A method for identifying the depth of underwater biological aggregation layers

Abstract

This application provides a method for identifying the depth location of underwater biological aggregation layers, comprising: identifying the upper and lower boundaries of the planktonic aggregation depth by using the position of the abrupt change in sound velocity of the temperature jump layer and the position of the change in acoustic impedance of the dissolved oxygen jump layer, obtaining the depth range of the aggregation layer boundary, and recording the depth location of the biological aggregation layer in the current cycle; determining the center depth location of the biological aggregation layer in the current cycle by combining the depth spacing, the peak depth of the scattering intensity, and the position of the abrupt change in sound velocity; identifying the depth offset between the center depth location and the depth location of the biological aggregation layer in the previous cycle, and when the offset exceeds the preset jump layer stability boundary, acquiring the echo intensity distribution of the temperature jump layer and the dissolved oxygen jump layer, and extracting the echo intensity difference value between the two jump layers; and identifying the final center depth location of the underwater biological aggregation layer using multibeam sonar by using the enhanced boundary depth location and the position coordinates of each depth point in the aggregation layer.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for identifying the depth location of underwater biological aggregation layers. Background Technology

[0002] In complex marine environments, accurately determining the depth of targets such as plankton is crucial not only for scientific research but also for providing indispensable technical support for practical operations. However, current methods in this field often struggle to adapt to the variability of the marine environment, particularly when environmental parameters interfere with each other, leading to measurement errors. Among the numerous technical challenges, the spatial misalignment of the dissolved oxygen stratum and the temperature stratum is particularly intractable. These two strata represent regions of rapid change in oxygen concentration and temperature in the water, respectively, and their positional differences directly affect the propagation path and signal characteristics of sound waves. Since these two strata do not always coincide, increased separation can have complex effects on the reception and interpretation of sound signals. Specifically, sound waves experience abrupt changes in propagation speed due to temperature variations when passing through the temperature stratum, while at the dissolved oxygen stratum, changes in water density cause fluctuations in signal intensity. This dual interference results in inconsistent depth signals for the target object under different detection methods, increasing the difficulty of interpretation. Taking a real-world scenario as an example, in an underwater plankton depth exploration mission, the thermocline might be located at a depth of 50 meters, while the dissolved oxygen clinchpin appears at 70 meters, a considerable distance apart. In this situation, the parallel acoustic signals become blurred due to the dispersion of organisms, making it difficult to clearly distinguish the specific layers of biological aggregation. While the vertical acoustic signals can capture the positional difference between the two clinches, the uneven signal intensity prevents accurate identification of the concentrated areas of organisms. Therefore, accurately identifying and integrating the characteristics of acoustic signals from different directions when the dissolved oxygen clinchpin and thermocline are spatially misaligned, thereby precisely locating the actual depth of underwater targets, becomes a crucial problem that this research urgently needs to solve. Summary of the Invention

[0003] This invention provides a method for identifying the depth location of underwater biological aggregation layers, comprising: acquiring acoustic wave data in the water body using a sonar device to obtain depth location information of a temperature stratum and a dissolved oxygen stratum; determining the depth distance between the two strata based on the depth location information of the temperature stratum and the dissolved oxygen stratum; extracting scattered echo intensity features from the acoustic wave data to identify the hierarchical distribution characteristics of the scattered layers; determining the clarity of the hierarchical boundaries based on the depth distance and the hierarchical distribution characteristics of the scattered layers; determining the boundary location of the planktonic aggregation depth using the hierarchical boundary clarity and sound velocity variation features to construct the aggregation layer depth range; calculating the center depth location of the biological aggregation layer by combining the depth distance and the scattered intensity features; analyzing the echo intensity difference between the two strata based on the offset amplitude of the center depth location from historical data; determining the actual depth distribution range of the aggregation layer based on the echo intensity difference to enhance the boundary depth location; and verifying and finally determining the center depth location of the underwater biological aggregation layer using multibeam sonar.

[0004] Furthermore, the step of acquiring sound wave data in the water body using sonar equipment to obtain the depth location information of the temperature gradient and dissolved oxygen gradient includes: emitting parallel and vertical beams using a dual-wave sonar, acquiring the scattered echo intensity data of the parallel wave at different depths, and recording the sound speed value of the vertical wave propagating in the water body and its curve changing with depth; when the sound speed changes abruptly at a certain depth and exceeds a preset sound speed change threshold, that depth is determined to be the temperature gradient depth; when the sound speed gradient undergoes a sign change at another depth, that depth is identified as the dissolved oxygen gradient depth; calculating the depth distance between the two gradients based on the temperature gradient depth and the dissolved oxygen gradient depth; verifying the accuracy of the two gradient positions based on the depth distance and the sound speed change curve, and recording the depth location information of the two gradients as basic data for subsequent analysis.

[0005] Furthermore, the step of extracting scattered echo intensity features from the acoustic wave data and identifying the hierarchical distribution features of the scattering layer includes: identifying depth points where the echo intensity changes significantly as texture boundary locations based on the parallel wave scattered echo intensity data; obtaining the specific depth of the region where the scattering intensity reaches its peak; if the intensity difference between adjacent texture boundaries exceeds a preset boundary recognition threshold, recording each depth point and its corresponding scattering intensity value within the region where the scattering intensity reaches its peak, forming a scattering dataset for the depth interval; performing layered processing on the scattering intensity values ​​according to the depth interval based on the texture boundary locations and peak scattering intensity depths in the scattering dataset; determining the uniformity of the intensity distribution within each layer by calculating the statistical characteristics of the scattering intensity within each layer; when the distribution features of a certain layer meet a preset stability condition, it is determined as a stable scattering layer, obtaining the depth range and intensity features of multiple stable scattering layers, forming the hierarchical distribution features of the scattering layer.

[0006] Furthermore, the step of determining the clarity of the layer boundary based on the depth spacing and the hierarchical distribution characteristics of the scattering layer includes: extracting the echo intensity difference between adjacent scattering layers based on the depth spacing and the hierarchical distribution characteristics of the scattering layer; calculating the vertical rate of change of the echo intensity difference; determining that the interlayer transition is slow when the rate of change is less than a preset threshold; comparing the depth range of the transition interval with the depth spacing; if the transition interval exceeds half of the depth spacing, determining that the clarity of the layer boundary is lower than a preset threshold, and confirming that the boundary is in a dispersed state; calculating the rate of change of sound velocity from the sound velocity change curve for the boundary region in the dispersed state; when the rate of change of sound velocity changes abruptly at a certain depth and the abrupt change amplitude exceeds a preset threshold, recording that depth as the sound velocity abrupt change position of the temperature jump layer; calculating the acoustic impedance value by combining the sound velocity value near the sound velocity abrupt change position with seawater density distribution data, and identifying the depth of the vertical gradient sign conversion of acoustic impedance as the acoustic impedance change position of the dissolved oxygen jump layer.

[0007] Furthermore, the step of determining the boundary position of the plankton aggregation depth by using the clarity of the layer boundary and the sound speed change characteristics, and constructing the aggregation layer depth interval, includes: comparing the depth values ​​at the locations of the abrupt change in sound speed of the temperature jump layer and the changes in acoustic impedance of the dissolved oxygen jump layer, taking the smaller of the two depth values ​​as the upper boundary position of the plankton aggregation depth, and taking the larger of the two depth values ​​as the lower boundary position; if the distance between the two positions exceeds a preset threshold, then an adaptively determined extension depth value is added to the lower boundary position as the corrected lower boundary position; constructing the depth interval of the aggregation layer boundary based on the upper and lower boundary positions; extracting the scattering intensity value sequence from the sonar echo data of each depth point within the depth interval of the aggregation layer boundary; calculating the difference between the intensity value of each depth point and the average intensity of the interval; when the difference within a continuous depth segment is less than a preset threshold, determining the upper and lower depths of the depth segment as the upper and lower boundaries of the main biological aggregation area, and recording the depth position data set.

[0008] Furthermore, the calculation of the center depth position of the biological aggregation layer by combining the depth spacing and scattering intensity characteristics includes: combining the depth spacing, the peak depth of scattering intensity, and the location of the abrupt change in sound velocity, and weighting each parameter according to a preset weighting coefficient to obtain an initial center depth value; extracting the scattering intensity data sequence within a preset range near the initial center depth value; calculating the actual aggregation center depth using an intensity-weighted average method; determining the actual aggregation center depth as the center depth position of the biological aggregation layer in the current period; recording the relationship between the center depth position and the upper and lower bounds of the depth interval as benchmark data for subsequent migration analysis; and verifying the stability of the calculation results by comparing the center depth position with historical period data.

[0009] Furthermore, the step of analyzing the echo intensity difference between the two thermoclines based on the offset amplitude of the center depth position and historical data includes: identifying the difference between the center depth position in the current period and the biological aggregation layer depth position in the previous period; calculating the absolute value of the difference as the depth offset amplitude; if the depth offset amplitude exceeds a preset thermocline stability boundary threshold, it is determined that the biological distribution has migrated significantly; collecting continuous sonar echo intensity sequence data at the depth position of the temperature thermocline; collecting echo intensity sequence data at the corresponding time at the depth position of the dissolved oxygen thermocline; calculating the average intensity value of the two sequence data respectively; obtaining the echo intensity difference between the two thermoclines by subtracting the average intensity value of the dissolved oxygen thermocline from the average intensity value of the temperature thermocline, and recording this difference value as a quantitative indicator of the biological distribution characteristics between the thermoclines.

[0010] Furthermore, determining the actual depth distribution range of the aggregation layer and enhancing the boundary depth position based on the echo intensity difference includes: comparing the echo intensity difference with a preset aggregation layer determination threshold; when the difference value is within the threshold range, taking the depth position where the difference value occurs as the center, extending it upwards and downwards by a preset distance to obtain the depth distribution range; extracting the depth values ​​of each sampling point within this range at fixed depth intervals as a position coordinate sequence; calculating the intensity change rate between adjacent depth points using the scattered echo intensity value of each depth point in the position coordinate sequence; when the change rate of multiple consecutive depth points is less than a preset stability threshold, marking the interval where these depth points are located as a stable distribution area; marking the interval where the change rate of depth points exceeds the stability threshold as a transition area; for depth points in the transition area, determining the fuzzy boundary by combining the upper and lower limits of the depth interval of the aggregation layer boundary; calculating the local gradient value within the fuzzy boundary, and amplifying it through an enhancement coefficient to obtain the enhanced boundary depth position.

[0011] Furthermore, the process of using multibeam sonar to verify and ultimately determine the center depth of the underwater biological aggregation layer includes: determining the upper and lower boundary values ​​based on the enhanced boundary depth; constructing a three-dimensional spatial distribution matrix by combining the coordinates of each depth point in the aggregation layer, with matrix elements including depth values, horizontal coordinates, and scattering intensity values; emitting multi-angle acoustic beams within the boundary range using multibeam sonar; extracting backscattering intensity data based on the echo signals, and weighting and summing the intensity data of the same depth layer according to the beam coverage area to obtain a comprehensive intensity distribution map; if the comprehensive intensity value of a certain depth layer exceeds a preset biological aggregation threshold, it is recorded as a candidate center layer; for the candidate center layer, calculating the intensity-weighted average depth of all locations within each layer; taking the arithmetic mean of the weighted center depths of all candidate layers to obtain a preliminary center depth value; and making judgments and linear corrections based on the relationship between the preliminary center depth value and the upper and lower boundaries to finally determine the center depth location.

[0012] Furthermore, determining the actual depth distribution range of the aggregation layer based on the echo intensity difference and enhancing the boundary depth position includes: extracting scattering intensity data of key depth points between the two layers based on the echo intensity difference; identifying depth intervals with significant intensity changes as preliminary distribution ranges using the scattering intensity data; calculating the intensity value of each depth point within the preliminary distribution range and the intensity difference between its upper and lower adjacent points to form a local gradient value sequence; identifying depth points with abrupt gradient value changes as boundary candidate points based on the local gradient value sequence; selecting boundary candidate points located within the depth intervals by combining the aggregation layer depth intervals; enhancing the gradient values ​​of the boundary candidate points and connecting adjacent boundary feature points using a linear interpolation method to obtain the final enhanced boundary depth position; and recording the correspondence between the enhanced boundary depth position and the depth distribution range as basic data for subsequent verification.

[0013] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0014] This invention discloses a method for identifying the depth location of underwater biological aggregation layers. Addressing the challenges of complex depth distribution, blurred layer boundaries, and dynamically changing depth shifts in operational scenarios, this method solves these problems through multi-dimensional sonar data fusion and dynamic analysis. First, this invention utilizes dual-wave sonar to collect parallel wave scattered echo intensity and vertical wave sound velocity data to identify the depth distance between the temperature gradient and dissolved oxygen gradient layers, as well as the distribution characteristics of the scattering layer, thereby determining the clarity of the layer boundaries. When the boundaries are blurred, the depth range of the aggregation layer is located through abrupt changes in sound velocity and acoustic impedance, and the center depth position is dynamically adjusted by combining the peak value of the scattered intensity with the difference in echo intensity. Finally, multi-beam sonar technology is employed, combined with the enhanced boundary depth and position coordinates, to accurately identify the center depth of the aggregation layer. This invention, through multi-level data analysis and boundary enhancement technology, significantly improves the accuracy and stability of underwater biological aggregation layer depth identification, providing reliable support for marine ecological monitoring. Attached Figure Description

[0015] Figure 1 This is a flowchart of a method for identifying the depth location of underwater biological aggregation layers according to the present invention.

[0016] Figure 2 This is a schematic diagram of a method for identifying the depth of an underwater biological aggregation layer according to the present invention.

[0017] Figure 3 This is another schematic diagram of a method for identifying the depth of an underwater biological aggregation layer according to the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figures 1-3 This embodiment of a method for identifying the depth of an underwater biological aggregation layer may specifically include:

[0020] Step S101: The parallel wave scattered echo intensity, vertical wave sound velocity and sound velocity variation curve are collected by dual-wave sonar. The depth of the temperature jump layer and the depth of the dissolved oxygen jump layer are identified from the vertical wave sound velocity. The depth distance between the two jump layers is extracted. The texture boundary position and the peak depth of the scattering intensity of the scattering layer are identified from the parallel wave scattered echo intensity, so as to obtain the hierarchical distribution characteristics of the scattering layer.

[0021] By emitting parallel and vertical beams using a dual-wave sonar, the intensity data of the scattered echoes from the parallel waves at different depths are collected. The sound velocity of the vertical waves propagating in the water and its variation with depth are recorded. When the sound velocity abruptly changes at a certain depth, exceeding the sound velocity jump threshold, this location is identified as the depth of a temperature jump layer. When the sound velocity gradient undergoes a sign change at another depth, this depth is identified as the depth of a dissolved oxygen jump layer, and the depth distance between the two jump layers is calculated. Based on the depth distance and the parallel wave scattered echo intensity data, depth points where the echo intensity changes significantly are identified as texture boundary locations. The specific depth at which the scattered intensity reaches its peak is obtained. If the intensity difference between adjacent texture boundaries exceeds the boundary identification threshold, each depth point in that region and its corresponding scattered intensity value are recorded, forming a scattered data set for that depth range. For the texture boundary positions and peak depths of scattering intensity in the scattering dataset, the scattering intensity values ​​are layered according to the depth interval. Each layer contains a scattering intensity sequence of continuous depth points. By calculating the statistical characteristics of the scattering intensity in each layer, the uniformity of the intensity distribution within the layer is determined. When the distribution characteristics of a certain layer meet the stability condition, it is determined as a stable scattering layer. The depth range and intensity characteristics of multiple stable scattering layers are obtained, forming the hierarchical distribution characteristics of the scattering layers.

[0022] Specifically, in one implementation, the dual-wave sonar system simultaneously emits parallel and vertical beams for marine environmental detection.

[0023] Specifically, the parallel beam is mainly used to acquire horizontal scattering information, and its frequency range is typically set between 38kHz and 120kHz, effectively capturing the scattering characteristics of planktonic communities. The vertical beam focuses on monitoring the vertical distribution of sound velocity, calculating the sound velocity values ​​at each depth layer by continuously recording the round-trip time and propagation distance of the sound waves. When the sound velocity value of the vertical wave at a certain depth shows a significant jump relative to adjacent depths, and the jump amplitude exceeds a first preset threshold, the system determines that the location is the depth of a temperature jump layer.

[0024] It should be noted that the identification of dissolved oxygen strata depends on the changing characteristics of the sound velocity gradient. In marine environments, rapid changes in dissolved oxygen concentration can cause changes in water density and acoustic properties, manifested as inflection points where the sound velocity gradient changes from positive to negative or vice versa.

[0025] For example, at a depth of 20 meters below the temperature gradient, when the sound velocity gradient changes from an increase of 0.5 m / s per meter of depth to a decrease of 0.3 m / s per meter of depth, this transition depth is identified as the location of the dissolved oxygen gradient. The system automatically calculates the vertical distance between the two gradients; this depth spacing parameter is of significant reference value for subsequent scattering layer analysis. Preferably, the identification of texture boundary locations is achieved using a sliding window scanning method. The input data for this method is a sequence of parallel wave scattered echo intensity, where each element contains a depth value and the corresponding echo intensity value. The processing consists of four steps: First, the window size is set to one-tenth of the depth spacing, typically 2 to 5 meters, to ensure that the window can cover sufficient sampling points; second, the window is slid downwards layer by layer from the shallowest depth, with each sliding step being the depth interval of one sampling point; third, the arithmetic mean of the intensity values ​​of all sampling points within the current window is calculated as the representative intensity of that window; finally, the representative intensities of two adjacent windows are compared, and when the intensity difference exceeds the boundary identification threshold, the depth at the intersection of the two windows is marked as the texture boundary location. The output of this method is a sequence of texture boundary positions, including the depth value of each boundary point and the intensity difference between the two sides of the boundary. Simultaneously, the maximum intensity value of each depth window is recorded throughout the scanning process, and the depth corresponding to the global maximum value is extracted as the peak depth of scattering intensity. This depth typically corresponds to a densely distributed area of ​​plankton.

[0026] In one embodiment, the system performs layered processing on the scattering data based on the acquired depth spacing parameters. Specifically, the layering process divides the entire detection depth range into multiple depth layers at fixed intervals, with each layer containing several consecutive depth sampling points and their corresponding scattering intensity values. The distribution characteristics of the scattering intensity within each layer are evaluated by calculating the arithmetic mean and variance of the intensity values ​​of all sampling points. When the variance value of a layer is less than a third preset threshold, it indicates that the scattering intensity distribution within that layer is relatively uniform, and the system classifies it as a stable scattering layer. The resulting layered distribution characteristics include the depth range, average intensity value, and intensity variation characteristics within each stable scattering layer; these parameters together constitute a complete description of the vertical distribution of plankton.

[0027] Step S102: Identify the clarity of the layer boundary of the scattered echo based on the depth spacing between the two lattice layers and the layer distribution characteristics of the scattering layer. When the clarity of the layer boundary is lower than the set threshold, it is determined that the boundary is in a dispersed state. Then, extract the position of the abrupt change in sound velocity of the temperature lattice layer and the position of the change in acoustic impedance of the dissolved oxygen lattice layer from the sound velocity change curve.

[0028] Based on the depth spacing and the hierarchical distribution characteristics of the scattering layers, the echo intensity difference between adjacent scattering layers is extracted, and the vertical rate of change of the echo intensity difference is calculated. When the rate of change is less than a preset threshold, it indicates a slow transition between layers. The depth range of the transition interval is compared with the depth spacing. If the transition interval exceeds half of the depth spacing, the boundary clarity is determined to be lower than a set threshold, indicating a dispersed boundary. For the dispersed boundary region, the rate of change of sound velocity is calculated from the sound velocity change curve. When the rate of change of sound velocity changes abruptly at a certain depth and the magnitude of the abrupt change exceeds a preset threshold, this depth is recorded as the sound velocity abrupt change location of the temperature jump layer. Using the sound velocity values ​​near the sound velocity abrupt change location of the temperature jump layer, combined with seawater density distribution data, the acoustic impedance values ​​at each depth are calculated. When the vertical gradient of the acoustic impedance value undergoes a sign reversal, the turning depth is identified as the acoustic impedance change location of the dissolved oxygen jump layer.

[0029] Specifically, in one implementation, the identification of boundary sharpness is based on a comprehensive evaluation of the depth spacing and the hierarchical distribution characteristics of the scattering layer.

[0030] Specifically, the system first extracts the echo intensity difference between adjacent scattering layers, and then calculates the rate of change of the echo intensity by measuring the change of this difference per unit depth. A small rate of change indicates a slow transition between layers and blurred boundaries. In actual oceanographic exploration, when plankton are distributed between the thermocline and the dissolved oxygen clinch, their scattered echoes exhibit a gradual change characteristic, with the transition zone potentially extending to depths of tens of meters.

[0031] It should be noted that the depth range of the transition interval is determined using the gradient threshold method. The system continuously monitors the echo intensity gradient value. When the gradient value first falls below a preset threshold, it is marked as the start of the transition interval. When the gradient value exceeds the threshold again, it is marked as the end of the transition interval. The depth difference between the two points is the range of the transition interval. If this range exceeds half of the depth interval, the boundary is considered to be dispersed, and conventional layer identification methods are insufficient to accurately locate the biological aggregation layer.

[0032] Preferably, for boundary regions in a dispersed state, the system performs secondary analysis on the recorded sound velocity variation curves. The rate of change of sound velocity is obtained by dividing the difference in sound velocity between adjacent depth sampling points by the depth interval, forming a continuous sequence of rates of change. At the location of a temperature abrupt change, the sharp drop in seawater temperature causes a significant abrupt change in sound velocity, with the rate of change potentially increasing abruptly from 0.2 m / s to over 1.5 m / s. The system automatically identifies and records the precise depth of these abrupt change locations using a set threshold for such changes.

[0033] In one embodiment, the acoustic impedance calculation method is based on acoustic theory and marine environmental parameters. The input data for this method includes three parts: first, a sequence of sound velocity values ​​at and near the locations of abrupt changes in sound velocity at temperature strata; second, standard seawater density distribution data, which is based on international oceanographic standards and uses temperature, salinity, and pressure parameters to construct a reference model of seawater density variation with depth, typically stored in the form of a lookup table or a polynomial fitting function; and third, the target depth range, i.e., the depth interval for which acoustic impedance needs to be calculated. The processing consists of four steps: first, extracting the corresponding sound velocity value from the sound velocity sequence based on the target depth value; if the depth is not at the sampling point, linear interpolation is used; second, querying or calculating the density value corresponding to the depth from the seawater density distribution model, with the density unit being kilograms per cubic meter; third, multiplying the sound velocity value by the density value to obtain the acoustic impedance value at that depth, with the unit being Rayleigh; and finally, repeating the above calculations for all target depth points to form a complete sequence of acoustic impedance values. The method outputs two parts: first, a sequence of acoustic impedance values ​​at each depth point; and second, a sequence of acoustic impedance gradients obtained by dividing the acoustic impedance difference between adjacent depth points by the depth difference. By analyzing the sign change of the gradient sequence, the inflection point depth at which the gradient changes from positive to negative or vice versa is identified. This depth marks the location of the acoustic impedance change in the dissolved oxygen cascade, signifying a significant shift in the acoustic properties of the water body. Changes in dissolved oxygen content alter the microstructure of the water body, leading to inflections in the acoustic impedance gradient. When the gradient changes from increasing to decreasing or vice versa, this inflection point depth indicates the location of the acoustic impedance change in the dissolved oxygen cascade. This location signifies a significant shift in the acoustic properties of the water body and plays a crucial indicative role in accurately locating phytoplankton accumulation layers.

[0034] Step S103: Identify the upper and lower boundaries of the phytoplankton aggregation depth by using the abrupt change in sound velocity of the temperature jump layer and the change in acoustic impedance of the dissolved oxygen jump layer, obtain the depth range of the aggregation layer boundary, and record the depth of the phytoplankton aggregation layer in the current cycle.

[0035] By comparing the depth values ​​at the locations of abrupt changes in sound velocity in the temperature jump layer and acoustic impedance changes in the dissolved oxygen jump layer, the smaller value is taken as the upper boundary of the phytoplankton aggregation depth, and the larger value is taken as the lower boundary. If the distance between the two locations exceeds a preset threshold, an extension depth value is added to the lower boundary, adaptively determined based on the distance between the two jump layers, serving as the corrected lower boundary. A depth interval for the aggregation layer boundary is constructed based on the upper and lower boundaries. A scattering intensity sequence is extracted from the sonar echo data at each depth point within this interval. The difference between the intensity value at each depth point and the average intensity of the interval is calculated. When the difference within consecutive depth segments is less than a preset threshold, the upper and lower depths of that depth segment are determined as the upper and lower boundaries of the main biological aggregation area, respectively. For the upper and lower boundaries of the main biological aggregation area, the upper boundary location, lower boundary location, upper boundary depth value, and lower boundary depth value are combined to form a depth location data set. Combined with the time information of the current detection time, a complete dataset containing the depth location data set and time information is recorded to obtain the biological aggregation layer depth location for the current period.

[0036] Specifically, in one implementation, the boundary of the phytoplankton aggregation depth is determined based on the relative positional relationship between the temperature stratum and the dissolved oxygen stratum.

[0037] Specifically, the location of the abrupt change in sound velocity in the temperature jump layer typically occurs at depths where water temperature drops sharply, while the location of the change in acoustic impedance in the dissolved oxygen jump layer corresponds to depths where dissolved oxygen levels change significantly. In actual marine environments, these two jump layer locations often exhibit vertical offsets, ranging from a few meters to tens of meters. The system compares the two depth values ​​and sets the shallower location as the upper bound, because plankton typically do not accumulate in large quantities in the homogeneous water layer above the jump layer.

[0038] It should be noted that the adaptive method for determining the extension depth is based on dynamic calculation of the thermocline spacing. When the spacing between the temperature thermocline and the dissolved oxygen thermocline is small, such as less than 10 meters, no extension is performed, and the deeper thermocline position is directly used as the lower limit. When the spacing exceeds 10 meters, it indicates that there is a large transition water layer between the two thermoclines, and plankton may extend and distribute to deeper areas. In this case, the system calculates the extension depth value. The specific calculation method is as follows: the extension depth value equals the thermocline spacing multiplied by the extension coefficient. The extension coefficient is set in segments according to the spacing size: when the spacing is 10 to 20 meters, the extension coefficient is 0.3; when the spacing is 20 to 30 meters, the extension coefficient is 0.4; and when the spacing exceeds 30 meters, the extension coefficient is 0.5. Through this piecewise function, the extension depth value can adaptively adjust with the change of the thermocline spacing, ensuring the accuracy of the lower limit position. For example, when the thermocline spacing is 25 meters, the extension depth value is 25 × 0.4 = 10 meters, and the lower limit position is adjusted by extending it downward by 10 meters from the original lower limit.

[0039] Preferably, after constructing the depth range of the aggregation layer, the system extracts the scattering intensity value of each depth point within that range from the sonar echo data. These intensity values ​​form a continuous depth-intensity sequence, reflecting the vertical distribution density of plankton. By calculating the deviation of the intensity value at each depth point from the average intensity of the entire range, continuous depth segments with relatively stable intensity are identified. When the deviation values ​​of all sampling points within a certain depth segment are less than a preset threshold, it indicates that the biological distribution within that depth segment is relatively uniform, and the system marks the upper and lower depths of that segment as the upper and lower boundaries of the main aggregation area, respectively. This method can effectively distinguish between densely distributed biological areas and sparse transitional areas.

[0040] In one embodiment, the depth location data is recorded in a structured data set format. The system combines the four depth values—upper boundary, lower boundary, upper boundary of the main aggregation area, and lower boundary—into a single data set, while also recording the current detection time information, including the date and specific time. This complete dataset not only contains spatial distribution information but also retains the temporal dimension, facilitating subsequent analysis of phytoplankton vertical migration patterns and comparative studies of data from different periods.

[0041] Step S104: Determine the center depth of the biological aggregation layer in the current cycle by combining the depth spacing, the peak depth of scattering intensity, and the location of the abrupt change in sound velocity.

[0042] By combining three parameters—depth spacing, peak scattering intensity depth, and abrupt change in sound velocity—an initial center depth value is obtained by weighting each parameter according to a preset weighting coefficient. Based on this initial center depth value, a scattering intensity data sequence within a certain range near this depth is extracted, and the actual aggregation center depth is calculated using an intensity-weighted averaging method. This weighted average depth is then determined as the center depth position of the biological aggregation layer in the current period.

[0043] Specifically, in one implementation, the depth of the center of the biological aggregation layer is determined using a multi-parameter weighted fusion method.

[0044] Specifically, the system combines three key parameters—depth spacing, peak scattering intensity depth, and abrupt change in sound velocity—with different weights for calculation. The peak scattering intensity depth is assigned a weight of 0.5, as this location typically corresponds to the area with the highest concentration of plankton; the abrupt change in sound velocity is assigned a weight of 0.3, reflecting the influence of the thermocline on biodistribution; and the midpoint of the depth spacing is assigned a weight of 0.2, representing the geometric center between the two thermoclines. Specifically, in one implementation, the multi-parameter weighted fusion method for the initial center depth determines the weight coefficients based on the degree of influence of each parameter on biodistribution. The weight coefficients consider the physical meaning and actual contribution of the three parameters: the peak scattering intensity depth directly reflects the area with the highest concentration of plankton and has the most explicit biological significance, therefore it is assigned the largest weight, typically 0.4 to 0.6; the abrupt change in sound velocity marks the location of the thermocline, which has a significant impact on plankton distribution, and is assigned a secondary weight, typically 0.2 to 0.4; the midpoint of the depth spacing represents the geometric center between the two thermoclines and serves as an auxiliary reference, assigned the smallest weight, typically 0.1 to 0.3. In typical marine applications, the weighting coefficients can be set as follows: peak depth 0.5, abrupt change location 0.3, and midpoint value 0.2. This set of coefficients has been verified through multiple field tests and can effectively reflect the actual location of the biological aggregation center. The weighted calculation formula is: initial center depth value = peak depth × weight 1 + abrupt change location × weight 2 + midpoint of depth interval × weight 3, where the sum of weight 1, weight 2, and weight 3 equals 1.

[0045] It should be noted that the intensity-weighted average depth calculation method is based on the principle of physical center of gravity and achieves precise positioning through a complete data processing flow. The input data for this method is the initial center depth value and the scattering intensity sequence within its vicinity, typically set to 5 to 10 meters above and below the initial depth. The processing consists of three steps: First, the depth values ​​and corresponding scattering intensity values ​​of all depth sampling points within the range are extracted to form a depth-intensity data pair sequence; second, the product of the depth value and intensity value in each data pair is calculated, and all products are summed to obtain a weighted depth sum, while all intensity values ​​are summed to obtain an intensity sum; finally, the weighted depth sum is divided by the intensity sum to obtain the intensity-weighted average depth. The output of this method is a weighted average depth value, which reflects the mass center of biological distribution, i.e., the actual aggregation center location after considering the differences in biological density at various depths. This method can accurately locate the actual aggregation center of plankton in the vertical direction, avoiding the bias that may be introduced by simple arithmetic averaging.

[0046] Step S105: Identify the depth offset between the center depth position and the previous cycle biological aggregation layer depth position. When the offset exceeds the preset cascade stability boundary, collect the echo intensity distribution of the temperature cascade and the dissolved oxygen cascade, and extract the echo intensity difference value between the two cascades.

[0047] The difference between the current cycle center depth and the previous cycle's biological aggregation layer depth is identified, and the absolute value of this difference is calculated as the depth offset amplitude. If the offset amplitude exceeds a preset threshold for thermocline stability, a significant migration of biological distribution is determined. Based on the determination that the depth offset amplitude exceeds the threshold, continuous sonar echo intensity sequence data is collected at the depth of the temperature thermocline and at the corresponding time point at the depth of the dissolved oxygen thermocline. The average intensity value of the two sequences is calculated. The difference in echo intensity between the two thermoclines is obtained by subtracting the average intensity value of the dissolved oxygen thermocline from the average intensity value of the temperature thermocline. This difference value is recorded as a quantitative indicator of the biological distribution characteristics between the thermoclines.

[0048] Specifically, in one implementation, the identification of depth offset magnitude is achieved based on data comparison over a continuous monitoring period.

[0049] Specifically, the system stores the center depth location data of the bioaccumulation layer for each detection cycle, forming a time series. After acquiring the center depth location for the current cycle, the difference between this location and the stored depth location from the previous cycle is calculated; the absolute value of this difference is the depth offset. The preset threshold for mesostratus stability is typically set to 30% to 50% of the mesostratus thickness; this threshold reflects the dynamic characteristics of the marine environment.

[0050] It should be noted that when the depth offset exceeds the stability boundary threshold, it indicates that the planktonic community may have undergone vertical migration due to environmental factors. This migration phenomenon is particularly evident during dawn and dusk, when plankton move vertically diurnally according to light conditions and dissolved oxygen distribution. Upon detecting significant migration, the system immediately initiates a refined acquisition program for the stratosphere echo intensity.

[0051] Preferably, the echo intensity data is acquired using a synchronous scanning method. At the depth of the thermocline, the system continuously transmits sonar pulses and receives echo signals. The echo intensity of each pulse forms a data point, and multiple consecutive pulses constitute an intensity sequence. Data acquisition at the dissolved oxygen thermocline uses the same time window and sampling frequency to ensure temporal correspondence between the two sets of data. The acquired raw intensity sequences are filtered to remove noise interference, and then their respective average intensity values ​​are calculated. This synchronous acquisition method can accurately reflect the real-time biological distribution status at the two thermocline locations.

[0052] In one embodiment, the echo intensity difference is calculated using the direct difference method. The average intensity value of the thermocline is typically higher because the temperature in this layer is suitable, resulting in a high density of plankton. The intensity value of the dissolved oxygen thermocline, however, varies considerably, depending on the influence of oxygen content on organism distribution. The difference between the two reflects the degree of unevenness in organism distribution between the thermoclines. A positive and large difference value indicates that organisms mainly accumulate near the thermocline; a difference value close to zero indicates that organisms are relatively evenly distributed between the two thermoclines. This quantitative indicator provides an important basis for subsequent organism density analysis and resource assessment.

[0053] Step S106: Identify the depth distribution range of the actual aggregation layer based on the echo intensity difference value, extract the position coordinates of each depth point in the aggregation layer, determine the fuzzy boundary by combining the depth interval of the aggregation layer boundary, obtain the boundary intensity gradient value by analyzing the scattered echo intensity, and enhance the boundary depth position based on the boundary intensity gradient value.

[0054] The echo intensity difference is compared with a preset aggregation layer determination threshold. When the difference is within the threshold range, the depth location where the difference occurs is taken as the center, and a preset distance is extended upwards and downwards to obtain the depth distribution range. Within this range, the depth values ​​of each sampling point are extracted at fixed depth intervals as a position coordinate sequence. Using the scattered echo intensity value of each depth point in the position coordinate sequence, the rate of intensity change between adjacent depth points is calculated. When the rate of change of multiple consecutive depth points is less than a preset stability threshold, the interval containing these depth points is marked as a stable distribution area, and the interval containing depth points with a rate of change exceeding the stability threshold is marked as a transition area. For depth points within the transition area, combined with the upper and lower limits of the depth interval at the aggregation layer boundary, the depth range within the transition area where the intensity value is between the average intensity of the stable distribution area and the average intensity outside the depth interval is determined as a fuzzy boundary. The difference between the intensity value of each depth point within the fuzzy boundary and the intensity values ​​of its upper and lower adjacent points is calculated to obtain the local gradient value of that point. Based on the local gradient value sequence, the gradient values ​​are multiplied by a preset enhancement coefficient to amplify them. The depth points with amplified gradient values ​​greater than a preset enhancement threshold are taken as boundary feature points. Adjacent boundary feature points are connected by a linear interpolation method to obtain the enhanced boundary depth position.

[0055] Specifically, in one implementation, the depth distribution range is identified based on a multi-level determination of echo intensity difference values.

[0056] Specifically, the system first compares the acquired echo intensity difference value with a preset aggregation layer determination threshold. This threshold is typically determined based on statistical analysis of historical detection data and reflects the typical characteristics of plankton aggregation in the sea area. When the difference value falls within the threshold range, it indicates the presence of biological aggregation at that depth. The system uses the depth location where the difference value occurs as the center point, extending upwards by a distance typically set to half the thickness of the thermocline, and downwards by considering the influence range of the dissolved oxygen thermocline. Both extension distances can be dynamically adjusted according to the characteristics of the sea area. Within the determined depth distribution range, depth sampling is performed at fixed intervals of 0.5 meters to 1 meter. The depth value of each sampling point is recorded as its vertical coordinate in three-dimensional space, while the horizontal coordinate corresponds to the geographical location detected by the sonar, forming a complete position coordinate sequence.

[0057] It should be noted that the scattered echo intensity information contained in the location coordinate sequence is crucial data for subsequent processing. The system identifies the uniformity of biological distribution by calculating the rate of change of intensity between adjacent depth points. The rate of change is calculated using a difference method, that is, subtracting the intensity value of the previous depth point from the intensity value of the subsequent depth point, and then dividing by the depth interval. When the rate of change of five or more consecutive depth points is less than a preset stability threshold, the depth interval containing these points is marked as a stable distribution area, indicating that the plankton distribution in this area is relatively uniform. Conversely, areas where the rate of change exceeds the threshold are identified as transition zones, usually corresponding to areas of abrupt changes in biological density.

[0058] Preferably, the determination of the fuzzy boundary involves a comprehensive analysis of multiple parameters. The system first calculates the arithmetic mean of the intensity values ​​of all depth points within the stable distribution area, using this as the characteristic intensity of the aggregation layer. Then, it extracts the intensity data from the region outside the depth interval and calculates its average value as a background intensity reference. Within the transition zone, the intensity value of each depth point is compared with these two reference values. When the intensity value of a depth point is greater than the background intensity but less than the characteristic intensity, that point is considered to be within the fuzzy boundary. The fuzzy boundary reflects the gradual transition of biological distribution from dense to sparse, and its spatial extent directly affects the accurate location of the aggregation layer boundary.

[0059] In one embodiment, the local gradient value is calculated using the three-point difference method. For each depth point within the fuzzy boundary, the system extracts the intensity values ​​of that point and its two adjacent points above and below it, and calculates half of the intensity difference between the upper and lower neighboring points as the local gradient value for that point. This method can effectively smooth out the influence of noise and improve the stability of gradient calculation. The sign of the gradient value indicates the direction of intensity change, and the absolute value reflects the degree of change. In actual marine environments, the gradient value near the temperature climax is usually large, while the gradient change at the dissolved oxygen climax is relatively gentle.

[0060] For example, boundary enhancement processing employs gradient amplification technology to achieve precise positioning. The system multiplies each local gradient value by a preset enhancement coefficient, which is dynamically adjusted based on sea turbidity and sonar frequency, typically between 1.5 and 3.0. Specifically, the adjustment process is as follows: when sea turbidity is high, the coefficient tends to a larger value to enhance the signal; when the sonar frequency is high, the coefficient is appropriately reduced to avoid noise interference. After adjustment, the amplified gradient value is compared with a preset enhancement threshold; depth points exceeding the threshold are marked as boundary feature points. These feature points represent key locations at the boundaries of biological distribution, but gaps may exist between feature points due to sampling interval limitations.

[0061] Understandably, the connection of boundary feature points employs a linear interpolation method to achieve continuous processing. The system traverses all feature points in depth order. For blank areas between two adjacent feature points, the interpolation slope is calculated based on the depth and intensity differences between the two points, and virtual feature points are inserted at the blank positions. The interpolation process considers the influence of ocean currents and internal waves. When the depth difference between adjacent feature points exceeds a preset range, the system introduces a secondary interpolation correction to make the boundary curve smoother. Furthermore, the enhanced boundary depth position is obtained by connecting all actual and virtual feature points. After sorting these points by depth, the system uses a piecewise linear connection method to construct a complete boundary curve. Each line segment represents the direction of the boundary within a specific depth range, and multiple line segments combine to form a continuous boundary contour. This processing method not only improves the accuracy of boundary identification but also preserves the detailed features of biological distribution, providing a reliable spatial range definition for accurately assessing phytoplankton resource abundance.

[0062] Step S107: Using the enhanced boundary depth position and the position coordinates of each depth point in the aggregation layer, multibeam sonar is used to identify the center depth position of the final underwater biological aggregation layer.

[0063] The upper and lower boundary values ​​are determined by the enhanced boundary depth location. A three-dimensional spatial distribution matrix is ​​constructed by combining the coordinates of each depth point in the aggregation layer. The matrix elements include depth values, horizontal coordinates, and scattering intensity values. Multi-beam sonar is used to emit multi-angle acoustic beams within the boundary range. Each beam covers a specific depth layer and a horizontal sector. Backscattering intensity data is extracted from the echo signals. The intensity data of the same depth layer are weighted and summed according to the beam coverage area to obtain a comprehensive intensity distribution map. If the comprehensive intensity value of a certain depth layer exceeds a preset biological aggregation threshold of 50, it is recorded as a candidate center layer. For the candidate center layers, the intensity-weighted average depth of all locations within each layer is calculated. The weight is the ratio of the scattering intensity of each point to the maximum intensity value of that layer, resulting in a weighted center depth. The arithmetic mean of the weighted center depths of all candidate layers is then taken to obtain the preliminary center depth value. The determination is made based on the relationship between the initial center depth value and the upper and lower boundaries: if it is located within the preset center interval between the upper and lower boundaries, that is, the distance from each of the upper and lower boundaries does not exceed 30% of the total boundary range, then it is directly determined as the final center depth position; otherwise, it is linearly corrected according to the ratio of its distance to the upper and lower boundaries. The correction formula is that the final depth value is equal to the initial depth value plus the correction amount. The correction amount is the ratio of the distance to the nearest boundary to the total boundary range multiplied by a fixed correction coefficient of 10, and the final center depth position is obtained.

[0064] Specifically, in one implementation, the construction of the three-dimensional spatial distribution matrix integrates the enhanced boundary depth location and relevant information of each depth point in the aggregation layer to achieve a precise spatial distribution representation.

[0065] Specifically, the system first extracts precise depth values ​​for the upper and lower boundaries from the enhanced boundary depth location data. These two boundary values ​​define the vertical range of underwater plankton aggregation. Within this range, the location coordinates of each depth point contain three dimensions of information: the vertical depth value represents the distance from the sea surface, the horizontal coordinates include longitude and latitude values, and the scattering intensity value reflects the biological density at that location. The system organizes this information into a matrix, where rows correspond to different depth layers, columns correspond to horizontal grid points, and each matrix element stores the scattering intensity value of that spatial point. Multibeam sonar emits multiple sound beams simultaneously during operation, typically configured as a fan-shaped array containing 64 to 256 independent beams, each with an opening angle between 1 and 3 degrees, resulting in a total coverage angle of 120 to 150 degrees.

[0066] It should be noted that the sound beams of multibeam sonar form a specific coverage pattern when propagating in water. Each beam is emitted at a preset angle, with the central beam pointing vertically downwards and the two side beams distributed at equal angular intervals. When the sound waves encounter a group of planktonic organisms, they are backscattered, and the scattering intensity is related to the size, density, and acoustic characteristics of the organisms. The distribution of organisms at different depths leads to time differences in the echo signals, and the system determines the depth location of the scattering organisms by accurately measuring the round-trip time. The width of the horizontal sector covered by each beam increases with depth; at a depth of 100 meters, the coverage diameter of a single beam can reach 3 to 5 meters.

[0067] In one possible implementation, the method for generating the integrated intensity distribution map of multi-beam sonar is based on the fusion processing of multi-angle echo signals. The input data of this method includes three parts: first, the echo signal sequence of each beam of the multi-beam sonar, with each beam containing time-series echo intensity values; second, the geometric configuration parameters of the beams, including the number of beams, opening angle, and fan array angle distribution; and third, the enhanced boundary depth position, used to define the depth range of data processing. The processing consists of six steps: First, time gain compensation is performed on the original echo signals of each beam. The compensation formula is: compensated intensity = original intensity × exp(absorption coefficient × distance), where the absorption coefficient is determined based on the sonar frequency and seawater characteristics. Second, the depth of the scatterer is calculated based on the echo round-trip time and sound speed, establishing a correspondence between echo intensity and depth. Third, the three-dimensional space is divided into layers according to depth, with each layer typically 0.5 to 1 meter thick, and echo data within each depth layer is collected. Then, for the same depth layer, the echo intensity values ​​of all beams in that layer are collected, and weighting coefficients are calculated based on the horizontal area covered by each beam. The beam coverage area is calculated using the beam opening angle, depth, and the geometric relationship of sound wave propagation. Next, the intensity values ​​of each beam are multiplied by their corresponding weighting coefficients, summed, and then divided by the sum of the weighting coefficients to obtain the comprehensive intensity value of that depth layer. Finally, the comprehensive intensity values ​​of all depth layers are arranged by depth to form a comprehensive intensity distribution map. The output of this method consists of two parts: first, a sequence of comprehensive intensity values ​​for each depth layer; and second, a list of candidate center layers that exceed a preset biological aggregation threshold, which is typically set to 1.5 to 2 times the average comprehensive intensity value.

[0068] Preferably, the acquisition of the comprehensive intensity distribution map involves complex signal processing. After receiving the echo signals from each beam, the system first performs time gain compensation to correct for the attenuation effect of sound waves propagating in water. For the same depth layer, the intensity data detected by different beams need to be spatially registered to ensure data consistency in geographical location. The weighting coefficients for the weighted summation are determined based on the coverage area of ​​the beams; the central beam has a higher weight due to its smaller coverage area, while the edge beams have relatively lower weights. The formula for calculating the comprehensive intensity value is the sum of the products of each beam's intensity value and its weighting coefficient, divided by the sum of the weighting coefficients. When the comprehensive intensity value of a certain depth layer exceeds a preset threshold, that layer is marked as a candidate center layer. The threshold is set based on historical detection data and biological distribution characteristics of the sea area.

[0069] In one embodiment, the weighted average depth calculation of the candidate center layer employs an intensity-weighted method. The system traverses all spatial locations within each candidate layer, extracting the scattering intensity value and depth coordinates of each point. First, the maximum intensity value within the layer is identified as the normalization benchmark. The weight coefficient for each location point is equal to its intensity value divided by the maximum intensity value, ensuring the weight is between 0 and 1. The weighted center depth is calculated by multiplying the depth value of each location point by its weight coefficient, summing all products, and then dividing by the sum of the weight coefficients. This method allows areas with high biological density to have a greater influence in determining the center depth, accurately reflecting the actual aggregation center of organisms.

[0070] For example, the system performs an arithmetic mean of the weighted center depths of all candidate layers to obtain a preliminary center depth value. This value takes into account the biological distribution at different depths and represents the vertical center position of the entire aggregation layer.

[0071] Understandably, determining the final center depth location requires considering the relative relationship between the initial value and the boundaries. The system first calculates the depth range between the upper and lower boundaries, dividing this range into several intervals. The preset center interval is typically defined as the middle area, located 40% of the total depth range from both the upper and lower boundaries. When the initial center depth value falls within this interval, it indicates a relatively uniform biological distribution, and the initial value is directly determined as the final center depth. Furthermore, to address deviations, the system sets the following parameters: a preset lower limit of 0.5, a preset upper limit of 2.0, and a preset correction coefficient of 0.3. These values ​​are based on empirical data on marine plankton distribution and optimization through multiple experiments. When the initial center depth value deviates from the preset center interval, the system uses a linear correction method for adjustment. The correction process first calculates the distances from the initial value to the upper and lower boundaries, denoted as upper distance and lower distance, respectively. The distance ratio is defined as the ratio of the upper distance to the lower distance. When the ratio is less than 0.5, it indicates that the center is too close to the upper boundary, and the system adjusts the center downwards according to a correction coefficient of 0.3; when the ratio is greater than 2.0, the adjustment is performed in the opposite direction. The magnitude of the correction is proportional to the degree of deviation, ensuring that the corrected center depth more accurately reflects the actual distribution center of the organisms. This multibeam sonar combined with boundary enhancement method enables precise positioning of the center depth of marine planktonic aggregation layers.

[0072] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several improvements and additions without departing from the principle of the present invention, and these improvements and additions should also be considered within the scope of protection of the present invention.

Claims

1. A method for identifying the depth position of a biological aggregation layer underwater, characterized by, include: By collecting acoustic data in the water body using sonar equipment, the depth and location information of the temperature gradient and dissolved oxygen gradient can be obtained. The depth spacing between the two temperature and dissolved oxygen quiescent layers is determined based on their depth location information. The scattered echo intensity features are extracted from the acoustic data to identify the hierarchical distribution characteristics of the scattering layer; the clarity of the layer boundaries is determined based on the depth spacing and the hierarchical distribution characteristics of the scattering layer; the boundary position of the planktonic aggregation depth is determined by the clarity of the layer boundaries and the sound velocity variation characteristics, and the aggregation layer depth range is constructed; the center depth position of the biological aggregation layer is calculated by combining the depth spacing and scattering intensity features; the difference in echo intensity between the two layers is analyzed based on the offset of the center depth position from historical data; the actual depth distribution range of the aggregation layer is determined based on the difference in echo intensity, and the boundary depth position is enhanced; the center depth position of the underwater biological aggregation layer is verified and finally determined using multibeam sonar, wherein the sonar equipment collects data from the water body... Acquiring depth location information of the temperature and dissolved oxygen strata using acoustic data includes: emitting parallel and vertical beams using a dual-wave sonar; collecting the scattered echo intensity data of the parallel wave at different depths; recording the sound velocity value of the vertical wave propagating in the water and its curve changing with depth; identifying the depth as the temperature strata depth when the sound velocity abruptly changes at a certain depth and exceeds a preset sound velocity abrupt change threshold; identifying the depth as the dissolved oxygen strata depth when the sound velocity gradient undergoes a sign change at another depth; calculating the depth distance between the two strata based on the temperature and dissolved oxygen strata depths; verifying the accuracy of the two strata locations using the sound velocity change curves, and recording the depth location information of the two strata as basic data for subsequent analysis.

2. The method of identifying the depth location of a biological aggregation layer underwater as claimed in claim 1, wherein, The step of extracting scattered echo intensity features from the acoustic data and identifying the hierarchical distribution features of the scattering layers includes: identifying depth points where the echo intensity changes significantly as texture boundary locations based on the parallel wave scattered echo intensity data; obtaining the specific depth of the region where the scattering intensity reaches its peak; if the intensity difference between adjacent texture boundaries exceeds a preset boundary recognition threshold, recording each depth point and its corresponding scattering intensity value within the region where the scattering intensity reaches its peak, forming a scattering dataset for the depth interval; performing layered processing on the scattering intensity values ​​according to the depth interval based on the texture boundary locations and peak scattering intensity depths in the scattering dataset; determining the uniformity of the intensity distribution within each layer by calculating the statistical characteristics of the scattering intensity within each layer; determining a stable scattering layer when the distribution features of a certain layer meet a preset stability condition, obtaining the depth range and intensity features of multiple stable scattering layers, and forming the hierarchical distribution features of the scattering layers.

3. The method of identifying the depth location of a biological aggregation layer underwater as claimed in claim 1, wherein, The step of determining the clarity of the layer boundary based on the depth spacing and the hierarchical distribution characteristics of the scattering layer includes: extracting the echo intensity difference between adjacent scattering layers based on the depth spacing and the hierarchical distribution characteristics of the scattering layer; calculating the vertical rate of change of the echo intensity difference; determining that the interlayer transition is slow when the rate of change is less than a preset threshold; comparing the depth range of the transition interval with the depth spacing; if the transition interval exceeds half of the depth spacing, determining that the clarity of the layer boundary is lower than a preset threshold, and confirming that the boundary is in a dispersed state; calculating the rate of change of sound velocity from the sound velocity change curve for the boundary region in the dispersed state; when the rate of change of sound velocity changes abruptly at a certain depth and the abrupt change amplitude exceeds a preset threshold, recording that depth as the sound velocity abrupt change position of the temperature jump layer; calculating the acoustic impedance value by combining the sound velocity value near the sound velocity abrupt change position with seawater density distribution data, and identifying the depth of the vertical gradient sign conversion of acoustic impedance as the acoustic impedance change position of the dissolved oxygen jump layer.

4. The method of identifying the depth location of a biological aggregation layer underwater as claimed in claim 1, wherein, The process of determining the boundary positions of phytoplankton aggregation depth based on the clarity of the layer boundaries and the sound velocity variation characteristics, and constructing the aggregation layer depth interval, includes: comparing the depth values ​​at the locations of abrupt changes in sound velocity in the temperature jump layer and acoustic impedance changes in the dissolved oxygen jump layer; taking the smaller of the two depth values ​​as the upper boundary position of the phytoplankton aggregation depth and the larger of the two depth values ​​as the lower boundary position; if the distance between the two positions exceeds a preset threshold, then adding an adaptively determined extension depth value to the lower boundary position as the corrected lower boundary position; constructing the depth interval of the aggregation layer boundary based on the upper and lower boundary positions; extracting the scattering intensity value sequence from the sonar echo data of each depth point within the depth interval of the aggregation layer boundary; calculating the difference between the intensity value of each depth point and the average intensity of the interval; when the difference within a continuous depth segment is less than a preset threshold, determining the upper and lower depths of that depth segment as the upper and lower boundaries of the main biological aggregation area, and recording the depth position data set.

5. The method for identifying the depth of underwater biological aggregation layers as described in claim 1, characterized in that, The calculation of the center depth position of the biological aggregation layer by combining the depth spacing and scattering intensity characteristics includes: combining the depth spacing, the peak depth of scattering intensity, and the location of abrupt changes in sound velocity, and weighting each parameter according to a preset weighting coefficient to obtain an initial center depth value; extracting the scattering intensity data sequence within a preset range near the initial center depth value; calculating the actual aggregation center depth using an intensity-weighted averaging method; determining the actual aggregation center depth as the center depth position of the biological aggregation layer in the current period; recording the relationship between the center depth position and the upper and lower bounds of the depth interval as benchmark data for subsequent migration analysis; and verifying the stability of the calculation results by comparing the center depth position with historical period data.

6. The method for identifying the depth of underwater biological aggregation layers as described in claim 1, characterized in that, The step of analyzing the echo intensity difference between the two thermoclines based on the offset magnitude of the center depth position and historical data includes: identifying the difference between the center depth position in the current period and the biological aggregation layer depth position in the previous period; calculating the absolute value of the difference as the depth offset magnitude; if the depth offset magnitude exceeds a preset thermocline stability boundary threshold, it is determined that the biological distribution has migrated significantly; collecting continuous sonar echo intensity sequence data at the depth position of the temperature thermocline; collecting echo intensity sequence data at the corresponding time at the depth position of the dissolved oxygen thermocline; calculating the average intensity value of the two sequence data respectively; obtaining the echo intensity difference between the two thermoclines by subtracting the average intensity value of the dissolved oxygen thermocline from the average intensity value of the temperature thermocline; and recording the echo intensity difference value between the two thermoclines as a quantitative indicator of the biological distribution characteristics between the thermoclines.

7. The method for identifying the depth of underwater biological aggregation layers as described in claim 1, characterized in that, The step of determining the actual depth distribution range of the aggregation layer and enhancing the boundary depth position based on the echo intensity difference includes: comparing the echo intensity difference with a preset aggregation layer determination threshold; when the difference value is within the threshold range, taking the depth position where the difference value is generated as the center, extending it upwards and downwards by a preset distance to obtain the depth distribution range; extracting the depth values ​​of each sampling point within this range at fixed depth intervals as a position coordinate sequence; calculating the intensity change rate between adjacent depth points using the scattered echo intensity value of each depth point in the position coordinate sequence; when the change rate of multiple consecutive depth points is less than a preset stability threshold, marking the interval where these depth points are located as a stable distribution area; marking the interval where the change rate of depth points exceeds the stability threshold as a transition area; for depth points in the transition area, determining the fuzzy boundary by combining the upper and lower limits of the depth interval of the aggregation layer boundary; calculating the local gradient value within the fuzzy boundary, and amplifying it through an enhancement coefficient to obtain the enhanced boundary depth position.

8. The method for identifying the depth location of underwater biological aggregation layers as described in claim 1, characterized in that, The process of using multibeam sonar to verify and ultimately determine the center depth of the underwater biomass accumulation layer includes: determining the upper and lower boundary values ​​based on the enhanced boundary depth; constructing a three-dimensional spatial distribution matrix by combining the coordinates of each depth point in the accumulation layer, with matrix elements including depth values, horizontal coordinates, and scattering intensity values; emitting multi-angle acoustic beams within the boundary range using multibeam sonar; extracting backscattering intensity data based on the echo signals, and weighting and summing the intensity data of the same depth layer according to the beam coverage area to obtain a comprehensive intensity distribution map; if the comprehensive intensity value of a certain depth layer exceeds a preset biomass accumulation threshold, it is recorded as a candidate center layer; for the candidate center layer, calculating the intensity-weighted average depth of all locations within each layer; taking the arithmetic mean of the weighted center depths of all candidate layers to obtain a preliminary center depth value; and making judgments and linear corrections based on the relationship between the preliminary center depth value and the upper and lower boundaries to finally determine the center depth location.

9. The method for identifying the depth location of underwater biological aggregation layers as described in claim 1, characterized in that, The step of determining the actual depth distribution range of the aggregation layer based on the echo intensity difference and enhancing the boundary depth position includes: extracting scattering intensity data of key depth points between the two layers based on the echo intensity difference; identifying depth intervals with significant intensity changes as preliminary distribution ranges using the scattering intensity data; calculating the intensity difference between the intensity value of each point and its adjacent points above and below for depth points within the preliminary distribution range to form a local gradient value sequence; identifying depth points with abrupt gradient value changes as boundary candidate points based on the local gradient value sequence; selecting boundary candidate points located within the depth intervals by combining the aggregation layer depth intervals; enhancing the gradient values ​​of the boundary candidate points and connecting adjacent boundary feature points using a linear interpolation method to obtain the final enhanced boundary depth position; and recording the correspondence between the enhanced boundary depth position and the depth distribution range as basic data for subsequent verification.

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