JP2023148976A - Disaster information acquisition device and disaster information acquisition system - Google Patents

Abstract

To provide a disaster information acquisition device and a disaster information acquisition system that can acquire plural pieces of disaster information with a simple structure.SOLUTION: The disaster information acquisition device of the present invention includes: a vibration meter which can detect a vibration in a set position; and a data processor for acquiring disaster information of the set position from the result of output of the vibration meter. The data processor includes two or more among a wind rate calculation unit for calculating the estimated value of the wind rate at the set position on the basis of the result of output of the vibration meter; a rainfall intensity calculation unit for calculating the estimated value of the rainfall intensity of the set position from the result of output of the vibration meter; and a sediment disaster detection unit for detecting generation of a sediment disaster in the set position and around the set position from the result of output of the vibration meter.SELECTED DRAWING: Figure 1

Description

The present disclosure relates to a disaster information acquisition device and a disaster information acquisition system.

Regional disaster (hazard) information is generally measured using different sensors for each type. In Japan, for example, information on earthquakes can be obtained from the Japan Meteorological Agency's seismic intensity measurement network or the National Institute for Earth Science and Disaster Prevention's strong motion observation network (K-NET, KiK-net), and information about strong winds and heavy rain can be obtained from the Japan Meteorological Agency's regional meteorological observation system ( Information on the occurrence of landslides is measured by wire sensors installed and under the jurisdiction of the Ministry of Land, Infrastructure, Transport and Tourism and local governments, and is used as information for preventing secondary disasters and evacuation in the event of a disaster. has been done.

However, as mentioned above, when individual measurements are taken at different locations for each type of disaster, countermeasures tend to be taken individually, and it may not be possible to take a comprehensive response when a complex disaster occurs. . In addition, different types of measurement means are used to measure each disaster that is to be detected, and the measurement networks are owned individually by the responsible ministries and agencies, local governments, etc., so installation costs and maintenance costs tend to be high. be.

In addition, because the owners of conventional measuring means are different from each other, the structure of the equipment related to the measuring means is complex, and the equipment is expensive, it is difficult to install new equipment or relocate it. Changes etc. are not easy. Therefore, it has been difficult, for example, to pinpoint information on various disasters in a relatively small area.

An object of the present disclosure is to provide a disaster information acquisition device and a disaster information acquisition system that can acquire a plurality of disaster information with a simple structure.

In order to achieve the above object, a disaster information acquisition device according to a first aspect of the present disclosure includes a vibration meter capable of detecting vibrations at an installation location, and acquires disaster information at the installation location from the output result of the vibration meter. a data processing unit for calculating an estimated value of the wind speed at the installation position from the output result of the vibration meter, and a wind speed calculation unit for calculating the estimated value of the wind speed at the installation position from the output result of the vibration meter a rainfall intensity calculation unit that calculates an estimated value of the rainfall intensity; and a landslide detection unit that detects the occurrence of a landslide at and around the installation position from the output results of the vibration meter. This includes:

In the disaster information acquisition device as described above, two or more pieces of disaster information such as information regarding strong winds, information regarding heavy rain, and information regarding the occurrence of landslides can be acquired using a single device. This will make it easier to take a comprehensive response even in the event of a complex disaster.

A disaster information acquisition device according to a second aspect of the present disclosure is the disaster information acquisition device according to the first aspect of the present disclosure, wherein the data processing unit calculates an earthquake at the installation location from the output result of the vibration meter. It further includes an earthquake detector for detecting an earthquake.

In addition to information on strong winds, heavy rain, and landslides, the disaster information acquisition device described above can also acquire information on earthquakes with a single device. become.

A disaster information acquisition device according to a third aspect of the present disclosure is the disaster information acquisition device according to the first or second aspect of the present disclosure, in which the vibration meter is attached to a structure disposed in an air fluid. The wind speed calculation unit includes a first vibration meter installed, and the wind speed calculation unit calculates the position of the installation position using a regression model shown in equation (1) below, which uses the output result detected by the first vibration meter as an explanatory variable. Calculate the estimated wind speed.

Here, x is the wind speed, y is the output result of the first vibration meter, and a, b, c, α, and β are arbitrary constants.

In the disaster information acquisition device as described above, the wind speed can be detected from the output result of the vibration meter without using a dedicated measuring means.

A disaster information acquisition device according to a fourth aspect of the present disclosure is the disaster information acquisition device according to the first or second aspect of the present disclosure, wherein the wind speed calculation unit converts the output result detected by the vibration meter into a Fourier An estimated value of the wind speed at the installation position is calculated from the amplitude value in a predetermined frequency region of the Fourier spectrum obtained by the conversion.

In the disaster information acquisition device as described above, the wind speed can be detected from the output result of the vibration meter without using a dedicated measuring means.

A disaster information acquisition device according to a fifth aspect of the present disclosure is the disaster information acquisition device according to the third or fourth aspect of the present disclosure, wherein the vibration meter includes a second vibration meter installed on the ground. The wind speed calculation unit further includes: an amplitude value of a Fourier spectrum obtained by Fourier-transforming the output result of the first vibrometer and a Fourier spectrum obtained by Fourier-transforming the output result of the second vibrometer. a first vibration determination unit that compares the amplitude value of An estimated value of the wind speed at the installation position is calculated from the output result of the first vibration meter at the time of detection.

In the disaster information acquisition device as described above, vibrations caused by wind can be determined with high accuracy.

A disaster information acquisition device according to a sixth aspect of the present disclosure is the disaster information acquisition device according to the third or fourth aspect of the present disclosure, in which the wind speed calculation unit is configured to detect vibrations detected by the first vibration meter. a second vibration determination section that detects whether or not the duration of the vibration is equal to or greater than a second threshold; An estimated value of the wind speed at the installation position is calculated from the output results of the vibration meter.

In the disaster information acquisition device as described above, vibrations caused by wind can be determined with high accuracy.

A disaster information acquisition device according to a seventh aspect of the present disclosure is such that in the disaster information acquisition device according to the sixth aspect of the present disclosure, the second vibration determination unit uses a Trifunac cumulative power method and a Jennings envelope function. The duration time is specified using at least one of the methods.

In the disaster information acquisition device as described above, the duration of vibration can be accurately determined.

A disaster information acquisition device according to an eighth aspect of the present disclosure is the disaster information acquisition device according to the first or second aspect of the present disclosure, in which the rainfall intensity calculation unit calculates the output result detected by the vibration meter. An estimated value of rainfall intensity at the installation position is calculated from the amplitude of the time history.

In the disaster information acquisition device as described above, rainfall intensity can be detected from the output results of the vibration meter without using a dedicated measuring means.

A disaster information acquisition device according to a ninth aspect of the present disclosure is the disaster information acquisition device according to the first or second aspect of the present disclosure, wherein the rainfall intensity calculation unit calculates the output result detected by the vibration meter. An estimated value of the rainfall intensity at the installation position is calculated from the amplitude value in a predetermined frequency region of the Fourier spectrum obtained by Fourier transformation.

In the disaster information acquisition device as described above, rainfall intensity can be detected from the output results of the vibration meter without using a dedicated measuring means.

A disaster information acquisition device according to a tenth aspect of the present disclosure is the disaster information acquisition device according to the first or second aspect of the present disclosure, in which the landslide detection unit detects an output result detected by the vibration meter. The occurrence of a landslide at and around the installation position is detected based on the number of times the third threshold value is exceeded within a unit time.

In the disaster information acquisition device as described above, the occurrence of a landslide can be detected from the output results of the vibration meter without using a dedicated measuring means.

A disaster information acquisition device according to an eleventh aspect of the present disclosure is the disaster information acquisition device according to any one of the first, second, and tenth aspects of the present disclosure, wherein the landslide detection unit includes the vibration meter. includes a high-pass filter that extracts vibration components with a frequency higher than a predetermined frequency from the output results detected by the device, and detects the occurrence of landslides at and around the installation location from the vibration components extracted by the high-pass filter. .

In the disaster information acquisition device as described above, it is possible to accurately determine vibrations caused by the occurrence of a landslide.

A disaster information acquisition device according to a twelfth aspect of the present disclosure is the disaster information acquisition device according to any one of the first, second, tenth, and eleventh aspects of the present disclosure, wherein the landslide detection unit includes: A fitting section is provided that fits a curve represented by a Gaussian function to a Fourier spectrum obtained by Fourier transforming the output result of the vibrometer and detects the difference, Detect the occurrence of landslides.

In the disaster information acquisition device as described above, the occurrence of a landslide can be detected from the output results of the vibration meter without using a dedicated measuring means. It is also possible to detect landslides that occur at a location far from the location where the vibration meter is installed.

A disaster information acquisition system according to a thirteenth aspect of the present disclosure includes a vibration meter that is connected to a communication network and is capable of detecting vibrations at an installation position, and is capable of acquiring output results of the vibration meter via the communication network. and a data processing unit for acquiring disaster information at the installation position from the output result of the vibration meter, the data processing unit acquiring an estimated value of wind speed at the installation position from the output result of the vibration meter. a rainfall intensity calculation section that calculates an estimated value of rainfall intensity at the installation position from the output result of the vibration meter; A landslide detection unit that detects the occurrence of a disaster, and two or more of the following.

In the disaster information acquisition system as described above, two or more pieces of disaster information, such as information regarding strong winds, information regarding heavy rain, and information regarding the occurrence of landslides, can be acquired using a single system. This will make it easier to take a comprehensive response even in the event of a complex disaster.

According to the disaster information acquisition device and disaster information acquisition system of the present disclosure, there is no need to prepare multiple measuring means, and it is possible to acquire multiple pieces of disaster information with a simple structure.

FIG. 1 is a functional block diagram showing an example of a disaster information acquisition device according to an embodiment of the present disclosure. 2 is a graph showing the relationship between the maximum acceleration detected by the first vibration meter shown in FIG. 1 and wind speed. 2 is a graph showing the relationship between RMS acceleration detected by the first vibration meter shown in FIG. 1 and wind speed. 2 is a graph showing Fourier spectra obtained by Fourier-transforming the output results detected by the first and second vibrometers shown in FIG. 1 for each of three directional components. It is a graph showing the results of measuring the duration of vibration due to an earthquake and the duration of vibration due to wind using Trifunac's cumulative power method. It is a graph showing the results of measuring the duration of vibration due to an earthquake and the duration of vibration due to wind using the Jennings envelope function method. 2 is a graph showing the relationship between acceleration detected by the second vibration meter shown in FIG. 1 and rainfall intensity. 2 is a graph showing a Fourier spectrum obtained by Fourier transforming the output results of the second vibrometer shown in FIG. 1 observed while changing the rainfall intensity. 2 is a graph showing the relationship between the amplitude of the Fourier spectrum obtained by Fourier-transforming the output result detected by the second vibrometer shown in FIG. 1 and the rainfall intensity. 2 is a graph showing an example of an acceleration waveform detected by the vibrometer shown in FIG. 1. FIG. 2 is a graph showing another example of the acceleration waveform detected by the vibrometer shown in FIG. 1 and the relationship between its pulse density. 12 is a graph showing side by side the time-series transition of the pulse density shown in FIG. 11(C) and the time-series transition of the pulse density of the acceleration waveform due to an earthquake. A graph similar to FIG. 12 was generated after applying a high-pass filter to the acceleration waveform detected by the vibration meter and the acceleration waveform caused by the earthquake. It is a graph showing the Fourier spectra of the results of two different vibration meters detecting vibrations caused by the occurrence of a landslide.

Embodiments for carrying out the present disclosure will be described below with reference to the drawings. In addition, below, the scope necessary for explanation to achieve the purpose of the present disclosure will be schematically shown, and the scope necessary for explanation of the relevant part of the present disclosure will be mainly explained, and parts where explanation is omitted will be omitted. It shall be based on known technology.

<Overall configuration of the device>
FIG. 1 is a functional block diagram showing an example of a disaster information acquisition device according to an embodiment of the present disclosure. As shown in FIG. 1, the disaster information acquisition device 1 according to the present embodiment includes a vibration meter 10 capable of detecting vibrations at a specific installation position, and acquires disaster information at the installation position from the output results of the vibration meter 10. The data processing unit 20 includes at least a data processing unit 20 for processing the data.

The vibration meter 10 can be installed at any installation location where disaster information is desired to be obtained, and is a means capable of measuring vibrations in three-dimensional directions (for example, the X, Y, and Z directions in FIG. 1) occurring at this installation location. Can be configured. As this vibration meter 10, for example, a piezoelectric type, servo type, electromagnetic type, or semiconductor type vibration sensor (acceleration sensor, speed sensor) or MEMS (Micro Electro Mechanical Systems) vibration sensor (acceleration sensor, speed sensor) is adopted. be able to. The vibration meter 10 according to the present embodiment includes two vibration meters, namely, a first vibration meter 11 installed in a structure 2 placed in an air fluid so as to be exposed to the wind flowing around it, and a A device including a second vibration meter 12 installed on the (ground) 3 is illustrated. Note that the number and arrangement of the vibration meters 10 are not limited to this, and can be changed as appropriate in consideration of the disaster information that is desired to be acquired.

The structure 2 in which the first vibration meter 11 is installed may be a columnar member installed in a position exposed to the wind, for example, erected upward from the ground 3. The shape and size of the structure 2 are not particularly limited as long as it can vibrate when exposed to wind. Further, as the structure 2, an existing structure such as a utility pole or a streetlight can be used. On the other hand, the "ground" on which the second vibration meter 12 is installed is understood to include not only the ground 3 but also structures installed on the ground 3 as long as it does not vibrate even when exposed to wind. Should.

The data processing unit 20 may be a member for acquiring desired disaster information based on the output results of the vibration meter 10. This data processing section 20 can be realized by, for example, a sequencer (Programmable Logic Controller, PLC) or a well-known computer connected to a power source (not shown). Therefore, various calculations performed within the data processing section 20 may be performed by a processor (not shown) within the data processing section 20. The data processing unit 20 according to the present embodiment includes a data collection unit 21 that collects the output results of the vibration meter 10, and an estimated value of the wind speed at the installation position from the output results of the vibration meter 10 collected by the data collection unit 21. A wind speed calculation unit 22 that calculates the rainfall intensity at the installation location from the output results of the vibration meter 10 collected by the data collection unit 21; A landslide detection unit 24 detects the occurrence of a landslide at and around the installation position from the output results of the vibration meter 10, and an earthquake at the installation position is detected from the output results of the vibration meter 10 collected by the data collection unit 21. An example including an earthquake detection unit 25, a memory 26 capable of storing various data, and a communication interface 27 is illustrated. Note that the data processing unit 20 does not need to have all of the above-mentioned components, and can appropriately select and employ them.

The data collection unit 21 may be electrically connected to the vibration meter 10 via wired or wireless communication, and may be capable of collecting output results detected by the vibration meter 10. The output result of the vibration meter 10 is data (specifically, an acceleration waveform or a vibration waveform) indicating a time-measured change in the detected acceleration (vibration), or information corresponding thereto, which is transmitted to the data collection unit 21 in the form of an electrical signal. It may be something that is done. The data collection unit 21 can function to at least temporarily store the received data in the memory 26.

The wind speed calculation unit 22, the rainfall intensity calculation unit 23, the landslide detection unit 24, and the earthquake detection unit 25 calculate desired disaster information, specifically strong wind information, from the output results of the vibration meter 10 collected by the data collection unit 21. It may be a component for calculating or detecting information regarding the presence or absence of heavy rain, information regarding the occurrence of a landslide, and information regarding the presence or absence of an earthquake. In this embodiment, a wind speed calculation section 22, a rainfall intensity calculation section 23, a landslide detection section 24, and an earthquake detection section 25 are provided in the data processing section 20. As a result, disaster information data can be generated within the disaster information acquisition device 1, so the amount of data transmitted via the communication interface 27 can be significantly reduced compared to the case where the output results of the vibration meter 10 are transmitted. This makes it possible to suppress an increase in communication traffic. A specific calculation method or detection method using each component will be described in detail later.

The memory 26 can be configured with a well-known volatile or nonvolatile recording medium, and can store data collected by the data collection unit 21 and information used when calculating or detecting various disaster information. . Further, the communication interface 27 may be for transmitting and receiving various disaster information calculated or detected within the data processing unit 20. Through this communication interface 27, for example, to transmit predetermined disaster information to the management server 4 and client terminal 5 via a communication network NW connected via wired or wireless communication, or to calculate or detect disaster information. It is also possible to receive control signals transmitted from the management server 4 or client terminal 5. Although this communication interface 27 is exemplified as one connected to the communication network NW via wired or wireless communication, it may also be one for locally connecting to the management server 4 or client terminal 5.

The disaster information acquisition device 1 according to the present embodiment having the above-described configuration is capable of identifying multiple types of disaster information from the output results of the vibration meter 10 as a measuring means. Therefore, a method for acquiring disaster information by the wind speed calculation section 22, the rainfall intensity calculation section 23, the landslide detection section 24, and the earthquake detection section 25 will be explained below in order.

<How to calculate estimated wind speed>
When measuring wind speed, it is common to use a dedicated measuring means for measuring wind speed, such as a windmill-type anemometer. On the other hand, the wind speed calculation unit 22 of the disaster information acquisition device 1 according to the present embodiment uses the output result of the vibration meter 10 to measure the wind speed, and more precisely to calculate the estimated value of the wind speed.

The wind speed calculation unit 22 according to the present embodiment calculates an estimated value of the wind speed based on the output results detected by the vibration meter 10, particularly the first vibration meter 11, collected by the data collection unit 21. can do. That is, the wind speed calculation unit 22 utilizes vibration waveforms detected when the first vibration meter 11 and the structure 2 in which the first vibration meter 11 is installed vibrate due to wind. It may be something that calculates an estimated value of wind speed.

In order to identify the correlation between the output result of the first vibration meter 11 and the wind speed of the wind generated around the first vibration meter 11, when wind of an arbitrary wind speed is applied to the first vibration meter 11. The output results are shown in FIGS. 2 and 3. Here, FIG. 2 is a graph showing the relationship between the maximum acceleration detected by the first vibration meter of the disaster information acquisition device shown in FIG. 1 and the wind speed, and FIG. The maximum acceleration observed by the first vibration meter 11 is plotted as an observation point, and FIG. 2(B) shows a function that models the observation points shown in FIG. 2(A). Further, FIG. 3 is a graph showing the relationship between the RMS (root mean square, also referred to as "effective value") acceleration detected by the first vibration meter of the disaster information acquisition device shown in FIG. 1 and the wind speed. 3(A) is a graph in which the RMS acceleration actually observed multiple times with the first vibration meter 11 is plotted as an observation point, and FIG. 3(B) is a graph plotted as the observation point. This shows a function that models the observation points shown. Incidentally, as can be seen by comparing FIGS. 2 and 3, it can be confirmed that the variation among observation points tends to be smaller for RMS acceleration than for maximum acceleration.

The points indicated by dots in FIGS. 2 and 3 are observation points. From these observation points, it can be seen that the first vibration meter 11 and the structure 2 exposed to the wind are forced to vibrate due to so-called gust response (or buffeting). Here, the forced vibration due to the gust response can be modeled using an exponential function, as shown in portions A and C surrounded by dotted lines in FIGS. 2(A) and 3(A).

On the other hand, the first vibration meter 11 and the structure 2 may cause vortex-excited vibration (or galloping) due to Karman vortices being emitted downstream when the first vibration meter 11 and the structure 2 are hit by wind. The increase in acceleration at a specific wind speed due to this vortex excitation vibration (portions indicated by arrows B and D in FIGS. 2(A) and 3(A)) can be modeled using a Gaussian function.

Based on the above-mentioned matters, the wind speed calculation unit 22 uses the regression model shown in the following equation (1), for example, to determine whether the first vibration meter 11 is installed based on the output result detected by the first vibration meter 11, for example, the RMS acceleration. The estimated value of the wind speed at the installation location can be calculated.

Here, x is the wind speed, y is the output result of the first vibration meter, and a, b, c, α, and β are arbitrary constants.

The method of calculating the wind speed by the wind speed calculation unit 22 is not limited to the method using equation (1) described above. Specifically, the estimated value of the wind speed at the installation position can also be calculated from the Fourier spectrum obtained by Fourier transforming the output result detected by the first vibration meter 11. A positive correlation is recognized between the spectral intensity of the Fourier spectrum obtained by Fourier transforming the output result detected by the first vibrometer 11 (the average value of the Fourier amplitude in a predetermined frequency domain) and the wind speed. Therefore, the spectral intensity in each direction can be modeled using the regression model shown in equation (1) above using regression analysis.

Therefore, by using the modeled regression equation (1) described above, the estimated value x of the wind speed at the installation position where the first vibration meter 11 is installed is calculated from the output result of the first vibration meter 11. be able to.

By the way, the vibrations detected by the first vibration meter 11 are not limited to those caused by wind. For example, when an earthquake occurs at the installation location where the first vibration meter 11 is installed, vibrations due to the earthquake (earthquake motion) can also be detected. Therefore, in order to accurately calculate the estimated value of the wind speed, it is necessary to identify the cause of the vibration detected by the first vibration meter 11. Therefore, as a method for calculating an estimated value of wind speed from the output result detected by the first vibration meter 11, a method for distinguishing between vibrations caused by the wind and vibrations caused by other factors will be explained below. .

In order to distinguish whether the vibration detected by the first vibration meter 11 is caused by the wind or by other factors, especially an earthquake, the disaster information acquisition device 1 according to the present embodiment exemplifies a vibrometer 10 that includes the second vibrometer 12 described above in addition to the first vibrometer 11. The second vibration meter 12 is preferably installed on the ground 3, particularly on the ground 3 relatively adjacent to the structure 2. Adjacent here means a positional relationship such that, if an earthquake were to occur, the seismic motion detected by the first vibration meter 11 and the earthquake motion detected by the second vibration meter 12 would be approximately the same. shall refer to. Therefore, the positions of the first vibrometer 11 and the second vibrometer 12 do not need to be strictly adjacent to each other as shown in FIG. 1, and may be separated by a certain distance.

In addition, as shown in FIG. It may include a first vibration determination unit 31 that determines whether the result includes a vibration component caused by wind. Specifically, the first vibration determination unit 31 converts the amplitude value of the Fourier spectrum obtained by Fourier transforming the output result of the first vibration meter 11 and the output result of the second vibration meter 12 into a Fourier transform. By comparing the amplitude value of the Fourier spectrum obtained by the conversion and detecting whether the difference is greater than or equal to the first threshold value, it is possible to determine whether the output result of the first vibration meter 11 is caused by wind. It may be possible to determine whether or not it includes.

FIG. 4 is a graph showing Fourier spectra obtained by Fourier-transforming the output results detected by the first and second vibrometers for each of the three directional components of X, Y, and Z. 4(C) shows the Fourier spectra of the X, Y, and Z direction components of the output results detected by the first vibrometer, and FIGS. 4(D) to 4(F) show the Fourier spectra of the output results detected by the first vibrometer. This figure shows the Fourier spectra of the X, Y, and Z direction components of the output results detected by the vibration meter. Note that Figures 4(A) to 4(F) show the output results when winds of different wind speeds (0.4 m/s, 3.1 m/s, and 6.2 m/s) are applied to each vibration meter. The Fourier spectrum of is also shown.

As can be seen from FIG. 4, the amplitude of the Fourier spectrum (Fourier amplitude) corresponding to the output result of the second vibration meter 12 installed on the ground 3 hardly changes even if the wind speed changes. On the other hand, the amplitude of the Fourier spectrum corresponding to the output result of the first vibration meter 11 installed in the structure 2 changes greatly in proportion to the wind speed. From this, the first vibration determination unit 31 determines that the vibration component that cannot be detected by the second vibration meter 12 among the output results of the first vibration meter 11 is a vibration component caused by the wind. It is possible to prevent the calculation unit 22 from misinterpreting vibration components other than vibrations caused by the wind as being caused by the wind.

The determination method by the first vibration determination unit 31 is, for example, by comparing the amplitude values of the Fourier spectra of the output results of the first and second vibrometers 11 and 12, and determining whether the difference is greater than or equal to a first threshold value. The determination can be made based on the following. The first threshold value may be set in advance based on experiments or the like. Then, the wind speed calculation unit 22 calculates the estimated value of the wind speed from the output result of the first vibration meter 11 when a difference equal to or more than the first threshold value is detected, thereby calculating the wind speed based on the vibration component caused by the wind. It is possible to calculate the estimated value of

In addition, in addition to calculating the estimated value of the wind speed by the wind speed calculation unit 22 described above, the earthquake detection unit 25 (described later) is used to calculate the estimated value of the wind speed based on the output result of the second vibration meter 11 when a difference greater than or equal to the first threshold is detected. Earthquakes may also be detected. If the wind speed calculation unit 22 calculates the estimated wind speed and detects the earthquake in parallel, it is possible to obtain a calculation result that takes into account the vibration component caused by the earthquake when the wind speed calculation unit 22 calculates the estimated wind speed. . Therefore, even if a combined disaster of an earthquake and strong wind occurs, information on both disasters can be provided to the user with high accuracy.

Another method for determining whether or not the output result of the first vibration meter 11 includes a vibration component due to the wind may be to use the duration of vibration. In this regard, the wind speed calculation unit 22 according to the present embodiment includes a second vibration determination unit 32 instead of or in addition to the first vibration determination unit 31 described above. can include.

The second vibration determination unit 32 may detect whether the duration of the vibration detected by the first vibration meter 11 is equal to or greater than a second threshold value. The method of measuring the duration of vibration by the second vibration determination unit 32 is not particularly limited, but for example, at least one of Trifunac's cumulative power method and Jennings' envelope function method may be used. Here, Trifunac's cumulative power method is defined as the time-integrated value of the amplitude squared value of the time history from the beginning to the end of the vibration measurement record as the cumulative power, and a fixed interval on the time axis of this cumulative power as the duration. It is defined. Generally, it is often set as an interval where the cumulative power is between 5% and 95%. In addition, Jennings' envelope function method is a method devised to simulate the temporal characteristics of the time history waveform of seismic waves, and the envelope function consists of an initial motion part, a main motion part, and a coda wave. . The vibration measurement record is applied to this envelope function, and the duration is defined as the time from the start time of the initial motion part to the end time of the coda part.

Figure 5 is a graph showing the results of measuring the duration of vibration detected by a vibration meter using Trifunac's cumulative power method. This figure shows the duration of the vibrations caused by the wind, and FIG. 5(B) shows the duration of the vibrations caused by the wind at three points in time. In addition, FIG. 6 is a graph showing the results of measuring the duration of vibrations detected by a vibration meter using Jennings' envelope function method, and FIG. It shows the duration of vibrations caused by an earthquake, and FIG. 6(B) shows the duration of vibrations caused by wind at three points in time. As can be seen from Figures 5 and 6, in both measurement methods, the vibrations caused by the earthquake had a relatively short duration of less than 100 seconds, whereas the vibrations caused by the wind lasted for more than 1500 seconds. It lasted a long time. Therefore, by setting the second threshold value to an arbitrary value within the range of 100 seconds or more and less than 1500 seconds (for example, 200 seconds), the second vibration determination unit 32 determines whether the vibration detected by the vibration meter is caused by the wind. It is possible to accurately determine whether a vibration component is included.

As explained above, according to the estimated wind speed calculation method of the disaster information acquisition device 1 according to the present embodiment, the wind speed calculation unit 22 calculates the estimated wind speed from the output result detected by the vibration meter 10. I can do it. Therefore, disaster information regarding strong winds can be obtained without using a dedicated measuring means such as a windmill-type anemometer. Note that the calculation method described above tends to have relatively lower accuracy than a method using a dedicated measuring means. However, in the disaster information acquisition device 1 according to the present embodiment, if it can be specified whether or not a strong wind with a wind speed exceeding 10 m/s is occurring, it can be sufficiently used as disaster information. Therefore, it can be said that even with the calculation method described above, it is possible to obtain disaster information with usable accuracy.

Further, according to the above-described method, since the vibration meter 10 is used as a means for measuring wind speed, the output result of the vibration meter 10 can be used to obtain disaster information other than wind speed, such as an earthquake. Therefore, it is possible to obtain multiple pieces of disaster information at once, thereby enabling comprehensive disaster countermeasures.

<How to calculate estimated rainfall intensity>
When measuring rainfall intensity, it is common to use specialized measuring means, such as a falling rain gauge that collects and measures the rain that actually falls, or a distrometer that measures using laser light. On the other hand, the rainfall intensity calculation unit 23 of the disaster information acquisition device 1 according to the present embodiment measures the rainfall intensity using the output results of the vibration meter 10 collected by the data collection unit 21, as in the case of wind speed. , strictly speaking, calculates the estimated value of rainfall intensity.

The rainfall intensity calculation unit 23 of the disaster information acquisition device 1 according to the present embodiment calculates an estimated value of rainfall intensity based on the output result detected by the vibration meter 10, for example, the second vibration meter 12. I can do it. In other words, the rainfall intensity calculation unit 23 calculates the estimated value of the rainfall intensity using the vibration waveform caused by raindrops falling on or near the second vibration meter 12. It can be anything. Note that when calculating the estimated value of the rainfall intensity in the rainfall intensity calculation unit 23, the output result detected by the first vibration meter 11 can also be used. However, since the first vibrometer 11 may include wind-induced vibrations as described above, it is easier to use the output results of the second vibrometer 12, which does not substantially include wind-induced vibrations, for easier and more accurate calculations. You can expect to get results.

In order to specify the correlation between the output result of the second vibrometer 12 and the rainfall intensity, the output result when rain was artificially caused on the second vibrometer 12 is shown in FIG. Here, FIG. 7 is a graph showing the relationship between the acceleration detected by the second vibration meter shown in FIG. 1 and the rainfall intensity, and FIGS. The acceleration of each component in the X, Y, and Z directions of the output results detected by the total 12, especially the RMS acceleration, is plotted as an observation point.

As can be seen from Figure 7, the observed results of the RMS acceleration for each rainfall intensity in each direction are obtained using regression analysis for each of the three directional components. It can be modeled into a model.

Here, x ₁ is the RMS acceleration, y ₁ is the rainfall intensity, and a and b are arbitrary constants.

Therefore, by using the modeled regression equation (2) described above, the amplitude of the time history of the output result of the second vibrometer 12 (specifically, acceleration or velocity) can be used to calculate the It is possible to calculate the estimated value y ₁ of the rainfall intensity at the installation location where is installed.

FIG. 8 is a graph showing Fourier spectra obtained by Fourier transforming the output results of the second vibrometer shown in FIG. 1, which were observed while changing the rainfall intensity. C) shows the Fourier spectrum of each component in the X, Y, and Z directions of the output result detected by the second vibration meter. In addition, the one indicated by R0 in FIGS. 8(A) to 8(C) shows the Fourier spectrum when the rainfall intensity is zero, and the other ones show the Fourier spectra with rainfall (the specific rainfall intensity is 15 mm/15 mm, respectively). Fig. 4 shows Fourier spectra when the speed is set to 75 mm/h, 75 mm/h, 135 mm/h, and 300 mm/h. From FIG. 8, it is recognized that the amplitude value of the Fourier spectrum of the second vibrometer 12 tends to increase in proportion to the rainfall intensity in a relatively high frequency region. Based on this point, FIG. 9 shows the relationship between the value of the spectral intensity of the Fourier spectrum (the average value of the Fourier amplitude in a predetermined high frequency band) and the rainfall intensity.

FIG. 9 is a graph showing the relationship between the value of the spectral intensity in a specific frequency region of the Fourier spectrum obtained by Fourier-transforming the output result detected by the second vibration meter shown in FIG. 1 and the rainfall intensity. (A) to FIG. 9(C) are plots of the spectral intensity values of the Fourier spectra of each component in the X, Y, and Z directions of the output results detected by the second vibration meter 12 as observation points. . Here, the above-mentioned specific frequency range can be set as appropriate within a relatively high frequency range. As can be seen from FIG. 9, the observation results in a specific frequency region of the spectral intensity for each direction of rainfall intensity are obtained using regression analysis for each of the three directional components, similar to those shown in FIG. It can be modeled into a linear regression model shown by the dotted line in the middle and the following equation (3).

Here, x ₂ is the spectral intensity, y ₂ is the rainfall intensity, and a and b are arbitrary constants.

Therefore, by using the modeled regression equation (3) described above, the estimated value y ₂ of the rainfall intensity at the installation location where the second vibration meter 12 is installed can be calculated from the output result of the second vibration meter 12. It can be calculated.

As explained above, according to the rainfall intensity estimation value calculation method of the disaster information acquisition device 1 according to the present embodiment, the rainfall intensity calculation unit 23 calculates the estimated rainfall intensity value from the output result detected by the vibration meter 10. It can be calculated. Therefore, disaster information related to heavy rain can be obtained without using special measuring means such as a tipping rain gauge or a distrometer that measures with laser light. In addition, since the above-mentioned calculation method calculates an estimated value, its accuracy tends to be relatively low compared to those that actually measure rainfall, such as the above-mentioned dedicated measuring means. However, in the disaster information acquisition device 1 according to the present embodiment, if the approximate rainfall intensity (for example, rainfall intensity with an accuracy of two digits) can be specified, it can be sufficiently used as disaster information. Therefore, it can be said that even with the calculation method described above, it is possible to obtain disaster information with usable accuracy.

Further, according to the above-described method, since the vibration meter 10 is used as a means for measuring rainfall intensity, the output result of the vibration meter 10 can be used to obtain disaster information other than rainfall intensity such as an earthquake. Therefore, it is possible to obtain multiple pieces of disaster information at once, thereby enabling comprehensive disaster countermeasures.

<Method for detecting landslide occurrence>
For landslide disasters such as landslides, landslides, and debris flows, it is common to use dedicated measuring means such as wire sensors that are installed in advance at locations where landslides are likely to occur. On the other hand, the landslide detection unit 24 of the disaster information acquisition device 1 according to the present embodiment detects the occurrence of a landslide using the output results of the vibration meter 10 collected by the data collection unit 21.

The landslide detection unit 24 according to the present embodiment may detect the occurrence of a landslide based on the output results detected by the vibration meter 10 collected by the data collection unit 21. In other words, the landslide detection unit 24 is capable of detecting whether the vibration detected by at least one of the first and second vibration meters 11 and 12 is due to the occurrence of a landslide. It's fine. Note that the landslide detection unit 24 may use the output results of either of the first and second vibration meters 11 and 12 to detect a landslide.

As a specific method for detecting the occurrence of a landslide, the landslide detection unit 24 detects the number of peaks of pulses exceeding the third threshold (i.e., the number of pulses in the vibration waveform detected by the vibration meter 10). A method of detecting the occurrence of a landslide based on the number of times the third threshold has been exceeded can be adopted. In order to detect the occurrence of landslide disasters with high precision using this method, it is preferable that the vibration meter 10 be placed at or near a place where landslide disasters are likely to occur.

FIG. 10 is a graph showing an example of an acceleration waveform detected by the vibration meter shown in FIG. Note that the acceleration waveform shown in FIG. 10 is an example of the acceleration waveform of the Z-direction component among the output results detected by the vibration meter 10. In the landslide detection unit 24 according to the present embodiment, as shown in FIG. 10, the third threshold value is set to ±100 cm/s ² . Then, the landslide detection unit 24 detects the peak of the pulse that exceeds this third threshold (the part marked with an arrow P in FIG. Based on the number (ie, pulse density), the occurrence of a landslide can be detected. Note that the specific value of the third threshold is not limited to the above value, and can be changed as appropriate in consideration of its detection accuracy.

FIG. 11 is a graph showing another example of the acceleration waveform caused by earth movement detected by the vibration meter shown in FIG. 1 and the relationship between its pulse density, and FIG. 11(A) shows the acceleration waveform. 11(B) shows the results of detecting the peak of the pulse exceeding the third threshold in the acceleration waveform of FIG. 11(A) in time series, and FIG. 11(C) 11(B) shows changes in the number of pulse peaks (pulse density) detected in FIG. 11(B) in time series. In the acceleration waveform shown in FIG. 11(A), the peak of the pulse exceeding the third threshold set to ±100 cm/ ^s2 appears at the timing shown in FIG. 11(B), The frequency is shown in FIG. 11(C). Here, if the pulse density for determining that the vibration is caused by a landslide is set to, for example, 40 times/second, the landslide detection unit 24 detects that the vibration meter 10 detects the acceleration waveform from FIG. 11(C). It can be determined that a landslide has occurred 12 seconds after the occurrence of a landslide. Note that the pulse density at which it is determined that the vibration is caused by a landslide can be adjusted as appropriate.

By the way, the vibrations detected by the vibration meter 10 are not limited to those caused by landslides, but can also detect vibrations caused by other disasters, such as earthquakes. FIG. 12 is a graph showing side by side the time series transition of the pulse density shown in FIG. 11(C) and the time series transition of the pulse density of the acceleration waveform due to the earthquake. 11(C), and Figures 12(B) and 12(C) show the results of seismic motions detected by two earthquakes that occurred in the past using a vibration meter using the same method as in Figure 11(C). The pulse density obtained by processing is shown in time series. As can be seen from FIG. 12, the pulse density derived from the acceleration waveform that can be detected by the vibration meter 10 when an earthquake occurs is the pulse density derived from the acceleration waveform that can be detected by the vibration meter 10 when a landslide occurs. At first glance, they are similar. For this reason, especially when an earthquake occurs at the location where the vibration meter 10 is installed, there is a possibility that vibrations caused by the earthquake (earthquake motion) may be mistaken for vibrations caused by a landslide. Therefore, as a method for the landslide detection unit 24 to reliably detect vibrations caused by the occurrence of a landslide from the output results of the vibration meter 10, the following describes how the landslide detection unit 24 can detect vibrations caused by landslides and earthquakes. A method for distinguishing between vibration and vibration will be explained.

When we analyzed the vibrations caused by earthquakes and the vibrations caused by landslides, we found that most of the vibrations caused by earthquakes are vibrations in a lower frequency range than the vibrations caused by landslides. Therefore, the landslide detection unit 24 according to the present embodiment uses a high-pass filter 41 that extracts vibration components with a frequency equal to or higher than a predetermined frequency from the output results detected by the vibration meter 10. The following is an example of how to identify whether the detected vibration is caused by the occurrence of a landslide or an earthquake.

The high-pass filter 41 may block vibration components in a relatively low frequency range among the output results detected by the vibration meter 10, and may pass vibration components in other frequency ranges. The threshold value of this high-pass filter 41 may be set to a frequency that can block vibration components caused by an earthquake. FIG. 13 shows the results of applying the above-described high-pass filter 41 to the graph of detecting vibrations caused by landslides and the graph of detecting vibrations caused by earthquakes shown in FIG. 12. As can be seen from FIG. 13, when the high-pass filter 41 is applied, there is almost no change in the pulse density of vibrations caused by landslides (see FIG. 13(A)), whereas vibrations caused by earthquakes substantially change. Since all the components cannot pass through the high-pass filter 41, the peak of the pulse exceeding the third threshold value is not detected (see FIGS. 13(B) and 13(C)).

Therefore, by analyzing the vibration component after passing through the high-pass filter 41, the landslide detection unit 24 prevents the vibration caused by an earthquake from being mistaken as the vibration caused by the occurrence of a landslide, and accurately detects the occurrence of a landslide. can be detected.

Regarding the detection of landslides occurring near the vibration meter 10, highly accurate detection can be achieved by the method described above. However, when a landslide occurs at a location far from the installation location of the vibration meter 10, the components in a relatively high frequency region (pulses with large amplitude) among the vibration components caused by the landslide, Attenuation occurs during the process of reaching the installation position of the vibration meter 10. Therefore, with only the landslide detection method using the pulse density described above, it may not be possible to accurately detect a landslide that occurs at a location away from the installation position of the vibration meter 10. Therefore, in addition to the above-described high-pass filter 41, the landslide detection unit 24 according to the present embodiment further includes a fitting unit 42 for detecting a landslide that occurs at a location away from the installation position of the vibration meter 10. be able to. The fitting unit 42 according to the present embodiment may fit an arbitrary curve to a Fourier spectrum obtained by Fourier transforming the output result detected by the vibration meter 10.

FIG. 14 is a graph showing Fourier spectra as a result of two different vibration meters detecting vibrations caused by the occurrence of a landslide. The Fourier spectra of the X, Y, and Z direction components of the detected output results are shown, respectively, and FIGS. 14(D) to 14(F) show the X, Y, and Z components of the output results detected by other vibration meters. The Fourier spectra of the directional components are shown respectively. FIG. 14 also shows approximate curves obtained as a result of fitting arbitrary curves to each Fourier spectrum. Further, the first vibration meter and the other vibration meters described above can both be configured with vibration meters installed on the ground like the second vibration meter 12, but their installation positions may be different. . From the observation records shown in FIG. 14, it can be said that the Fourier spectrum of vibrations caused by landslides has a stable single peak convex shape. Therefore, it can be said that the Fourier spectrum of vibrations caused by landslides can be modeled, for example, by a Gaussian function shown in equation (4) below.

Here, c is the amplitude of the spectral peak (cm/s ² ·s), α is the frequency of the spectral peak (Hz), and β is the width of the spectral peak (Hz). In addition, in this embodiment, since the output result of the vibration meter includes an acceleration record, the unit of the spectrum peak amplitude corresponding to the above c is (cm/s ² · s). However, when the output result of the vibrometer includes a velocity record, the unit of the spectrum peak amplitude corresponding to the above c is (cm/s·s).

Considering the above-mentioned matters, the landslide detection unit 24 causes the fitting unit 42 to fit a curve represented by a Gaussian function shown in equation (4) to the Fourier spectrum of the output result of the vibration meter 10, and both That is, based on the difference between the Fourier spectrum and the Gaussian function, it can be specified that the vibration detected by the vibration meter 10 is caused by the occurrence of a landslide. In this identification, for example, the above-mentioned difference may be compared with a preset fourth threshold. Further, for the above-mentioned difference, for example, the residual sum of squares, normalized RMSE (root mean square error), or the like can be used as an index.

Therefore, the landslide detection unit 24 continuously creates an approximate curve expressed by a Gaussian function from the detection results of the vibration meter 10 in the fitting unit 42, and compares it with the Fourier spectrum of the measured vibration. It will be possible to detect landslides that occur at a distance of 10.

As explained above, according to the landslide occurrence detection method of the disaster information acquisition device 1 according to the present embodiment, the landslide detection unit 24 determines the installation position of the vibration meter 10 from the output result detected by the vibration meter 10. It is possible to detect not only landslides that occur in a nearby location, but also landslides that occur at a location far from the installation location of the vibration meter 10. Therefore, disaster information regarding landslides can be obtained without using a dedicated measuring means such as a wire sensor. Furthermore, according to the above-described method for detecting the occurrence of landslides, it is possible to detect landslides that occur at a location far from the installation location of the vibration meter 10, so it is easier to detect landslides over a wide range than by installing wire sensors. It becomes possible. Note that in order to further improve the accuracy of detecting the occurrence of landslides, a well-known geophone (geophone) or the like may be supplementarily employed.

Furthermore, according to the method described above, since the vibration meter 10 is used as a means for measuring landslide disasters, the output results of the vibration meter 10 can be used to obtain disaster information other than landslide disasters such as earthquakes. Therefore, it is possible to obtain multiple pieces of disaster information at once, thereby enabling comprehensive disaster countermeasures.

<Earthquake detection method>
Finally, a case where an earthquake is detected using the disaster information acquisition device 1 according to the present embodiment will be briefly described. The disaster information acquisition device 1 according to the present embodiment can include an earthquake detection unit 25 that detects an earthquake at the installation position of the vibration meter 10 from the output result of the vibration meter 10. When the earthquake detection unit 25 detects the seismic intensity of an earthquake from the acceleration waveform detected by the vibration meter 10, a conventionally known conversion method may be employed. Specifically, the acceleration in the X, Y, and Z directions detected by the vibrometer is processed in the order of Fourier transform, filter processing, and inverse Fourier transform, and a vector waveform is synthesized from the obtained values. Then, the seismic intensity of the earthquake may be detected by calculating I=2logA+0.94 using the resultant vector waveform composite value A and obtaining the measured seismic intensity I. Further, in order to further improve the earthquake detection accuracy, a known magnetic sensor or the like may be separately employed.

The disaster information acquired by the earthquake detection method described above does not interfere with the acquisition of other disaster information calculated or detected by the disaster information acquisition device 1 according to the present embodiment. Specifically, when the wind speed calculating unit 22 calculates the estimated wind speed and the earthquake detecting unit 25 detects an earthquake at the same time, for example, the first vibration meter 11 is used to calculate the estimated wind speed. The output results detected by the second vibration meter 12 may be used to detect an earthquake. Alternatively, as described above, taking into account that wind-induced vibrations have a longer duration than earthquake-induced vibrations, the wind speed calculation unit 22 may detect an earthquake from among the data detected by the first vibration meter 11. By calculating the estimated value of wind speed based on time data other than time, both earthquakes and wind speed can be calculated and detected with high accuracy.

In addition, when calculating the estimated value of rainfall intensity by the rainfall intensity calculation unit 23 and detecting an earthquake by the earthquake detection unit 25 at the same time, it is possible to determine whether the vibration is caused by the rain based on the frequency characteristics of the vibration detected by the vibration meter. All you have to do is to determine whether it is caused by an earthquake or by an earthquake. Specifically, when it is desired to detect earthquakes during periods of rainfall, components with desired frequency characteristics are extracted from the output results detected by the vibration meter using a low-pass filter that passes only relatively low frequencies. After that, the earthquake detection section 25 may detect an earthquake. Furthermore, when detecting the occurrence of a landslide by the landslide detection unit 24 and detecting an earthquake by the earthquake detection unit 25 at the same time, for example, the frequency range of the vibration detected by the vibration meter 10 may be taken into account to distinguish between the two. It is sufficient to detect them separately.

As explained above, according to the earthquake detection method of the disaster information acquisition device 1 according to the present embodiment, the earthquake detection unit 25 detects an earthquake that occurred at the installation position of the vibration meter 10 from the output result detected by the vibration meter 10. can be detected without interfering with the detection of other disaster information. Therefore, the disaster information acquisition device 1 can acquire a plurality of pieces of disaster information.

In addition, the disaster information acquisition device 1 according to the present embodiment can calculate or detect disaster information other than earthquakes in parallel without interfering with each other's acquisition. Specifically, for example, calculating the estimated value of wind speed and calculating the estimated value of rainfall intensity can be calculated separately by performing the calculations using the output results of different vibration meters. Further, since the magnitude of the vibration component between calculating the estimated value of rainfall intensity and detecting the occurrence of a landslide is completely different, calculation and detection may be performed by distinguishing between the two based on the magnitude of the vibration component. Furthermore, the calculation of the estimated value of wind speed and the detection of the occurrence of landslides are calculated and detected using the output results of different vibration meters, similar to the calculation of the estimated value of wind speed and the calculation of the estimated value of rainfall intensity. By doing so, both can be calculated and detected separately.

Further, although the disaster information acquisition device 1 according to the embodiment described above has been described as one device in which the vibration meter 10 and the data processing section 20 are locally connected, this disaster information acquisition device 1 , it is also possible to change to a system in which the vibration meter 10 and the data processing section 20 exist separately. Specifically, the disaster information acquisition system may be configured such that an information terminal (for example, the server 4 shown in FIG. 1) that is installed at a location apart from the vibration meter 10 functions as the data processing unit 20. In this case, the information terminal functioning as the data processing unit 20 and the vibration meter 10 are connected via a communication network, and the output results of the vibration meter 10 can be transmitted to the information terminal, so that the information terminal can generate vibrations. It will be possible to obtain disaster information for the installation locations where a total of 10 devices have been installed.

Furthermore, the regression equations used in some of the calculation methods or detection methods exemplified in the present embodiment described above can be replaced by those based on other regression analyses. That is, parametric regression equations other than those described above may be alternatively employed, or non-parametric regression may be used to obtain similar results.

The present disclosure is not limited to the embodiments described above, and various modifications can be made without departing from the spirit of the present disclosure. All of them are included in the technical idea of the present disclosure.

1 Disaster information acquisition device 2 Structure 3 Ground 10 Vibration meter 11 First vibration meter 12 Second vibration meter 20 Data processing section 21 Data collection section 22 Wind speed calculation section 23 Rainfall intensity calculation section 24 Landslide detection section 25 Earthquake detection Section 26 Memory 27 Communication interface 31 First vibration determination section 32 Second vibration determination section 41 High pass filter 42 Fitting section

Claims (13)

A vibration meter that can detect vibrations at the installation location,
a data processing unit for acquiring disaster information at the installation location from the output results of the vibration meter,
The data processing unit includes a wind speed calculation unit that calculates an estimated value of wind speed at the installation position from the output result of the vibration meter, and a rainfall intensity calculation unit that calculates an estimated value of rainfall intensity at the installation position from the output result of the vibration meter. comprising two or more of a calculation unit and a landslide detection unit that detects the occurrence of a landslide at and around the installation position from the output results of the vibration meter;
Disaster information acquisition device. The data processing unit further includes an earthquake detection unit that detects an earthquake at the installation position from the output result of the vibration meter.
The disaster information acquisition device according to claim 1. The vibrometer includes a first vibrometer installed in a structure disposed in an air fluid;
The wind speed calculation unit calculates an estimated value of the wind speed at the installation position using a regression model shown in the following formula (1) using the output result detected by the first vibration meter as an explanatory variable.
The disaster information acquisition device according to claim 1 or claim 2.

Here, x is the wind speed, y is the output result of the first vibration meter, and a, b, c, α, and β are arbitrary constants. The vibrometer includes a first vibrometer installed in a structure disposed in an air fluid;
The wind speed calculation unit calculates an estimated value of the wind speed at the installation position from the amplitude value in a predetermined frequency region of a Fourier spectrum obtained by Fourier transforming the output result detected by the first vibration meter.
The disaster information acquisition device according to claim 1 or claim 2. The vibration meter further includes a second vibration meter installed on the ground,
The wind speed calculating section calculates the amplitude value of the Fourier spectrum obtained by Fourier transforming the output result of the first vibrometer and the amplitude value of the Fourier spectrum obtained by Fourier transforming the output result of the second vibrometer. a first vibration determination unit that compares the difference between the vibration determination unit and the first vibration determination unit and detects whether the difference is equal to or greater than a first threshold value, and when the first vibration determination unit detects a difference equal to or greater than the first threshold value. calculating an estimated value of the wind speed at the installation position from the output result of the first vibration meter;
The disaster information acquisition device according to claim 3 or 4. The wind speed calculation unit includes a second vibration determination unit that detects whether the duration of vibration detected by the first vibration meter is equal to or greater than a second threshold, and the second vibration determination unit Calculating an estimated value of the wind speed at the installation position from the output result of the first vibration meter when a duration equal to or greater than a second threshold is detected;
The disaster information acquisition device according to claim 3 or 4. The second vibration determination unit specifies the duration using at least one of Trifunac's cumulative power method and Jennings' envelope function method.
The disaster information acquisition device according to claim 6. The rainfall intensity calculation unit calculates an estimated value of the rainfall intensity at the installation position from the amplitude of the time history of the output results detected by the vibration meter.
The disaster information acquisition device according to claim 1 or claim 2. The rainfall intensity calculation unit calculates an estimated value of the rainfall intensity at the installation position from the amplitude value in a predetermined frequency region of a Fourier spectrum obtained by Fourier transforming the output result detected by the vibration meter.
The disaster information acquisition device according to claim 1 or claim 2. The landslide detection unit detects the occurrence of a landslide at and around the installation position based on the number of times the output result detected by the vibration meter exceeds a third threshold within a unit time.
The disaster information acquisition device according to claim 1 or claim 2. The landslide detection unit includes a high-pass filter that extracts a vibration component having a frequency higher than a predetermined frequency from the output result detected by the vibration meter, and detects the installation position and the installation from the vibration component extracted by the high-pass filter. Detecting the occurrence of landslides around a location,
The disaster information acquisition device according to any one of claims 1, 2 and 10. The landslide detection unit includes a fitting unit that fits a curve represented by a Gaussian function to a Fourier spectrum obtained by Fourier transforming the output result of the vibration meter, and detects a difference between the two. Detecting the occurrence of landslides at and around the installation location;
The disaster information acquisition device according to any one of claims 1, 2, 10 and 11. A vibration meter connected to a communication network and capable of detecting vibrations at the installation location;
a data processing unit capable of acquiring the output results of the vibration meter via the communication network, and for acquiring disaster information of the installation location from the output results of the vibration meter,
The data processing unit includes a wind speed calculation unit that calculates an estimated value of wind speed at the installation position from the output result of the vibration meter, and a rainfall intensity calculation unit that calculates an estimated value of rainfall intensity at the installation position from the output result of the vibration meter. comprising two or more of a calculation unit and a landslide detection unit that detects the occurrence of a landslide at and around the installation position from the output results of the vibration meter;
Disaster information acquisition system.