Green peacock ecology monitoring method and system based on chirp

Abstract

The invention discloses a green peacock ecology monitoring method and system based on chirp. The method comprises the following steps: firstly, constructing a target data set; the target data set comprises a green peacock chirp data set, a bird sound data set, an interference sound data set and an environment sound data set; constructing a deep learning network model according to the target data set; carrying out sound data acquisition on the target monitoring area to obtain target sound data; performing sound information extraction on the target sound data to obtain target information, and identifying the target information through a deep learning network model to obtain a sound identification result; the voice recognition result comprises green peacock whistling, various bird whistling, interference sound and environment sound; and finally, according to a voice recognition result, green peacock ecological data statistics is carried out. The green peacock ecological monitoring method can improve the green peacock ecological monitoring accuracy, and can be widely applied to the technical field of sound signal processing.

Description

Technical Field

[0001] The present invention relates to the technical field of sound signal processing, and in particular to a method and system for monitoring the ecology of green peacocks based on sound. Background Art

[0002] Green peacocks are currently only distributed in Yunnan Province. They are extremely endangered species and have been listed as a national first-class protected wild animal. They mainly live in tropical and subtropical evergreen broad-leaved forests and mixed forests below 2,000 meters above sea level, and especially like to move around in open areas such as sparse woodlands, grasslands, and river banks.

[0003] In order to promote the protection of green peacocks and their habitats, it is very important to monitor their distribution, behavior and dynamics. However, green peacocks are alert by nature, making it difficult to carry out systematic manual monitoring.

[0004] Deploying infrared cameras to monitor green peacocks is a relatively advanced technology that has little interference with green peacocks, but the monitoring range of the camera is limited and affected by the viewing angle. Calling is an important way for green peacocks to communicate with each other, especially during the breeding season, when their calls will increase significantly. At the same time, the volume of the green peacock's call is loud and the transmission distance is long, so it is highly feasible to monitor green peacocks through their calls.

[0005] Passive bird monitoring technology based on singing is to deploy recorders in the monitoring area. The recorders record according to the set schedule, and the recording data is stored in the SD card. The staff retrieves the SD card after a period of time. Use machine learning (deep learning) to build a bird singing recognition model to identify the bird species corresponding to the recording data. Some products also support real-time transmission of recording data via 4G wireless. However, there is no monitoring method and system solution specifically for the singing of green peacocks.

[0006] Therefore, the related prior art has the following disadvantages:

[0007] 1. In the existing bird monitoring methods based on singing, due to the influence of small amount of label data, the deep learning recognition model generally does not support the green peacock species, or the recognition rate of the green peacock species is low.

[0008] 2. Existing bird monitoring methods based on calls only output the identified bird species information and do not perform ecological information statistics. Summary of the invention

[0009] In view of this, an embodiment of the present invention provides a green peacock ecology monitoring method and system based on sound, which can improve monitoring accuracy and efficiency.

[0010] An aspect of an embodiment of the present invention provides a method for monitoring the ecology of green peacocks based on singing, comprising the following steps:

[0011] Constructing a target data set; the target data set includes a green peacock sound data set, a bird sound data set, an interference sound data set, and an environmental sound data set;

[0012] Constructing a deep learning network model according to the target data set; the deep learning network model includes a sound event detection model, a bird sound recognition model, and a green peacock sound recognition model;

[0013] Collect sound data of the target monitoring area to obtain target sound data;

[0014] Extracting sound information from the target sound data to obtain target information, and identifying the target information through the deep learning network model to obtain a sound recognition result; the sound recognition result includes the sound of a green peacock, various bird sounds, interference sounds, and environmental sounds;

[0015] Green peacock ecological data statistics are performed based on the sound recognition results.

[0016] In some embodiments, constructing the target data set comprises the following steps:

[0017] Deploy a mono recording device in areas where green peacocks may appear, wherein the microphone of the mono recording device is omnidirectional, the operating frequency band range is 200Hz-16000Hz, the sampling rate is set to be above 32000Hz, and the sampling accuracy is 16 bits;

[0018] According to the audio data collected by the monophonic recording device, the types of green peacock calls are identified and the behavioral information corresponding to the calls is annotated to generate label data for each type of green peacock calls;

[0019] Based on the public information on the public platform, determine the list of bird species in the monitored area and obtain the call data of these bird species;

[0020] According to the audio data collected by the monophonic recording device, various types of interference sounds are screened out, including: car driving sound, car horn sound, dog barking, human voice, and construction sound, and the screened interference sound data are annotated to generate label data of the interference sound;

[0021] Download rain, wind, and thunder data from open source websites to build an environmental sound dataset;

[0022] The target data set is obtained by summarizing the label data of each type of green peacock's call, the call data of bird species, the label data of interference sounds, and the environmental sound data set.

[0023] In some embodiments, constructing a deep learning network model according to the target data set comprises the following steps:

[0024] All types of green peacock calls are regarded as an acoustic event, each type of environmental sound is regarded as an acoustic event, each type of interference sound is regarded as an acoustic event, and some bird sound data are selected and combined as a bird sound event; the pre-trained logmelBase-Frame-Level model is used as the initial sound event detection model, and the collected acoustic event detection data set is used to retrain the logmelBase-Frame-Level model to obtain the final sound event detection model;

[0025] Each type of green peacock sound data set and various types of bird sound data sets were sliced ​​into 2s slices and randomly divided into training set, validation set and test set in a ratio of 7:2:1. The bird sound recognition model and the green peacock sound recognition model were trained.

[0026] In some embodiments, retraining the logmelBase-Frame-Level model with the collected acoustic event detection dataset to obtain the final sound event detection model comprises the following steps:

[0027] In data preprocessing, the input signal is first subjected to Fourier transform to obtain the spectrum of the input signal, and then the power spectrum density is calculated and the power spectrum is processed by a Mel filter; wherein the transfer function of the Mel filter is defined as:

[0028]

[0029] Where H(mk) represents the frequency response function of the Mel filter; F center[m] and F width[m] are the center frequency and width of the mth filter respectively;

[0030] Take the logarithm of the processing result of the Mel filter to get V[m], where V[m] represents the output of the mth Mel filter group. Serialize the processed Mel filter output V[m] into a feature vector, which is used as the input of the logmelBase-Frame-Level model.

[0031] Based on the features extracted by the Log-Mel process, an embedded representation at the segment level or frame level is obtained;

[0032] The embedding representation of the query set is multiplied by the initialized weight matrix, and the prediction result is obtained through the softmax function, and the sound event detection model is obtained; among them, the sound event detection results output by the prediction result have four types: green peacock sound, environmental sound, interference sound, and bird sound;

[0033] Among them, the network structure of the sound event detection model includes 2 BasicBlock layers, 2 CNN layers and 1 linear layer.

[0034] In some embodiments, the data set of each type of green peacock sound and various types of bird sound data sets are sliced ​​into 2s slices, and randomly divided into a training set, a validation set, and a test set in a ratio of 7:2:1, and the bird sound recognition model and the green peacock sound recognition model are trained, including the following steps:

[0035] The data set is filtered by a bandpass filtering method, and a signal y[n] is obtained after filtering; wherein, the filtering adopts a window function method to realize a bandpass filter, and the window function adopts a Hanning window;

[0036] Perform Chirplet transformation on the filtered signal to obtain a Chirplet graph;

[0037] Among them, Chirplet transformation first needs to define Chirplet atoms. A Chirplet atom can be expressed as: Where f0 represents the center frequency; α represents the frequency modulation rate; t0 represents the center time of the atom; represents a window function, which is 1 from time t0-Δ to t0+Δ, otherwise it is 0; ψ[n] represents a Chirplet atomic operator;

[0038] After the Chirplet atom library is constructed, for each atom in the y[n] sequence obtained after filtering and in the atom library Calculate discrete Chirplet transform coefficients The calculation expression is: in, is the complex conjugate of the atom;

[0039] The Chirplet graph is constructed by taking the modulus of the calculated Chirplet transformation coefficients;

[0040] The obtained Chirplet spectrogram is input into the VGGish model for feature extraction. After feature extraction in the VGGish model, the feature data is input into the XGBoost algorithm for classification, and the corresponding probability of each type of green peacock sound and the probability of each bird species are output.

[0041] In some embodiments, the green peacock ecological data statistics are performed according to the sound recognition result, comprising the following steps:

[0042] The green peacock's calls were counted over time by monitoring area, and the frequency of calls at different time periods of the day and seasonal variation factors were analyzed to generate a rhythmic diagram to show the green peacock's call activity pattern in each monitoring area.

[0043] The various types of green peacock calls are counted by time, and the collected sound data and corresponding labels will be subjected to time series analysis. The frequency of occurrence of each sound type is counted and associated with time to determine the frequency of the green peacock's calls in a specific time period. The time when human interference sounds appear is also counted to analyze the impact of interference sounds on the activities of green peacocks.

[0044] In some embodiments, the green peacock ecological data statistics according to the sound recognition result further includes the following steps:

[0045] Statistical analysis was used to evaluate the relationship between the intensity and frequency of the disturbance sounds and the singing patterns of the green peacocks, and to analyze the effects of the disturbance sounds on the singing behavior and daily activities of the green peacocks.

[0046] Calculate the sound pressure level and spectrum components of human interference sounds, and analyze the impact of the interference sound size and frequency components on the behavior of green peacocks;

[0047] The potential effects of sound intensity on the behavior of green peacocks were evaluated by analyzing the sound pressure levels of audio clips measured by a sound level meter;

[0048] The frequency distribution of the sound was measured by a spectrum analyzer to analyze the effects of sounds in different frequency ranges on the behavior of the green peacock;

[0049] Observe and count other bird species coexisting in the monitoring area, and analyze the interactions between coexisting bird species.

[0050] Another aspect of the embodiment of the present invention further provides a green peacock ecology monitoring system based on singing, comprising:

[0051] The first module is used to construct a target data set; the target data set includes a green peacock sound data set, a bird sound data set, an interference sound data set, and an environmental sound data set;

[0052] The second module is used to construct a deep learning network model according to the target data set; the deep learning network model includes a sound event detection model, a bird sound recognition model, and a green peacock sound recognition model;

[0053] The third module is used to collect sound data of the target monitoring area to obtain target sound data;

[0054] The fourth module is used to extract sound information from the target sound data to obtain target information, and recognize the target information through the deep learning network model to obtain a sound recognition result; the sound recognition result includes the sound of green peacocks, various bird sounds, interference sounds and environmental sounds;

[0055] The fifth module is used to collect ecological data of green peacocks according to the sound recognition results.

[0056] Another aspect of an embodiment of the present invention further provides an electronic device, including a processor and a memory;

[0057] The memory is used to store programs;

[0058] The processor executes the program to implement the method described above.

[0059] Another aspect of the embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the method described above.

[0060] The embodiment of the present invention also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the above method.

[0061] The embodiment of the present invention first constructs a target data set; the target data set includes a green peacock singing data set, a bird sound data set, an interference sound data set, and an environmental sound data set; then a deep learning network model is constructed according to the target data set; the deep learning network model includes a sound event detection model, a bird sound recognition model, and a green peacock singing recognition model; then sound data is collected in the target monitoring area to obtain target sound data; and sound information is extracted from the target sound data to obtain target information, and the target information is recognized by the deep learning network model to obtain a sound recognition result; the sound recognition result includes the green peacock singing, various bird sounds, interference sounds, and environmental sounds; finally, according to the sound recognition result, green peacock ecological data statistics are performed. The embodiment of the present invention can improve the accuracy of green peacock ecological monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0063] Figure 1 A schematic diagram of an implementation environment provided for an embodiment of the present invention;

[0064] Figure 2 An overall step flow chart provided for an embodiment of the present invention;

[0065] Figure 3 A schematic diagram of the framework of the logmelBase-Frame-Level model provided in an embodiment of the present invention;

[0066] Figure 4 A process flow chart for forming a Chirplet graph provided by an embodiment of the present invention;

[0067] Figure 5 A flowchart of a bird song network training method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present invention. They are only examples of devices and methods consistent with some aspects of the embodiments of the present invention as detailed in the attached claims.

[0069] It is understood that the terms "first", "second", etc. used in the present invention may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0070] The terms "at least one", "multiple", "each", "any", etc. used in the present invention, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0071] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0072] The green peacock ecology monitoring method based on singing provided by the embodiment of the present invention relates to the field of acoustic signal processing technology. The green peacock ecology monitoring method based on singing provided by the embodiment of the present invention can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements the green peacock ecology monitoring method based on singing, etc., but is not limited to the above forms.

[0073] The present invention can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0074] like Figure 1 FIG. 1 is a schematic diagram of an implementation environment provided by an embodiment of the invention. Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected to a network wirelessly or wired to complete data transmission and exchange.

[0075] Server 101 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0076] In addition, the server 101 can also be a node server in the blockchain network. Blockchain is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm.

[0077] The terminal 102 may be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc. The terminal 102 may also be a vehicle-mounted terminal of various device types described above, but is not limited thereto. The terminal 102 and the server 101 may be directly or indirectly connected via wired or wireless communication, which is not limited in this embodiment of the present invention.

[0078] Based on the example Figure 1 In the implementation environment shown, an embodiment of the present invention provides a green peacock ecological monitoring method based on sound. The following is explained using the green peacock ecological monitoring method based on sound applied to the server 101 as an example. It can be understood that the method can also be applied to the terminal 102.

[0079] Reference Figure 2 , Figure 2 The flowchart of the green peacock ecology monitoring method based on singing applied to the server provided in the embodiment of the present invention, the execution subject of the method can be any of the aforementioned computer devices (including servers or terminals). Figure 2 , the method may include the following steps:

[0080] Constructing a target data set; the target data set includes a green peacock sound data set, a bird sound data set, an interference sound data set, and an environmental sound data set;

[0081] Constructing a deep learning network model according to the target data set; the deep learning network model includes a sound event detection model, a bird sound recognition model, and a green peacock sound recognition model;

[0082] Collect sound data of the target monitoring area to obtain target sound data;

[0083] Extracting sound information from the target sound data to obtain target information, and identifying the target information through the deep learning network model to obtain a sound recognition result; the sound recognition result includes the sound of a green peacock, various bird sounds, interference sounds, and environmental sounds;

[0084] Green peacock ecological data statistics are performed based on the sound recognition results.

[0085] In some embodiments, constructing the target data set comprises the following steps:

[0086] Deploy a mono recording device in areas where green peacocks may appear, wherein the microphone of the mono recording device is omnidirectional, the operating frequency band range is 200Hz-16000Hz, the sampling rate is set to be above 32000Hz, and the sampling accuracy is 16 bits;

[0087] According to the audio data collected by the monophonic recording device, the types of green peacock calls are identified and the behavioral information corresponding to the calls is annotated to generate label data for each type of green peacock calls;

[0088] Based on the public information on the public platform, determine the list of bird species in the monitored area and obtain the call data of these bird species;

[0089] According to the audio data collected by the monophonic recording device, various types of interference sounds are screened out, including: car driving sound, car horn sound, dog barking, human voice, and construction sound, and the screened interference sound data are annotated to generate label data of the interference sound;

[0090] Download rain, wind, and thunder data from open source websites to build an environmental sound dataset;

[0091] The target data set is obtained by summarizing the label data of each type of green peacock's call, the call data of bird species, the label data of interference sounds, and the environmental sound data set.

[0092] In some embodiments, constructing a deep learning network model according to the target data set comprises the following steps:

[0093] All types of green peacock calls are regarded as an acoustic event, each type of environmental sound is regarded as an acoustic event, each type of interference sound is regarded as an acoustic event, and some bird sound data are selected and combined as a bird sound event; the pre-trained logmelBase-Frame-Level model is used as the initial sound event detection model, and the collected acoustic event detection data set is used to retrain the logmelBase-Frame-Level model to obtain the final sound event detection model;

[0094] Each type of green peacock sound data set and various types of bird sound data sets were sliced ​​into 2s slices and randomly divided into training set, validation set and test set in a ratio of 7:2:1. The bird sound recognition model and the green peacock sound recognition model were trained.

[0095] In some embodiments, retraining the logmelBase-Frame-Level model with the collected acoustic event detection dataset to obtain the final sound event detection model comprises the following steps:

[0096] In data preprocessing, the input signal is first subjected to Fourier transform to obtain the spectrum of the input signal, and then the power spectrum density is calculated and the power spectrum is processed by a Mel filter; wherein the transfer function of the Mel filter is defined as:

[0097]

[0098] Where H(mk) represents the frequency response function of the Mel filter; F center[m] and F width[m] are the center frequency and width of the mth filter respectively;

[0099] Take the logarithm of the processing result of the Mel filter to get V[m], where V[m] represents the output of the mth Mel filter group. Serialize the processed Mel filter output V[m] into a feature vector, which is used as the input of the logmelBase-Frame-Level model.

[0100] Based on the features extracted by the Log-Mel process, an embedded representation at the segment level or frame level is obtained;

[0101] The embedding representation of the query set is multiplied by the initialized weight matrix, and the prediction result is obtained through the softmax function, and the sound event detection model is obtained; among them, the sound event detection results output by the prediction result have four types: green peacock sound, environmental sound, interference sound, and bird sound;

[0102] Among them, the network structure of the sound event detection model includes 2 BasicBlock layers, 2 CNN layers and 1 linear layer.

[0103] In some embodiments, the data set of each type of green peacock sound and various types of bird sound data sets are sliced ​​into 2s slices, and randomly divided into a training set, a validation set, and a test set in a ratio of 7:2:1, and the bird sound recognition model and the green peacock sound recognition model are trained, including the following steps:

[0104] The data set is filtered by a bandpass filtering method, and a signal y[n] is obtained after filtering; wherein, the filtering adopts a window function method to realize a bandpass filter, and the window function adopts a Hanning window;

[0105] Perform Chirplet transformation on the filtered signal to obtain a Chirplet graph;

[0106] Among them, Chirplet transformation first needs to define Chirplet atoms. A Chirplet atom can be expressed as: Where f0 represents the center frequency; α represents the frequency modulation rate; t0 represents the center time of the atom; represents a window function, which is 1 from time t0-Δ to t0+α, otherwise it is 0; ψ[n] represents a Chirplet atomic operator;

[0107] After the Chirplet atom library is constructed, for each atom in the y[n] sequence obtained after filtering and in the atom library Calculate discrete Chirplet transform coefficients The calculation expression is: in, is the complex conjugate of the atom;

[0108] The Chirplet graph is constructed by taking the modulus of the calculated Chirplet transformation coefficients;

[0109] The obtained Chirplet spectrogram is input into the VGGish model for feature extraction. After feature extraction in the VGGish model, the feature data is input into the XGBoost algorithm for classification, and the corresponding probability of each type of green peacock sound and the probability of each bird species are output.

[0110] In some embodiments, the green peacock ecological data statistics are performed according to the sound recognition result, comprising the following steps:

[0111] The green peacock's calls were counted over time by monitoring area, and the frequency of calls at different time periods of the day and seasonal variation factors were analyzed to generate a rhythmic diagram to show the green peacock's call activity pattern in each monitoring area.

[0112] The various types of green peacock calls are counted by time, and the collected sound data and corresponding labels will be subjected to time series analysis. The frequency of occurrence of each sound type is counted and associated with time to determine the frequency of the green peacock's calls in a specific time period. The time when human interference sounds appear is also counted to analyze the impact of interference sounds on the activities of green peacocks.

[0113] In some embodiments, the green peacock ecological data statistics according to the sound recognition result further includes the following steps:

[0114] Statistical analysis was used to evaluate the relationship between the intensity and frequency of the disturbance sounds and the singing patterns of the green peacocks, and to analyze the effects of the disturbance sounds on the singing behavior and daily activities of the green peacocks.

[0115] Calculate the sound pressure level and spectrum components of human interference sounds, and analyze the impact of the interference sound size and frequency components on the behavior of green peacocks;

[0116] The potential effects of sound intensity on the behavior of green peacocks were evaluated by analyzing the sound pressure levels of audio clips measured by a sound level meter;

[0117] The frequency distribution of the sound was measured by a spectrum analyzer to analyze the effects of sounds in different frequency ranges on the behavior of the green peacock;

[0118] Observe and count other bird species coexisting in the monitoring area, and analyze the interactions between coexisting bird species.

[0119] The implementation process of the present invention in a specific application scenario is described in detail below in conjunction with the accompanying drawings of the specification:

[0120] A method for monitoring the ecology of green peacocks based on their singing, comprising the following steps:

[0121] S1. Build a dataset;

[0122] S2, build deep learning network models (sound event detection, bird sound recognition, green peacock sound recognition);

[0123] S3, sound data collection in monitoring area;

[0124] S4, sound data information extraction;

[0125] S5. Green peacock ecological data statistics.

[0126] 1. The specific details of S1 are:

[0127] 1) Recording equipment performance. Deploy mono recording equipment in areas where green peacocks may appear. The microphone of the mono recording equipment is omnidirectional, with an operating frequency band range of 200Hz-16000Hz, a sampling rate of more than 32000Hz, and a sampling accuracy of 16 bits.

[0128] 2) Construction of a green peacock sound dataset. The audio output by the monophonic recording device is manually identified by the type of green peacock sound in the audio and the behavioral information corresponding to the sound is manually annotated, thereby generating label data for each type of green peacock sound. To supplement the less than 100 types of green peacock sounds, a monophonic recording device is deployed in the zoo to collect less than 100 types of green peacock sound data, so that each type of green peacock sound data is no less than 100. The audio file format uses lossless compressed wav format.

[0129] 3) Construction of bird sound dataset. The list of bird species in the monitored area is determined through relevant information published by government departments, academic literature, China Bird Watching Record Center, etc. The sound data of these bird species are downloaded from the open source database website, with a unified sampling frequency of 32kHz and a quantization accuracy of 16 bits. In the constructed bird sound dataset, there are no less than 100 data for each bird sound. Each data is saved as an audio file, and the audio file format uses lossless compression wav format.

[0130] 4) Construction of interference sound data set. The audio files collected by the recording equipment are manually screened to identify various types of interference sounds, including: car driving sounds, car horns, dog barking, human voices, and construction sounds. The manually screened interference sound data are labeled to generate label data for the interference sound. For interference sounds with less than 100 data, they are downloaded and supplemented from the open source sound website, so that each type of interference sound data is no less than 100. Each data is saved as an audio file, and the audio file format uses lossless compressed wav format.

[0131] 5) Construction of environmental sound dataset. Download rain, wind, and thunder data from open source websites, with a unified sampling frequency of 32kHz and a quantization accuracy of 16 bits. When constructing the environmental sound dataset, ensure that there are no less than 100 data for each type of environmental sound. If there are less than 100 data, collect and label them through a single-channel recording device. Each data is saved as an audio file, and the audio file format uses lossless compression wav format.

[0132] 2. The specific details of S2 are:

[0133] 1) Sound event detection. All types of green peacock calls are considered as an acoustic event; each type of environmental sound is considered as an acoustic event; each type of interference sound is considered as an acoustic event; and some of the various bird sound data are selected and combined together as a bird sound event. The sound event detection model uses the logmelBase-Frame-Level model and is pre-trained with an existing open source dataset. The collected acoustic event detection dataset is then used to retrain the logmelBase-Frame-Level model. The framework of the logmelBase-Frame-Level model is as follows: Figure 3 shown.

[0134] In data preprocessing, the model first performs Fourier transform on the input signal to obtain its spectrum, then calculates the power spectrum density and passes the power spectrum through the Mel filter. The transfer function of the Mel filter is defined as:

[0135]

[0136] Among them, Fcenter[m] and Fwidth[m] are the center frequency and width of the mth filter respectively. The logarithm of the result after passing through the Mel filter is taken to get V[m] to simulate the logarithmic perception characteristics of the human ear to the sound intensity. Here V[m] represents the output of the mth Mel filter group. Finally, the processed Mel filter output V[m] is serialized into a feature vector, which is used as the input of the logmelBase-Frame-Level model. This serialized feature vector is the final output of the Log-Mel feature.

[0137] Through the Log-Mel feature extraction above, the extracted features are input into the network to obtain a segment-level (or frame-level) embedding representation. Finally, the embedding representation of the query set is multiplied by the initialized weight matrix and the prediction result is obtained through the softmax function. The sound event detection results output by this prediction result have four types: green peacock singing, environmental sound, interference sound, and bird sound.

[0138] The model network structure includes 2 BasicBlock layers, 2 CNN layers and 1 linear layer. BasicBlock is the core building block in ResNet (residual network). It directly adds input to output through skip connection, which helps to alleviate the gradient vanishing problem and speed up network convergence.

[0139] 2) Green peacock and bird song recognition network. Each type of green peacock song data set and various types of bird song data sets were sliced ​​into 2s slices and randomly divided into training set, validation set and test set in a ratio of 7:2:1. After investigation and research, the frequency of bird songs with the same habits as green peacock and green peacock is concentrated between 0.4kHz-16kHz. The data set is filtered by bandpass filtering method, which helps to further improve the recognition accuracy of the model. The filtering adopts the window function method to realize the bandpass filter, and the window function adopts the Hanning window. After filtering, the signal y[n] is obtained.

[0140] refer to Figure 4 , the filtered signal is subjected to Chirplet transformation to obtain a Chirplet graph. Chirplet transformation first requires the definition of Chirplet atoms, a Chirplet atom can be expressed as:

[0141]

[0142] Where f0 is the center frequency, α is the frequency modulation rate (chirprate), t0 is the center time of the atom, is a window function, usually a rectangular window, which is 1 in the time range from t0-Δ to t0+Δ, and 0 otherwise. A series of parameters with different values ​​are selected to construct the atom library, where these parameters include f0, α and t0. After the Chirplet atom library is constructed, for each atom in the y[n] sequence obtained after filtering and in the atom library Compute discrete Chirplet transform coefficients:

[0143]

[0144] in, is the complex conjugate of the atom. The Chirplet graph is constructed by taking the modulus of the calculated Chirplet transform coefficients.

[0145] The obtained Chirplet spectrogram is input into the VGGish model for feature extraction. After the VGGish model performs feature extraction, the feature data is input into the XGBoost algorithm for classification, and the corresponding probability of each type of green peacock sound and the probability of each bird species are output. The overall process of green peacock and bird sound recognition model training is as follows Figure 5 shown.

[0146] 3. The specific details of S3 are:

[0147] 1) Deployment of recording equipment. A mono recording device is deployed in the monitoring area of ​​the green peacock. The mono recording microphone is omnidirectional and has an operating frequency band range of 200Hz-16000Hz. The sampling rate of the recording device is set to above 32000Hz, and the sampling accuracy is 16 bits. In order to avoid human factors affecting the activities of the green peacock, the recording device supports 4G wireless network data transmission and can be powered by a variety of methods (solar energy, 220V AC power, battery). The recording equipment is installed in the green peacock monitoring area, including streams, woodlands and woodland boundaries, and the microphone is 1.5 meters to 3 meters above the ground.

[0148] 2) Recording device data storage and transmission. The recording device works by triggering sound events. When a sound signal is detected, it records the sound. The recorded data is transmitted to the remote server via 4G wireless and stored in the SD card for backup. Other metadata corresponding to the recorded data (geographic location, recording time, temperature, humidity) are also transmitted and stored.

[0149] 4. The specific details of S4 are as follows:

[0150] 1) The recording data sent back by the recording device is processed in the remote server, and sound event detection is performed on the recording data through the trained sound event detection model, including the sound of green peacocks, bird sounds, interference sounds (including car driving sounds, car horns, dog barking, human voices, construction sounds, bird sounds), and environmental sounds (rain sounds, wind sounds, thunder sounds).

[0151] 2) Save the detected green peacock sounds of various types, bird sounds, interference sounds (including car driving sounds, car horns, dog barking, human voices, construction sounds), environmental sound data fragments, and the corresponding time, geographical location of the recording equipment, temperature, humidity and other data.

[0152] 3) Green peacock call recognition: The data corresponding to the green peacock call event detected above is re-recognized through the green peacock call recognition network model, and if the confidence level exceeds 0.8, the green peacock call data and its call type information are saved.

[0153] 4) Bird song recognition: The detected bird song segment data is used to identify the specific species again through the bird song recognition network model. If the confidence level exceeds 0.8, the species song data and its species information are saved.

[0154] 5. The specific details of S5 are:

[0155] 1) Time statistics of the green peacock's calls are collected by monitoring area, including analysis of the call frequency at different time periods of the day and possible seasonal changes. Based on these data, the embodiment of the present invention can draw a detailed rhythm map to more intuitively display the call activity pattern of the green peacock in each monitoring area.

[0156] 2) Statistics are collected for various types of green peacock sounds by time. The collected sound data and corresponding labels are analyzed in time series, and the frequency of each type of sound is counted and associated with time. In this way, the embodiment of the present invention can determine whether the green peacock's sound is more frequent in certain time periods. Statistics are also collected for the time when various human interference sounds such as dog barking / construction / cars appear, and the impact of interference sounds on the activities of green peacocks is analyzed.

[0157] 3) In addition, the embodiments of the present invention will also use statistical analysis methods, such as correlation analysis or regression models, to evaluate the relationship between the intensity and frequency of the interfering sounds and the singing patterns of the green peacocks. This will help the embodiments of the present invention understand whether the interfering sounds have changed the singing behavior of the green peacocks and whether they have affected the daily activities of the green peacocks.

[0158] 4) Calculate the sound pressure level and spectral components of various human interference sounds such as dog barking / construction / cars, and analyze the impact of the interference sound size and frequency components on the behavior of the green peacock. By performing sound pressure level analysis on the audio clips measured by the sound level meter, the embodiment of the present invention can evaluate the potential impact of sound intensity on the behavior of the green peacock. Sound pressure level is a physical quantity that measures the energy of sound, which reflects the amplitude of the sound wave. In animal behavior studies, changes in sound pressure level may have a significant impact on the communication, foraging and reproductive behavior of animals. For example, high-decibel noise may interfere with the call of the green peacock, affect its reproductive success rate, or cause a stress response, thereby affecting its daily activity pattern.

[0159] Furthermore, by measuring the frequency distribution of the sound with a spectrum analyzer, the embodiment of the present invention can more carefully understand the effects of sounds of different frequency ranges on the behavior of the green peacock. The frequency distribution diagram of the sound can help the embodiment of the present invention identify the main frequency components that may affect the behavior of the green peacock. For example, sounds of certain frequencies may stimulate the vigilance behavior of the green peacock, while sounds of other frequencies may interfere with its foraging or resting behavior.

[0160] By analyzing the above data, researchers can evaluate the specific effects of specific frequencies and sound pressure levels on the behavior of green peacocks and propose corresponding protection measures.

[0161] 5) Statistics of coexisting bird species and activity rhythms. While conducting the green peacock behavior study, the embodiment of the present invention will also observe and count other bird species coexisting in the monitoring area. This includes recording their activity rhythms, such as active time of day, seasonal activity changes, etc. Through time series analysis, the embodiment of the present invention can reveal the interactions between coexisting bird species. For example, some species may choose to rest when the green peacock is active to avoid competition. This will help the embodiment of the present invention understand whether different bird species share similar ecological niches, or whether they have developed different behavioral strategies to share the same living environment. The data statistics table constructed by collating the above information is shown in Table 1:

[0162] Table 1 Data statistics

[0163]

[0164] Another aspect of the embodiment of the present invention further provides a green peacock ecology monitoring system based on singing, comprising:

[0165] The first module is used to construct a target data set; the target data set includes a green peacock sound data set, a bird sound data set, an interference sound data set, and an environmental sound data set;

[0166] The second module is used to construct a deep learning network model according to the target data set; the deep learning network model includes a sound event detection model, a bird sound recognition model, and a green peacock sound recognition model;

[0167] The third module is used to collect sound data of the target monitoring area to obtain target sound data;

[0168] The fourth module is used to extract sound information from the target sound data to obtain target information, and recognize the target information through the deep learning network model to obtain a sound recognition result; the sound recognition result includes the sound of green peacocks, various bird sounds, interference sounds and environmental sounds;

[0169] The fifth module is used to collect ecological data of green peacocks according to the sound recognition results.

[0170] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0171] The embodiment of the present invention further provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned green peacock ecology monitoring method based on singing when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0172] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0173] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned green peacock ecological monitoring method based on sound.

[0174] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0175] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0176] It should be noted that in various specific embodiments of the present invention, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present invention needs to obtain the user's sensitive personal information, it will obtain the user's separate permission or consent through a pop-up window or jump to a confirmation page, and after clearly obtaining the user's separate permission or consent, it will obtain the necessary user-related data for the normal operation of the embodiment of the present invention.

[0177] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0178] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0179] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0180] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0181] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0182] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0183] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0184] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0185] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A method for monitoring the ecology of green peacocks based on their singing, characterized in that: The following steps are involved: Build the target dataset; The target data set includes a green peacock sound data set, a bird sound data set, an interference sound data set, and an environmental sound data set; Constructing a deep learning network model according to the target data set; the deep learning network model includes a sound event detection model, a bird sound recognition model, and a green peacock sound recognition model; Collect sound data of the target monitoring area to obtain target sound data; Extracting sound information from the target sound data to obtain target information, and identifying the target information through the deep learning network model to obtain a sound recognition result; the sound recognition result includes the sound of a green peacock, various bird sounds, interference sounds, and environmental sounds; Green peacock ecological data statistics are performed based on the sound recognition results.

2. The method for monitoring green peacock ecology based on sound according to claim 1, characterized in that: The construction of the target data set comprises the following steps: Deploy a mono recording device in areas where green peacocks may appear, wherein the microphone of the mono recording device is omnidirectional, the operating frequency band range is 200 Hz-16000 Hz, the sampling rate is set to be above 32000 Hz, and the sampling accuracy is 16 bits; According to the audio data collected by the monophonic recording device, the types of green peacock calls are identified and the behavioral information corresponding to the calls is annotated to generate label data for each type of green peacock calls; Based on the public information on the public platform, determine the list of bird species in the monitored area and obtain the call data of these bird species; According to the audio data collected by the monophonic recording device, various types of interference sounds are screened out, including: car driving sound, car horn sound, dog barking, human voice, and construction sound, and the screened interference sound data are annotated to generate label data of the interference sound; Download rain, wind, and thunder data from open source websites to build an environmental sound dataset; The target data set is obtained by summarizing the label data of each type of green peacock's call, the call data of bird species, the label data of interference sounds, and the environmental sound data set.

3. The method for monitoring green peacock ecology based on sound according to claim 1, characterized in that: The step of constructing a deep learning network model according to the target data set comprises the following steps: All types of green peacock calls are regarded as an acoustic event, each type of environmental sound is regarded as an acoustic event, each type of interference sound is regarded as an acoustic event, and some bird sound data are selected and combined as a bird sound event; the pre-trained logmelBase-Frame-Level model is used as the initial sound event detection model, and the collected acoustic event detection data set is used to retrain the logmelBase-Frame-Level model to obtain the final sound event detection model; Each type of green peacock sound data set and various types of bird sound data sets were sliced ​​into 2s slices and randomly divided into training set, validation set and test set in a ratio of 7:2:

1. The bird sound recognition model and the green peacock sound recognition model were trained.

4. The method for monitoring green peacock ecology based on sound according to claim 3 is characterized in that: The method of retraining the logmelBase-Frame-Level model with the collected acoustic event detection data set to obtain the final sound event detection model includes the following steps: In data preprocessing, the input signal is first subjected to Fourier transform to obtain the spectrum of the input signal, and then the power spectrum density is calculated and the power spectrum is processed by a Mel filter; wherein the transfer function of the Mel filter is defined as: Where H(mk) represents the frequency response function of the Mel filter; F center[m] and F width[m] are the center frequency and width of the mth filter respectively; Take the logarithm of the processing result of the Mel filter to get V[m], where V[m] represents the output of the mth Mel filter group. Serialize the processed Mel filter output V[m] into a feature vector, which is used as the input of the logmelBase-Frame-Level model. Based on the features extracted by the Log-Mel process, an embedded representation at the segment level or frame level is obtained; The embedding representation of the query set is multiplied by the initialized weight matrix, and the prediction result is obtained through the softmax function, and the sound event detection model is obtained; among them, the sound event detection results output by the prediction result have four types: green peacock sound, environmental sound, interference sound, and bird sound; Among them, the network structure of the sound event detection model includes 2 BasicBlock layers, 2 CNN layers and 1 linear layer.

5. The method for monitoring green peacock ecology based on sound according to claim 3 is characterized in that: The data sets of each type of green peacock sound and various types of bird sound data sets are sliced ​​into 2s slices, and randomly divided into a training set, a validation set and a test set in a ratio of 7:2:1, and a bird sound recognition model and a green peacock sound recognition model are obtained by training, including the following steps: The data set is filtered by a bandpass filtering method, and a signal y[n] is obtained after filtering; wherein, the filtering adopts a window function method to realize a bandpass filter, and the window function adopts a Hanning window; Perform Chirplet transformation on the filtered signal to obtain a Chirplet graph; Among them, Chirplet transformation first needs to define Chirplet atoms. A Chirplet atom can be expressed as: Where f0 represents the center frequency; α represents the frequency modulation rate; t0 represents the center time of the atom; represents a window function, which is 1 from time t0-Δ to t0+Δ, otherwise it is 0; ψ[n] represents a Chirplet atomic operator; After the Chirplet atom library is constructed, for each atom in the y[n] sequence obtained after filtering and in the atom library Calculate discrete Chirplet transform coefficients The calculation expression is: in, is the complex conjugate of the atom; The Chirplet graph is constructed by taking the modulus of the calculated Chirplet transformation coefficients; The obtained Chirplet spectrogram is input into the VGGish model for feature extraction. After feature extraction in the VGGish model, the feature data is input into the XGBoost algorithm for classification, and the corresponding probability of each type of green peacock sound and the probability of each bird species are output.

6. The method for monitoring green peacock ecology based on sound according to claim 1, characterized in that: The method of performing green peacock ecological data statistics according to the sound recognition result comprises the following steps: The green peacock's calls were counted over time by monitoring area, and the frequency of calls at different time periods of the day and seasonal variation factors were analyzed to generate a rhythmic diagram to show the green peacock's call activity pattern in each monitoring area. The various types of green peacock calls are counted by time, and the collected sound data and corresponding labels will be subjected to time series analysis. The frequency of occurrence of each sound type is counted and associated with time to determine the frequency of the green peacock's calls in a specific time period. The time when human interference sounds appear is also counted to analyze the impact of interference sounds on the activities of green peacocks.

7. The method for monitoring green peacock ecology based on sound according to claim 6, characterized in that: The method of performing green peacock ecological data statistics according to the sound recognition result also includes the following steps: Statistical analysis was used to evaluate the relationship between the intensity and frequency of the disturbance sounds and the singing patterns of the green peacocks, and to analyze the effects of the disturbance sounds on the singing behavior and daily activities of the green peacocks. Calculate the sound pressure level and spectrum components of human interference sounds, and analyze the impact of the interference sound size and frequency components on the behavior of green peacocks; The potential effects of sound intensity on the behavior of green peacocks were evaluated by analyzing the sound pressure levels of audio clips measured by a sound level meter; The frequency distribution of the sound was measured by a spectrum analyzer to analyze the effects of sounds in different frequency ranges on the behavior of the green peacock; Observe and count other bird species coexisting in the monitoring area, and analyze the interactions between coexisting bird species.

8. A green peacock ecological monitoring system based on singing, characterized in that: include: The first module is used to construct the target data set; The target data set includes a green peacock sound data set, a bird sound data set, an interference sound data set, and an environmental sound data set; The second module is used to construct a deep learning network model according to the target data set; the deep learning network model includes a sound event detection model, a bird sound recognition model, and a green peacock sound recognition model; The third module is used to collect sound data of the target monitoring area to obtain target sound data; The fourth module is used to extract sound information from the target sound data to obtain target information, and recognize the target information through the deep learning network model to obtain a sound recognition result; the sound recognition result includes the sound of green peacocks, various bird sounds, interference sounds and environmental sounds; The fifth module is used to collect ecological data of green peacocks according to the sound recognition results.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 7.

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