CN113688276A - Whale real-time monitoring and distinguishing method and system - Google Patents
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Abstract
The invention relates to a method and a system for monitoring and distinguishing whales in real time, which comprises the following steps: a database establishing step, namely recording original sonar signals of different types of whales, analyzing the original sonar signals, and establishing a database in which the original sonar signals are associated with the whale types; and a comparison and judgment step, acquiring and recording collected sonar signals, analyzing the collected sonar signals, comparing the collected sonar signals with the database, and judging the type of the whale. According to the method, the variety of the odontocetia can be quickly and effectively judged by acquiring the sonar signals in the water area in the subsequent water area monitoring through establishing the database related to the odontocetia sonar signals and associating the sonar signals with the variety of the odontocetia.
Description
Technical Field
The invention relates to the field of whale identification, in particular to a real-time monitoring and identification method and system for whales.
Background
Small or medium-sized whales, which widely live in all oceans in the world, are distributed in the salt and fresh water near the entrances of inland seas and rivers, and individual species are found in inland rivers. Dolphins are a common whale.
Marine fishery fishing is an important protein source for humans, but fishery fishing is often interfered and affected by dolphins. Dolphins seek fish schools and are therefore often mistakenly caught by fishing nets in fishery fishing operations. The existing whale field judgment mainly depends on ship tracking to carry out photographing storage and identification, so that population judgment is carried out. The survey of ships needs to utilize a telescope to observe the sea surface in real time, wait for the whale to float out of the water surface, and then track, thereby limiting the survey work to a great extent.
The invention aims to provide a method and a system for monitoring and distinguishing whales in real time aiming at the problems in the prior art.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method and a system for monitoring and distinguishing a whale in real time, which can effectively solve the problems in the prior art.
The technical scheme of the invention is as follows:
a method for monitoring and distinguishing whales in real time comprises the following steps:
a database establishing step, namely recording original sonar signals of different types of whales, analyzing the original sonar signals, and establishing a database in which the original sonar signals are associated with the whale types;
and a comparison and judgment step, acquiring and recording collected sonar signals, analyzing the collected sonar signals, comparing the collected sonar signals with the database, and judging the type of the whale.
Further, the specific method for analyzing the original sonar signals is as follows:
carrying out peak value sound pressure level calculation on the original sonar signal, and intercepting data to be analyzed from the original sonar signal if the original sonar signal is greater than a first peak value sound pressure threshold value;
analyzing the frequency spectrum of the data to be analyzed to obtain a signal frequency spectrum, acquiring a peak frequency of the signal frequency spectrum, acquiring a frequency distribution range of a first decibel bandwidth and a frequency distribution range of a second decibel bandwidth from the signal frequency spectrum by taking the peak frequency as a reference, acquiring a frequency bandwidth numerical value of the frequency distribution range of the first decibel bandwidth and defining the frequency bandwidth numerical value as a first characteristic value, acquiring a frequency bandwidth numerical value of the frequency distribution range of the second decibel bandwidth and defining the frequency bandwidth numerical value as a second characteristic value, and defining the peak frequency as a third characteristic value;
and performing time domain energy integration on the data to be analyzed to obtain energy accumulation distribution, counting the time difference T of the energy accumulation distribution in a first energy range, and defining T as a fourth characteristic value.
Further, the first peak sound pressure threshold is 160dB to 180dB, and the step of intercepting the data to be analyzed from the original sonar signal specifically includes: and intercepting the data of 70 us-80 us before and after the peak value of the original sonar signal as data to be analyzed.
Further, the obtaining of the frequency distribution range of the first decibel bandwidth and the frequency distribution range of the second decibel bandwidth from the signal spectrum with the peak frequency as a reference specifically includes: and acquiring a frequency distribution range of a first decibel bandwidth and a frequency distribution range of a second decibel bandwidth from the signal frequency spectrum by taking the peak frequency as a center, wherein the first decibel bandwidth is a-3 decibel bandwidth, and the second decibel bandwidth is a-10 decibel bandwidth.
Further, the first energy range is 2.5% -97.5%, and the time difference v ∑ T of the energy accumulation distribution within the first energy range is counted as: obtaining a time point T at which said cumulative distribution of energy corresponds to 2.5% of the energy1Time point T corresponding to 97.5% of energy of said cumulative distribution of energy2Time difference ═ T2-T1。
Further, the specific method for analyzing the collected sonar signals is the same as the specific method for analyzing the original sonar signals.
Further, the establishing of the database of the correlation between the original sonar signals and the beluga species is specifically as follows: the first characteristic value, the second characteristic value, the third characteristic value and the fourth characteristic value are associated with the whale type, and a database is established; the analysis of the sonar signal acquisition and the comparison with the database specifically comprises the following steps: and analyzing the first characteristic value, the second characteristic value, the third characteristic value and the fourth characteristic value of the collected sonar signals, and comparing the first characteristic value, the second characteristic value, the third characteristic value and the fourth characteristic value with the database to obtain the whale type matched with the characteristic values.
Further, the original sonar signals and the collected sonar signals are one or more acoustic signals with the length of 0.8s-1.2 s.
Further provides a whale real-time monitoring and distinguishing system, which comprises the following modules:
the system comprises a database establishing module, a data acquisition module and a data processing module, wherein the database establishing module is used for recording original sonar signals of different types of whales, analyzing the original sonar signals and establishing a database in which the original sonar signals are associated with the types of the whales;
and the comparison and judgment module is used for acquiring and recording the collected sonar signals, analyzing the collected sonar signals, comparing the collected sonar signals with the database and judging the type of the whale.
Further, the system also comprises an analysis module for analyzing the original sonar signals or analyzing the collected sonar signals, wherein the analysis module specifically comprises:
the data to be analyzed acquisition submodule is used for carrying out peak sound pressure level calculation on the original sonar signal, and if the original sonar signal is greater than a first peak sound pressure threshold value, intercepting data to be analyzed from the original sonar signal;
a frequency spectrum characteristic value obtaining sub-module, configured to perform frequency spectrum analysis on the data to be analyzed to obtain a signal frequency spectrum, obtain a peak frequency of the signal frequency spectrum, obtain a frequency distribution range of a first decibel bandwidth and a frequency distribution range of a second decibel bandwidth from the signal frequency spectrum with the peak frequency as a reference, obtain a frequency bandwidth numerical value of the frequency distribution range of the first decibel bandwidth and define the frequency bandwidth numerical value as a first characteristic value, obtain a frequency bandwidth numerical value of the frequency distribution range of the second decibel bandwidth and define the frequency bandwidth numerical value as a second characteristic value, and define the peak frequency as a third characteristic value;
the energy characteristic value acquisition submodule is used for integrating the energy of the data to be analyzed to obtain energy accumulation distribution, counting the time difference V T of the energy accumulation distribution in a first energy range, and defining V T as a fourth characteristic value.
Accordingly, the present invention provides the following effects and/or advantages:
according to the method, the variety of the odontocetia can be quickly and effectively judged by acquiring the sonar signals in the water area in the subsequent water area monitoring through establishing the database related to the odontocetia sonar signals and associating the sonar signals with the variety of the odontocetia.
The method provided by the invention is used for matching the characteristics of the sonar signals of the whales, only the sonar signals above 160 dB-180 dB are acquired, and simultaneously 70 us-80 us of data before and after the sonar signal interception peak value corresponds to the sonar signals, so that the characteristics of the sonar signals of most of the whales can be met, noise doped in the sonar signals is eliminated, and data more beneficial to subsequent processing is obtained.
According to the method, the collected acoustic signals are subjected to parameter analysis of time difference, peak frequency, -3dB bandwidth and-10 dB bandwidth, the parameters are stored, a database is built, meanwhile, the variety of the whale is identified by using the parameters of the time difference, the peak frequency, -3dB bandwidth and-10 dB bandwidth, the traditional method for identifying the type of the whale by using images is broken through, the activity of the whale can be detected under different weather and time, the influence of whether the whale is floating on the water surface is avoided, and convenience is provided for field investigation of the whale.
It is to be understood that both the foregoing general description and the following detailed description of the present invention are exemplary and explanatory and are intended to provide further explanation of the invention as claimed.
Drawings
FIG. 1 is a schematic flow diagram of the process.
Fig. 2 is a waveform diagram of an original sonar signal.
Fig. 3 is a sonar waveform of the Chinese white dolphin in the time domain.
Fig. 4 shows a sonar waveform of eastern asian finless porpoise in the time domain.
Fig. 5 shows the sound pressure levels of the sonar waveforms of the chinese white dolphin and the east asian finless porpoise.
Fig. 6 shows the duration of the sonar waveform of the chinese white dolphin and the east asian finless porpoise.
Fig. 7 is a waveform diagram of data to be analyzed.
Fig. 8 is a frequency domain graph of fig. 7 after FFT.
Fig. 9 is an energy accumulation graph obtained by integration of fig. 7.
FIG. 10 shows the frequency domain characteristics of sonar signals from Chinese white dolphin and east Asia finless dolphin.
FIG. 11 is a plot of the peak frequency, -3dB bandwidth and-10 dB bandwidth profiles of FIG. 10.
FIG. 12 is a table of characteristic values of Chinese white dolphin and east Asia finless porphin.
Detailed Description
To facilitate understanding of those skilled in the art, the structure of the present invention will now be described in further detail by way of examples in conjunction with the accompanying drawings:
referring to fig. 1, a method for monitoring and distinguishing whales in real time includes the following steps:
s1, a database establishing step, namely recording original sonar signals of different types of whales, analyzing the original sonar signals, and establishing a database in which the original sonar signals are associated with the whale types;
and S2, a comparison and judgment step, wherein the collected sonar signals are obtained and recorded, and the collected sonar signals are analyzed and compared with the database to judge the type of the whale.
Specifically, the underwater acoustic transducer, the power amplifier and the digital-to-analog conversion unit are adopted in the implementation, the real-time collected acoustic signals can be transmitted to the software part for post-processing, the collection coverage frequency range can reach 200kHz, and the requirement of whale sonar signal collection with different frequencies and different bandwidths can be met. An underwater acoustic transducer is a tool that can perform electric energy-acoustic energy conversion underwater. The electric signal is transmitted to the digital-to-analog conversion unit and converted into an analog signal, the sampling rate is 400kS/s, and the conversion from low, medium and high frequency digital signals to the analog signal can be realized.
And laying cloth drainage acoustic transducers in a working sea area by using an investigation ship or a field experiment carrying platform. Underwater sound signals, namely sonar, are collected by an underwater acoustic transducer.
Firstly, a database is established, original sonar signals of different types of whales are recorded in a water area through a water-sound transducer, and the signal source of the original sonar signals can be the recorded sonar signals of the different types of whales in advance or the sonar signals of the different types of whales which are recorded in the water area in real time. Through continuously collecting one or more original sonar signals with the length of 1s, threshold setting is carried out on various acoustic parameters of the original sonar signals of different types of whales, the obtained original sonar signals are associated with the whale types, and a database is established.
Then, record the collection sonar signal at corresponding waters through underwater acoustic transducer, through continuous real-time, gather one or more collection sonar signals of 1s length, whether the analysis is gathered sonar signal belongs to the sonar signal that tooth whale sent, if, then each item acoustic parameter of the analysis collection sonar signal, judge whether each item acoustic parameter of gathering the sonar signal falls into the threshold value scope that certain tooth whale kind corresponds in the database, if then can judge out tooth whale kind according to the threshold value scope that corresponds.
And finally, prompting the workers to have the whales in the water area through a display screen and the like, wherein the whales are the types of the whales.
The original sonar signals and the collected sonar signals are one or more acoustic signals with the length of 0.8s-1.2 s. In this embodiment, both the original sonar signals and the collected sonar signals are 1s, and in other embodiments, the time may be 0.8s or 1.2 s. As shown in fig. 2.
The specific method for analyzing the collected sonar signals is the same as the specific method for analyzing the original sonar signals, and only the specific method for analyzing the original sonar signals is specifically described herein, and the specific method for analyzing the collected sonar signals can be analogized in turn.
The specific method for analyzing the original sonar signals comprises the following steps:
s1.1, performing peak value sound pressure level calculation on the original sonar signal, and if the original sonar signal is greater than a first peak value sound pressure threshold value, intercepting 70-80 us of data before and after the original sonar signal corresponding to the peak value as data to be analyzed; the first peak sound pressure threshold value is 160 dB-180 dB.
Specifically, peak sound pressure level calculation is performed on the original sonar signal, and if the original sonar signal is greater than 170dB, 75us of data before and after the original sonar signal corresponding to the peak value is intercepted from the original sonar signal, and 150us of data in total is used as data to be analyzed and stored and used for processing in subsequent steps. This is because underwater sonar signals are many and complicated, and it is necessary to filter out irrelevant signals by setting a threshold value and simply determine whether the sonar signals belong to sonar signals from whales. Referring to fig. 3-6, fig. 3 is a sonar waveform of a chinese white dolphin in the time domain, and fig. 4 is a sonar waveform of a east asian finless dolphin in the time domain, where the abscissa is time and the ordinate is amplitude. Fig. 5 shows sound pressure levels of sonar waveforms of the chinese white dolphin and the east asian finless porpoise, and fig. 6 shows durations of the sonar waveforms of the chinese white dolphin and the east asian finless porpoise. Through a large amount of sound signal analysis of whale, the applicant finds that the distribution interval of the Chinese white dolphin in the duration is approximately 20.6-57.5 microseconds, the sound pressure level of the Chinese white dolphin is 190.8-198.6dB, and the sound pressure level of the Chinese white dolphin is 163.58-179.5 dB. Because the energy of the acoustic signal emitted by the dolphin attenuates with distance of propagation, an optimal threshold is set at 170dB based on the monitored coverage distance. Therefore, the sonar signal is defined to be more than 170dB, so that the sound waveform of the whale is considered to be contained, and 150us is the most researched value before and after the peak value of the sound waveform. According to the embodiment, most of useless waveforms can be filtered through the two parameter settings, so that the optimal sonar signals are obtained, as shown by a dotted line frame in fig. 2, and the data to be analyzed obtained through interception is shown in fig. 7.
S1.2, analyzing the frequency spectrum of the data to be analyzed to obtain a signal frequency spectrum, obtaining the peak frequency of the signal frequency spectrum, and obtaining the frequency distribution range of a first decibel bandwidth and the frequency distribution range of a second decibel bandwidth from the signal frequency spectrum by taking the peak frequency as a center, wherein the first decibel bandwidth is-3 decibel bandwidth, and the second decibel bandwidth is-10 decibel bandwidth. And acquiring a frequency bandwidth numerical value of the frequency distribution range of the-3 dB bandwidth and defining the frequency bandwidth numerical value as a first characteristic value, acquiring a frequency bandwidth numerical value of the frequency distribution range of the-10 dB bandwidth and defining the frequency bandwidth numerical value as a second characteristic value, and defining the peak frequency as a third characteristic value.
Specifically, the data to be analyzed is subjected to FFT to obtain a frequency domain graph as shown in fig. 8, where the abscissa is frequency and the ordinate is decibel. The frequency point corresponding to the spectral peak is defined as the peak frequency fp, and the spectral energy value P is read. It can be obtained that the peak frequency is 100kHz and a frequency distribution range of-3 db bandwidth and a frequency distribution range of-10 db bandwidth are obtained from the signal spectrum centered at 100 kHz. The decibel bandwidth refers to a bandwidth corresponding to a drop of a corresponding decibel in a spectrogram with a certain point as a reference. In this embodiment, the peak frequency is used as a key point, and is reduced by 3dB and 10dB to obtain bandwidths corresponding to the two dashed lines shown in fig. 8, and a value corresponding to the dashed line is obtained on the X axis, and a range corresponding to the value, that is, a frequency bandwidth value, is obtained to obtain a first eigenvalue, a second eigenvalue, and a third eigenvalue.
S1.3, performing time domain energy integration on the data to be analyzed to obtain energy accumulation distribution, counting time difference T of the energy accumulation distribution in a first energy range, and defining T as a fourth characteristic value.
Specifically, the waveform shown in fig. 7 is integrated, resulting in an energy accumulation map as shown in fig. 9. The energy of the pulse is integrated, assuming that the pulse is x (n), and the energy E is defined as
It can be seen that the graph of the energy accumulation of the sonar signal of the whale initially has a slope close to 0, then the slope rises suddenly and rapidly, and finally the slope approaches 0 again, and the energy is concentrated in the middle part. Duration is defined as E2.5％Corresponding time point T1To E97.5％Corresponding time point T2Time difference between ═ T2-T1. The calculated time difference ∑ T is also an important parameter of the sonar signal of the whale, and is defined as a fourth feature value.
The applicant finds that the four characteristic values are closely related to the sound of the dolphin, and the kinds of dolphin can be distinguished through the four characteristic values. Then, a database is established by associating the first characteristic value, the second characteristic value, the third characteristic value and the fourth characteristic value with the whale species.
Through four eigenvalues, sonar signals can be detected in real time for different water areas, the collected sonar signals are analyzed, and the steps of analysis are similar to the steps S1.1-S1.3, which are not described again. And obtaining four characteristic values of the collected sonar signals, and comparing the first characteristic value, the second characteristic value, the third characteristic value and the fourth characteristic value with the database to obtain the whale type matched with the characteristic values.
Further provides a whale real-time monitoring and distinguishing system, which comprises the following modules:
the system comprises a database establishing module, a data acquisition module and a data processing module, wherein the database establishing module is used for recording original sonar signals of different types of whales, analyzing the original sonar signals and establishing a database in which the original sonar signals are associated with the types of the whales;
and the comparison and judgment module is used for acquiring and recording the collected sonar signals, analyzing the collected sonar signals, comparing the collected sonar signals with the database and judging the type of the whale.
Further, the system also comprises an analysis module for analyzing the original sonar signals or analyzing the collected sonar signals, wherein the analysis module specifically comprises:
the data to be analyzed acquisition submodule is used for carrying out peak sound pressure level calculation on the original sonar signal, and if the original sonar signal is greater than a first peak sound pressure threshold value, intercepting data to be analyzed from the original sonar signal;
a frequency spectrum characteristic value obtaining sub-module, configured to perform frequency spectrum analysis on the data to be analyzed to obtain a signal frequency spectrum, obtain a peak frequency of the signal frequency spectrum, obtain a frequency distribution range of a first decibel bandwidth and a frequency distribution range of a second decibel bandwidth from the signal frequency spectrum with the peak frequency as a reference, obtain a frequency bandwidth numerical value of the frequency distribution range of the first decibel bandwidth and define the frequency bandwidth numerical value as a first characteristic value, obtain a frequency bandwidth numerical value of the frequency distribution range of the second decibel bandwidth and define the frequency bandwidth numerical value as a second characteristic value, and define the peak frequency as a third characteristic value;
the energy characteristic value acquisition submodule is used for integrating the energy of the data to be analyzed to obtain energy accumulation distribution, counting the time difference V T of the energy accumulation distribution in a first energy range, and defining V T as a fourth characteristic value.
Example one
The specific embodiment of the invention takes the Chinese white dolphin and east Asia finless porphin which are present and absent in the Xiamen sea area as examples. Chinese white dolphin and east Asia finless porpoise live in coastal shallow waters, a sea area where human economic activities are intensive. The visibility of the shallow sea water body is limited, the traditional image acquisition and judgment method is difficult to monitor, and the Chinese white dolphin and the east Asia finless dolphin both rely on sonar signals emitted by the Chinese white dolphin and the east Asia finless dolphin to detect and prey. However, the environmental noise of shallow sea is strong, and the existence of the white Chinese dolphin and the east Asia dolphin is affected by shipping noise, ocean engineering noise (such as piling and wind power noise), underwater explosion and the like. The whale emits a series of sonar signal pulses during detection. The invention can utilize sonar signals sent by the whales to arrange the device to a working sea area, monitor and forecast the present whales, and carry out self-adaptive control on the noise intensity of the construction sea area, thereby monitoring the activities of the dolphin and the finless porpoise.
Referring to fig. 10-11, a database is established by the above method, the database at least comprises the chinese white dolphin and the east asia finless porphin, wherein the peak frequency of the chinese white dolphin is 31.435k-135.041k, and the average value is 80.461 k; peak frequency 127.29k-140.448k of finless porpoise, mean 133.345 k; the-3 dB bandwidth of the Chinese white dolphin is 8.900kHz-81.281kHz, and the average value is 39.334 kHz; the-3 dB bandwidth of the finless porpoise is 7.795kHz-21.355kHz, and the average value is 11.753 kHz; the-10 dB bandwidth of the Chinese white dolphin is 60.143kHz-158.086kHz, and the average value is 98.467 kHz; the-10 dB bandwidth of the finless porpoise is 15.3-36.6kHz, and the average value is 26.9 kHz. The time difference of the Chinese white dolphin is 20.6-57.5 microseconds, and the time difference of the Jiangtong dolphin is 47.5-115.6 microseconds, which is specifically referred to fig. 12.
By establishing the database, acquiring sonar signals in different water areas in real time, analyzing four characteristic values of the acquired sonar signals, and comparing the four characteristic values with the database, the type of the whale can be accurately judged.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.
Claims (10)
1. A method for monitoring and distinguishing whales in real time is characterized by comprising the following steps: comprises the following steps:
a database establishing step, namely recording original sonar signals of different types of whales, analyzing the original sonar signals, and establishing a database in which the original sonar signals are associated with the whale types;
and a comparison and judgment step, acquiring and recording collected sonar signals, analyzing the collected sonar signals, comparing the collected sonar signals with the database, and judging the type of the whale.
2. The method for monitoring and distinguishing whales in real time according to claim 1, wherein the method comprises the following steps: the specific method for analyzing the original sonar signals comprises the following steps:
carrying out peak value sound pressure level calculation on the original sonar signal, and intercepting data to be analyzed from the original sonar signal if the original sonar signal is greater than a first peak value sound pressure threshold value;
analyzing the frequency spectrum of the data to be analyzed to obtain a signal frequency spectrum, acquiring a peak frequency of the signal frequency spectrum, acquiring a frequency distribution range of a first decibel bandwidth and a frequency distribution range of a second decibel bandwidth from the signal frequency spectrum by taking the peak frequency as a reference, acquiring a frequency bandwidth numerical value of the frequency distribution range of the first decibel bandwidth and defining the frequency bandwidth numerical value as a first characteristic value, acquiring a frequency bandwidth numerical value of the frequency distribution range of the second decibel bandwidth and defining the frequency bandwidth numerical value as a second characteristic value, and defining the peak frequency as a third characteristic value;
and performing time domain energy integration on the data to be analyzed to obtain energy accumulation distribution, counting the time difference T of the energy accumulation distribution in a first energy range, and defining T as a fourth characteristic value.
3. The method for monitoring and distinguishing whales in real time according to claim 2, wherein the method comprises the following steps: the first peak sound pressure threshold value is 160 dB-180 dB, and the step of intercepting the data to be analyzed from the original sonar signal specifically comprises the following steps: and intercepting the data of 70 us-80 us before and after the peak value of the original sonar signal as data to be analyzed.
4. The method for monitoring and distinguishing whales in real time according to claim 2, wherein the method comprises the following steps:
the obtaining of the frequency distribution range of the first decibel bandwidth and the frequency distribution range of the second decibel bandwidth from the signal spectrum with the peak frequency as a reference specifically includes: and acquiring a frequency distribution range of a first decibel bandwidth and a frequency distribution range of a second decibel bandwidth from the signal frequency spectrum by taking the peak frequency as a center, wherein the first decibel bandwidth is a-3 decibel bandwidth, and the second decibel bandwidth is a-10 decibel bandwidth.
5. The method for monitoring and distinguishing whales in real time according to claim 2, wherein the method comprises the following steps: the first energy range is 2.5% -97.5%, and the time difference v ^ T of the energy cumulative distribution in the first energy range is counted as: obtaining a time point T at which said cumulative distribution of energy corresponds to 2.5% of the energy1Time point T corresponding to 97.5% of energy of said cumulative distribution of energy2Time difference ═ T2-T1。
6. A method for real-time monitoring and discrimination of whales as claimed in any one of claims 2 to 5, wherein: the specific method for analyzing the collected sonar signals is the same as the specific method for analyzing the original sonar signals.
7. The method for monitoring and distinguishing whales in real time according to claim 6, wherein the method comprises the following steps: the establishing of the database of the correlation between the original sonar signals and the whale species specifically comprises the following steps: the first characteristic value, the second characteristic value, the third characteristic value and the fourth characteristic value are associated with the whale type, and a database is established; the analysis of the sonar signal acquisition and the comparison with the database specifically comprises the following steps: and analyzing the first characteristic value, the second characteristic value, the third characteristic value and the fourth characteristic value of the collected sonar signals, and comparing the first characteristic value, the second characteristic value, the third characteristic value and the fourth characteristic value with the database to obtain the whale type matched with the characteristic values.
8. The method for monitoring and distinguishing whales in real time according to claim 1, wherein the method comprises the following steps:
the original sonar signals and the collected sonar signals are one or more acoustic signals with the length of 0.8s-1.2 s.
9. The utility model provides a whale real-time supervision and discrimination system which characterized in that: the system comprises the following modules:
the system comprises a database establishing module, a data acquisition module and a data processing module, wherein the database establishing module is used for recording original sonar signals of different types of whales, analyzing the original sonar signals and establishing a database in which the original sonar signals are associated with the types of the whales;
and the comparison and judgment module is used for acquiring and recording the collected sonar signals, analyzing the collected sonar signals, comparing the collected sonar signals with the database and judging the type of the whale.
10. The system for real-time monitoring and distinguishing of whales as claimed in claim 9, wherein: the system also comprises an analysis module for analyzing the original sonar signals or the collected sonar signals, wherein the analysis module specifically comprises:
the data to be analyzed acquisition submodule is used for carrying out peak sound pressure level calculation on the original sonar signal, and if the original sonar signal is greater than a first peak sound pressure threshold value, intercepting data to be analyzed from the original sonar signal;
a frequency spectrum characteristic value obtaining sub-module, configured to perform frequency spectrum analysis on the data to be analyzed to obtain a signal frequency spectrum, obtain a peak frequency of the signal frequency spectrum, obtain a frequency distribution range of a first decibel bandwidth and a frequency distribution range of a second decibel bandwidth from the signal frequency spectrum with the peak frequency as a reference, obtain a frequency bandwidth numerical value of the frequency distribution range of the first decibel bandwidth and define the frequency bandwidth numerical value as a first characteristic value, obtain a frequency bandwidth numerical value of the frequency distribution range of the second decibel bandwidth and define the frequency bandwidth numerical value as a second characteristic value, and define the peak frequency as a third characteristic value;
the energy characteristic value acquisition submodule is used for integrating the energy of the data to be analyzed to obtain energy accumulation distribution, counting the time difference V T of the energy accumulation distribution in a first energy range, and defining V T as a fourth characteristic value.
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