical fiber attached along the power transmission line;
monitoring in real time whether or not there is an aspect caused by infrasound in the temporal change in vibration intensity for each frequency at the measurement point, and
outputting information predicting the occurrence of a disaster when the aspect is detected;
A method for predicting the occurrence of disasters. The disaster occurrence prediction method according to claim 7,
The information processing device
identifying the type of disaster by performing data analysis on the aspect; and
outputting information indicating the identified type of disaster;
A method for predicting the occurrence of disasters. The disaster occurrence prediction method according to claim 7,
The information processing device,
storing information indicating a correspondence between the measurement points and the spans of the power transmission line; and
outputting information indicating a span having the measurement point, the information including a feature attributable to infrasound in the time change of the vibration intensity for each frequency;
The disaster occurrence prediction method further comprises: The disaster occurrence prediction method according to claim 7,
storing information indicating a correspondence between the measurement point and a span of the power transmission line, information indicating a position where the span exists, and geographic information around the power transmission line;
identifying a span having the measurement point that includes an aspect caused by infrasound in the temporal change in vibration intensity for each frequency, and
outputting information predicting the occurrence of a disaster in the area surrounding the identified span;
A method for predicting the occurrence of disasters.

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