JP2020153793A - Debris flow notification system and debris flow sensor - Google Patents

Abstract

To provide a debris flow notification system which can appropriately determine the level of danger of a debris flow and notify a user of the danger by separating a debris flow generated in a river between mountains and noise not caused by a debris flow.SOLUTION: The present invention includes: voice acquisition means for acquiring voice information; compression means for compressing the voice information and reducing the information amount; wireless transmission means for wirelessly transmitting the voice information compressed by the compression means; reception means for receiving the voice information wirelessly transmitted by the wireless transmission means; determination means for determining the situation of a debris flow by using the voice information received by the reception means; and notification means for notifying a user of the output of the determination means.SELECTED DRAWING: Figure 1

Description

The present invention relates to a debris flow notification system and a debris flow sensor.

Debris flow causes enormous damage to mountain rivers, so construction of sabo dams has been continued since the Meiji era. However, many rivers still do not have sabo dams, and according to the Ministry of Land, Infrastructure, Transport and Tourism, there were 3,451 landslide disasters in 1 metropolitan area, 2 prefectures and 41 prefectures, and the total was started in 1982. It has greatly exceeded the maximum number of cases from (2,537 cases in 2004).

In order to detect the occurrence of debris flow, a contact type debris flow detector using a wire and a non-contact type debris flow detector using a luminous flux are used. These debris flow detectors detect the occurrence of debris flow, etc. by detecting that the debris flow has cut the wire, or by detecting that the debris flow has blocked the luminous flux, and alert people living downstream. Is configured to emit.

However, these debris flow detectors can be erroneously detected by animals such as deer. Furthermore, with a debris flow detector that uses a wire, it is necessary to re-tension the wire installed in the mountains every time an animal cuts the wire, and maintenance costs are high. There is also the problem that there is not enough time to evacuate even if an alarm is issued after the debris flow occurs.

"Comparison of Plate Geophone and Pipe Hydrophone in Quicksand Observation", Disaster Prevention Research Institute, Kyoto University Annual Report No. 57 B June 2014

Japanese Unexamined Patent Publication No. 2001-281015 Japanese Unexamined Patent Publication No. 11-230792

As described in "Non-Patent Paper 1", in Japanese sabo rivers, attempts to measure the amount of quicksand by analyzing the sound of quicksand colliding with a hydrophone (metal pipe) are being studied. In Switzerland, plate-type geophones have been developed for a long time and are used in European countries.

The hydrophone extracts the sound of particles colliding with a metal tube installed on the riverbed in a specific frequency band, and measures the number of times the amplitude value exceeds the set amplitude threshold to estimate the amount of quicksand. is there. It is expected that the hydrophone can be used as an alarm for debris flow by capturing the phenomenon of gravel flowing before the debris flow occurs. In addition, there is no problem that the wire is cut by the animal, and there is an advantage that the data can be easily handled. However, if a large gravel collides with the pipe, the metal pipe will be deformed or damaged and will need to be replaced. In addition, it is necessary to set the amplitude threshold appropriately depending on the river conditions, but there is also the problem that the correct set value cannot be known unless an actual debris flow occurs.

The geophone developed in Switzerland has a structure in which an iron plate is installed on the top plate of the river floor and the number of gravel is counted by measuring the vibration caused by the gravel passing over it. With this structure, the geophone will not be destroyed even if a debris flow occurs. However, floor consolidation work is required in mountain rivers, which is difficult and extremely expensive.

Therefore, the debris flow / mudflow generation detection device described in Patent Document 1 transmits ultrasonic waves and radio waves to the flowing body flowing down on the inclined surface, receives the reflected wave from the flowing body, and receives the spectrum of the received wave. Focusing on the behavior during distribution, the presence or absence of debris flow or mudflow is determined.

According to the debris flow / mudflow generation detection device described in Patent Document 1, it is considered that the debris flow / mudflow generation can be detected at a relatively low installation cost. However, the spread of the spectral distribution varies greatly depending on the structure of the river. It is difficult to correctly set the criteria for spectral distribution until a debris flow actually occurs.

The debris flow detection system described in Patent Document 2 is a system in which a sound wave detector is installed on an object in contact with the water surface of a target river, a sound wave level is transmitted wirelessly, and a debris flow is determined from the received sound wave level. This system has the advantage of being inexpensive to install. However, when the sound wave level increases due to a phenomenon other than debris flow (for example, when there is a lightning strike, when a helicopter flies near the detector, when a heavy machine such as a bulldozer runs, etc.), an erroneous report is made. There is a problem that it ends up.

Therefore, the present invention provides a debris flow reporting system and a debris flow sensor capable of appropriately determining and reporting the risk of debris flow by distinguishing between debris flow generated in a river in a mountainous area and noise other than debris flow. The purpose is.

The earth and stone flow reporting system of the present invention includes a voice acquisition means for acquiring voice information, a compression means for compressing the voice information to reduce the amount of information, and a wireless transmission means for wirelessly transmitting the voice information compressed by the compression means. A receiving means for receiving the voice information wirelessly transmitted by the wireless transmission means, a determination means for determining the state of the earth and stone flow using the voice information received by the receiving means, and an output of the determination means. It has a notification means for notifying the user.

The earth and stone flow sensor of the present invention includes a voice acquisition means for acquiring voice information, a compression means for compressing the voice information to reduce the amount of information, and a wireless transmission means for wirelessly transmitting the voice information compressed by the compression means. The compression means compresses the amount of information by using the audio information according to a predetermined waveform model.

In the debris flow reporting system and the debris flow sensor of the present invention, a microphone is installed at a position away from the target river to acquire a voice signal of the debris flowing in the river. Voice information (amount of information) is compressed by detecting a signal peculiar to a gravel collision from the acquired voice signal, and transmitted to a cloud computer via a receiver at the foot of the LPWA (Low Power Wide Area) radio. .. LPWA wireless is a wireless communication technology that can be applied when the amount of information to be transmitted is small (approximately 100 bits), and is a technology that enables long-distance wireless transmission with low power consumption.

The cloud computer is equipped with a judgment engine, which uses compressed audio information transmitted by LPWA to make judgments regarding debris flow and report the degree of risk of debris flow. The cloud computer accumulates compressed audio information transmitted by LPWA in a database. Since the cloud computer updates (learns) the judgment engine by comparing it with the actual debris flow generation, the performance of the judgment engine is gradually improved. As a result of repeating the learning of debris flow in this way, it is a system that correctly determines and notifies the degree of risk regarding debris flow.

According to the present invention, voice information in which gravel collides with gravel is detected at a place away from a river, voice information is compressed by using the voice waveform peculiar to gravel, and wireless to a receiver at the foot of the river. By transmitting, the risk of debris flow can be appropriately determined and reported by distinguishing between debris flow and noise other than debris flow. In addition, since the amount of information transmitted wirelessly is small, wireless technology (LPWA) that operates over long distances and with low power consumption can be applied, and it becomes possible to determine the danger due to debris flow in the mountains that mobile phones cannot reach. In addition, by updating (learning) the database of the judgment engine using the actual debris flow occurrence status as teacher data, it is possible to provide a system that is far more reliable than the conventional debris flow detection system at a low cost. It becomes.

It is a figure explaining the whole structure of the debris flow report system 1. It is a figure which shows the circuit structure of the sensor information compression transmitter 20. It is a flowchart which shows the operation algorithm of CPU 24. It is a figure which shows the structure of the 128-bit payload data 30. It is a figure which shows the compression algorithm of voice information. It is a figure which shows the collision waveform (A) of gravel and gravel, and the modeled waveform (B). It is a figure which shows an example of the collision spectrum distribution of a gravel and a gravel. It is a schematic diagram which shows the structural example of the determination engine 11.

Hereinafter, a mode for carrying out the present disclosure (hereinafter referred to as an embodiment) will be described.

<Overall configuration>
FIG. 1 is a diagram for explaining the overall configuration of the debris flow notification system 1.
As shown in FIG. 1, the debris flow notification system 1 is a system that detects the state of debris flow that may occur in mountainous areas or the precursory phenomenon of debris flow and notifies the user terminal 8.

In the debris flow notification system 1, a plurality of sensor information compression transmitters 20 (compression means) are installed at a location away from the river 14 in the upstream portion of the river 14 in the mountainous area 13. The sensor information compression transmitter 20 detects information such as voice information, odor information, and rainfall information around the installation location, compresses it into 128-bit payload data, and transmits it over a long distance to the receiving station 7 at the foot of the device by LPWA radio. ..

The LPWA receiver 7 installed in the urban area receives the payload data 30 (see FIG. 4 described later) transmitted from the sensor information compression transmitter 20, decodes it, and transmits it to the cloud computer 10 on the Internet. The cloud computer 10 cuts out information of various sensors from the payload data 30 and stores it in the database 12. The cloud computer 10 also supplies data obtained from various sensors to the determination engine 11 (determination means).

The determination engine 11 is a recognition program executed by the cloud computer 10, and is realized by, for example, a neural network. When the determination engine 11 determines that a debris flow has occurred or is likely to occur, a mail is transmitted to the user terminal 8 to increase the risk of debris flow to local residents and local governments. Inform you that you are. This e-mail includes GPS position information and information indicating the state of debris flow determined by the determination engine 11.

With reference to the information displayed on the user terminal 8, the local government and the like will issue evacuation instructions as appropriate. Of course, it is expected that debris flow will occur as it is displayed on the user terminal 8, but there is a possibility that the result will be different from that displayed on the user terminal 8 due to a judgment error of the judgment engine 11. At this time, the user operates the user terminal 8 to feed back the actual situation to the cloud computer 10. The cloud computer 10 accumulates the fed-back information as teacher data in the database 12.

When direct feedback cannot be obtained from the user, the surroundings of the river 14 are photographed by a drone before and after the rainfall, and the image information of the gravel on the riverbed is compared to know the information of the gravel flowing downstream. Can be done. Such gravel movement information is also fed back to the cloud computer 10 and utilized as teacher data.

In this way, information on various sensors and teacher data are accumulated in the database 12. Using these sensor information and teacher data, the cloud computer 10 updates (learns) the judgment engine 11. That is, the judgment engine 11 is configured to improve its judgment performance by feedback from the user. By sequentially improving (learning) the performance of the determination engine 11 in this way, this system finally constructs a system that enables debris flow determination with high accuracy. It is desirable to update the determination engine 11 frequently in the summer when heavy rains that cause debris flow occur frequently.

Since the hydrophones that have been used conventionally have been installed in the basin of the river 14, there is a high possibility that the hydrophones will be broken or washed away when a debris flow occurs. On the other hand, in this embodiment, the sensor information compression transmitter 20 is installed at a location distant from the river 14 within the range where the sound can reach, and the sound waveform generated by the collision of the gravel and the gravel is extracted and compressed. By transmitting from the sensor information compression transmitter 20, the possibility that the sensor information compression transmitter 20 is broken is reduced even when a gravel flow occurs.

Further, in this embodiment , since the voice waveform is compressed, low-bit and long-distance wireless technology such as LPWA can be applied, and the sensor information compression transmitter 20 can be installed in a remote mountain. It has become. Further, since the waveform in which the gravel collides with the gravel is extracted, there is a low possibility of malfunction due to noise unlike the conventional number of pulses, and a reliable judgment can be made.

<Structure of sensor information compression transmitter 20>
FIG. 2 is a diagram showing a circuit configuration of the sensor information compression transmitter 20.
As shown in FIG. 2, the sensor information compression transmitter 20 has a built-in battery 27 and supplies power to each part of the WakeUp circuit 26 and the sensor information compression transmitter 20. The voice sensor 23 is composed of a small microphone and an AD converter, and detects voice information in which gravel collides with gravel in or near the river 11 and sends it to the CPU 24. The odor sensor 22 is a so-called malodor sensing sensor that detects sulfur compound gas and the like, and AD-converts the detection output and sends it to the CPU 24. The rainfall sensor 21 detects the amount of rainfall, converts it to AD, and sends it to the CPU 24.

In the past debris flow, it has been reported that "smell" and "sound of rolling stones" were observed about 1 hour before the debris flow occurred. The voice sensor 23 and the odor sensor 22 are useful for more accurately determining the danger of debris flow by capturing such a precursory phenomenon.

The GPS receiver 29 receives radio waves from a plurality of GPS satellites orbiting the earth, calculates latitude and longitude information of the place where the sensor information compression transmitter 20 is installed, and sends the radio waves to the CPU 24. The radio transmitter 25 transmits the 128-bit payload data generated by the CPU 24 as LPWA radio.

The CPU 24 compresses the information from the voice sensor 23 by an algorithm described later, adds GPS latitude / longitude information and an identification code (ID), and adds the information of the odor sensor 22 and the rainfall sensor 21 to obtain a 128-bit payload. The data 30 is created and sent to the wireless transmitter 25.

The WakeUp circuit 26 monitors the output level of the voice sensor 23, and when the level falls below a predetermined value, issues an instruction to the CPU 24 and the wireless transmitter 25 to put the sensor information compression transmitter 20 into sleep mode. Reduce power consumption.

FIG. 3 is a flowchart showing an operation algorithm of the CPU 24.
In step SP1, the CPU 24 executes a signal processing algorithm described later every minute. In step SP2, the CPU 24 measures the voice level obtained from the voice sensor 23, and if the level is equal to or less than a predetermined value, the processing is interrupted to reduce the power consumption of the sensor information compression transmitter 20.

If the signal level obtained from the voice sensor 23 (voice acquisition means) exceeds a predetermined value, that is, if there is a possibility that a rustling sound due to rainfall or a sound of gravel rolling on a slope is detected, a step is taken. In SP3, the power of various sensors is turned on, the subsequent processing is executed, and information measurement related to the earth and stone flow and LPWA wireless communication are performed.

In step SP4, the CPU 24 acquires the latitude and longitude information of the installation location from the GPS receiver 29. For example, information such as latitude 36.030160 degrees north and longitude 138.155298 degrees east can be obtained from the GPS receiver 29. The CPU 24 compresses latitude and longitude into 24-bit latitude information and longitude information by representing 6-digit information (“030160” and “155298” in the above example) in BCD (Binary Coded Decimal), respectively. ..

The digits above the decimal point of latitude and longitude (36 degrees north latitude, 138 degrees east longitude) can be restored by the receiver 7, and may be deleted in this way. In addition, the latitude and longitude information obtained in this way can be used to know the fact that the sensor information compression transmitter 20 has been "flowed" because the latitude and longitude change if it is washed away by a debris flow. , It is important information to show that the debris flow has already occurred.

In step SP5, the data of the odor sensor 22 is processed. By converting the output value of the odor sensor 22 into 6 bits, it is added to the payload data 30 as odor level information SCENT.

As mentioned above, in the past debris flow occurrence, a peculiar odor has been reported from 1 hour ago. There, odor levels can be a precursor to debris flow. However, since wild animals such as bears also emit extremely strong odors, it is not appropriate to estimate the possibility of debris flow based only on the odor level. Therefore, in this system, the odor level is transmitted to the cloud computer 10 as one of the reference information, stored in the database 12, and combined with the information from other sensors and the past results such as whether debris flow occurred or not. Therefore, the determination engine 11 mounted on the cloud computer 10 is made to learn.

In step SP6, the data of the rainfall sensor 21 is processed and added to the payload data 30 as 6-bit data (Prescription). Precipitation is the root cause of debris flow, but whether or not debris flow occurs is also affected by soil composition and groundwater flow.
Therefore, it is not appropriate to judge the occurrence of debris flow based only on the amount of rainfall. Therefore, in this system, the amount of rainfall is transmitted to the cloud computer 10 as one of the reference information, stored in the database 12, and combined with the information from other sensors and the actual information such as whether or not the debris flow occurred. The determination engine 11 is configured to learn.

In step SP7, the CPU 24 processes the information of the voice sensor 23. As described above, the voice sensor 23 is composed of a small microphone and an AD converter, and detects surrounding voice information. If the conversion rate of the AD converter is 10 kHz and the number of quantization bits is 10 bits, the data will be as much as 6 megabits in 1 minute. Although this data contains various information and is useful, it cannot be transmitted by LPWA due to the large amount of data. Therefore, the CPU 24 extracts audio information useful for debris flow detection as will be described later in FIG. 5, and adds it to the payload data 30 as a 42-bit compressed audio information Sound.

In step SP8, the 128-bit payload data 30 is configured as shown in FIG. 4, which will be described later. The beginning of the payload data 30 is 16-bit ID information, and a unique number recorded in the internal non-volatile memory of the CPU 24 can be used. Next, status information (5 bits), GPS latitude information (24 bits) and longitude information (24 bits), ambient temperature information Temp (5 bits), rainfall information Precipitation (6 bits), and odor information. The payload data is composed of SCENT (6 bits) and compressed audio information (42 bits).

FIG. 4 is a diagram showing a configuration of 128-bit payload data 30.
As shown in the lower left of FIG. 4, the status information is composed of TEST (1 bit) and BAT information (4 bits) indicating the remaining battery level in 16 steps. A test switch 28 is mounted on the sensor information compression transmitter 20. When confirming that the system operates correctly, the operation test of the system can be performed by pressing the test switch 20 and setting the TEST information of the payload data to "1".

In step SP9, the payload data 30 is transmitted by LPWA radio. This radio signal is received by the LPWA receiver 7 installed in an urban area or the like and transmitted to the cloud computer 10. The cloud computer 10 determines the risk of debris flow by the determination engine 11, and notifies the residents and local governments by sending an e-mail to the user terminal 8. This mail includes the position information by GPS and the state of the debris flow determined by the determination engine 11. The local government can make necessary decisions such as the start of evacuation preparations comprehensively by referring to not only the information sent by this e-mail but also the information such as the amount of precipitation in the entire area by the X-band radar.

FIG. 5 is a diagram showing a compression algorithm for voice information.
FIG. 6 is a diagram showing a gravel-to-grave collision waveform (A) and a modeled waveform (B).
In FIG. 5, the process of step SP7 (compression of information obtained from the voice sensor 23) will be described in detail. In step SP20, the CPU 24 regards the entire waveform captured in one minute as noise and calculates the noise level LVN. Assuming that the AD conversion result of N points at the time interval Δ is Au (n) (n = 0, 1, ... N-1), the noise level LVN is calculated by Equation 1.

LVN = {Σ (Au (n) ・ Au (n))} ÷ N ・ ・ Equation 1

Here, "Σ" is an operator that represents the sum of N points, and "・" is an operator that represents multiplication.

In step SP21, the value of the counter CNT is reset to zero. The counter CNT is a counter that counts voice pulses caused by collision of gravel with gravel. CNT = 0 indicates that no pulse sound due to collision was generated during the measurement time.

In step SP22, the waveform data Au (n) (see FIG. 6A) is scanned to search for peaks of the waveform that exceed a predetermined level. The peak of the waveform may be caused by the collision of gravel with gravel. When a waveform peak that has not been processed in steps SP24 to SP28 is found, the processing after step SP24 is performed to extract a voice waveform due to a collision between gravel and gravel.

In step SP24, 1 is added to the counter CNT. The value of the counter CNT represents a pulse in which the gravel seems to have collided with the gravel.

In step SP25, waveform data Au (n) before and after the peak is cut out. FIG. 6A shows an example of the collision waveform of the gravel and the gravel cut out in this way. It can be seen that the waveform peaks immediately after the collision and then decays rapidly, that is, it is a free vibration accompanied by damping. Such a waveform is completely different from the sound of an animal or a helicopter, and is modeled by the following equation 2.

R (n-τ) =
A ・ EXP (-β ・ n) ・ Sin (2π ・ Fpeak ・ Δ ・ n) ・ ・ Equation 2

Here, A is the amplitude of the peak, β is the damping coefficient, Fpeak is the free vibration frequency, and τ is the time delay.

In step SP26, the CPU 24 calculates the spectrum by Fourier transforming the cut out voice information Au (n).

FIG. 7 is a diagram showing an example of the collision spectrum distribution of gravel and gravel. FIG. 7 shows an example of the spectrum obtained by Fourier transforming the voice information Au (n). In FIG. 7, the peak of the spectrum is observed at a frequency of 2200 Hz (Fpeak). In general, the larger the gravel, the lower the peak frequency Fpeak tends to be.

In step SP27, the attenuation coefficient β and the time delay τ are estimated. That is, the waveform R (n) is obtained by Equation 2 and the correlation coefficient with Au (n) is obtained by using β and τ that are sequentially changed stepwise from a predetermined initial value. The combination of β and τ that maximizes this correlation coefficient is obtained and adopted as the attenuation coefficient β and the time delay τ. Further, as the peak amplitude A, the peak value of the cut-out voice waveform Au (n) can be used.

FIG. 6B described above shows an example of R (n−τ) thus obtained. It can be seen that the waveform shown in FIG. 6 (B) is almost the same as the actually observed gravel-grave collision waveform (FIG. 6 (A)).

In step SP28, the SNR is calculated according to Equations 3 and 4.

Err (n) = Au (n) -R (n-τ) ... Equation 3

SNR = Σ {R (n-τ) · R (n-τ)}
÷ Σ {Err (n) ・ Err (n)} ・ ・ Equation 4

The SNR obtained in this way has a large value if it is a voice waveform due to collision of gravel and gravel. On the contrary, when it is an animal bark, the SNR becomes a small value.

When step SP28 is completed, the process returns to SP22, the waveform data Au (n) is scanned again, and it is determined in step SP23 whether or not there is a waveform peak that has not yet been processed. When an untreated peak remains, SNR, β, and τ are obtained by repeating the processes of steps SP24 to SP28.

When the processing for all the waveform data is completed, the compression processing of the voice waveform Au is completed. In step SP29, the payload data 30 is set. That is, as shown in the lower right of FIG. 4, the count CNT is stored as 10-bit data. Subsequently, the SNR, the peak frequency Fpeak, the attenuation coefficient β, and the noise level LVN are each stored as 8-bit data. When the counter CNT is 2 or more, Fpeak, β, and LVN when the SNR is maximized are stored. The accuracy of each value is adjusted so that the number of bits is predetermined.

In this way, in this embodiment, the voice information Au (n), which is 6 megabits, is compressed into 42 bits of information (Sound) and then transmitted. When compressing data, it uses the waveform characteristics peculiar to the collision sound between gravel and gravel. Further, in order to know whether or not the compression process is appropriate, SNR is added as an evaluation index so that the determination engine 11 can make a more accurate determination.

<Structure of judgment engine 11>
FIG. 8 is a schematic diagram showing a configuration example of the determination engine 11. FIG. 8 shows the configuration of the determination engine 11 that determines the degree of risk by processing the information of various sensors extracted from the payload data. By inputting the information of various sensors into a so-called feed-forward type neural network, the degree of risk is determined. The internal setting of the neural network is performed by learning (backpropagation) using the past sensor information stored in the database 12 and the teacher data. A detailed description of the feed-forward type neural network will be omitted.

In the above-mentioned debris flow notification system 1, a plurality of sensor information compression transmitters 20 are installed in a place away from the river 14 in the upstream part of the river 14 in the mountainous area 13, so that the hide has been conventionally used. It is possible to capture the risk of debris flow by compensating for the shortcomings of the phone. In addition, the debris flow reporting system 1 makes it possible to utilize long-distance communication means such as LPWA by compressing and transmitting voice information generated when gravel collides with gravel using its characteristics, and is a mobile phone. The danger of debris flow can be detected even in mountainous areas where lines cannot be used. Further, in the debris flow reporting system 1, it is possible to construct a system that enables highly accurate debris flow judgment by accumulating the fed-back information as teacher data and updating (learning) the judgment engine 11. ..

In the above examples, it is assumed that the collision sound is a free vibration with damping. In reality, the voice sensor 23 has a response speed and cannot cope with a very rapid signal change. Therefore, by considering the response speed of the voice sensor 23, it becomes possible to more accurately model the collision sound detected by the voice sensor 23. In this case, it can be dealt with by modifying the conventional equation 2 as in the following equation 5.

R (n-τ) =
A {1-EXP (-α ・ n)} ・ EXP (-β ・ n) ・ Sin (2π ・ Fpeak ・ Δ ・ n) ・ ・ Equation 5

Here, α is a parameter determined by the response speed of the voice sensor 23 or the like.

Further, it is also possible to simplify the equation 5 and express it by the following equation 6.

R (n-τ) =
A (θ ・ n) ・ EXP (-β ・ n) ・ Sin (2π ・ Fpeak ・ Δ ・ n) ・ ・ Equation 6

Here, θ is a parameter determined by the response speed of the voice sensor 23 or the like.

1 Earth and stone flow notification system, 7 LPWA receiver, 8 user terminal, 10 cloud computer, 11 judgment engine, 12 database, 13 mountainous area, 14 rivers, 20 sensor information compression transmitter, 21 rain sensor, 22 odor sensor, 23 voice sensor , 24 CPU, 25 LPWA wireless transmitter, 26 WakeUp circuit, 27 battery, 28 test switch, 29 GPS receiver

Claims (8)

Voice acquisition means to acquire voice information and
A compression means that compresses the voice information to reduce the amount of information,
A wireless transmission means for wirelessly transmitting the voice information compressed by the compression means, and
A receiving means for receiving the voice information wirelessly transmitted by the wireless transmission means, and
A determination means for determining the state of debris flow using the voice information received by the reception means, and
A debris flow notification system characterized by having a notification means for notifying the user of the output of the determination means. The debris flow reporting system according to claim 1, wherein the compression means compresses an amount of information by using the voice information to follow a predetermined waveform model. The debris flow reporting system according to claim 2, wherein the waveform model is an equation of free vibration accompanied by damping. The compression means includes an evaluation means for evaluating the similarity between the voice information and a predetermined voice model when compressing the voice information, and the wireless transmission means transmits the evaluation result of the evaluation means. The earth and stone flow reporting system according to claim 1, which is characterized. The debris flow according to claim 1, wherein the determination means is equipped with a learning means for accumulating and learning information on a past movement state of earth and stone, and the internal state of the determination means is updated by the learning means. Reporting system. Voice acquisition means to acquire voice information and
A compression means that compresses the voice information to reduce the amount of information,
It has a wireless transmission means for wirelessly transmitting the voice information compressed by the compression means.
The compression means is a debris flow sensor characterized in that the amount of information is compressed by using the voice information according to a predetermined waveform model. The debris flow sensor according to claim 6, wherein the predetermined model is a free vibration equation with damping. The debris flow sensor according to claim 6, wherein the compression means outputs an evaluation index representing a degree of matching between the voice information and the predetermined waveform model, and the wireless transmission means transmits the evaluation index.