CN117877213A - Real-time monitoring and early warning system and method for bank collapse based on acoustic sensor - Google Patents
Real-time monitoring and early warning system and method for bank collapse based on acoustic sensor Download PDFInfo
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Abstract
The invention discloses an acoustic sensor-based bank collapse real-time monitoring and early warning system and method, and belongs to the technical field of intelligent monitoring and early warning of geological disasters. The bank collapse real-time monitoring and early warning system comprises an acoustic sensor array, a distributed signal collector and a data fusion early warning server. The method comprises the following steps: firstly, fixedly burying an acoustic sensor array on the surface layer of a river beach or a embankment, and collecting acoustic signals in real time; then the signal transmission is completed through the distributed signal collector; the data fusion early warning server performs data analysis processing and extracts acoustic characteristics such as monitoring signal frequency spectrum, time domain and the like; and establishing a bank collapse monitoring acoustic signal characteristic database by fusing the frequency spectrum characteristics, the time domain characteristics and the like, identifying suspected bank collapse signals through acoustic signal characteristic similarity analysis, and sending out early warning information. The invention realizes early warning of dangerous case of bank collapse, is favorable for taking dangerous removal and reinforcement measures in advance, and reduces the degree of bank collapse hazard and economic loss.
Description
Technical Field
The invention relates to an acoustic sensor-based real-time monitoring and early warning system and method for bank collapse, and belongs to the technical field of intelligent monitoring and early warning of geological disasters.
Background
The sand binary structure river bank is poor in geological condition, and under the influence of multiple adverse factors such as rainfall, water flow washing, rapid water level drop or earthquake induction, the stability of the bank slope is reduced, and dangerous bank collapse easily occurs. The bank collapse not only endangers the safety of river embankment and coastal infrastructure, but also can induce dangerous situations of breach in serious cases, and forms serious threat to the life and property safety of people, thereby being one of natural disasters needing important defense. In the past, a manual inspection mode is generally adopted, so that the difficulty that dangerous situations can be found early is greatly increased, and the application of a scientific and technological means in monitoring dangerous situations on the bank collapse is urgently needed. Dangerous case monitoring and early warning are important technical means for realizing forward movement of disaster prevention gateway, but dangerous case occurrence of bank collapse has gradual change and burst property, and the difficulty of monitoring and early warning is high.
The river bank collapse is complex in formation and various in forms, and is easy to cause collapse of an upper viscous soil layer after water current on the coast washes a lower sand soil layer, the underwater bank slope is damaged generally earlier than an above water part, and the underwater bank slope collapse monitoring is favorable for judging the stability of the bank slope in advance, so that more time is won for dangerous case defense of the bank collapse. The bank collapse monitoring by utilizing satellite remote sensing, GNSS positioning, oblique photography and other technologies is mainly suitable for monitoring the collapse of the water bank slope, and the pressure change monitoring based on the pressure sensing technology is applied to the water and underwater bank collapse monitoring. However, due to the fact that the construction difficulty of the underwater embedded pressure sensor is high, the underwater bank slope has the problem of weak applicability in a steep bank collapse high risk section. The patent application No. CN202010610338.4 discloses a multi-source sonar-positioning-based underwater landslide body deformation monitoring method, which is a method for monitoring underwater landslide by adopting a floating multi-source active sonar, but has the advantages of limited monitoring range, smaller monitoring area range and relatively higher cost.
Disclosure of Invention
In order to solve the problems, the invention discloses a bank collapse real-time monitoring and early warning system and method based on an acoustic sensor, and the specific technical scheme is as follows:
an acoustic sensor-based real-time monitoring and early warning system for bank collapse comprises an acoustic sensor, a distributed signal acquisition unit, a memory and a data fusion early warning server,
the plurality of acoustic sensors are buried in the surface layer of a river dike or a beach to form an acoustic sensor array, and the acoustic sensor array is used for acquiring acoustic signals;
the distributed signal acquisition device is connected with the acoustic sensor through a network to realize synchronous acquisition of acoustic signals of the acoustic sensor distributed at different positions, a main controller is arranged in the distributed signal acquisition device and is used for receiving a control command and a programming instruction of the data fusion early warning server, controlling the acoustic sensor array to complete signal acquisition work and sending time sequence data of the acquired acoustic signals to the data fusion early warning server;
the memory is used for storing the acoustic signal data acquired by the acoustic sensor and ensuring that the data is not lost under the condition of network interruption;
the data fusion early warning server is used for analyzing and processing the mass acoustic signal data, positioning suspected bank collapse points and sending early warning information.
A bank collapse real-time monitoring and early warning method of a bank collapse real-time monitoring and early warning system based on an acoustic sensor comprises the following steps:
step 1: selecting a bank collapse monitoring bank section, and arranging a plurality of rows and columns of acoustic sensors in the direction of a bank collapse monitoring bank section along a bank line to form an acoustic sensor array;
step 2: positioning all acoustic sensors by adopting a global satellite positioning system (GPS-RTK) to acquire position coordinates;
step 3: the acoustic sensor array collects acoustic signals when bank collapse dangerous situations do not occur in a monitoring range in real time, surveys and analyzes noise sources of monitoring bank sections, and the acoustic sensors collect acoustic signals of the noise sources, namely noise signals;
step 4: performing a simulated bank collapse field test on the monitoring bank section, and acquiring a simulated bank collapse acoustic signal by using an acoustic sensor;
step 5: processing the noise signals acquired in the step 3 and the acoustic signals acquired in the step 4, extracting frequency spectrum and time domain signal characteristics, and respectively constructing a noise signal characteristic matrix N 1 And suspected bank collapse signal feature matrix F 1 Establishing a bank collapse monitoring acoustic signal characteristic database;
step 6: developing real-time monitoring of bank collapse, applying acoustic signals acquired in real time in the monitoring process to the processing method in step 5, and constructing a real-time signal characteristic matrix A n Respectively calculating a real-time signal characteristic matrix A n Feature matrix F of suspected bank collapse signal 1 And noise signal characteristic matrix N 1 Calculating the fusion similarity, comparing with a preset threshold value, and judging whether the real-time signal is a suspected bank collapse signal or not;
step 7: and combining the sensor positions for monitoring the suspected bank collapse signals, positioning the suspected bank collapse points, and sending out early warning information to realize early warning of dangerous bank collapse.
Further, the steps 1-5 are to build a bank collapse monitoring acoustic signal characteristic database, which comprises the following steps:
step (1): the acoustic signal characteristics are classified by combining modes of manual inspection, multi-beam underwater topography measurement, side-scan sonar underwater topography measurement and the like: if the occurrence of the dangerous case of the bank collapse is not monitored in the mode in the acoustic signal acquisition period, judging that the signal in the time period is a noise signal, extracting signal characteristics and recording the signal characteristics into a noise signal characteristic library;
step (2): performing an analog bank collapse field test, taking acoustic signals acquired in the test process as suspected bank collapse signals, extracting signal characteristics and recording the signal characteristics into a suspected bank collapse signal characteristic library;
step (3): and the acoustic signal characteristics collected in real time are gradually accumulated, so that the bank collapse monitoring acoustic signal characteristic database is continuously perfected.
Further, in the step 5, the acoustic signal processing includes preprocessing and spectrum transformation, the preprocessing mainly includes audio segmentation and windowing processing, a hamming window is selected for audio segmentation, in order to ensure timeliness of early warning and reduce computational complexity, audio data are segmented in units of 10s, each 10s of data are used as a basic unit for analysis and early warning, and discrete fourier transformation is performed on each segmented audio data to obtain a spectrogram and a time domain diagram.
Further, the method for extracting the frequency spectrum and time domain signal features comprises the following steps: and detecting the spectrum energy peak point based on the spectrogram, recording time-frequency position information of the spectrum energy peak point, and constructing a feature matrix of the section of audio signal.
Further, in the step 6, the similarity analysis with the suspected bank collapse signal feature and the noise signal feature includes: respectively calculating to obtain a real-time audio signal characteristic matrix A n Characteristic matrix F of suspected bank collapse audio signal 1 And noise signal characteristic matrix N 1 Is used for the degree of similarity of (c) to (c),
real-time audio signal feature matrix A n Characteristic matrix F of suspected bank collapse audio signal 1 Similarity of (2)，
Real-time audio signal feature matrix A n And noise signal characteristic matrix N 1 Similarity of (2)，
Comprehensive similarity。
Further, the threshold setting of the comprehensive similarity is implemented by sequentially adopting different threshold values to test all data sets, combining on-site actual measurement modes such as manual inspection, multi-beam underwater topography measurement, side-scan sonar underwater topography measurement and the like, assisting in completing acoustic signal characteristic classification, and taking an average value of similarity values with the matching degree of a similarity calculation result and an actual measurement classification result being greater than 0.6 as a preset threshold value；
If it isGenerating a bank collapse early warning signal, and incorporating the signal characteristics into a suspected bank collapse signal characteristic library;
if it isThe signal features are incorporated into a noise signal feature library.
Further, in the step 7, the following method is adopted to perform suspected bank point location:
step 7.1: preliminary positioning of suspected bank collapse points is carried out, and the m coordinate of an acoustic sensor is assumed to beThe approximate coordinates of the suspected breakpoints are +.>According to the position coordinates of 4 observed acoustic sensors, the speed of sound c and the time required from the emission of the acoustic signal to the reception of the acoustic signal by acoustic sensor m +.>Solving approximate coordinates of suspected breakouts>For the geometrical distance between the approximate coordinates of the acoustic sensor m and the suspected breakup point,
，
sequential subtraction
，
Obtaining the approximate coordinates of the suspected bank collapse points，
Step 7.2: acoustic ray tracing, assuming that the acoustic velocity profile consists of multiple acoustic velocity layers,a sonic gradient for the i-th layer;the sound velocity and the corresponding depth of the i-th layer sound velocity profile, respectively,>sound velocity of sound velocity profile of the i+1th layer，
The radius of curvature of sound ray of the ith layer, p is Snell's theorem constant,an incident angle of the ith layer sound ray
，/>，
，/>The propagation distance of the acoustic signal in the horizontal direction and in the vertical direction of the i-th layer, +.>For the incident angle of the i +1 layer sound ray,
，
，
step 7.3: newton iteration method for solving initial incidence angle of sound rayCalculating the propagation time of the acoustic signal of the ith layer as +.>，/>，
Incident angle of sound rayUsing Snell constant +.7.2>Instead, then
，
The time required from the emission of the acoustic signal to the reception of the acoustic signal by the acoustic sensor m isThen
，
，
，
，
，
Iterative computation results inInfinite approaching 0, the Snell constant +.>To find the initial incidence angle of the sound ray +.>；
Step 7.4: accurate positioning of suspected bank collapse points is carried out, and the actual coordinates of the suspected bank collapse points are assumed to beThe approximate coordinates of the suspected bank collapse points are obtained in the step 7.1>The acoustic sensor m-coordinate is known as +.>，/>For acoustic sensor m andgeometric distance between actual coordinates of suspected breakpoints, +.>For the geometrical distance between the approximate coordinates of the acoustic sensor m and the suspected breakup point, then
，
，
Taylor series expansion:
，
，
obtaining the actual coordinates of the suspected bank collapse points。
The beneficial effects of the invention are as follows:
1. the invention realizes the integrated monitoring of water and underwater bank collapse: according to the invention, the acoustic sensor is adopted to collect the water and underwater bank collapse acoustic signals in the coverage area of the sensor in real time, so that the monitoring coverage area is further enlarged.
2. The equipment installation and arrangement difficulty and the cost are reduced, and nondestructive monitoring can be realized: the acoustic sensor adopted by the invention is only required to be buried in a river dike or beach soil body by about 50cm, has low installation difficulty, can be widely distributed, further expands the range of monitoring river reach, effectively avoids equipment from being damaged, and has small safety influence on the dike or beach; and the passive acoustic sensor is adopted in the invention, so that the cost is lower compared with active sonar monitoring.
3. The invention can realize the remote control of the equipment: according to the invention, the working state of each sensor can be monitored through the front-end acquisition system, and the signal acquisition frequency can be remotely set in real time according to the early warning signal grade, so that the monitoring efficiency is improved.
4. The early warning accuracy of the system can be optimized and improved: the bank collapse acoustic signal feature library established by the invention is continuously expanded and perfected along with the accumulation of the acquired acoustic signal features, so that the accuracy of system early warning can be improved.
Drawings
FIG. 1 is a schematic diagram of a monitoring system according to the present invention.
FIG. 2 is a flow chart of the monitoring method of the present invention.
Figure 3 is a schematic diagram of the acoustic ray tracing of the present invention,
in fig. 3, the left is a sound velocity profile, the right is a sound ray trace,is sound speed (I)>For depth->Sound velocity of sound velocity profile of ith layer, +.>Sound velocity of sound velocity profile of the i+1th layer, +.>Is the sound velocity gradient of the i-th layer, +.>For the propagation distance of the acoustic signal in the horizontal direction of the i-th layer,/a>For the propagation distance of the acoustic signal in the vertical direction of the i-th layer +.>For the i-th layer sound ray incidence angle, +.>Is the firsti+1 layer sound ray incidence angle, +.>Is the sound ray curvature radius of the i-th layer, < >>Is the center of the sound ray curvature circle.
Detailed Description
The invention is further elucidated below in connection with the drawings and the detailed description. It should be understood that the following detailed description is merely illustrative of the invention and is not intended to limit the scope of the invention.
According to the bank collapse real-time monitoring and early warning system and method based on the acoustic sensor, acoustic signals are collected in real time through the acoustic sensor buried in the river beach or the surface layer of the embankment, so that real-time monitoring and early warning of dangerous situations of water and underwater bank collapse are realized, the monitoring range is enlarged, the installation difficulty and cost of monitoring equipment are reduced, the adoption of dangerous and reinforcement measures in advance is facilitated, and the bank collapse hazard degree and economic loss are reduced.
Referring to fig. 1, it can be seen that the bank collapse real-time monitoring and early warning system based on the acoustic sensor comprises: an acoustic sensor array and a distributed signal collector which are buried on a river dike or beach, and a data fusion early warning server;
the acoustic sensor array comprises a plurality of acoustic sensor cells for acoustic signal acquisition;
the distributed signal collector is used for realizing the synchronous collection of multichannel acoustic signals distributed at different positions through network connection, and comprises:
the main controller is used for receiving the control command and the programming command of the server, controlling the acquisition station to complete signal acquisition work and transmitting the acquired time sequence data to the server;
the memory is used for storing the acoustic signal data acquired by the acoustic sensor and ensuring that the data is not lost under the condition of network interruption;
the data fusion early warning server is used for storing historical data of each sensor, providing analysis processing for mass acoustic signal data and sending early warning information.
Referring to fig. 2, the method for monitoring and early warning system in real time based on bank collapse of acoustic sensor comprises the following steps:
step 1: selecting a bank collapse monitoring bank section, and arranging n acoustic sensors in the direction of a bank line of the bank collapse monitoring bank section to form an acoustic sensor array; if the river width H is more than or equal to 500 meters and the near shore water flow velocity V is more than or equal to 0.8m/S, the arrangement distance S of adjacent sensors is less than or equal to H/2; if the river width H is less than 500 m and the flow velocity V of the near shore water flow is less than 0.8m/s, the arrangement distance of adjacent sensors can be H/2<S less than or equal to H.
Step 2: positioning all acoustic sensors by adopting a global satellite positioning system (GPS-RTK) to acquire position coordinates;
step 3: the acoustic sensor array collects acoustic signals when bank collapse dangerous situations do not occur in a monitoring range in real time, surveys and analyzes noise sources of monitoring bank sections, and the acoustic sensors collect acoustic signals of the noise sources, namely noise signals;
step 4: performing a simulated bank collapse field test on the monitoring bank section, and acquiring a simulated bank collapse acoustic signal by using an acoustic sensor;
step 1-5 is to build a bank collapse monitoring acoustic signal characteristic database, which comprises the following steps:
step (1): the acoustic signal characteristics are classified by combining modes of manual inspection, multi-beam underwater topography measurement, side-scan sonar underwater topography measurement and the like: if the occurrence of the dangerous case of the bank collapse is not monitored in the mode in the acoustic signal acquisition period, judging that the signal in the time period is a noise signal, extracting signal characteristics and recording the signal characteristics into a noise signal characteristic library;
step (2): performing an analog bank collapse field test, taking acoustic signals acquired in the test process as suspected bank collapse signals, extracting signal characteristics and recording the signal characteristics into a suspected bank collapse signal characteristic library;
step (3): and the acoustic signal characteristics collected in real time are gradually accumulated, so that the bank collapse monitoring acoustic signal characteristic database is continuously perfected.
Step 5: processing the acoustic signals acquired in the step 3 and the step 4, and extracting frequency spectrum and time domain signalsNumber features, respectively constructing suspected bank collapse signal feature matrix F 1 And noise signal characteristic matrix N 1 The method comprises the steps of carrying out a first treatment on the surface of the The acoustic signal processing comprises preprocessing and frequency spectrum transformation, the preprocessing mainly comprises audio segmentation and windowing processing, a clear window is selected for audio segmentation, in order to ensure timeliness of early warning and reduce computational complexity, audio data are segmented in units of 10s, each 10s of data are used as a basic unit for analysis and early warning, and discrete Fourier transformation is carried out on each segmented audio data to obtain a spectrogram and a time domain graph. The method for extracting the frequency spectrum and time domain signal features comprises the following steps: and detecting the spectrum energy peak point based on the spectrogram, recording time-frequency position information of the spectrum energy peak point, and constructing a feature matrix of the section of audio signal.
Step 6: developing real-time monitoring of the bank collapse, and constructing a real-time signal characteristic matrix A by applying the acoustic signals acquired in real time in the monitoring process to the same processing method as in the step 5 n Respectively calculating a real-time signal characteristic matrix A n Feature matrix F of suspected bank collapse signal 1 And noise signal characteristic matrix N 1 Calculating the fusion similarity, comparing with a preset threshold value, and judging whether the real-time signal is a suspected bank collapse signal or not; the similarity analysis with the suspected bank collapse signal features and the noise signal features comprises the following steps: respectively calculating to obtain a real-time audio signal characteristic matrix A n Characteristic matrix F of suspected bank collapse audio signal 1 And noise signal characteristic matrix N 1 Is used for the degree of similarity of (c) to (c),
real-time audio signal feature matrix A n Characteristic matrix F of suspected bank collapse audio signal 1 Similarity of (2)，
Real-time audio signal feature matrix A n And noise signal characteristic matrix N 1 Similarity of (2)，
Comprehensive similarity。
The threshold setting of the comprehensive similarity is carried out by sequentially adopting different threshold values to test all data sets, combining on-site actual measurement modes such as manual inspection, multi-beam underwater topography measurement, side-scan sonar underwater topography measurement and the like, assisting in completing the characteristic classification of acoustic signals, and taking the average value of similarity values with the matching degree of similarity calculation results and actual measurement classification results being more than 0.6 as a preset threshold value；
If it isGenerating a bank collapse early warning signal, and incorporating the signal characteristics into a suspected bank collapse signal characteristic library;
if it isThe signal features are incorporated into a noise signal feature library.
Step 7: and combining the sensor positions for monitoring the suspected bank collapse signals, positioning the suspected bank collapse points, and sending out early warning information to realize early warning of dangerous bank collapse.
The suspected bank point location is carried out by adopting the following method:
step 7.1: preliminary positioning of suspected bank collapse points is carried out, and the m coordinate of an acoustic sensor is assumed to beThe approximate coordinates of the suspected breakpoints are +.>According to the position coordinates of 4 observed acoustic sensors, the speed of sound c and the time required from the emission of the acoustic signal to the reception of the acoustic signal by acoustic sensor m +.>The geometrical distance between the approximate coordinates of the acoustic sensor m and the suspected breakup point is +.>Solving the approximate coordinates of the suspected bank collapse points,
，
the order of the steps is subtracted from each other,
，
obtaining the approximate coordinates of the suspected bank collapse points；
Step 7.2: acoustic ray tracing, assuming that the acoustic velocity profile consists of multiple acoustic velocity layers,is the sonic velocity gradient of the i-th layer,the sound velocity and the corresponding depth of the i-th layer sound velocity profile, respectively,>sound velocity of sound velocity profile of the i+1th layer
，
Is the sound ray curvature radius of the i-th layer, < >>Is Snell theorem constant, < ->An incident angle of the ith layer sound ray
，
，
，/>The propagation distance of the acoustic signal in the horizontal direction and in the vertical direction of the i-th layer, respectively +.>An incident angle of the (i+1) -th layer sound ray
，
，
Step 7.3: newton iteration method for solving initial incidence angle of sound rayCalculating the propagation time of the acoustic signal of the ith layer as +.>Then
，
Incident angle of sound rayUsing Snell constant +.7.2>Instead, then
，
Self-acoustic signalingThe time required for the acoustic sensor m to receive the acoustic signal isThen
，
，
，
，
，
Iterative computation results inInfinite approaching 0, the Snell constant +.>To find the initial incidence angle of the sound ray +.>；
Step 7.4: accurate positioning of suspected bank collapse points is carried out, and the actual coordinates of the suspected bank collapse points are assumed to beThe approximate coordinates of the suspected bank points are +.7.1>The acoustic sensor m-coordinate is known as +.>，/>For the geometrical distance between the acoustic sensor m and the actual coordinates of the suspected breakpoints, +.>For the geometrical distance between the approximate coordinates of the acoustic sensor m and the suspected breakup point, then
，
，
Taylor series expansion:
，
，
obtaining the actual coordinates of the suspected bank collapse points。
The technical means disclosed by the scheme of the invention is not limited to the technical means disclosed by the technical means, and also comprises the technical scheme formed by any combination of the technical features.
With the above-described preferred embodiments according to the present invention as a teaching, the above-described descriptions will enable workers to make various changes and modifications without departing from the scope of the technical idea of the present invention. The technical scope of the present invention is not limited to the description, but must be determined according to the scope of claims.
Claims (8)
1. A bank collapse real-time monitoring and early warning system based on an acoustic sensor is characterized by comprising the acoustic sensor, a distributed signal acquisition device, a memory and a data fusion early warning server,
the plurality of acoustic sensors are buried in the surface layer of a river dike or a beach to form an acoustic sensor array, and the acoustic sensor array is used for acquiring acoustic signals;
the distributed signal acquisition device is connected with the acoustic sensor through a network to realize synchronous acquisition of acoustic signals of the acoustic sensor distributed at different positions, a main controller is arranged in the distributed signal acquisition device and is used for receiving a control command and a programming instruction of the data fusion early warning server, controlling the acoustic sensor array to complete signal acquisition work and sending time sequence data of the acquired acoustic signals to the data fusion early warning server;
the memory is used for storing acoustic signal data acquired by the acoustic sensor, so that the data is not lost under the condition of network interruption;
the data fusion early warning server is used for analyzing and processing the massive acoustic signal data, positioning suspected bank collapse points and sending early warning information.
2. The bank collapse real-time monitoring and early warning method based on the bank collapse real-time monitoring and early warning system based on the acoustic sensor as claimed in claim 1 is characterized by comprising the following steps:
step 1: selecting a bank collapse monitoring bank section, and arranging a plurality of rows and columns of acoustic sensors in the direction of a bank collapse monitoring bank section along a bank line to form an acoustic sensor array;
step 2: positioning all acoustic sensors by adopting a global satellite positioning system (GPS-RTK) to acquire position coordinates;
step 3: the acoustic sensor array collects acoustic signals when bank collapse dangerous situations do not occur in a monitoring range in real time, surveys and analyzes noise sources of monitoring bank sections, and the acoustic sensors collect acoustic signals of the noise sources, namely noise signals;
step 4: performing a simulated bank collapse field test on the monitoring bank section, and acquiring a simulated bank collapse acoustic signal by using an acoustic sensor;
step 5: for the noise collected in the step 3Processing the acoustic signals and the acoustic signals acquired in the step 4, extracting frequency spectrum and time domain signal characteristics, and respectively constructing a noise signal characteristic matrix N 1 And suspected bank collapse signal feature matrix F 1 Establishing a bank collapse monitoring acoustic signal characteristic database;
step 6: developing real-time monitoring of the bank collapse, and constructing a real-time signal characteristic matrix A by applying the acoustic signals acquired in real time in the monitoring process to the same processing method as in the step 5 n Respectively calculating a real-time signal characteristic matrix A n Feature matrix F of suspected bank collapse signal 1 And noise signal characteristic matrix N 1 Calculating the fusion similarity, comparing with a preset threshold value, and judging whether the real-time signal is a suspected bank collapse signal or not;
step 7: and combining the sensor positions for monitoring the suspected bank collapse signals, positioning the suspected bank collapse points, and sending out early warning information to realize early warning of dangerous bank collapse.
3. The method for real-time monitoring and early warning of bank collapse according to claim 2, wherein the steps 1-5 are to build a bank collapse monitoring acoustic signal characteristic database, and further comprising the steps of:
step (1): the acoustic signal characteristics are classified by combining manual inspection, multi-beam underwater topography measurement and side-scan sonar underwater topography measurement: if the occurrence of the dangerous case of the bank collapse is not monitored in the mode in the acoustic signal acquisition period, judging that the signal in the time period is a noise signal, extracting signal characteristics and recording the signal characteristics into a noise signal characteristic library;
step (2): performing an analog bank collapse field test, taking acoustic signals acquired in the test process as suspected bank collapse signals, extracting signal characteristics and recording the signal characteristics into a suspected bank collapse signal characteristic library;
step (3): and the acoustic signal characteristics collected in real time are gradually accumulated, so that the bank collapse monitoring acoustic signal characteristic database is continuously perfected.
4. The method according to claim 2, wherein in step 5, the acoustic signal processing includes preprocessing and spectrum transformation, the preprocessing includes audio segmentation and windowing processing, a hamming window is selected for audio segmentation, in order to ensure timeliness of early warning and reduce computational complexity, audio data are segmented in units of 10s, data of every 10s are used as a basic unit of analysis and early warning, and discrete fourier transformation is performed on each segmented audio data to obtain a spectrogram and a time domain diagram.
5. The method for real-time monitoring and early warning of bank collapse according to claim 4, wherein the method for extracting the frequency spectrum and time domain signal features is as follows: and detecting the spectrum energy peak point based on the spectrogram, recording time-frequency position information of the spectrum energy peak point, and constructing a feature matrix of the section of audio signal.
6. The method according to claim 2, wherein the analysis of the similarity between the signal characteristics of the suspected bank collapse and the noise signal characteristics in the step 6 comprises: respectively calculating to obtain a real-time audio signal characteristic matrix A n Characteristic matrix F of suspected bank collapse audio signal 1 And noise signal characteristic matrix N 1 Is used for the degree of similarity of (c) to (c),
real-time audio signal feature matrix A n Characteristic matrix F of suspected bank collapse audio signal 1 Similarity of (2)，
Real-time audio signal feature matrix A n And noise signal characteristic matrix N 1 Similarity of (2),
Comprehensive similarity。
7. According to claim 6The bank collapse real-time monitoring and early warning method is characterized in that the threshold setting of the comprehensive similarity is carried out by sequentially adopting different threshold values to test all data sets, combining manual inspection, multi-beam underwater topography measurement and side-scan sonar underwater topography measurement, assisting in completing acoustic signal characteristic classification, and taking an average value of similarity values with the matching degree of similarity calculation results and actual measurement classification results being more than 0.6 as a preset threshold value；
If it isGenerating a bank collapse early warning signal, and incorporating the signal characteristics into a suspected bank collapse signal characteristic library;
if it isThe signal features are incorporated into a noise signal feature library.
8. The method for real-time monitoring and early warning of bank collapse according to claim 2, wherein in the step 7, the suspicious bank collapse point positioning is performed by adopting the following method:
step 7.1: preliminary positioning of suspected bank collapse points is carried out, and the m coordinate of an acoustic sensor is assumed to beThe approximate coordinates of the suspected breakpoints are +.>According to the position coordinates of 4 observed acoustic sensors, the speed of sound c and the time required from the emission of the acoustic signal to the reception of the acoustic signal by acoustic sensor m +.>The geometrical distance between the approximate coordinates of the acoustic sensor m and the suspected breakup point is +.>Solving the approximate coordinates of the suspected bank collapse points,
，
the order of the steps is subtracted from each other,
，
obtaining the approximate coordinates of the suspected bank collapse points；
Step 7.2: acoustic ray tracing, assuming that the acoustic velocity profile consists of multiple acoustic velocity layers,is the sound velocity gradient of the i-th layer, +.>The sound velocity and the corresponding depth of the i-th layer sound velocity profile, respectively,>sound velocity of sound velocity profile of the i+1th layer
，
Is the sound ray curvature radius of the i-th layer, < >>Is Snell theorem constant, < ->Is the ith layer sound rayIncident angle of (1)
，
，
，/>The propagation distance of the acoustic signal in the horizontal direction and in the vertical direction of the i-th layer, respectively +.>An incident angle of the (i+1) -th layer sound ray
，
，
Step 7.3: newton iteration method for solving initial incidence angle of sound rayCalculating the propagation time of the acoustic signal of the ith layer as +.>Then
，
Incident angle of sound rayUsing Snell constant +.7.2>Instead, then
，
The time required from the emission of the acoustic signal to the reception of the acoustic signal by the acoustic sensor m isThen
，
，
，
，
，
Iterative computation results inInfinite approaching 0, the Snell constant +.>To find the initial incidence angle of the sound ray +.>；
Step 7.4: accurate positioning of suspected bank collapse points is carried out, and the actual coordinates of the suspected bank collapse points are assumed to beThe approximate coordinates of the suspected bank points are +.7.1>The acoustic sensor m-coordinate is known as +.>，/>For the geometrical distance between the acoustic sensor m and the actual coordinates of the suspected breakpoints, +.>For the geometrical distance between the approximate coordinates of the acoustic sensor m and the suspected breakup point, then
，
，
Taylor series expansion:
，
，
obtaining the actual coordinates of the suspected bank collapse points。
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