CN111986683A - Method and system for evaluating deep sea ecosystem by using bioacoustic characteristics - Google Patents
Method and system for evaluating deep sea ecosystem by using bioacoustic characteristics Download PDFInfo
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Abstract
The invention relates to a method and a system for evaluating a deep sea ecosystem by using bioacoustic characteristics. The method comprises the following steps: acquiring acoustic signals of various organisms in a deep sea ecosystem; determining the deep sea organism type and the quantity of the deep sea organisms according to the acoustic signals; training a neural network model according to the acoustic signals, the deep sea organism types and the deep sea organism quantity to obtain a trained neural network model; acquiring a deep sea bioacoustic signal of a sea area to be detected; inputting the deep sea bioacoustic signals of the sea area to be tested into the trained neural network model for prediction to obtain the type and quantity of the deep sea organisms of the sea area to be tested. The invention can observe a specific sea area for a long time and does not damage the surrounding environment.
Description
Technical Field
The invention relates to the technical field of deep sea ecosystem evaluation, in particular to a method and a system for evaluating a deep sea ecosystem by using bioacoustic characteristics.
Background
As human activities gradually extend from offshore to deep and distant sea, particularly mining of deep sea mineral resources, the deep sea environment is inevitably affected. Deep sea mining has a significantly greater impact on the underlying ecosystem than the overlying ecosystem, and thus the impact on the underlying ecosystem is a focus of environmental problems in deep sea mining. At present, the research on the bottom ecological system mainly depends on the conventional scientific investigation means, such as manned submersible, unmanned cableless remote control submersible, optical towed body, television grab bucket, biological trawl and the like, to shoot and sample the benthos. However, manned submersible vehicles, unmanned remote-controlled submersible vehicles with cables, optical towed bodies, and the like are expensive to use, have a limited working time, are mainly suitable for scientific research, and cannot observe a specific sea area for a long time. The biological trawl and the like can cause great damage to the benthos, and the biological trawl is generally dragged for a long time and a long distance, so that the position of the specific creature cannot be determined.
Disclosure of Invention
The invention aims to provide a method and a system for evaluating a deep sea ecosystem by using bioacoustic characteristics, which can observe a specific sea area for a long time and can not damage the surrounding environment.
In order to achieve the purpose, the invention provides the following scheme:
a method for evaluating a deep sea ecosystem by using bioacoustic features comprises the following steps:
acquiring acoustic signals of various organisms in a deep sea ecosystem;
determining the deep sea organism type and the quantity of the deep sea organisms according to the acoustic signals;
training a neural network model according to the acoustic signals, the deep sea organism types and the deep sea organism quantity to obtain a trained neural network model;
acquiring a deep sea bioacoustic signal of a sea area to be detected;
inputting the deep sea bioacoustic signals of the sea area to be tested into the trained neural network model for prediction to obtain the type and quantity of the deep sea organisms of the sea area to be tested.
Optionally, the acquiring acoustic signals of various organisms in the deep sea ecosystem specifically includes:
acoustic signals of various organisms in the deep sea ecosystem are acquired through the self-contained hydrophone.
Optionally, the determining the deep sea creature type and quantity according to the acoustic signal specifically comprises:
analyzing the acoustic signal by adopting a voice feature extraction method, and extracting acoustic features of the deep-sea creatures;
and matching the acoustic characteristics of the deep-sea creatures with the deep-sea creatures by combining the synchronously acquired image data and the species and the number of the creatures captured by the biological trapper to obtain the classification result of the acoustic characteristics of the deep-sea creatures, wherein the classification result comprises the species and the number of the deep-sea creatures.
Optionally, the training the neural network model according to the acoustic signal, the deep sea organism type, and the deep sea organism number to obtain the trained neural network model specifically includes:
and taking the acoustic signal as input, taking the deep sea organism type and the deep sea organism quantity as output, and training a neural network model to obtain the trained neural network model.
A system for deep sea ecosystem assessment using bioacoustic features, comprising:
the first acoustic signal acquisition module is used for acquiring acoustic signals of various organisms in a deep sea ecosystem;
the biological species and biological quantity determining module is used for determining the deep sea biological species and the deep sea biological quantity according to the acoustic signals;
the network training module is used for training the neural network model according to the acoustic signals, the deep sea organism types and the deep sea organism quantity to obtain the trained neural network model;
the second acoustic signal acquisition module is used for acquiring deep sea bioacoustic signals of the sea area to be detected;
and the prediction module is used for inputting the deep sea bioacoustic signals of the to-be-detected sea area into the trained neural network model for prediction to obtain the type and the quantity of the deep sea organisms of the to-be-detected sea area.
Optionally, the first acoustic signal acquiring module specifically includes:
the first acoustic signal acquisition unit is used for acquiring acoustic signals of various organisms in the deep sea ecosystem through the self-contained hydrophone.
Optionally, the biological type and biological quantity determining module specifically includes:
the acoustic feature extraction unit is used for analyzing the acoustic signal by adopting a voice feature extraction method and extracting the acoustic features of the deep-sea creatures;
and the biological type and biological quantity determining unit is used for combining the synchronously acquired image data and the biological type and quantity captured by the biological trap, pairing the acoustic characteristics of the deep-sea organisms with the deep-sea organisms to obtain the classification result of the acoustic characteristics of the deep-sea organisms, wherein the classification result comprises the type and quantity of the deep-sea organisms.
Optionally, the network training module specifically includes:
and the network training unit is used for taking the acoustic signal as input, taking the deep sea organism type and the deep sea organism quantity as output, and training the neural network model to obtain the trained neural network model.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a method and a system for evaluating a deep sea ecosystem by using bioacoustic characteristics. The method firstly proposes that a neural network is trained by acoustic signals of various organisms in the deep sea ecosystem, deep sea organism types and deep sea organism quantity, and the types and the quantity of the organisms in the deep sea ecosystem are predicted by utilizing a trained neural network model. The invention can observe the specific sea area for a long time under the condition of not damaging the surrounding environment, and improves the accuracy of prediction.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flow chart of the method for evaluating the deep sea ecosystem by using bioacoustic characteristics according to the invention;
FIG. 2 is a system configuration diagram of deep sea ecosystem evaluation using bioacoustic features according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for evaluating a deep sea ecosystem by using bioacoustic characteristics, which can observe a specific sea area for a long time and can not damage the surrounding environment.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Sound is the most efficient form of energy transmission underwater. Most marine organisms (including invertebrates, fish and marine mammals) produce sound, and actions such as coupling, foraging, competition and social communication are mainly performed by means of the sound. In invertebrates, crustaceans such as crabs and shrimps are the most loud. Such animals are typically bumped and rubbed to make a snap, click or rasp with tongs and tentacles, and the like. This noise, most commonly emitted by invertebrates during feeding and exercise, has a frequency spectrum between 20Hz and 20 kHz. In mollusks, shellfish make a collision sound when their shells open and close; barnacles and sea urchins also make a click when moving. In addition, certain vocalizations of invertebrates may be related to reproduction or as a warning signal. The fish sounds mainly include friction sound, swimming bladder vibration sound and hydrodynamic sound generated during swimming. Many fishes make rasping sound when biting, peeling food from rocks, shaking fins or rubbing convex parts of pharyngeal portion, and the main frequency is 100-4000 Hz.
FIG. 1 is a flow chart of the method for evaluating the deep sea ecosystem by using the bioacoustic characteristics. As shown in fig. 1, a method for evaluating a deep sea ecosystem using bioacoustic features includes:
step 101: acquiring acoustic signals of various organisms in a deep sea ecosystem, which specifically comprises the following steps:
acoustic signals of various organisms in the deep sea ecosystem are acquired through the self-contained hydrophone.
The method for evaluating the deep sea ecosystem by using the bioacoustic characteristics of the invention depends on a set of deep sea bioacoustic characteristic acquisition system, which comprises self-contained hydrophones (recording acoustic signals), deep sea cameras (3, each of which is matched with 1 deep sea LED illuminating lamp for recording surrounding biological videos), large-capacity battery units (supplying power to the hydrophones, the cameras and the cameras), data storage units (storing acoustic data, videos and photos), biological traps (inducing the coming of organisms in a certain range so as to better record the acoustic signals and shoot image data of the organisms), GPS beacons (recording GPS longitude and latitude of the water surface), acoustic beacons and releasers (2 are connected in parallel and matched with a shipborne ultra-short baseline positioning system to provide positioning for the acquisition system and provide release weights when the acquisition system is recovered, a depth meter (recording system depth), a titanium alloy frame (structure of the acquisition system, support for instrumentation), buoyancy material (providing buoyancy for the acquisition system), floating balls (providing additional buoyancy for the acquisition system), weights (blocks of cement or iron, providing gravity for the acquisition system), cables (connecting weights to an acoustic release), and the like. The connection relationship among the above components is common, and is not described herein.
Step 102: according to the acoustic signals, the deep sea organism type and the deep sea organism number are determined, and the method specifically comprises the following steps:
and analyzing the acoustic signals by adopting a voice feature extraction method, and extracting the acoustic features of the deep-sea creatures.
And matching the acoustic characteristics of the deep-sea creatures with the deep-sea creatures by combining the synchronously acquired image data and the species and the number of the creatures captured by the biological trapper to obtain the classification result of the acoustic characteristics of the deep-sea creatures, wherein the classification result comprises the species and the number of the deep-sea creatures.
101 and 102, selecting typical sea areas (such as a deep sea manganese nodule area and a deep sea cobalt-rich crust area), carrying out marine test measurement on deep sea bioacoustic characteristics, recording deep sea bioacoustic data and image data by using the acquisition system, acquiring measured deep sea bioacoustic characteristic data of the area, and capturing a deep sea biological sample; analyzing the recorded deep sea bioacoustic signals by using a mature voice feature extraction technology, extracting the acoustic features of deep sea organisms, and matching the extracted deep sea bioacoustic features with the deep sea organisms by combining synchronously acquired image data and the species and the quantity of the organisms captured by the organism trap to realize the classification and the identification of the deep sea bioacoustic features; and after a period of time, fully acquiring the acoustic characteristics of the abyssal creatures in a certain sea area, and establishing an acoustic characteristic database of different types of abyssal creatures. That is, step 101 and step 102 are the creation process of the sample database.
Step 103: training a neural network model according to the acoustic signals, the deep sea organism types and the deep sea organism quantity to obtain the trained neural network model, and specifically comprising the following steps:
and taking the acoustic signal as input, taking the deep sea organism type and the deep sea organism quantity as output, and training a neural network model to obtain the trained neural network model.
Step 104: and acquiring deep sea bioacoustic signals of the sea area to be detected.
Step 105: inputting the deep sea bioacoustic signals of the sea area to be tested into the trained neural network model for prediction to obtain the type and quantity of the deep sea organisms of the sea area to be tested.
FIG. 2 is a system configuration diagram of deep sea ecosystem evaluation using bioacoustic features according to the present invention. As shown in fig. 2, a system for deep sea ecosystem assessment using bioacoustic features includes:
the first acoustic signal acquisition module 201 is used for acquiring acoustic signals of various organisms in the deep sea ecosystem.
And a biological species and biological quantity determining module 202 for determining the deep sea biological species and the deep sea biological quantity according to the acoustic signals.
And the network training module 203 is used for training the neural network model according to the acoustic signals, the deep sea organism types and the deep sea organism quantity to obtain the trained neural network model.
And the second acoustic signal acquisition module 204 is used for acquiring the deep sea bioacoustic signal of the sea area to be detected.
And the prediction module 205 is configured to input the deep sea bioacoustic signal of the to-be-detected sea area to the trained neural network model for prediction, so as to obtain the type and quantity of the deep sea organisms of the to-be-detected sea area.
The first acoustic signal obtaining module 204 specifically includes:
the first acoustic signal acquisition unit is used for acquiring acoustic signals of various organisms in the deep sea ecosystem through the self-contained hydrophone.
The biological type and biological quantity determining module 202 specifically includes:
and the acoustic feature extraction unit is used for analyzing the acoustic signals by adopting a voice feature extraction method and extracting the acoustic features of the deep-sea creatures.
And the biological type and biological quantity determining unit is used for combining the synchronously acquired image data and the biological type and quantity captured by the biological trap, pairing the acoustic characteristics of the deep-sea organisms with the deep-sea organisms to obtain the classification result of the acoustic characteristics of the deep-sea organisms, wherein the classification result comprises the type and quantity of the deep-sea organisms.
The network training module 203 specifically includes:
and the network training unit is used for taking the acoustic signal as input, taking the deep sea organism type and the deep sea organism quantity as output, and training the neural network model to obtain the trained neural network model.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (8)
1. A method for evaluating a deep sea ecosystem by using bioacoustic characteristics is characterized by comprising the following steps:
acquiring acoustic signals of various organisms in a deep sea ecosystem;
determining the deep sea organism type and the quantity of the deep sea organisms according to the acoustic signals;
training a neural network model according to the acoustic signals, the deep sea organism types and the deep sea organism quantity to obtain a trained neural network model;
acquiring a deep sea bioacoustic signal of a sea area to be detected;
inputting the deep sea bioacoustic signals of the sea area to be tested into the trained neural network model for prediction to obtain the type and quantity of the deep sea organisms of the sea area to be tested.
2. The method for evaluating the deep sea ecosystem by using bioacoustic features according to claim 1, wherein the acquiring acoustic signals of various organisms in the deep sea ecosystem specifically comprises:
acoustic signals of various organisms in the deep sea ecosystem are acquired through the self-contained hydrophone.
3. The method for evaluating the deep sea ecosystem by using bioacoustic features according to claim 1, wherein the determining the deep sea organism type and quantity according to the acoustic signals specifically comprises:
analyzing the acoustic signal by adopting a voice feature extraction method, and extracting acoustic features of the deep-sea creatures;
and matching the acoustic characteristics of the deep-sea creatures with the deep-sea creatures by combining the synchronously acquired image data and the species and the number of the creatures captured by the biological trapper to obtain the classification result of the acoustic characteristics of the deep-sea creatures, wherein the classification result comprises the species and the number of the deep-sea creatures.
4. The method for evaluating the deep sea ecosystem by using bioacoustic features according to claim 1, wherein the training of the neural network model according to the acoustic signals, the deep sea organism species and the deep sea organism number to obtain the trained neural network model specifically comprises:
and taking the acoustic signal as input, taking the deep sea organism type and the deep sea organism quantity as output, and training a neural network model to obtain the trained neural network model.
5. A system for deep sea ecosystem assessment using bioacoustic features, comprising:
the first acoustic signal acquisition module is used for acquiring acoustic signals of various organisms in a deep sea ecosystem;
the biological species and biological quantity determining module is used for determining the deep sea biological species and the deep sea biological quantity according to the acoustic signals;
the network training module is used for training the neural network model according to the acoustic signals, the deep sea organism types and the deep sea organism quantity to obtain the trained neural network model;
the second acoustic signal acquisition module is used for acquiring deep sea bioacoustic signals of the sea area to be detected;
and the prediction module is used for inputting the deep sea bioacoustic signals of the to-be-detected sea area into the trained neural network model for prediction to obtain the type and the quantity of the deep sea organisms of the to-be-detected sea area.
6. The system for deep sea ecosystem evaluation according to claim 5, wherein the first acoustic signal acquisition module specifically comprises:
the first acoustic signal acquisition unit is used for acquiring acoustic signals of various organisms in the deep sea ecosystem through the self-contained hydrophone.
7. The system for deep sea ecosystem assessment using bioacoustic features of claim 5, wherein the biological species and biological number determination module specifically comprises:
the acoustic feature extraction unit is used for analyzing the acoustic signal by adopting a voice feature extraction method and extracting the acoustic features of the deep-sea creatures;
and the biological type and biological quantity determining unit is used for combining the synchronously acquired image data and the biological type and quantity captured by the biological trap, pairing the acoustic characteristics of the deep-sea organisms with the deep-sea organisms to obtain the classification result of the acoustic characteristics of the deep-sea organisms, wherein the classification result comprises the type and quantity of the deep-sea organisms.
8. The system for deep sea ecosystem evaluation by using bioacoustic features according to claim 5, wherein the network training module specifically comprises:
and the network training unit is used for taking the acoustic signal as input, taking the deep sea organism type and the deep sea organism quantity as output, and training the neural network model to obtain the trained neural network model.
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| WO2023228266A1 (en) * | 2022-05-24 | 2023-11-30 | 日本電信電話株式会社 | Observation evaluation device, observation evaluation method, and program |
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