Multi-beam water plume three-dimensional target extraction method, device, electronic device and storage medium

Abstract

The present invention discloses a method, device, electronic device, and storage medium for extracting three-dimensional targets from a multi-beam water plume. The method comprises: determining a target range based on a water plume target image in a multi-beam water stacked image; determining reference water image data based on the target range; performing backscatter intensity correction on the reference water image data to obtain corrected water data; determining a beam sequence acoustic intensity threshold based on the corrected water data; performing preliminary screening on the corrected water data based on the beam sequence acoustic intensity threshold to obtain two-dimensional image data under noise; calculating three-dimensional geographic coordinates of the multi-beam water data based on the water plume target in the two-dimensional image data to obtain a three-dimensional point cloud of the water plume; and performing point cloud clustering on the three-dimensional point cloud of the water plume to obtain a three-dimensional water plume target. This method can improve the efficiency of water data acquisition while also increasing the degree of refinement in water plume target extraction.

Description

[0001] Technology Neighborhood

[0002] The present invention relates to the field of ocean surveying and mapping technology, and in particular to a method, device, electronic equipment and storage medium for extracting three-dimensional targets from a multi-beam water plume. Background Art

[0003] As an acoustic remote sensing method, multibeam bathymetry systems have been widely used in fields such as marine geological surveys due to their high resolution, high precision, and full coverage. Currently, most multibeam bathymetry systems support the collection and recording of water data. Water data reflects the echo information of sampling points in the water volume and can image targets within the water. Therefore, many researchers at home and abroad are committed to the application of multibeam water data in underwater exploration, such as shipwreck detection, fluid target detection, and fish detection. Underwater fluid targets mainly include cold seeps, hydrothermal seeps, and pipeline leaks in the deep sea. The detection and identification of fluid targets is of great significance for resource exploration, monitoring, and the location of leak sources.

[0004] In multi-beam water imagery, target extraction often relies on noise suppression. Multi-beam water data is subject to significant interference due to sidelobe interference, ship noise, and other factors. When using multi-beam water data to detect cold seep plumes, only central beam data or data within the minimum slant range (MSR) are often used. These data processing methods typically lack error mitigation or other pre-processing techniques, making target recognition more difficult and less reliable. Furthermore, target extraction methods are often based on single-ping image processing, disregarding the three-dimensional spatial distribution of targets and noise. Furthermore, water plume targets are often identified based on sound intensity, disregarding their geometric characteristics, resulting in a low degree of refinement in the resulting water plume images. Summary of the Invention

[0005] The present invention provides a multi-beam water plume three-dimensional target extraction method, device, electronic equipment and storage medium to solve the problem that the water plume target refinement is not high.

[0006] According to one aspect of the present invention, a method for extracting three-dimensional targets from a multi-beam water plume is provided, comprising:

[0007] The target range is determined based on the water plume target image in the multi-beam water stack image; the water plume target is a plume formed by natural gas hydrates in the seabed formation that leaks in the form of gas and migrates upward; the target range is the range of water image data that can characterize the water plume target; the water image data is the data range corresponding to each water data acquisition by the acoustic geological acquisition device;

[0008] Determining reference water body image data based on the target range; the reference water body image data is obtained by matching the multi-beam water body data based on the target range; the multi-beam water body data is data collected by an acoustic geological acquisition device that can characterize changes in water plumes in the ocean;

[0009] Performing backscatter intensity correction on the reference water body image data to obtain corrected water body data;

[0010] Determining a beam sequence sound intensity threshold according to the corrected water body data;

[0011] The corrected water body data is preliminarily screened according to the beam sequence sound intensity threshold to obtain two-dimensional image data under noise; the beam sequence sound intensity threshold is used to characterize the difference between the sound wave intensity returned by the target sampling point and the background noise intensity;

[0012] Based on the water plume target in the two-dimensional image data, three-dimensional geographic coordinates are calculated for the multi-beam water data to obtain a three-dimensional point cloud of the water plume;

[0013] Point cloud clustering is performed on the three-dimensional point cloud of the water body plume to obtain a three-dimensional water body plume target.

[0014] According to another aspect of the present invention, a multi-beam water plume three-dimensional target extraction device is provided, comprising:

[0015] The sampling point acquisition module is used to determine the target range based on the water plume target image in the multi-beam water body stacking image; the water plume target is a plume formed by the upward migration of natural gas hydrates in the marine seabed formation in the form of gas leakage; the target range is the range of water image data that can characterize the water plume target; the water image data is the data range corresponding to each water data acquisition by the acoustic geological acquisition device;

[0016] a reference water body image data determination module, configured to determine reference water body image data based on the target range; the reference water body image data is obtained by matching the multi-beam water body data based on the target range; the multi-beam water body data is data collected by an acoustic geological acquisition device that can characterize changes in water plume flow in the ocean;

[0017] a corrected water body data determination module, configured to perform backscatter intensity correction on the reference water body image data to obtain corrected water body data;

[0018] an acoustic intensity threshold determination module, configured to determine a beam sequence acoustic intensity threshold based on the corrected water body data;

[0019] a two-dimensional image data determination module, configured to preliminarily screen the corrected water body data based on the beam sequence sound intensity threshold to obtain two-dimensional image data under noise; the beam sequence sound intensity threshold is used to characterize the difference between the intensity of the sound wave returned by the target sampling point and the intensity of the background noise;

[0020] a water plume three-dimensional point cloud determination module, configured to calculate the three-dimensional geographic coordinates of the multi-beam water data based on the water plume target in the two-dimensional image data to obtain a water plume three-dimensional point cloud;

[0021] The three-dimensional water plume target determination module is used to perform point cloud clustering on the three-dimensional point cloud of the water plume to obtain a three-dimensional water plume target.

[0022] According to another aspect of the present invention, an electronic device is provided, comprising:

[0023] at least one processor; and

[0024] a memory communicatively connected to the at least one processor; wherein,

[0025] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the multi-beam water plume three-dimensional target extraction method according to any embodiment of the present invention.

[0026] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the multi-beam water plume three-dimensional target extraction method according to any embodiment of the present invention when executed.

[0027] The technical solution of the embodiment of the present invention determines the target range based on the water plume target image in the multi-beam water body stacking image; determines reference water body image data based on the target range; performs backscatter intensity correction on the reference water body image data to obtain corrected water body data, eliminating the influence of TVG and sidelobe effects on water plume target extraction; determines the beam sequence sound intensity threshold based on the corrected water body data; performs preliminary screening on the corrected water body data based on the beam sequence sound intensity threshold to obtain two-dimensional image data under noise, which can effectively remove complex noise interference; calculates the three-dimensional geographic coordinates of the multi-beam water body data based on the water plume target in the two-dimensional image data to obtain a three-dimensional point cloud of the water plume; and performs point cloud clustering on the three-dimensional point cloud of the water plume to obtain a three-dimensional water plume target, which can improve the refinement of water plume extraction. By performing noise correction on the reference water body image data, this method can effectively remove complex noise interference and automatically extract three-dimensional targets from a large number of multi-beam water body sampling points, thereby improving water acquisition efficiency and the refinement of water plume target extraction.

[0028] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0030] Figure 1 A flowchart of a multi-beam water plume three-dimensional target extraction method provided by an embodiment of the present invention;

[0031] Figure 2 A schematic diagram of a stacked diagram of original water bodies provided by an embodiment of the present invention;

[0032] Figure 3 A schematic diagram of a target range provided by an embodiment of the present invention;

[0033] Figure 4 A schematic diagram of a three-dimensional point cloud of a water plume provided by an embodiment of the present invention;

[0034] Figure 5 A schematic diagram of a three-dimensional target of a water plume provided by an embodiment of the present invention;

[0035] Figure 6An original water body stacking map provided by an embodiment of the present invention;

[0036] Figure 7 A sound intensity correction result diagram provided by an embodiment of the present invention;

[0037] Figure 8 A sidelobe effect correction result diagram provided by an embodiment of the present invention;

[0038] Figure 9 A flowchart for obtaining two-dimensional image data provided by an embodiment of the present invention;

[0039] Figure 10 A flow chart for acquiring a three-dimensional target of a water plume provided by an embodiment of the present invention;

[0040] Figure 11 A schematic structural diagram of a multi-beam water plume three-dimensional target extraction device provided by an embodiment of the present invention;

[0041] Figure 12 A schematic structural diagram of an electronic device for implementing the multi-beam water plume three-dimensional target extraction method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0043] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0044] Figure 1This is a flowchart of a multi-beam water plume 3D target extraction method provided by an embodiment of the present invention. This embodiment is applicable to the extraction of water plume targets in the ocean from acoustic detection data. The method can be performed by a multi-beam water plume 3D target extraction device. The multi-beam water plume 3D target extraction device can be implemented in the form of hardware and / or software. The multi-beam water plume 3D target extraction device can be configured in any electronic device with network communication capabilities. Figure 1 As shown, the method includes:

[0045] S110 , determining a target range based on the water plume target image in the multi-beam water body stacking image.

[0046] Among them, the water plume target is the natural gas hydrate existing in the seabed formation in the ocean, which leaks in the form of gas and migrates upward to form a plume.

[0047] The target range is the range of water body image data that can characterize the water body plume target.

[0048] Furthermore, the target range includes: the target sampling point ping number.

[0049] The water body image data is the data range corresponding to each time the acoustic geological acquisition device collects water body data.

[0050] Specifically, determine whether there is a water plume target from the original water body stacking map, such as Figure 2 As shown in , if it exists, the range where the water plume target is located is intercepted to obtain the target range. Figure 3 As shown, the ping range where the target range is located is [p1, p2].

[0051] S120: Determine reference water body image data according to the target range.

[0052] The reference water body image data is obtained by matching the multi-beam water body data according to the target range.

[0053] Among them, the multi-beam water data is data collected by the acoustic geological acquisition device that can characterize the changes in water plume in the ocean.

[0054] Specifically, the water body data of corresponding points are matched from the multi-beam water body data according to the target range to obtain the reference water body image data.

[0055] S130 , performing backscattering intensity correction on the reference water body image data to obtain corrected water body data.

[0056] Backscatter intensity is the amount of returned sound energy received by the transducer when a sound wave propagates through the ocean, or seawater, and interacts with particles or inhomogeneous structures in the medium. Backscatter refers to a scattering process in which the direction of scattering is completely opposite to the direction of incidence.

[0057] The backscatter intensity correction includes propagation loss correction and sidelobe effect correction.

[0058] Specifically, the reference water body image data is corrected for propagation loss and sidelobe effect to obtain corrected water body data.

[0059] S140: Determine the beam sequence sound intensity threshold according to the corrected water body data.

[0060] Specifically, the ping number, beam number, and sampling point number of the target range corresponding to the corrected water body data are obtained from the corrected water body data, and the beam sequence sound intensity threshold is determined based on the obtained ping number, beam number, sampling point number, and threshold parameter.

[0061] Furthermore, the beam sequence sound intensity threshold can be expressed by the following formula:

[0062] T=a×μ b,s +b×σ b,s ;

[0063] Among them, μ b,s is the mean backscatter intensity; σ b,s is the standard deviation of the backscattering intensity; a and b are threshold parameters.

[0064] in:

[0065]

[0066] Among them, P is the Ping number of the target range corresponding to the corrected water body data; b is the beam number of the corrected water body data; s is the sampling point number of the corrected water body data; B is the maximum beam number of the corrected water body data; S is the maximum sampling point number of the corrected water body data.

[0067] S150 , preliminarily screening the corrected water body data according to the beam sequence sound intensity threshold to obtain two-dimensional image data under noise.

[0068] The beam sequence sound intensity threshold is used to characterize the difference between the sound wave intensity returned by the target sampling point and the background noise intensity.

[0069] Specifically, if the sound intensity of the corrected water data is greater than or equal to the beam sequence sound intensity threshold, the corresponding corrected water data is retained; if the sound intensity of the corrected water data is less than the beam sequence sound intensity threshold, the corresponding corrected water data is discarded. The retained corrected water data is used to generate two-dimensional image data under noise.

[0070] S160 , based on the water plume target in the two-dimensional image data, performing three-dimensional geographic coordinate calculation on the multi-beam water data to obtain a three-dimensional point cloud of the water plume.

[0071] Specifically, based on the water plume target in the two-dimensional image data, the multi-beam water data is first converted to transducer coordinates to obtain transducer coordinate data. The transducer coordinate data is then converted to a spatial rectangular coordinate system to obtain a three-dimensional point cloud of the water plume.

[0072] Furthermore, the transducer coordinate data can be obtained by the following formula:

[0073]

[0074] Where x, y, and z represent the coordinates of the sampling point in the transducer coordinate system; θ represents the beam receiving steering angle; s is the sampling point number; c represents the sound velocity; and f represents the sampling rate of the acoustic geological acquisition device.

[0075] S170: performing point cloud clustering on the three-dimensional point cloud of the water body plume to obtain a three-dimensional water body plume target.

[0076] Specifically, the three-dimensional point cloud of the water plume is clustered using DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to distinguish residual noise from the target and obtain the three-dimensional water plume target.

[0077] Among them, the basic idea of ​​DBSCAN is to cluster point clouds based on the point density in three-dimensional point clouds. If the density of point clouds in a region exceeds a certain threshold, these points are divided into a cluster.

[0078] Furthermore, since the corrected water data are screened by the beam sequence sound intensity threshold, not all noise can be removed, such as Figure 4 As shown in Figure 3, the obtained 3D point cloud of the water plume contains not only the water plume target but also some noise points, as shown by the blue circle. Therefore, DBSCAN can remove the residual noise and construct the 3D water plume target.

[0079] For example, Figure 5 As shown in FIG, the constructed three-dimensional water plume target is shown. It can be seen from the figure that the detailed information of the water plume target is retained.

[0080] Optionally, determining the target range based on the water plume target image in the multi-beam water body stack image includes steps A1-A4:

[0081] Step A1: Determine the water body stacking map.

[0082] Among them, the water body stacking map is used to obtain high signal-to-noise ratio water body data

[0083] Furthermore, the water stacking map can characterize the changes in the water plume in the ocean based on the distribution of high signal-to-noise ratio stacking sound intensity.

[0084] The sound intensity is the sound energy returned by the sampling point.

[0085] Specifically, the original water body stacking map is obtained from the acoustic geological acquisition device. According to the original water body stacking map, the cross-section position is selected, and the multi-beam water body data at the cross-section position with a high sound intensity signal-to-noise ratio is used to generate the water body stacking map.

[0086] For example, Figure 6 The figure shows the original water stacking diagram. From the figure, we can see that different objects in the ocean have different corresponding sound intensities. For example, the water plume target is shown in the red box and the color is green.

[0087] Step A2: grayscale mapping is performed on the water body stacking map to obtain a grayscale heading stacking map.

[0088] Specifically, the distribution of sound intensity in the water body stacking map is determined to determine the maximum and minimum values ​​of the sound intensity, grayscale mapping is performed based on the obtained maximum and minimum values ​​of the sound intensity, and the water body stacking map is converted into a grayscale map to obtain a grayscale heading stacking map.

[0089] Furthermore, grayscale mapping can be performed as follows:

[0090]

[0091] img BS For water body stacking map; img gray Grayscale heading stacking map; BS min is the minimum value of sound intensity, BS max is the maximum value of the sound intensity.

[0092] Step A3: Determine the cross-sectional positions where the water plume target appears and disappears based on the pixel values ​​of the stacked image and the reference threshold, and generate a classification labeling map based on the cross-sectional positions.

[0093] The pixel value of the stacked image is the pixel value of the pixel point in the grayscale heading stacked image.

[0094] The section position is the column number in the stacked image.

[0095] Specifically, the pixel values ​​of the stacked image are screened according to the reference threshold, and the pixel points whose pixel values ​​are greater than or equal to the reference threshold are taken as target points and marked as 1; the other points are marked as 0, and a classified labeling image is obtained.

[0096] Furthermore, the classification labeling map is expressed as follows:

[0097]

[0098] Among them, img bin is the classification label map; T is the reference threshold.

[0099] Step A4: Determine the target range based on the classification labeling diagram.

[0100] Specifically, the classification label map is traversed, and the column numbers that are not all 0 are taken as the target range.

[0101] For example, Figure 3 As shown in the figure, it is the target range, that is, the target ping range in the figure. It can be seen from the figure that the divided target ping range can completely contain the water plume target.

[0102] Optionally, the process of determining the reference threshold includes steps B1-B3:

[0103] Step B1: determine the grayscale threshold.

[0104] The grayscale threshold is one of the grayscale ranges.

[0105] Specifically, a grayscale value is selected from the grayscale range as the grayscale threshold so that the difference between the target and the noise is maximized.

[0106] Exemplarily, the grayscale range is [0, 255].

[0107] Step B2: Calculate the overall variance based on the grayscale threshold and the reference pixel value probability.

[0108] The reference pixel value probability is the probability of the reference pixel value appearing in the water body stacking map. The reference pixel value is any pixel value within the range of zero to the grayscale threshold.

[0109] Specifically, the water body stacking map is traversed according to the grayscale threshold, and the probability of the grayscale threshold appearing in the water body stacking map is calculated, that is, the reference pixel value probability. The overall variance is calculated using the variance formula based on the reference item numerical probability.

[0110] Furthermore, the variance formula can be expressed as follows:

[0111]

[0112] Among them, p iis the probability of gray threshold i, σ t is the variance.

[0113] Step B3: loop through the overall variance acquisition process, determine the target variance, and use the grayscale threshold corresponding to the target variance as the reference threshold.

[0114] The target variance is the minimum value among the obtained population variances.

[0115] Specifically, the process of obtaining the population variance is circulated, the minimum value of the calculated population variance is taken, and the grayscale threshold corresponding to the minimum value of the population variance is used as the reference threshold.

[0116] For example, the reference threshold can be expressed by the following formula:

[0117]

[0118] Optionally, backscatter intensity correction is performed on the reference water body image data to obtain corrected water body data, including steps C1-C2:

[0119] Step C1: Perform propagation loss correction on the reference water body image data according to the propagation loss correction model to obtain sound intensity correction data.

[0120] Among them, the propagation loss correction model is used to correct the backscattering intensity of the reference water body image data according to the time-varying gain and offset factor.

[0121] Furthermore, the propagation loss correction model can be expressed as follows:

[0122] TS r =A-(X-40)lgR-C

[0123] Among them, TS r represents the corrected backscatter intensity; A represents the backscatter intensity of the reference water image data; X is the TVG propagation loss factor set during the measurement, where TVG (Time-Variable Gain) is automatically added to the multibeam water data by the acoustic geological acquisition device during multibeam water data acquisition; and C is the offset factor obtained through calibration.

[0124] Specifically, the reference water body image data is analyzed to obtain backscattering intensity, and the backscattering intensity is input into a propagation loss correction model to correct the reference water body image data to obtain sound intensity correction data.

[0125] For example, Figure 7 The data shown in FIG. 1 are sound intensity correction data. It can be seen from the figure that the backscattering intensity caused during the propagation process has been removed.

[0126] Step C2: performing propagation loss correction and sidelobe effect correction on the sound intensity correction data to obtain corrected water body data.

[0127] Among them, the propagation loss correction is used to compensate for the energy loss during the propagation of sound waves.

[0128] Among them, sidelobe effect correction is used to remove mirror noise and sound intensity deviation caused by sidelobes.

[0129] Among them, the side lobe is a series of smaller lobe-shaped energy distributions that appear around the main lobe of the sound wave (the main direction of energy concentration).

[0130] Specifically, the sampling number, beam number, and ping number of the sampling point within the target range determined in the sound intensity correction data are input into the sidelobe correction model to correct the sidelobe effect and obtain the corrected water body data.

[0131] Among them, the sidelobe correction model can be expressed by the following formula:

[0132]

[0133] Among them, BS P,b,s is the sound intensity of the water body data; BS0 is the sound intensity of the sound intensity correction data; N B is the number of beams; N S is the sampling point number; P is the Ping number of the sampling point within the target range; b is the beam number of the sampling point within the target range; s is the sampling point number of the sampling point within the target range; B is the maximum beam number of the sampling point within the target range; S is the maximum sampling point number of the sampling point within the target range.

[0134] For example, Figure 8 As shown in the figure, it is a schematic diagram of the correction of water body data. It can be seen from the figure that Figure 7 In comparison, the water noise that can affect the water plume target has been weakened.

[0135] Optionally, preliminary screening of the corrected water body data is performed according to the beam sequence sound intensity threshold to obtain two-dimensional image data under noise, including steps D1-D3:

[0136] Step D1: Determine the mean and standard deviation of the backscatter intensity of each beam sequence based on the corrected water body data.

[0137] Specifically, the ping number, beam number, and sampling point number of the target range are obtained from the calibrated water data. The mean and standard deviation of the obtained ping number, beam number, and sampling point number are calculated to obtain the mean and standard deviation of the backscatter intensity of the beam sequence.

[0138] Furthermore, the mean and standard deviation of the backscatter intensity can be expressed by the following formula:

[0139]

[0140] Where P is the Ping number corresponding to the corrected water data; b is the beam number of the corrected water data; s is the sampling point number of the corrected water data; μ b,s is the mean backscatter intensity; σ b,s is the standard deviation of the backscatter intensity.

[0141] Step D2: Determine the beam sequence sound intensity threshold according to the mean and standard deviation of the beam sequence backscatter intensity.

[0142] Specifically, a threshold parameter is set, and the beam sequence sound intensity threshold is calculated based on the threshold parameter and the mean and standard deviation of the beam sequence backscatter intensity.

[0143] Furthermore, the beam sequence sound intensity threshold can be obtained by the following formula:

[0144] T=A1×μ b,s +A2×σ b,s .

[0145] Among them, A1 and A2 are threshold parameters.

[0146] Step D3: Compare the beam sequence sound intensity threshold with the sound intensity of the corrected water body data, remove the corrected water body data, and obtain two-dimensional image data under noise.

[0147] Specifically, the beam sequence sound intensity threshold is compared with the sound intensity of the corrected water body data. If the sound intensity of the corrected water body data is greater than or equal to the beam sequence sound intensity threshold, it is retained; if the sound intensity of the corrected water body data is less than the beam sequence sound intensity threshold, it is eliminated to obtain the two-dimensional image data under noise.

[0148] For example, assuming that the sound intensity threshold is t P,s , then the elimination process can be expressed by the following formula:

[0149]

[0150] For example, Figure 9As shown, input threshold parameters A1 and A2, obtain the sampling point number s=1, ..., S corresponding to the corrected water body data, judge whether the sampling point number s is greater than the maximum sampling point number S, if it is greater, directly end; if it is less, obtain the beam number b=1, ..., B corresponding to the corrected water body data, judge whether the beam number b is greater than the maximum beam number B, if it is less, calculate the sum of the sound intensities of the corrected water body data; if it is greater, calculate the mean and standard deviation of the backscattering intensity, calculate the beam sequence sound intensity threshold according to the obtained mean and standard deviation of the backscattering intensity, obtain the beam number at this time, if the obtained beam number is greater than the maximum beam number B, re-obtain the sampling point number s; if the obtained beam number is less than the maximum beam number B, calculate and judge whether the sound intensity of the corrected water body data corresponding to the beam number b is greater than the beam sequence sound intensity threshold, if it is greater, retain it; if it is less, remove it.

[0151] Optionally, performing point cloud clustering on the three-dimensional point cloud of the water plume to obtain a three-dimensional water plume target includes steps E1-E2:

[0152] Step E1: Filter each point cloud in the water plume three-dimensional point cloud to obtain a cluster corresponding to each point.

[0153] Specifically, a reference neighborhood is constructed for each point cloud in the three-dimensional point cloud of the water plume, the point cloud data within the reference neighborhood is screened, the noise data is removed, and the non-noise data is added to the preset set to obtain the cluster corresponding to each point.

[0154] Step E2: determining a cluster direction threshold according to the clusters, and determining the cluster where the three-dimensional water plume target is located according to the cluster direction threshold and the preset direction threshold.

[0155] Specifically, a cluster direction threshold is determined for each cluster corresponding to each point obtained, and the cluster direction threshold is compared with the preset direction threshold. If it is greater than, the cluster corresponding to the cluster direction threshold is retained, that is, the cluster where the three-dimensional water plume target is located.

[0156] Furthermore, the cluster direction threshold can be expressed as follows:

[0157]

[0158] Among them, X k , Z k 、Y k Represents the three-dimensional point cloud coordinates of the kth set, D k Represents the set C k direction.

[0159] Furthermore, the cluster where the three-dimensional water plume target is located can be obtained by the following formula:

[0160]

[0161] Among them, D T is the preset direction threshold.

[0162] Optionally, each point cloud in the water plume three-dimensional point cloud is screened to obtain a cluster corresponding to each point, including steps F1-F4:

[0163] Step F1: Determine the neighborhood radius and density threshold.

[0164] The neighborhood radius is determined by the sound velocity and the sampling frequency of the acoustic geological acquisition device. The density threshold is used to represent the threshold value of the number of data points contained in the neighborhood of each point cloud in the three-dimensional point cloud of the water plume.

[0165] Specifically, the neighborhood radius is determined based on the speed of sound waves and the sampling frequency of the acoustic geological acquisition device, and the density threshold is determined based on the distribution of the three-dimensional point cloud of the water plume.

[0166] Furthermore, the neighborhood radius and density threshold can be expressed by the following formula:

[0167]

[0168] Wherein, K is the coefficient; c is the speed of sound; and f is the sampling frequency of the acoustic geological acquisition device.

[0169] Step F2: Determine the cluster points, and determine the reference neighborhood based on the cluster points and the neighborhood radius.

[0170] Among them, the cluster point is any point in the three-dimensional point cloud of the water plume.

[0171] The reference neighborhood is a set composed of the distances between cluster points that meet the density threshold and other point clouds.

[0172] Specifically, a point is selected from the 3D point cloud of the water plume as a cluster point. The distance between the cluster point cloud and other point clouds is calculated. If the distance is less than the neighborhood radius, it is retained; if the distance is greater than the neighborhood radius, it is removed. A reference neighborhood is defined based on the retained point cloud. The distance between each point cloud within the reference neighborhood and the cluster point is calculated.

[0173] For example, for cluster point p i (i=1,2,…,m), determine point p by Euclidean distance metric i Ep neighborhood:

[0174] Ep(p i )={dis(p i ,q)≤Ep}.

[0175] Among them, q is the other point cloud in the three-dimensional point cloud of the water plume.

[0176] Step F3: If the reference neighborhood is greater than or equal to the density threshold, the cluster points are added to the preset set.

[0177] Specifically, the distance from each point in the reference neighborhood to the point pi is calculated. If the distance between each point cloud in the reference neighborhood is greater than or equal to the density threshold, the second target point is added to the preset set.

[0178] For example, if point p i The neighborhood of Ep(p i )|≧M, then create a cluster C k , that is, the preset set, put p i Add to Ck; if the condition is not met, then p i Marked as noise.

[0179] Step F4: If the distance between each point cloud in the reference neighborhood is greater than or equal to the density threshold, each point in the reference neighborhood is added to the preset set to obtain an updated cluster.

[0180] Specifically, the distance between each point cloud in the reference neighborhood is calculated. If the distance between each point cloud in the reference neighborhood is greater than or equal to the density threshold, each point in the reference neighborhood is added to the preset set to obtain an updated cluster.

[0181] For example, each point cloud in the reference neighborhood is recorded as a set N, and the distance between each point cloud in the reference neighborhood |Ep(N j )|, that is, if it satisfies: |Ep(N j )|≧M, each point N in N j (j=1,2,…,n) added to cluster C k middle.

[0182] For example, Figure 10 As shown, obtain the parameters K, c, and f. Calculate the neighborhood radius and density threshold based on the obtained parameters. Number each point cloud data to obtain a point cloud set p. Select any point cloud from the point cloud data p as a cluster, and compare the point number of the second target point with the maximum point number P. If all points have been traversed, directly calculate the cluster direction, that is, the direction of the point cloud cluster. If not all points have been traversed, determine the neighborhood Ep based on the neighborhood radius. If the number of points in Ep is greater than M, create a cluster C. k , and add the point cloud that meets the requirements to the cluster, then obtain the point clouds in Ep to form a set N, calculate the distance between each point in the set N, if the calculated distance is greater than M, then add the corresponding point cloud to the cluster C k Finally, according to the cluster C kThe cluster direction, i.e., the cluster direction, is calculated, and the target cluster, i.e., the target cluster, is selected based on the cluster direction. A three-dimensional water plume target is generated based on the target cluster.

[0183] The technical solution of this embodiment determines the target range based on the water plume target image in the multi-beam water body stacking image; determines reference water body image data based on the target range; performs backscatter intensity correction on the reference water body image data to obtain corrected water body data, eliminating the influence of TVG and sidelobe effects on water plume target extraction; determines the beam sequence sound intensity threshold based on the corrected water body data; performs preliminary screening on the corrected water body data based on the beam sequence sound intensity threshold to obtain two-dimensional image data under noise, which can effectively remove complex noise interference; calculates the three-dimensional geographic coordinates of the multi-beam water body data based on the water plume target in the two-dimensional image data to obtain a three-dimensional point cloud of the water plume; and performs point cloud clustering on the three-dimensional point cloud of the water plume to obtain a three-dimensional water plume target, which can improve the refinement of water plume extraction. By performing noise correction on the reference water body image data, this method can effectively remove complex noise interference and automatically extract three-dimensional targets from a large number of multi-beam water body sampling points, which can improve water acquisition efficiency and the refinement of water plume target extraction.

[0184] Figure 11 This is a schematic diagram of the structure of a multi-beam water plume 3D target extraction device provided by an embodiment of the present invention. This embodiment is applicable to the extraction of water plumes in the ocean. The multi-beam water plume 3D target extraction device can be implemented in the form of hardware and / or software. The multi-beam water plume 3D target extraction device can be configured in any electronic device with network communication capabilities. Figure 11 As shown, the device includes: a sampling point acquisition module 210, a reference water body image data determination module 220, a correction water body data determination module 230, an acoustic intensity threshold determination module 240, a two-dimensional image data determination module 250, a water body plume three-dimensional point cloud determination module 260, and a three-dimensional water body plume target determination module 270, wherein:

[0185] Sampling point acquisition module 210 is used to determine a target range based on the water plume target image in the multi-beam water body stacking image; the water plume target is a plume formed by the upward migration of natural gas hydrates in the ocean floor formation in the form of gas leakage; the target range is the range of water image data that can represent the water plume target; the water image data is the data range corresponding to each water data acquisition by the acoustic geological acquisition device;

[0186] Reference water image data determination module 220: used to determine reference water image data based on the target range; the reference water image data is obtained by matching the multi-beam water data based on the target range; the multi-beam water data is data collected by the acoustic geological acquisition device and can represent the changes in the water plume in the ocean;

[0187] Corrected water body data determination module 230: used to perform backscatter intensity correction on the reference water body image data to obtain corrected water body data;

[0188] Sound intensity threshold determination module 240: used to determine the beam sequence sound intensity threshold according to the corrected water body data;

[0189] 2D image data determination module 250: used to preliminarily screen the corrected water body data based on the beam sequence sound intensity threshold to obtain 2D image data under noise; the beam sequence sound intensity threshold is used to represent the difference between the sound wave intensity returned by the target sampling point and the background noise intensity;

[0190] The water plume 3D point cloud determination module 260 is used to calculate the 3D geographic coordinates of the multi-beam water data based on the water plume target in the 2D image data to obtain the 3D point cloud of the water plume;

[0191] The three-dimensional water plume target determination module 270 is used to perform point cloud clustering on the three-dimensional point cloud of the water plume to obtain the three-dimensional water plume target.

[0192] Optionally, the sampling point acquisition module 210 includes:

[0193] Water body stacking map determining unit: used to determine the water body stacking map; the water body stacking map is used to obtain high signal-to-noise ratio water body data;

[0194] Grayscale heading stacking map determining unit: used for performing grayscale mapping on the water body stacking map to obtain a grayscale heading stacking map;

[0195] Classification mark map determination unit: used to determine the cross-sectional position where the water plume target appears and disappears based on the pixel value of the stacked image and the reference threshold, and generate a classification mark map based on the cross-sectional position; the pixel value of the stacked image is the pixel value of the pixel point in the grayscale heading stacked image; the cross-sectional position is the column number in the stacked image;

[0196] Target range determination unit: used to determine the target range based on the classification labeling map.

[0197] Optionally, the classification label map determination unit includes:

[0198] Grayscale threshold determination subunit: used to determine the grayscale threshold; the grayscale threshold is one of the grayscale ranges;

[0199] Population variance determination subunit: used to calculate the population variance based on the grayscale threshold and the reference pixel value probability; the reference pixel value probability is the probability of the reference pixel value appearing in the water body stacking map; the reference pixel value is any pixel value within the range from zero to the grayscale threshold;

[0200] Reference threshold determination subunit: used to loop the overall variance acquisition process, determine the target variance, and use the grayscale threshold corresponding to the target variance as the reference threshold; the target variance is the minimum value of the acquired overall variance.

[0201] Optionally, the corrected water body data determination module 230 includes:

[0202] Sound intensity correction data determination unit: used to perform propagation loss correction on the reference water body image data according to the propagation loss correction model to obtain sound intensity correction data; the propagation loss correction model is used to correct the backscatter intensity of the reference water body image data according to the time-varying gain and offset factor;

[0203] Corrected water body data determination unit: used to perform propagation loss correction and sidelobe effect correction on the sound intensity correction data to obtain corrected water body data; propagation loss correction is used to compensate for energy loss during sound wave propagation; sidelobe effect correction is used to remove mirror noise and sound intensity deviation caused by sidelobes.

[0204] Optionally, the two-dimensional image data determination module 250 includes:

[0205] Mean determination unit: used to determine the mean and standard deviation of the backscatter intensity of each beam sequence based on the corrected water body data;

[0206] A beam sequence sound intensity threshold determination unit is used to determine the beam sequence sound intensity threshold according to the mean and standard deviation of the beam sequence backscatter intensity;

[0207] Two-dimensional image data determination unit: used to compare the beam sequence sound intensity threshold with the sound intensity of the corrected water body data, eliminate the corrected water body data, and obtain two-dimensional image data under noise.

[0208] Optionally, the three-dimensional water plume target determination module 270 includes:

[0209] Cluster determination unit: used to screen each point cloud in the three-dimensional point cloud of the water plume to obtain the cluster corresponding to each point;

[0210] The three-dimensional water plume target cluster determination unit is used to determine the cluster direction threshold according to the cluster, and determine the cluster where the three-dimensional water plume target is located according to the cluster direction threshold and the preset direction threshold.

[0211] Optionally, the cluster determination unit includes:

[0212] Parameter determination subunit: used to determine the neighborhood radius and density threshold; the neighborhood radius is determined by the sound velocity and the sampling frequency of the acoustic geological acquisition device; the density threshold is used to represent the threshold value of the number of data points contained in the neighborhood of each point cloud in the three-dimensional point cloud of the water plume;

[0213] Reference neighborhood determination subunit: used to determine cluster points and determine the reference neighborhood based on the cluster points and the neighborhood radius; a cluster point is any point in the 3D point cloud of the water plume; the reference neighborhood is a set of distances between cluster points that meet the density threshold and other point clouds;

[0214] Target point adding subunit: used to add cluster points to the preset set if the reference neighborhood is greater than or equal to the density threshold;

[0215] Cluster update subunit: used to add each point in the reference neighborhood to the preset set if the distance between each point cloud in the reference neighborhood is greater than or equal to the density threshold, to obtain the updated cluster.

[0216] The multi-beam water plume three-dimensional target extraction device provided in the embodiments of the present invention can execute the multi-beam water plume three-dimensional target extraction method provided in any of the above-mentioned embodiments of the present invention, and has the corresponding functions and beneficial effects of executing the multi-beam water plume three-dimensional target extraction method. For detailed processes, please refer to the relevant operations of the multi-beam water plume three-dimensional target extraction method in the above-mentioned embodiments.

[0217] Figure 12 A schematic diagram of the structure of an electronic device for implementing the multi-beam water plume three-dimensional target extraction method according to an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0218] like Figure 12As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0219] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0220] Processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. Processor 11 executes the various methods and processes described above, such as the multi-beam water plume three-dimensional target extraction method.

[0221] In some embodiments, the multi-beam water plume 3D target extraction method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the multi-beam water plume 3D target extraction method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the multi-beam water plume 3D target extraction method in any other suitable manner (e.g., via firmware).

[0222] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0223] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0224] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0225] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0226] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0227] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0228] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0229] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A multi-beam water plume three-dimensional target extraction method, characterized in that: include: The target range is determined based on the water plume target image in the multi-beam water stack image; the water plume target is a plume formed by natural gas hydrates in the seabed formation that leaks in the form of gas and migrates upward; the target range is the range of water image data that can characterize the water plume target; the water image data is the data range corresponding to each water data acquisition by the acoustic geological acquisition device; Determining reference water body image data based on the target range; the reference water body image data is obtained by matching the multi-beam water body data based on the target range; the multi-beam water body data is data collected by an acoustic geological acquisition device that can characterize changes in water plumes in the ocean; Performing backscatter intensity correction on the reference water body image data to obtain corrected water body data; Determining a beam sequence sound intensity threshold according to the corrected water body data; The corrected water body data is preliminarily screened according to the beam sequence sound intensity threshold to obtain two-dimensional image data under noise; the beam sequence sound intensity threshold is used to characterize the difference between the sound wave intensity returned by the target sampling point and the background noise intensity; Based on the water plume target in the two-dimensional image data, three-dimensional geographic coordinates are calculated for the multi-beam water data to obtain a three-dimensional point cloud of the water plume; Point cloud clustering is performed on the three-dimensional point cloud of the water body plume to obtain a three-dimensional water body plume target.

2. The method according to claim 1, characterized in that Determining the target range based on the water plume target image in the multi-beam water body stacking image includes: Determine a water body stacking map; the water body stacking map is used to obtain high signal-to-noise ratio water body data; Performing grayscale mapping on the water body stacking map to obtain a grayscale heading stacking map; Determine the cross-sectional position where the water plume target appears and disappears based on the stacked image pixel value and a reference threshold, and generate a classification label map based on the cross-sectional position; the stacked image pixel value is the pixel value of the pixel point in the grayscale heading stacked image; the cross-sectional position is the column number in the stacked image; A target range is determined based on the classification labeling map.

3. The method according to claim 2, characterized in that The process of determining the reference threshold includes: Determine a grayscale threshold; the grayscale threshold is one of the grayscale ranges; The overall variance is calculated based on the grayscale threshold and the reference pixel value probability; the reference pixel value probability is the probability of the reference pixel value appearing in the water body stacking map; the reference pixel value is any pixel value within the range from zero to the grayscale threshold; The process of obtaining the population variance is repeated, a target variance is determined, and a grayscale threshold corresponding to the target variance is used as a reference threshold; the target variance is the minimum value among the obtained population variances.

4. The method according to claim 1, wherein The step of performing backscattering intensity correction on the reference water body image data to obtain corrected water body data includes: Performing propagation loss correction on the reference water body image data according to a propagation loss correction model to obtain sound intensity correction data; the propagation loss correction model is used to correct the backscatter intensity of the reference water body image data according to a time-varying gain and an offset factor; The sound intensity correction data is subjected to propagation loss correction and sidelobe effect correction to obtain corrected water body data; the propagation loss correction is used to compensate for energy loss during sound wave propagation; the sidelobe effect correction is used to remove mirror noise and sound intensity deviation caused by sidelobes.

5. The method according to claim 1, wherein The preliminarily screening of the corrected water body data according to the beam sequence sound intensity threshold to obtain two-dimensional image data under noise includes: Determine the mean and standard deviation of the backscatter intensity of each beam sequence based on the corrected water data; Determining a beam sequence acoustic intensity threshold according to the mean and standard deviation of the backscatter intensity of the beam sequence; The beam sequence sound intensity threshold is compared with the sound intensity of the corrected water body data, and the corrected water body data is eliminated to obtain two-dimensional image data under noise.

6. The method according to claim 1, characterized in that The step of clustering the three-dimensional point cloud of the water plume to obtain a three-dimensional water plume target includes: Filter each point cloud in the three-dimensional point cloud of the water plume to obtain the cluster corresponding to each point; A cluster direction threshold is determined according to the cluster, and the cluster where the water plume three-dimensional target is located is determined according to the cluster direction threshold and a preset direction threshold.

7. The method according to claim 6, characterized in that The step of screening each point cloud in the three-dimensional point cloud of the water plume to obtain a cluster corresponding to each point includes: Determine a neighborhood radius and a density threshold; the neighborhood radius is determined by the sound velocity and the sampling frequency of the acoustic geological acquisition device; the density threshold is used to represent the threshold value of the number of data points contained in the neighborhood of each point cloud in the three-dimensional point cloud of the water plume; Determine cluster points, and determine a reference neighborhood based on the cluster points and the neighborhood radius; the cluster points are any points in the three-dimensional point cloud of the water plume; the reference neighborhood is a set of distances between cluster points that meet a density threshold and other point clouds; If the reference neighborhood is greater than or equal to the density threshold, the cluster points are added to a preset set; If the distance between each point cloud in the reference neighborhood is greater than or equal to the density threshold, each point in the reference neighborhood is added to a preset set to obtain an updated cluster.

8. A multi-beam water plume three-dimensional target extraction device, characterized in that: include: The sampling point acquisition module is used to determine the target range based on the water plume target image in the multi-beam water body stacking image; the water plume target is a plume formed by the upward migration of natural gas hydrates in the marine seabed formation in the form of gas leakage; the target range is the range of water image data that can characterize the water plume target; the water image data is the data range corresponding to each water data acquisition by the acoustic geological acquisition device; a reference water body image data determination module, configured to determine reference water body image data based on the target range; the reference water body image data is obtained by matching the multi-beam water body data based on the target range; the multi-beam water body data is data collected by an acoustic geological acquisition device that can characterize changes in water plume flow in the ocean; a corrected water body data determination module, configured to perform backscatter intensity correction on the reference water body image data to obtain corrected water body data; an acoustic intensity threshold determination module, configured to determine a beam sequence acoustic intensity threshold based on the corrected water body data; a two-dimensional image data determination module, configured to preliminarily screen the corrected water body data based on the beam sequence sound intensity threshold to obtain two-dimensional image data under noise; the beam sequence sound intensity threshold is used to characterize the difference between the intensity of the sound wave returned by the target sampling point and the intensity of the background noise; a water plume three-dimensional point cloud determination module, configured to calculate the three-dimensional geographic coordinates of the multi-beam water data based on the water plume target in the two-dimensional image data to obtain a water plume three-dimensional point cloud; The three-dimensional water plume target determination module is used to perform point cloud clustering on the three-dimensional point cloud of the water plume to obtain a three-dimensional water plume target.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so as to enable the at least one processor to perform the multi-beam water plume three-dimensional target extraction method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the multi-beam water plume three-dimensional target extraction method according to any one of claims 1 to 7 when executed.

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