Disaster occurrence prediction system and disaster occurrence prediction method

Abstract

The present invention achieves, efficiently and at low cost, an arrangement for disaster occurrence prediction using infrasound. This disaster occurrence prediction system is configured to include an optical analysis unit and an information processing device, uses distributed acoustic sensing (DAS) to acquire the change over time in vibration intensity for each frequency of an optical fiber at measurement points set along optical fibers that are provided along a power transmission line, monitors, in real time, whether or not a state caused by infrasound is present in the change over time in vibration intensity for each frequency at the measurement points, and outputs information for prediction of the occurrence of a disaster when the state has been detected.

Description

Disaster occurrence prediction system and disaster occurrence prediction method

[0001] The present invention relates to a disaster occurrence prediction system and a disaster occurrence prediction method.

[0002] Patent Document 1 describes an abnormality detection device that acquires backward Rayleigh scattered light from an OPGW (optical fiber composite overhead ground wire), generates vibration information for a frequency range including the natural frequency of the optical fiber composite overhead ground wire based on the acquired backward Rayleigh scattered light, and detects abnormalities in power transmission equipment based on the generated vibration information.

[0003] Patent Document 2 describes a tsunami detection device that acquires information about tsunamis using infrasound (infrasound, micro-pressure waves). The tsunami detection device determines whether an earthquake detected by an earthquake detection unit is of a predetermined magnitude or greater, measures infrasound, and, if an earthquake of a predetermined magnitude or greater is detected, determines whether a tsunami has occurred based on the magnitude of the change in sound pressure of the measured infrasound.

[0004] Non-Patent Document 1 describes micro-pressure oscillation data that is being experimentally observed in a study titled "Research on early detection of tsunamis using observation results of micro-pressure oscillations."

[0005] JP 2023-50257 A JP 2016-8865 A

[0006] "Infrasound Monitoring Network", Japan Weather Association, [online], Internet <URL: https: / / micos-sc.jwa.or.jp / infrasound-net / observed / >, retrieved November 1, 2023

[0007] 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 through the atmosphere. Therefore, it is expected to be used as a remote sensing tool to obtain disaster prevention information from remote locations. Furthermore, as described in Patent Document 2 and Non-Patent Document 1, research and development are underway on disaster prevention systems that utilize infrasound.

[0008] However, if one were to try to realize a disaster prevention system using infrasound using the mechanisms described in Patent Document 2 and Non-Patent Document 1, it would be necessary to prepare a large number of expensive tsunami detection devices equipped with seismometers as earthquake sensing units. It would also be necessary to secure installation locations where power can be supplied to operate the tsunami detection devices at all times. Furthermore, installing and maintaining the tsunami detection devices requires a great deal of effort and cost.

[0009] 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.

[0010] One means for solving the above problem is a disaster occurrence prediction system that utilizes power transmission facilities that are installed over a wide area, and is composed of an optical analysis unit and an information processing device.The system uses DAS (Distributed Acoustic Sensing) to obtain the time change in vibration intensity for each frequency of an optical fiber at measurement points set along the optical fiber attached to the power transmission line, monitors in real time whether there is any aspect of the time change in vibration intensity for each frequency at the measurement point that is caused by infrasound, and outputs information that predicts the occurrence of a disaster if such aspect is detected.

[0011] Other problems and solutions disclosed in the present application will be made clear in the detailed description and drawings.

[0012] According to the present invention, a disaster occurrence prediction system using infrasound can be realized efficiently and at low cost.

[0013] FIG. 1 is a diagram illustrating a schematic configuration of a disaster occurrence prediction system. FIG. 2 is a diagram illustrating a mechanism for measuring vibration states using a DAS. FIG. 3 is a graph illustrating temporal changes in vibration states at each measurement point for each frequency. FIG. 4 is a diagram illustrating the main configuration of a disaster occurrence prediction device. FIG. 5 is a diagram illustrating the main functions of a disaster occurrence prediction device. FIG. 6 is a diagram illustrating the main configuration of an alarm device. FIG. 7 is a diagram illustrating the main functions of an alarm device. FIG. 8 is a flowchart illustrating disaster occurrence prediction processing. FIG. 9 is a flowchart illustrating disaster occurrence prediction information output processing. FIG. 10 is an example of a disaster occurrence prediction information output screen.

[0014] The present invention will be described below in accordance with one embodiment with reference to the accompanying drawings. At least the following matters will become clear from the description of this specification and the accompanying drawings. In the following description, the letter "S" added before a reference numeral means a processing step.

[0015] 1 shows a schematic configuration of a disaster prediction system 1 according to an embodiment of the present invention. The disaster prediction system 1 includes a disaster prediction device 100 provided in a substation 6 or the like, and one or more alarm devices 300.

[0016] The disaster prediction device 100 is configured using an information processing device (computer). The disaster prediction device 100 is communicably connected to an alarm device 300 via a communication network 5. The communication network may be, for example, a local area network (LAN), a wide area network (WAN), the internet, a power line communication (PLC), a public communication network, a dedicated line, or the like.

[0017] The disaster prediction device 100 uses an optical fiber 4 a of an optical fiber composite overhead ground wire (OPGW) 4 installed on the power transmission line 3 as a vibration detection sensor, and acquires the vibration state at each of a plurality of measurement positions (hereinafter referred to as "measurement points") set along the optical fiber 4 a using a technique (distributed acoustic sensing (DAS)) that measures the vibration state (vibration intensity, vibration frequency) based on the expansion and contraction of the optical fiber 4 a at each of the measurement points. The DAS acquires the vibration state at each measurement point using, for example, the principle of a coherent detection optical time domain reflectometer (C-OTDR).

[0018] FIG. 2 illustrates how the disaster prediction device 100 measures the vibration state at each measurement point using a DAS. As shown in the figure, the disaster prediction device 100 applies a light pulse (laser pulse, hereinafter also referred to as "incident light") to the end face of the optical fiber 4a and measures the rate of change (≒ expansion / contraction 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 interference between the backscattered lights. Based on the measured rate of change, the disaster prediction device 100 then determines the vibration frequency (e.g., vibration frequency in the range of up to 10 kHz) of the longitudinal and transverse waves of the optical fiber 4a at each measurement point. The disaster prediction device 100 also determines the vibration intensity (spectral intensity, vibration amplitude) at each measurement point based on the phase difference for each vibration frequency. The disaster prediction device 100 also determines the position (distance from the end face) of each measurement point based on the elapsed time between when the incident light is incident on the end face and when the returned light is received.

[0019] The measurement points are set, for example, along the optical fiber at predetermined intervals d (m) 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.

[0020] The vibration state of the optical fiber 4a at each measurement point is affected by the sound (sound pressure) received by the optical fiber 4a from outside. That is, the optical fiber 4a between adjacent transmission towers 2 (span) is a string with the transmission towers 2 at both ends as fixed ends, and resonates with the sound of a specific frequency received from outside, generating natural vibrations.

[0021] 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, in order to observe infrasound (20 Hz or less), the length of the power line 3 needs to be at least 1 / 2 (= 170 m) of the wavelength (= 340 (m / s) / 1 (Hz)). Most existing power lines 3 meet this condition.

[0022] 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.

[0023] Returning to Fig. 1 , the warning device 300 is an information processing device (computer) capable of communicating with the disaster prediction device 100 via the communication network 5, and is, for example, a personal computer, a smartphone, a tablet, various server devices, a mainframe, etc. The warning device 300 is operated, for example, by a facility of an organization that monitors disasters (a disaster prevention center, a facility operated by a city, ward, town, or village, etc.). Upon receiving disaster prediction information sent from the disaster prediction device 100, the warning device 300 outputs information based on the received disaster prediction information (display on a display device, audio output from an audio output device, etc.).

[0024] Next, the relationship between infrasound and the time change (time change of vibration intensity for each frequency) of the vibration state (vibration intensity, vibration frequency) of each measurement point acquired by the disaster prediction device 100 using DAS will be described.

[0025] FIG. 3 is a graph showing the time variation of vibration intensity for each frequency at each measurement point on a power transmission line 3 stretching from Hiroshima Prefecture to Shimane Prefecture, acquired by the disaster prediction device 100 using the DAS when an earthquake (seismic intensity "4") occurred in the Hyuga-nada Sea at approximately 9:14 PM on July 22, 2023. The three graphs show the time variation of vibration intensity for each frequency of the optical fiber 4a, measured at measurement points on three adjacent spans (spans with identifiers (hereinafter referred to as "span IDs"): "K1," "K2," and "K3") of the power transmission line 3 near Hatsukaichi City, Hiroshima Prefecture. In each graph, time flows from top to bottom on the page. The shades of color in the figure represent the vibration intensity (arbitrary units) for each frequency (the lighter the color, the greater the vibration intensity).

[0026] As shown in the figure, vibrations with a frequency of 3 to 4 Hz caused by infrasound were observed at all measurement points around 21:15:30. Also, during the same time period, vibrations caused by infrasound were observed across a wide frequency band at measurement points on 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 from 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 approximately 10 seconds during the same time period, and that the standard deviation of atmospheric pressure fluctuations peaked at 21:15:33.

[0027] Here, since the propagation speed of infrasound is almost the same as the speed of sound, 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 that fact and information based on the observed infrasound (such as the predicted arrival of a tsunami, the predicted time of arrival of the tsunami, and the scale of the tsunami (wave height, etc.)) as disaster occurrence prediction information in advance to the warning device 300 installed in the area where the disaster is predicted to occur.

[0028] 4A is a diagram showing the main configuration of the disaster prediction device 100. As shown in the figure, the disaster 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 unit 107. These are communicatively connected via a bus, a communication cable, or the like. Note that all or part of the disaster prediction device 100 may be realized using virtual information processing resources, such as a virtual server provided by a cloud system.

[0029] The processor 101 is configured using, 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.

[0030] The main memory device 102 is a memory device used when the processor 101 executes a program, and is, for example, a read-only memory (ROM), a random access memory (RAM), or a non-volatile memory (NVRAM).

[0031] The auxiliary storage device 103 is a device that stores programs and data, and can be configured, for example, with an SSD (Solid State Drive), a hard disk drive, or an optical storage device (CD (Compact Disc), DVD (Digital Versatile Disc), etc.). Programs and data can be read into the auxiliary storage device 103 from other information processing devices equipped with non-transitory recording media or non-transitory storage devices via a recording medium reading device or a communication device 106. The programs and data stored in the auxiliary storage device 103 are read into the main storage device 102 as needed.

[0032] The input device 104 is an interface that accepts input of information from the outside, and is, for example, a keyboard, a mouse, a touch panel, a voice input device, or the like.

[0033] 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 (liquid crystal monitor, LCD (Liquid Crystal Display) or the like) that visualizes the various information, a device that converts the various information into audio (audio output device (speaker or the like)), or a device that converts the various information into text (printer or the like). Note that, for example, the information processing device 10 may be configured to input and output information to and from other devices via the communication device 106.

[0034] The input device 104 and the output device 105 constitute a user interface that realizes interactive processing with the user (receiving information, providing information, etc.).

[0035] The communication device 106 is a device that realizes communication with other devices via a communication network 5 (such as a local area network (LAN), a wide area network (WAN), the Internet, a public communication network, or a dedicated line). The communication device 106 is a wired or wireless communication interface that realizes communication with other devices via a communication medium, and is, for example, a network interface card (NIC), a wireless communication module, or a universal serial bus (USB) module.

[0036] The optical analyzing unit 107 is a device that measures the vibration state of a measurement point using a DAS and includes a vibration measuring device using a C-OTDR and a signal processing circuit. The optical analyzing 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 devices (photodetector, optical interferometer), a signal processing circuit (phase calculation circuit, etc.), etc. The optical analyzing unit 107 and the optical fiber 4a are connected, for example, by optically connecting the output of the laser light source of the optical analyzing unit 107 to a connection port (socket) of the core wire of an OPGW installed in a substation. Therefore, the connection does not cause any impact on the power system, such as a power outage.

[0037] The disaster prediction device 100 may be equipped with, for example, an operating system, a file system, a DBMS (DataBase Management System) (relational database, NoSQL, etc.), a KVS (Key-Value Store), etc.

[0038] The various functions of the disaster prediction device 100 are realized by the processor 101 reading and executing programs stored in the main memory device 102, or by the hardware (FPGA, ASIC, AI chip, etc.) that constitutes the disaster prediction device 100. The disaster prediction device 100 stores various pieces of information (data), for example, as tables in a database or files managed by a file system.

[0039] 4B is a block diagram illustrating the main functions of the disaster prediction device 100. As shown in the figure, the disaster prediction device 100 includes a memory unit 110, a vibration state measurement unit 120, an infrasound detection unit 125, a disaster type identification unit 130, a disaster prediction information generation unit 132, and a disaster prediction information transmission unit 135.

[0040] Of the above functions, the storage unit 110 stores vibration status for each measurement point 111, infrasound detection information 112, disaster type information 113, disaster occurrence prediction information 114, facility information 115, and geographic information 116. Of these, the vibration status for each measurement point 111, infrasound detection information 112, disaster type information 113, and disaster occurrence prediction information 114 will be described later. The facility information 115 includes information related to the power transmission facility (e.g., the correspondence between each measurement point and each span, the type of power transmission facility, and the positions (latitude, longitude) of the power transmission facility and spans). Furthermore, the geographic information 116 includes information related to a map and geography of the surrounding area where the power transmission facility is deployed.

[0041] The vibration state measurement unit 120 uses the DAS to measure the change over time in vibration intensity for each frequency at each measurement point on each span, and manages the measurement results as a vibration state for each measurement point 111. The vibration state for each measurement point 111 includes, for example, information on the graph shown in FIG.

[0042] The infrasound detection unit 125 detects infrasound based on the time change in 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 a feature attributable to infrasound in the time change in 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 .

[0043] When the infrasound detection unit 125 detects infrasound, it manages information indicating that infrasound has been detected and information based on aspects of the change over time in the vibration intensity for each frequency at the measurement point that are attributable to infrasound, as infrasound detection information 112. For example, the infrasound detection unit 125 manages, as infrasound detection information 112, information indicating the date and time when infrasound was detected and information used as the basis for determining that infrasound has been detected (for example, information acquired from the graph shown in FIG. 3 ).

[0044] For example, the infrasound detection unit 125 determines that infrasound has been detected when vibration intensity equal to or greater than a predetermined magnitude is observed in the infrasound frequency band (20 Hz or less) at each measurement point on a predetermined number or more adjacent spans. Furthermore, for example, the infrasound detection unit 125 determines that infrasound has been detected when vibration equal to or greater than a predetermined magnitude is observed across a wide frequency band at each measurement point on 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 temporal change in vibration intensity for each frequency at each measurement point. Furthermore, multiple methods and criteria may be combined to improve detection accuracy.

[0045] When the infrasound detection unit 125 detects infrasound, the disaster type identification unit 130 identifies the type of disaster (earthquake, tsunami, typhoon, volcanic eruption, meteorite fall, etc.) based on information (for example, information obtained from the graph shown in Figure 3) that is the basis for determining that infrasound has been detected, and manages the identified type of disaster as disaster type information 113.

[0046] 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 are acquired from the infrasound detection information 112. The disaster occurrence prediction information 114 also includes, for example, information acquired from the disaster type information 113, such as the type of disaster, information specifying areas where a disaster is predicted to occur, the predicted time when the disaster will occur for each specified area, the predicted scale of the disaster for each specified area, and areas where a disaster is predicted (expected) to occur.

[0047] The disaster occurrence prediction information generation unit 132 identifies, for example, the location (latitude, longitude) of the span where infrasound was detected from the facility information 115, and compares the identified location with the geographic information 116 to identify an area where a disaster is predicted to occur. For example, the disaster occurrence prediction information generation unit 132 identifies an area located within a predetermined distance from the location of the span where infrasound was detected as an area where a disaster is predicted to occur. Furthermore, the disaster occurrence prediction information generation 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 generation unit 132 determines the scale based on, for example, vibration intensity obtained from the graph shown in FIG. 3.

[0048] The disaster occurrence prediction information transmission unit 135 shown in the same figure transmits the disaster occurrence prediction information 114 generated by the disaster occurrence prediction information generation unit 132 to the alarm device 300 via the communication network 5.

[0049] Fig. 5A is a diagram showing the main configuration of the warning device 300. Each element shown in the figure is basically the same as the element with the same name in the disaster prediction device 100 shown in Fig. 4A, so a description thereof will be omitted.

[0050] 5B is a block diagram illustrating the main functions of the warning device 300. As shown in the figure, the warning device 300 has the functions of a storage unit 310, a disaster occurrence prediction information receiving unit 320, and a disaster occurrence prediction information output unit 325.

[0051] Of the above functions, the storage unit 110 stores disaster occurrence prediction information 311 .

[0052] The disaster prediction information receiving unit 320 receives the disaster prediction information 114 sent from the disaster prediction device 100 and manages the received disaster prediction information 114 as disaster prediction information 311 .

[0053] 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.).

[0054] 6 is a flowchart illustrating the process performed by the disaster prediction device 100 (hereinafter referred to as "disaster prediction process S600"). The disaster prediction process S600 will be described below with reference to FIG. It should be noted that the following description is premised on the assumption that the vibration state measurement unit 120 of the disaster prediction device 100 measures the time variation (time variation of vibration strength for each frequency) of the vibration state (vibration intensity, vibration frequency) at each measurement point on each span in real time using a DAS, and manages the latest vibration state of each measurement point as the vibration state for each measurement point 111.

[0055] 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).

[0056] Next, the disaster type identification unit 130 identifies the type of disaster based on the information that was 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).

[0057] Next, the disaster occurrence prediction information generating unit 132 generates and manages disaster occurrence prediction information 114 based on the infrasound detection information 112 and the disaster type information 113 (S615).

[0058] Next, the disaster prediction information transmitting unit 135 transmits the disaster prediction information 114 generated by the disaster prediction information generating unit 132 to the alarm device 300 via the communication network 5 (S616). Then, the process returns to S611.

[0059] 7 is a flowchart for explaining the process (hereinafter referred to as "disaster occurrence prediction information output process S700") performed by the warning device 300. The disaster occurrence prediction information output process S700 will be explained below with reference to this figure.

[0060] As shown in the figure, the disaster prediction information receiving unit 320 waits in real time to receive the disaster prediction information 114 sent from the disaster prediction device 100 (S711: No). Upon receiving the disaster prediction information 114 (S711: Yes), the disaster prediction information receiving unit 320 manages the received disaster prediction information 114 as disaster prediction information 311 (S712).

[0061] Next, 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.

[0062] FIG. 8 shows an example of a screen (hereinafter referred to as a "disaster occurrence prediction information output screen 800") that is displayed when the disaster occurrence prediction information output unit 325 outputs the above information.

[0063] As shown in the figure, the illustrated 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 disaster type, 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 disaster scale.

[0064] Among these, the infrasound detection date and time display field 811 displays the date and time when the infrasound detection unit 125 detected infrasound.

[0065] In addition, the detection span display field 812 displays the span ID of the span where infrasound was detected.

[0066] In addition, the disaster type display field 813 displays the contents of the disaster type information 113 (information indicating the type of disaster).

[0067] Further, the display field 814 for predicted disaster areas displays information indicating areas where disasters are predicted to occur.

[0068] Further, the predicted time of occurrence of a disaster is displayed in a display field 815 for the predicted time of occurrence of a disaster.

[0069] Furthermore, the disaster scale display field 816 displays the scale of the disaster that will occur.

[0070] As described above, the disaster prediction system 1 of this embodiment monitors in real time whether or not there is a phenomenon attributable to infrasound in the time change (time change of vibration intensity for each frequency) of the vibration state (vibration intensity, vibration frequency) at each measurement point acquired by the DAS, and outputs information predicting the occurrence of a disaster if the above phenomenon is detected. In this way, the disaster prediction system 1 quickly provides information predicting the occurrence of a disaster using existing power transmission facilities, making it possible to efficiently and low-costly realize a disaster prediction mechanism using infrasound.

[0071] Furthermore, the disaster occurrence prediction system 1 outputs information such as the span where infrasound was observed, the type of disaster, the area where the disaster is predicted to occur, the time when the disaster is predicted to occur, and the scale of the disaster as disaster occurrence prediction information 114, thereby enabling useful information to be quickly provided to people in areas where a disaster is predicted to occur.

[0072] The above-described embodiments are provided to facilitate understanding of the present invention and are not intended to limit the present invention. The present invention may be modified or improved without departing from the spirit thereof, and equivalents thereof are also included in the present invention.

[0073] For example, when the infrasound detection unit 125 inputs the feature quantities extracted by performing image recognition processing on the graph (image) shown in Figure 3 as explanatory variables, it may use a machine learning model that has been trained to output whether or not infrasound has been detected as a target variable.

[0074] For example, when the disaster type identification unit 130 inputs the feature quantities extracted by performing image recognition processing on the graph (image) shown in Figure 3 as explanatory variables, it may use a machine learning model that has been trained to output information indicating the type of disaster as a target variable.

[0075] For example, the disaster occurrence prediction device 100 may take into account information provided from other systems (for example, the "infrasound monitoring network" described in non-patent document 1) via a communication network 5 or the like to improve the accuracy of the disaster occurrence prediction information.

[0076] Furthermore, for example, the information provided by the disaster prediction system 1 may be linked with earthquake early warnings, warning information, etc. issued by public institutions, etc., and used to identify events and prevent 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.

[0077] REFERENCE SIGNS LIST 1 Disaster occurrence 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 Memory unit 111 Vibration state at each measurement point 112 Infrasound detection information 113 Disaster type information 114 Disaster occurrence prediction information 115 Facility information 116 Geographical information 120 Vibration state measurement unit 125 Infrasound detection unit 130 Disaster type identification unit 132 Disaster occurrence prediction information generation unit 135 Disaster occurrence prediction information transmission unit 300 Alarm device 310 Memory unit 320 Disaster occurrence prediction information reception unit 325 Disaster occurrence prediction information output unit S600 Disaster occurrence prediction processing S700 Disaster occurrence prediction information output processing 800 Disaster occurrence prediction information output screen

Claims

1. A disaster occurrence prediction system comprising an optical analysis unit and an information processing device, which acquires, by DAS (Distributed Acoustic Sensing), the time change of the vibration intensity for each frequency of the optical fiber at a measurement point set along an optical fiber attached along a transmission line, monitors in real time whether there is a mode caused by infrasound in the time change of the vibration intensity for each frequency of the measurement point, and outputs information for predicting the occurrence of a disaster when the mode is detected.

2. The disaster occurrence prediction system according to claim 1, which specifies the type of the disaster by performing data analysis on the mode and outputs information indicating the specified type of the disaster.

3. The disaster occurrence prediction system according to claim 1, which stores information indicating the correspondence between the measurement point and the span of the transmission line, and outputs information indicating the span having the measurement point including the mode caused by infrasound in the time change of the vibration intensity for each frequency.

4. The disaster occurrence prediction system according to claim 1, which stores information indicating the correspondence between the measurement point and the span of the transmission line, information indicating the position where the span exists, and geographical information around the transmission line, specifies the span having the measurement point including the mode caused by infrasound in the time change of the vibration intensity for each frequency, and outputs information for predicting the occurrence of a disaster in the area around the specified span.

5. The disaster occurrence prediction system according to claim 4, which obtains the time when the occurrence of the disaster is predicted in the surrounding area based on the distance from the specified span and outputs information indicating the obtained time.

6. The disaster occurrence prediction system according to claim 4, which obtains the scale of the disaster in the surrounding area based on the distance from the specified span and outputs information indicating the obtained scale.

7. In a disaster occurrence prediction system configured to include an optical analysis unit and an information processing apparatus, the information processing apparatus performs the steps of: acquiring, by DAS (Distributed Acoustic Sensing), a time change of vibration intensity for each frequency of the optical fiber at a measurement point set along the optical fiber attached along a power transmission line; monitoring in real time whether there is a mode caused by infrasound in the time change of vibration intensity for each frequency of the measurement point; and outputting information for predicting the occurrence of a disaster when the mode is detected. A disaster occurrence prediction method.

8. The disaster occurrence prediction method according to claim 7, wherein the information processing apparatus further performs the steps of: specifying the type of the disaster by performing data analysis on the mode; and outputting information indicating the specified type of the disaster. A disaster occurrence prediction method.

9. The disaster occurrence prediction method according to claim 7, wherein the information processing apparatus further performs the steps of: storing information indicating the correspondence between the measurement point and the span of the power transmission line; and outputting information indicating the span having the measurement point including the mode caused by infrasound in the time change of vibration intensity for each frequency. A disaster occurrence prediction method.

10. The disaster occurrence prediction method according to claim 7, wherein the information processing apparatus performs the steps of: storing information indicating the correspondence between the measurement point and the span of the power transmission line, information indicating the position where the span exists, and geographical information around the power transmission line; specifying the span having the measurement point including the mode caused by infrasound in the time change of vibration intensity for each frequency; and outputting information for predicting the occurrence of a disaster in the area around the specified span. A disaster occurrence prediction method.

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