Artificial fish reef pile distribution characteristic identification and analysis method

Abstract

The invention relates to the technical field of marine ecological monitoring, in particular to an artificial fish reef pile distribution feature recognition and analysis method, which comprises the following steps of S1, acquiring echo signal data including time delay, signal intensity and frequency information; s2, preprocessing the echo signal data obtained in the step S1; s3, based on the preprocessed echo signal data, identifying a characteristic signal related to the artificial fish reef pile; s4, extracting the distribution characteristics of the artificial fish reef pile, wherein the distribution characteristics comprise depth, form and distribution density; s5, analyzing and identifying different types of fish reef pile distribution characteristics; and S6, generating a three-dimensional distribution diagram according to an analysis result in S5 through a visualization technology. According to the method, the distribution characteristics of the artificial fish reef pile can be efficiently identified and analyzed by combining an echo detection technology and a K-means clustering algorithm, and the distribution condition of the artificial fish reef pile is visually displayed through a three-dimensional visualization technology, so that the monitoring precision of the artificial fish reef pile is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of marine ecological monitoring, and in particular to a method for identifying and analyzing the distribution characteristics of artificial fish reefs. Background Art

[0002] With the increasing attention paid to marine ecological protection and fishery resource management, the construction and monitoring of artificial reefs has become an important direction for marine environmental protection and sustainable development of fishery resources. Artificial reefs can provide habitats for marine organisms and promote the recovery and proliferation of fishery resources. Traditional methods for monitoring the distribution of artificial reefs mainly rely on manual diving, trawl sampling and other methods. These methods are not only cumbersome and costly to operate, but also have low monitoring efficiency in large sea areas, making it difficult to achieve real-time and large-scale monitoring. In addition, with the increasing complexity of the marine environment, traditional methods have limited ability to accurately locate and extract distribution characteristics of fish reefs in complex waters. There is an urgent need for an efficient and accurate technical means to monitor and analyze marine artificial reefs.

[0003] However, although the existing echo detection technology can be used to detect the bottom terrain of the ocean, there are still many technical difficulties in the monitoring of artificial fish reefs. First, most of the existing echo detection technologies use simple echo signal processing methods, which cannot effectively distinguish different types of artificial fish reefs and their distribution characteristics. Secondly, in the monitoring of large-scale sea areas, how to accurately extract the depth, shape, distribution density and other characteristics of artificial fish reefs and classify and identify them is still a technical problem. Summary of the invention

[0004] Based on the above purpose, the present invention provides a method for identifying and analyzing the distribution characteristics of artificial fish reefs.

[0005] A method for identifying and analyzing the distribution characteristics of artificial reefs comprises the following steps: S1: Use echo detection equipment to perform echo detection on the target sea area and obtain echo signal data, including time delay, signal strength and frequency information; S2: preprocessing the echo signal data obtained by S1, including signal denoising, filtering and time domain-frequency domain conversion; S3: Based on the pre-processed echo signal data, identifying characteristic signals related to the artificial reef pile; S4: Extract features of the target signal detected by S3 to extract the distribution features of the artificial reef pile, including depth, shape and distribution density; S5: Use K-means clustering algorithm to analyze the extracted distribution features to classify and identify the distribution characteristics of different types of fish reef piles; S6: Generate a three-dimensional distribution map using visualization technology based on the analysis results in S5 to show the distribution characteristics of the artificial reef piles.

[0006] Optionally, the S1 specifically includes: S11: deploying echo detection equipment in the target sea area, including a sonar transmitter and a sonar receiver, wherein the sonar transmitter is used to transmit a sound wave signal to the seabed, and the sonar receiver is used to receive the reflected echo signal; S12: transmitting a sound wave signal through a sonar transmitter according to predetermined frequency and power parameters, wherein the frequency range is 10kHz to 500kHz, and the power range is 1W to 1000W, and the sound wave signal propagates in the seawater and generates a reflected echo when encountering an artificial reef pile; S13: The sonar receiver receives the echo signal reflected from the artificial reef pile, wherein the echo signal includes time delay, signal strength and frequency information; S14: Synchronize the received echo signal data with the time reference of the transmitted signal to accurately calculate the time delay of the echo signal and ensure the accuracy of the signal strength and frequency information.

[0007] Optionally, the S14 specifically includes: S141: When the sonar transmitter emits a sound wave signal, record the exact time point of the signal emission ; S142: When the sonar receiver receives the echo signal, the time point of receiving the signal is recorded ; S143: According to the recorded launch time and receiving time , calculate the time delay of the echo signal : S144: Time delay calculated by comparison With the preset time threshold , to determine whether the echo signal is valid, if , the echo signal is considered valid and the corrected signal data is used in subsequent steps; otherwise, the echo signal data is discarded.

[0008] Optionally, the S2 specifically includes: S21: performing denoising processing on the echo signal by using a wavelet transform method, specifically including applying discrete wavelet transform to the echo signal, decomposing the signal into multiple frequency bands, and removing noise components by using a soft threshold denoising technique, and only retaining designated characteristic components of the signal; S22: applying a bandpass filter to the denoised echo signal, with a cutoff frequency range set to 10 kHz to 500 kHz, so as to filter out frequency components below 10 kHz and above 500 kHz; S23: performing fast Fourier transform on the filtered echo signal to convert the time domain signal into a frequency domain signal to generate spectrum data; S24: The converted frequency domain signal data is standardized by using a Z-score standardization method to normalize the signal strength to a range of a mean of 0 and a standard deviation of 1 to ensure comparability between different signals.

[0009] Optionally, the S3 specifically includes: S31: using the preprocessed echo signal data to train a target detection model, the target detection model is constructed by a convolutional neural network, including an input layer, multiple convolutional layers, a pooling layer and a fully connected layer, for identifying characteristic signals related to artificial reefs; S32: extracting spatial features and temporal features from the echo signal; S33: Input the extracted features into the classification layer, and use the Softmax function to output the probability that the signal belongs to the artificial fish reef pile; S34: Filter out signals with probabilities higher than the threshold according to a preset probability threshold, and then determine that these signals correspond to artificial fish reef piles.

[0010] Optionally, the S31 specifically includes: S311: Label the preprocessed echo signal data according to artificial fish reef piles and non-fish reef piles to form a labeled data set ,in represents the i-th echo signal sample, Indicates the corresponding label, which is an artificial fish reef or a non-fish reef, and N is the number of echo signal samples; S312: Divide the data set D into training sets in proportion and validation set , the split ratio is 80% training set and 20% validation set; S313: Construct a convolutional neural network model, i.e., a target detection model structure, including an input layer, multiple convolutional layers, a pooling layer, a fully connected layer, and an output layer; S314: Using the training set Train the target detection model; specifically include the following steps: S3141: Input data Through the target detection model, calculate the predicted output ; S3142: Use the cross entropy loss function L to calculate the error between the predicted value and the true label; S3143: performing gradient calculation on the model parameters according to the loss function L, and updating the model parameters through an optimization algorithm to minimize the loss function; S3144: Repeat the above S3141-S3143 until the loss function converges or reaches a preset number of training rounds.

[0011] Optionally, the S4 specifically includes: S41: Using the time delay of the echo signal and the known sound wave propagation speed v, according to the formula: depth , calculate and extract the depth information of artificial reef piles; S42: performing morphological analysis on the detected echo signal, and processing the signal image with a morphological filter to extract geometric shape features of the artificial fish reef pile, including the size, contour and surface texture of the pile; S43: Based on the extracted depth and morphological features, a spatial statistical analysis method is applied to calculate the distribution density of artificial reef piles in the target sea area, specifically including counting the number of piles within a preset grid range and calculating the average density of the piles in each grid unit; S44: The extracted depth, morphology and distribution density features are standardized. The Z-score standardization method is used to convert each feature into a standard normal distribution with a mean of 0 and a standard deviation of 1 to eliminate the influence of different feature dimensions on subsequent analysis.

[0012] Optionally, the S43 specifically includes: S431: Divide the target sea area into a number of grid cells of equal size; S432: Count the number of artificial reefs in each grid cell, recorded as , where i represents the i-th grid unit; S433: Calculate the area of ​​each grid cell , the unit is square meters; S434: Based on the number of piles in each grid unit and area , calculate the distribution density within the grid unit, the formula is: ,in, is the distribution density of the ith grid unit, in units of piles per square meter.

[0013] Optionally, the S5 specifically includes: S51: Randomly initialize the centroids of K clusters according to the predetermined number of clusters , , , ; S52: For each sample point , calculate its relative position to each centroid Euclidean distance , and Assign to the cluster with the closest cluster center ; S53: For each cluster , recalculate its cluster center is the mean of all sample points in the cluster; S54: Repeat S52-S53 until the cluster center position does not change or the preset maximum number of iterations is reached; S55: Based on the clustering results, the distribution characteristics of artificial reef piles are classified into different distribution patterns, including high-density piles, low-density piles and linear distribution piles.

[0014] Optionally, the S6 specifically includes: S61: Selecting a software system for three-dimensional data visualization, wherein the software system includes geographic information system software or three-dimensional modeling software; S62: importing the classification recognition results and corresponding distribution feature data obtained in the data analysis of S5 into the visualization software system; S63: According to the depth, shape and distribution density characteristics of the artificial reef, corresponding visualization parameter mapping is set, including: Depth mapping: Mapping depth data into vertical coordinates in three-dimensional space to reflect the depth distribution of fish reef piles; Morphological mapping: converting morphological features into geometric shape parameters of a three-dimensional model; Distribution density mapping: Map distribution density data to visual attributes such as color, transparency, or size to distinguish areas of different densities; S64: Based on the mapped feature data, a three-dimensional distribution model of the artificial fish reef pile is generated using the modeling function of the visualization software, specifically including integrating the location information, depth and morphological characteristics of each fish reef pile into the three-dimensional space by establishing a three-dimensional coordinate system, thereby generating a three-dimensional distribution map of the artificial fish reef pile.

[0015] Beneficial effects of the present invention: The present invention provides an efficient and accurate method for identifying and analyzing the distribution characteristics of artificial fish reefs by combining echo detection technology with a data analysis algorithm. The echo signal data of artificial fish reefs in the sea area is obtained by using echo detection equipment, and data preprocessing is performed through precise signal processing steps, which can effectively remove noise and interference and improve the accuracy of the data. The characteristic signals related to the artificial fish reefs are identified through a target detection model, and key characteristics such as the depth, shape and distribution density of the reefs are further extracted. The efficiency and accuracy of monitoring artificial fish reefs are improved, and compared with traditional manual diving and trawling sampling, real-time monitoring can be achieved and a wider range of sea areas can be covered. The present invention, by adopting the K-means clustering algorithm to classify and analyze the extracted distribution characteristics, can accurately identify the distribution characteristics of different types of fish reef piles, and present the analysis results as a three-dimensional distribution map through visualization technology; this visualization method not only provides researchers with an intuitive distribution of fish reef piles, facilitates monitoring and evaluation of different areas, but also helps decision makers formulate more scientific fishery resource management and ecological protection measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 Schematic diagram of a method for identifying and analyzing the distribution characteristics of artificial fish reefs according to an embodiment of the present invention; Figure 2 Schematic diagram of a method for identifying characteristic signals related to artificial fish reefs according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0019] like Figure 1-Figure 2 As shown, a method for identifying and analyzing the distribution characteristics of artificial reef piles includes the following steps: S1: Use echo detection equipment to perform echo detection on the target sea area and obtain echo signal data, including time delay, signal strength and frequency information; S2: preprocessing the echo signal data obtained by S1, including signal denoising, filtering and time domain-frequency domain conversion; S3: Based on the pre-processed echo signal data, identifying characteristic signals related to the artificial reef pile; S4: Extract features of the target signal detected by S3 to extract the distribution features of the artificial reef pile, including depth, shape and distribution density; S5: Use K-means clustering algorithm to analyze the extracted distribution features to classify and identify the distribution characteristics of different types of fish reef piles; S6: Generate a three-dimensional distribution map using visualization technology based on the analysis results in S5 to show the distribution characteristics of the artificial reef piles.

[0020] S1 specifically includes: S11: Deploy echo detection equipment in the target sea area, including sonar transmitters and sonar receivers. The sonar transmitter is used to transmit sound wave signals to the seabed, and the sonar receiver is used to receive the reflected echo signals. S12: transmitting a sound wave signal through a sonar transmitter according to predetermined frequency and power parameters, wherein the frequency range is 10kHz to 500kHz, and the power range is 1W to 1000W, and the sound wave signal propagates in the seawater and generates a reflected echo when encountering an artificial reef pile; S13: The sonar receiver receives the echo signal reflected from the artificial reef, and the echo signal includes time delay, signal strength and frequency information; S14: Synchronize the received echo signal data with the time reference of the transmitted signal to accurately calculate the time delay of the echo signal and ensure the accuracy of the signal strength and frequency information. Through the above steps, the echo detection equipment can accurately and real-timely perform echo detection on the target sea area, obtain high-quality echo signal data, and provide precise signal input for subsequent data preprocessing steps.

[0021] The synchronization processing in S14 specifically includes: S141: When the sonar transmitter emits a sound wave signal, record the exact time point of the signal emission ; S142: When the sonar receiver receives the echo signal, the time point of receiving the signal is recorded ; S143: According to the recorded launch time and receiving time , calculate the time delay of the echo signal ; The calculation formula is: : S144: Time delay calculated by comparison With the preset time threshold , to determine whether the echo signal is valid, if , the echo signal is considered valid, and the corrected signal data is used in subsequent steps; otherwise, the echo signal data is discarded; through the above steps, the method realizes the precise synchronization processing of the echo signal data and the time reference of the transmitted signal, ensures the accurate calculation of the time delay, signal strength and frequency information, and provides reliable data support for the precise identification and analysis of the distribution characteristics of artificial fish reefs.

[0022] S2 specifically includes: S21: using the wavelet transform method to denoise the echo signal, specifically including applying discrete wavelet transform (DWT) to the echo signal, decomposing the signal into multiple frequency bands, and removing the noise component by the soft threshold denoising technique, retaining only the specified characteristic components of the signal, thereby improving the signal-to-noise ratio of the signal; S22: applying a bandpass filter to the denoised echo signal, with a cutoff frequency range set to 10 kHz to 500 kHz, to filter out frequency components below 10 kHz and above 500 kHz, to ensure that the signal frequency range meets the requirements of subsequent analysis steps and effectively remove environmental noise interference; S23: Performing Fast Fourier Transform (FFT) on the filtered echo signal to convert the time domain signal into a frequency domain signal and generate spectrum data, so as to facilitate the analysis and processing of the signal frequency characteristics in the subsequent target detection and feature extraction steps; S24: The converted frequency domain signal data is standardized, and the signal strength is normalized to a range of a mean of 0 and a standard deviation of 1 using the Z-score standardization method to ensure comparability between different signals; through the above steps, the data preprocessing process can effectively remove noise and unnecessary frequency components in the echo signal, ensuring that the data used in subsequent target detection and feature extraction steps are of high quality and accuracy.

[0023] S3 specifically includes: S31: using the preprocessed echo signal data to train a target detection model, the target detection model is constructed by a convolutional neural network, including an input layer, multiple convolutional layers, a pooling layer and a fully connected layer, for identifying characteristic signals related to artificial reefs; S32: extracting spatial features and temporal features from the echo signal; S33: Input the extracted features into the classification layer, and use the Softmax function to output the probability that the signal belongs to the artificial fish reef pile; S34: According to a preset probability threshold, signals with probabilities higher than the threshold are screened out, and then it is determined that these signals correspond to artificial fish reef piles; through the above steps, the echo signal characteristics related to the artificial fish reef piles can be effectively identified, the accuracy and reliability of the identification are improved, and the data quality relied on by the subsequent feature extraction and data analysis steps is ensured.

[0024] S31 specifically includes: S311: Label the preprocessed echo signal data according to artificial fish reef piles and non-fish reef piles to form a labeled data set ,in represents the i-th echo signal sample, Indicates the corresponding label, which is an artificial fish reef or a non-fish reef, and N is the number of echo signal samples; S312: Divide the data set D into training sets in proportion and validation set , where the training set is used for training the target detection model, and the validation set is used for performance evaluation and hyperparameter adjustment of the target detection model, with a split ratio of 80% training set and 20% validation set; S313: Construct a convolutional neural network model, i.e., a target detection model structure, including an input layer, multiple convolutional layers, a pooling layer, a fully connected layer, and an output layer; the specific structure includes: Input layer: used to receive the preprocessed echo signal data, with a shape of (H, W, C), where H is the height, W is the width, and C is the number of channels; Convolutional layer: used to apply multiple convolution kernels to extract features and output feature maps; Pooling layer: used to adopt maximum pooling or average pooling method to reduce the dimension of feature map and improve the generalization ability of the model; Fully connected layer: used to expand the pooled feature map, connect it to the fully connected neurons, and perform high-level feature combination; Output layer: used to use the Softmax activation function to output classification probability distribution .

[0025] S314: Using the training set Train the target detection model; specifically include the following steps: S3141: Input data Through the target detection model, calculate the predicted output ; S3142: The cross entropy loss function L is used to calculate the error between the predicted value and the true label. The expression of the cross entropy loss function L is: , where L represents the total loss of the model; Indicates the total number of samples (or batch size); Represents the true label (target value) of the i-th sample. For a binary classification task, The value is 0 or 1; represents the predicted probability of the i-th sample, that is, the probability that the model predicts that sample i belongs to category 1; is the natural logarithm of the probability that the i-th sample is predicted to belong to category 1; is the natural logarithm of the probability that the i-th sample is predicted to belong to category 0; the cross entropy loss measures the difference between the probability distribution output by the model and the actual label; if the probability predicted by the model matches the true label, the loss is small; if the prediction result deviates greatly from the actual label, the loss is large; When , the loss term is , that is, the closer the model is to the true label 1, the smaller the loss; if the probability predicted by the model is very small (i.e. Close to 0), the loss will be huge; When , the loss term is , that is, when the model prediction is close to 0, the loss is small, and when the prediction is close to 1, the loss is large; S3143: Calculate the gradient of the model parameters according to the loss function L, and update the model parameters through an optimization algorithm (such as stochastic gradient descent SGD or Adam optimizer) to minimize the loss function; S3144: Repeat the above S3141-S3143 until the loss function converges or reaches a preset number of training rounds; S315: Using a Validation Set Evaluate the performance of the trained target detection model, calculate the accuracy, recall and F1 score indicators, and ensure that the model has good generalization ability and recognition accuracy; through the above steps, it is possible to use the labeled pre-processed echo signal data to train the convolutional neural network target detection model, ensuring that the model can accurately identify the echo signal characteristics related to artificial fish reefs, thereby improving the accuracy and reliability of target detection.

[0026] S4 specifically includes: S41: Using the time delay of the echo signal And the known sound wave propagation speed v (usually 1500m / s in seawater), according to the formula: depth , calculate and extract the depth information of artificial reef piles to ensure the accuracy and reliability of depth measurement; S42: performing morphological analysis on the detected echo signal, and processing the signal image using a morphological filter (such as an opening operation and a closing operation) to extract geometric shape features of the artificial fish reef pile, including the size, contour and surface texture of the pile; S43: Based on the extracted depth and morphological features, a spatial statistical analysis method is applied to calculate the distribution density of artificial reef piles in the target sea area, specifically including counting the number of piles within a preset grid range and calculating the average density of the piles in each grid unit; S44: The extracted depth, morphology and distribution density features are standardized. The Z-score standardization method is used to convert each feature into a standard normal distribution with a mean of 0 and a standard deviation of 1, so as to eliminate the influence of different feature dimensions on subsequent analysis and ensure the consistency and comparability of feature data. Through the above steps, comprehensive feature extraction of the target signal detected by S3 is achieved, and key distribution features such as the depth, morphology and distribution density of artificial fish reef piles are accurately obtained, providing high-quality feature data support for subsequent data analysis and visualization.

[0027] S43 specifically includes: S431: Divide the target sea area into a number of grid cells of equal size, where the size of the grid cells is pre-set according to the specific scope and resolution requirements of the target sea area to ensure the accuracy of distribution density calculation; S432: Count the number of artificial reefs in each grid cell, recorded as , where i represents the i-th grid unit; S433: Calculate the area of ​​each grid cell , the unit is square meters; S434: Based on the number of piles in each grid unit and area , calculate the distribution density within the grid unit, the formula is: ,in, is the distribution density of the ith grid unit, and the unit is the number of piles per square meter. Through the above steps, the accurate calculation of the distribution density of artificial fish reef piles in the target sea area is achieved, which provides an accurate and comparable data basis for the subsequent cluster analysis steps.

[0028] S5 specifically includes: S51: Randomly initialize the centroids of K clusters according to the predetermined number of clusters , , , ; S52: For each sample point , calculate its relative position to each centroid Euclidean distance , and Assign to the cluster with the closest cluster center , the formula is: , where M is the feature dimension, is the j-th eigenvalue of the ith sample, is the jth eigenvalue of the kth cluster centroid; S53: For each cluster , recalculate its cluster center is the mean of all sample points in the cluster, and the formula is: ; S54: Repeat S52-S53 until the cluster center position does not change or the preset maximum number of iterations is reached; S55: According to the clustering results, the distribution characteristics of artificial fish reef piles are classified into different distribution patterns, including high-density piles, low-density piles and linear distribution piles; through the above steps, the K-means clustering algorithm can effectively classify and identify the extracted distribution characteristics of artificial fish reef piles, accurately distinguish different types of fish reef pile distribution patterns, and improve the accuracy and reliability of distribution characteristic analysis.

[0029] S6 specifically includes: S61: Select a software system for three-dimensional data visualization, including geographic information system (GIS) software or three-dimensional modeling software to support the generation of high-precision three-dimensional distribution maps; S62: Import the classification recognition results and corresponding distribution feature data (depth, morphology, distribution density) obtained in the S5 data analysis into the visualization software system to ensure that the data format matches the software system requirements; S63: According to the depth, shape and distribution density characteristics of the artificial reef, corresponding visualization parameter mapping is set, including: Depth mapping: Mapping depth data into vertical coordinates in three-dimensional space to reflect the depth distribution of fish reef piles; Morphological mapping: converting morphological features into geometric shape parameters of 3D models, such as size, contour, and surface texture; Distribution density mapping: Map distribution density data to visual attributes such as color, transparency, or size to distinguish areas of different densities; S64: Based on the mapped feature data, a three-dimensional distribution model of the artificial fish reef piles is generated using the modeling function of the visualization software, specifically including integrating the location information, depth and morphological characteristics of each fish reef pile into the three-dimensional space by establishing a three-dimensional coordinate system, thereby generating a three-dimensional distribution map of the artificial fish reef piles; through the above steps, the spatial distribution, morphology and density characteristics of the artificial fish reef piles can be accurately displayed in three dimensions, providing an intuitive and efficient decision support tool for subsequent research and application.

[0030] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for identifying and analyzing the distribution characteristics of artificial reefs, characterized in that: The following steps are involved: S1: Use echo detection equipment to perform echo detection on the target sea area and obtain echo signal data, including time delay, signal strength and frequency information; S2: preprocessing the echo signal data obtained by S1, including signal denoising, filtering and time domain-frequency domain conversion; S3: Based on the pre-processed echo signal data, identifying characteristic signals related to the artificial reef pile; S4: Extract features of the target signal detected by S3 to extract the distribution features of the artificial reef pile, including depth, shape and distribution density; S5: Use K-means clustering algorithm to analyze the extracted distribution features to classify and identify the distribution characteristics of different types of fish reef piles; S6: Generate a three-dimensional distribution map using visualization technology based on the analysis results in S5 to show the distribution characteristics of the artificial reef piles.

2. The method for identifying and analyzing the distribution characteristics of artificial reefs according to claim 1, characterized in that: The S1 specifically includes: S11: deploying echo detection equipment in the target sea area, including a sonar transmitter and a sonar receiver, wherein the sonar transmitter is used to transmit a sound wave signal to the seabed, and the sonar receiver is used to receive the reflected echo signal; S12: transmitting a sound wave signal through a sonar transmitter according to predetermined frequency and power parameters, wherein the frequency range is 10kHz to 500kHz, and the power range is 1W to 1000W, and the sound wave signal propagates in the seawater and generates a reflected echo when encountering an artificial reef pile; S13: The sonar receiver receives the echo signal reflected from the artificial reef pile, wherein the echo signal includes time delay, signal strength and frequency information; S14: Synchronize the received echo signal data with the time reference of the transmitted signal to accurately calculate the time delay of the echo signal and ensure the accuracy of the signal strength and frequency information.

3. The method for identifying and analyzing the distribution characteristics of artificial reefs according to claim 2, characterized in that: The S14 specifically includes: S141: When the sonar transmitter emits a sound wave signal, record the exact time point of the signal emission ; S142: When the sonar receiver receives the echo signal, the time point of receiving the signal is recorded ; S143: According to the recorded launch time and receiving time , calculate the time delay of the echo signal : S144: Time delay calculated by comparison With the preset time threshold , to determine whether the echo signal is valid, if , the echo signal is considered valid and the corrected signal data is used in subsequent steps; otherwise, the echo signal data is discarded.

4. The method for identifying and analyzing the distribution characteristics of artificial reefs according to claim 1, characterized in that: The S2 specifically includes: S21: performing denoising processing on the echo signal by using a wavelet transform method, specifically including applying discrete wavelet transform to the echo signal, decomposing the signal into multiple frequency bands, and removing noise components by using a soft threshold denoising technique, and only retaining designated characteristic components of the signal; S22: applying a bandpass filter to the denoised echo signal, with a cutoff frequency range set to 10 kHz to 500 kHz, so as to filter out frequency components below 10 kHz and above 500 kHz; S23: performing fast Fourier transform on the filtered echo signal to convert the time domain signal into a frequency domain signal to generate spectrum data; S24: The converted frequency domain signal data is standardized by using a Z-score standardization method to normalize the signal strength to a range of a mean of 0 and a standard deviation of 1 to ensure comparability between different signals.

5. The method for identifying and analyzing the distribution characteristics of artificial reefs according to claim 1, characterized in that: The S3 specifically includes: S31: using the preprocessed echo signal data to train a target detection model, the target detection model is constructed by a convolutional neural network, including an input layer, multiple convolutional layers, a pooling layer and a fully connected layer, and is used to identify characteristic signals related to artificial reefs; S32: extracting spatial features and temporal features from the echo signal; S33: Input the extracted features into the classification layer, and use the Softmax function to output the probability that the signal belongs to the artificial fish reef pile; S34: Filter out signals with probabilities higher than the threshold according to a preset probability threshold, and then determine that these signals correspond to artificial fish reef piles.

6. The method for identifying and analyzing the distribution characteristics of artificial reefs according to claim 5, characterized in that: The S31 specifically includes: S311: Label the preprocessed echo signal data according to artificial fish reef piles and non-fish reef piles to form a labeled data set ,in represents the i-th echo signal sample, Indicates the corresponding label, which is an artificial fish reef or a non-fish reef, and N is the number of echo signal samples; S312: Divide the data set D into training sets in proportion and validation set , the split ratio is 80% training set and 20% validation set; S313: Construct a convolutional neural network model, i.e., a target detection model structure, including an input layer, multiple convolutional layers, a pooling layer, a fully connected layer, and an output layer; S314: Using the training set Train the target detection model; specifically include the following steps: S3141: Input data Through the target detection model, calculate the predicted output ; S3142: Use the cross entropy loss function L to calculate the error between the predicted value and the true label; S3143: performing gradient calculation on the model parameters according to the loss function L, and updating the model parameters through an optimization algorithm to minimize the loss function; S3144: Repeat the above S3141-S3143 until the loss function converges or reaches a preset number of training rounds.

7. The method for identifying and analyzing the distribution characteristics of artificial reefs according to claim 1, characterized in that: The S4 specifically includes: S41: Using the time delay of the echo signal and the known sound wave propagation speed v, according to the formula: depth , calculate and extract the depth information of artificial reef piles; S42: performing morphological analysis on the detected echo signal, and processing the signal image with a morphological filter to extract geometric shape features of the artificial fish reef pile, including the size, contour and surface texture of the pile; S43: Based on the extracted depth and morphological features, a spatial statistical analysis method is applied to calculate the distribution density of artificial reef piles in the target sea area, specifically including counting the number of piles within a preset grid range and calculating the average density of the piles in each grid unit; S44: The extracted depth, morphology and distribution density features are standardized. The Z-score standardization method is used to convert each feature into a standard normal distribution with a mean of 0 and a standard deviation of 1 to eliminate the influence of different feature dimensions on subsequent analysis.

8. The method for identifying and analyzing the distribution characteristics of artificial reefs according to claim 7, characterized in that: The S43 specifically includes: S431: Divide the target sea area into a number of grid cells of equal size; S432: Count the number of artificial reefs in each grid cell, recorded as , where i represents the i-th grid unit; S433: Calculate the area of ​​each grid cell , the unit is square meters; S434: Based on the number of piles in each grid unit and area , calculate the distribution density within the grid unit, the formula is: ,in, is the distribution density of the ith grid unit, in units of piles per square meter.

9. The method for identifying and analyzing the distribution characteristics of artificial reefs according to claim 8, characterized in that: The S5 specifically includes: S51: Randomly initialize the centroids of K clusters according to the predetermined number of clusters , , , ; S52: For each sample point , calculate its relative position to each centroid Euclidean distance , and Assign to the cluster with the closest cluster center ; S53: For each cluster , recalculate its cluster center is the mean of all sample points in the cluster; S54: Repeat S52-S53 until the cluster center position does not change or the preset maximum number of iterations is reached; S55: Based on the clustering results, the distribution characteristics of artificial reef piles are classified into different distribution patterns, including high-density piles, low-density piles and linear distribution piles.

10. The method for identifying and analyzing the distribution characteristics of artificial reefs according to claim 1, characterized in that: The S6 specifically includes: S61: Selecting a software system for three-dimensional data visualization, wherein the software system includes geographic information system software or three-dimensional modeling software; S62: importing the classification recognition results and corresponding distribution feature data obtained in the data analysis of S5 into the visualization software system; S63: According to the depth, shape and distribution density characteristics of the artificial reef, corresponding visualization parameter mapping is set, including: Depth mapping: Mapping depth data into vertical coordinates in three-dimensional space to reflect the depth distribution of fish reef piles; Morphological mapping: converting morphological features into geometric shape parameters of a three-dimensional model; Distribution density mapping: Map distribution density data to visual attributes such as color, transparency, or size to distinguish areas of different densities; S64: Based on the mapped feature data, a three-dimensional distribution model of the artificial fish reef pile is generated using the modeling function of the visualization software, specifically including integrating the location information, depth and morphological characteristics of each fish reef pile into the three-dimensional space by establishing a three-dimensional coordinate system, thereby generating a three-dimensional distribution map of the artificial fish reef pile.

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