Marine Mapping Method and System Based on Multi-Beam Bathymetric Radar

Abstract

The present invention relates to the field of marine surveying and mapping technology, and particularly relates to a marine surveying and mapping method and system based on a multi-beam bathymetric radar, comprising the following steps: obtaining in real time the echo signals of the multi-beam bathymetric radar, and adjusting the beam emission angle and emission frequency; constructing a multi-level spatial spectrum analysis model, and converting multi-beam data into spectral feature vectors; establishing a seabed terrain topological structure model, identifying regions with potential measurement errors, and optimizing the seabed terrain topological structure model based on the identified regions with potential measurement errors; and processing the bathymetric data according to the optimized seabed terrain topological structure model and combining with the spectral feature vectors of the multi-beam echo signals to generate a marine surveying and mapping map. In the present invention, the multi-level analysis can more comprehensively and accurately extract the diversity and complex features of the seabed terrain, especially in regions with large terrain undulations, significantly improving the accuracy and reliability of the surveying and mapping.

Description

Technical Field

[0001] The present invention relates to the field of ocean surveying and mapping technology, and in particular to an ocean surveying and mapping method and system based on a multi-beam bathymetric radar. Background Art

[0002] Ocean surveying and mapping is an important foundational work in the fields of marine scientific research, resource development, and environmental protection. Currently, the widely used ocean surveying and mapping technology mainly relies on bathymetric radar, which can measure the topography and depth of the seabed by emitting sound wave beams and receiving the echo signals reflected from the seabed. However, the existing bathymetric technology still has some technical bottlenecks and shortcomings when facing the complex and changeable seabed environment.

[0003] Traditional multi-beam bathymetry systems typically only utilize fixed beam angles and frequencies for seabed measurements. This method works well under simple terrain conditions, but is prone to measurement errors when faced with seabed environments with large terrain undulations or high complexity. Existing technologies also typically generate a large amount of redundant information when processing multi-beam data, which not only increases the burden of data storage and processing but may also reduce surveying and mapping accuracy.

[0004] The ocean environment is subject to dynamic changes, such as changes in seabed topography and the influence of tides. These factors will affect the accuracy of bathymetric data. Existing multi-beam bathymetric technology lacks effective prediction and error correction mechanisms when responding to dynamic environmental changes, which can easily lead to inaccurate measurement results. Especially in complex seabed areas, measurement errors are more significant.

[0005] In existing technologies, the processing of bathymetric data often relies on traditional interpolation and gridding methods, which are prone to noise and errors when the data volume is large or the terrain is complex. In addition, existing marine survey maps are usually difficult to reflect the true characteristics of the seabed topography in detail, especially in terms of multi-level terrain structure and resource distribution. Summary of the Invention

[0006] The present invention provides an ocean surveying and mapping method and system based on a multi-beam bathymetric radar.

[0007] The ocean mapping method based on multi-beam bathymetric radar includes the following steps:

[0008] S1 acquires the echo signal of the multi-beam bathymetric radar in real time and adjusts the beam transmission angle and frequency according to the seabed terrain characteristics and radar detection depth. This ensures that the radar can obtain high-precision multi-beam echo signals under different depths and terrain conditions, avoiding the measurement errors of traditional fixed angles and frequencies.

[0009] S2. Construct a multi-level spatial spectrum analysis model to perform spectrum analysis and data compression on the collected multi-beam echo signals. Combining sparse representation and multi-scale decomposition techniques, convert the multi-beam data into a spectral feature vector V to reduce data redundancy and storage requirements, and retain important spatial frequency information for seabed terrain reconstruction;

[0010] S3. Dynamic prediction and optimization based on the seabed terrain topological structure: Based on the spectral feature vector V, establish a seabed terrain topological structure model. Combine the terrain change trend to dynamically predict the bathymetric data in complex seabed terrain areas, identify areas with potential measurement errors, and optimize the seabed terrain topological structure model based on the identified potential measurement error areas;

[0011] S4. According to the optimized seabed terrain topological structure model, combined with the spectral feature vector of the multi-beam echo signal, process the bathymetric data to generate a marine surveying and mapping map.

[0012] Optionally, the sound waves emitted by the multi-beam bathymetric radar reflect multi-beam echo signals after encountering the seabed terrain, and record the intensity, time delay of the multi-beam echo signals and their corresponding beam angles.

[0013] Transmit the collected multi-beam echo signals to the dynamic environment analysis unit, and combine the seabed terrain complexity data and detection depth information at the current measurement position to analyze the terrain features of the seabed, including seabed slope, protrusion, and depression features.

[0014] Optionally, in S1, use the adaptive beam control algorithm to automatically adjust the beam emission angle and emission frequency of the multi-beam bathymetric radar according to the seabed terrain feature analysis results and radar detection depth, specifically including:

[0015] S11. Beam angle adjustment: Dynamically adjust the beam emission angle according to the slope of the terrain and the seabed undulation to ensure that each beam covers different parts of the seabed;

[0016] S12. Emission frequency adjustment: Dynamically adjust the emission frequency according to the radar detection depth and the acoustic velocity characteristics of the water body. Use a high frequency in shallow water areas to improve resolution, and use a low frequency in deep water areas to increase detection depth;

[0017] S13. Through a real-time feedback control loop, continuously monitor the quality of the echo signal. If the quality of the echo signal does not meet the preset accuracy requirements, automatic secondary adjustment will be performed to optimize the beam angle and emission frequency.

[0018] Optionally, S2 specifically includes:

[0019] S21, perform spatial spectrum analysis on the collected multi-beam echo signals, build a multi-level spectrum analysis model, and extract spectrum information on each spatial scale by performing spectrum transformation on the echo signals at different spatial scales. i The corresponding spectral components are Expressed as:

[0020] Among them, x(t,l i ) are signal components at different spatial scales, is Fourier transform, which is used to convert time domain signals into frequency domain signals;

[0021] S22, Application of sparse representation technology: Based on spectrum analysis, sparse representation technology is used to compress spectrum components and convert redundant information into sparse features;

[0022] S23, combination of multi-scale decomposition technology: Multi-scale decomposition technology based on wavelet transform decomposes the echo signal at multiple scales, extracting high-frequency and low-frequency components at different scales to describe the spatial characteristics of the seabed topography;

[0023] S24, generation of spectral feature vector: Integrate the sparse representation and multi-scale decomposition spectral information to generate a spectral feature vector for describing the seabed topography. The final spectral feature vector V is represented as a combination of the sparse representation and multi-scale decomposition results at each scale:

[0024] in, are sparse features at different spatial scales, W1, W2, ..., W m are the wavelet coefficients obtained by multi-scale decomposition.

[0025] Optionally, the step of establishing the seabed topology model based on the spectrum feature vector V in S3 specifically includes:

[0026] S31, define nodes: Each spectral feature vector V corresponds to a feature point in the seabed topography, and the feature point is defined as a node N in the model. i , the spectrum feature vector V includes spatial frequency information and the results of sparse representation and multi-scale decomposition;

[0027] S32, build edge: connect the nodes of adjacent feature points through edge E ij To connect, the weight of the edge is w ij The Euclidean distance d between nodes ij express;

[0028] S33, model generation: set all nodes N i Its adjacent node N j Through the edge Eij Connect to generate a seabed terrain topological structure model G=(N, E) representing the entire seabed terrain, where N represents nodes and E represents edges. This model represents the spatial distribution and characteristic relationship of the seabed terrain through the relationship between nodes and edges.

[0029] Optionally, the dynamic prediction of the bathymetric data for complex seabed terrain areas in S3 by combining the terrain change trend specifically includes:

[0030] S34: Use the spectral feature vectors V(t), V(t - 1), V(t - 2),..., V(t - n) measured at multiple time points as inputs to construct a time series analysis model. Adopt the autoregressive integrated moving average model (ARIMA) time series prediction method to analyze the trend of terrain feature points changing over time, and obtain the change amount ΔV(t) of the spectral feature vector over time;

[0031] Based on historical data and current data, predict the feature vector V(t + Δt) and the corresponding node position change at the future time t + Δt;

[0032] S35: According to the predicted terrain changes, perform dynamic prediction on complex terrain areas and identify potential measurement error areas. The identification of potential measurement error areas is achieved by comparing the predicted position with the actual measurement position to calculate the deviation ∈ i : It also includes an identification of potential measurement error areas. If ∈ i exceeds the preset error threshold ∈ threshold , then mark the node N i and its adjacent areas as potential measurement error areas.

[0033] Optionally, S3 also includes correcting and optimizing the model for the identified potential measurement error areas. The correction includes data correction for the nodes marked as error areas through interpolation;

[0034] Update the corrected node values into the topological structure model and recalculate the edge weights w ij .

[0035] Optionally, S4 specifically includes:

[0036] S41: Fuse the optimized seabed terrain topological structure model G=(N, E) with the spectral feature vector V i generated by the multibeam echo signal. During the fusion, associate the spatial position of each node N i with its corresponding spectral feature vector V iPerform matching to obtain a node set {N including terrain information and spectral characteristics i , V i};

[0037] S42. Based on the node information in the seabed topology structure model, combine the spectral information in the spectral feature vector to correct the bathymetric data in the multibeam echo signal. By calculating the spectral feature difference ΔV ij and bathymetric difference Δd ij , adjust the bathymetric data d i :

[0038] Among them, is the adjusted bathymetric data, and λ ij is the adjustment coefficient;

[0039] S43. Perform interpolation processing on the corrected bathymetric data to grid the irregularly distributed bathymetric data and generate continuous seabed terrain data. Grid the bathymetric data into regular grid points G x，y . Based on the gridded seabed terrain data G x，y , generate a marine surveying and mapping chart.

[0040] Optionally, the interpolation processing in S43 uses the Kriging interpolation algorithm to grid the bathymetric data into regular grid points G x，y , expressed as: Among them, λ i is the interpolation weight.

[0041] A marine surveying and mapping system based on a multibeam bathymetric radar, used to implement the above-mentioned marine surveying and mapping method based on a multibeam bathymetric radar, includes the following modules:

[0042] Multibeam bathymetric radar module: used to emit multiple acoustic beams and receive the echo signals reflected from the seabed, and obtain the initial bathymetric data under different depths and terrain conditions;

[0043] Real-time signal acquisition and analysis module: perform real-time monitoring and analysis on the echo signals collected by the bathymetric radar, and adjust the beam emission angle and frequency according to the seabed terrain complexity and detection depth to ensure high-precision echo signal acquisition;

[0044] Multilevel spatial spectrum analysis module: through multilevel spatial spectrum analysis, perform spectrum analysis and data compression on the collected echo signals, and generate a spectral feature vector to reduce data redundancy and retain key spatial frequency information;

[0045] Submarine Terrain Topological Structure Model Module: Construct a submarine terrain topological structure model based on spectral feature vectors, combine time series analysis to dynamically predict terrain changes, and identify and correct measurement errors;

[0046] Bathymetric Data Processing and Optimization Module: Use the topological structure model and spectral feature vectors to process and correct bathymetric data, optimize the bathymetric results by adjusting coefficients to improve the mapping accuracy;

[0047] Marine Mapping Chart Generation Module: Interpolate and grid the processed bathymetric data to generate a marine mapping chart, and display the terrain structure at different depth levels through a visualization tool.

[0048] Advantages of the present invention:

[0049] In the present invention, through multi-level spatial spectral analysis, the multi-beam echo signal is converted into spectral feature vectors. Combining sparse representation and multi-scale decomposition techniques, it effectively reduces data redundancy and retains key spatial frequency information. Compared with traditional single-scale analysis methods, this multi-level analysis can extract the diversity and complex features of the submarine terrain more comprehensively and accurately, especially in areas with large terrain fluctuations, significantly improving the mapping accuracy and reliability.

[0050] In the present invention, by establishing a submarine terrain topological structure model, combining spectral feature vectors, performing dynamic prediction of terrain changes and error identification and correction, and conducting time series analysis of the terrain change trend through an autoregressive moving average model, it can identify potential measurement error areas in advance and correct them through the optimized topological structure model, greatly improving the mapping accuracy in complex submarine environments, ensuring the accuracy and consistency of mapping data. Especially in a dynamically changing submarine environment, it can effectively respond to unforeseen terrain changes.

[0051] In the present invention, on the basis of combining the topological structure model and spectral feature vectors, the bathymetric data is processed and optimized to achieve precise correction of the bathymetric data. Through interpolation algorithms and gridding techniques, a high-resolution marine mapping chart is generated. Compared with traditional bathymetric data processing methods, it can more precisely reflect the true features of the submarine terrain, reduce noise and errors in the data. The finally generated marine mapping chart not only has high accuracy but also can intuitively display the terrain structure at different depth levels, providing strong decision-making support for marine research and development. Description of the Drawings

[0052] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0053] Figure 1 Schematic diagram of the surveying and mapping method for the embodiment of the present invention;

[0054] Figure 2 Schematic diagram of the surveying and mapping system module for the embodiment of the present invention. Detailed implementation manners

[0055] The present invention will be described in detail below in conjunction with the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; moreover, the drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0056] It should be pointed out that in the specification, when referring to "an embodiment", "embodiments", "exemplary embodiments", "some embodiments", etc., it indicates that the described embodiments may include specific features, structures or characteristics, but not necessarily every embodiment includes such specific features, structures or characteristics. In addition, when combining embodiments to describe specific features, structures or characteristics, implementing such features, structures or characteristics in combination with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the relevant art.

[0057] Generally, the terms can be understood at least in part from their use in the context. For example, at least in part depending on the context, the term "one or more" used herein can be used to describe any feature, structure or characteristic in a singular sense, or can be used to describe a combination of features, structures or characteristics in a plural sense. In addition, the term "based on" can be understood as not necessarily intended to convey a set of exclusive factors, but instead, at least in part depending on the context, allowing for the existence of other factors that may not be explicitly described.

[0058] As Figure 1 shown, the marine surveying and mapping method based on a multi-beam bathymetric radar includes the following steps:

[0059] S1, obtain the echo signal of the multi-beam bathymetric radar in real time, and adjust the beam emission angle and emission frequency according to the seabed terrain characteristics and the radar detection depth to ensure that the radar can obtain high-precision multi-beam echo signals under different depth and terrain conditions, and avoid measurement errors under traditional fixed angles and frequencies;

[0060] S2, construct a multi-level spatial spectrum analysis model to perform spectrum analysis and data compression on the collected multi-beam echo signals. Combine sparse representation and multi-scale decomposition techniques to convert the multi-beam data into a spectral feature vector V, so as to reduce data redundancy and storage requirements and retain important spatial frequency information for seabed terrain reconstruction;

[0061] S3, dynamic prediction and optimization based on the seabed terrain topological structure: Based on the spectral feature vector V, establish a seabed terrain topological structure model. Combine the terrain change trend to dynamically predict the bathymetric data in complex seabed terrain areas, identify areas with potential measurement errors, and optimize the seabed terrain topological structure model based on the identified potential measurement error areas to reduce the impact of complex terrain on surveying and mapping accuracy; The feature vector contains the optimized key terrain information, enabling the effective construction of the topological structure model;

[0062] S4, according to the optimized seabed terrain topological structure model, combine with the spectral feature vector of the multi-beam echo signal to process the bathymetric data and generate a marine surveying and mapping map.

[0063] The sound waves emitted by the multi-beam bathymetric radar reflect multi-beam echo signals after encountering the seabed terrain, and record the intensity, time delay of the multi-beam echo signals and their corresponding beam angles;

[0064] Collect the time delay and intensity of the echo signal to calculate the bathymetric result of each beam. Assume that the initial time of the emitted sound wave is t0 and the time when the echo signal returns is t r , the propagation speed of the sound wave in water is v, then the calculation formula for the bathymetric distance d is:

[0065] where v is the speed of sound, which depends on water temperature, salinity and pressure, about 1500m / s, t T -t0 is the round-trip time of the sound wave, in seconds.

[0066] Transmit the collected multi-beam echo signals to the dynamic environment analysis unit. Combine the seabed terrain complexity data and detection depth information at the current measurement position to analyze the terrain features of the seabed, including seabed slope, protrusion, and depression features.

[0067] In the dynamic environment analysis unit, analyze the complexity of the seabed terrain according to the collected bathymetric data. The terrain complexity is represented by the slope (i.e., the speed of change of the seabed height). Let the depths of two adjacent points measured be d1 and d2, and their horizontal distance be Δx, then the slope S is calculated as: When S is too large, the terrain is complex and the emission angle of the beam needs to be adjusted.

[0068] In S1, the adaptive beam control algorithm is used to automatically adjust the beam emission angle and emission frequency of the multi-beam bathymetric radar according to the analysis results of the seabed terrain features and the radar detection depth, specifically including:

[0069] S11, beam angle adjustment: According to the slope of the terrain and the seabed undulation, dynamically adjust the emission angle of the beam to ensure that each beam covers different parts of the seabed and avoid blind areas or overlapping coverage caused by a fixed angle; Let the emission angle of the original radar beam be θ0, and the adjusted emission angle θ according to the terrain complexity S is: θ = θ0 + k1×S, where θ0 is the initial emission angle and k1 is an adjustment coefficient related to the terrain complexity, which is determined according to empirical data;

[0070] The coefficient k1 is an adjustment coefficient related to the terrain complexity S, and its specific value is determined through the following steps:

[0071] 1. Experimental measurement and data collection:

[0072] First, in different seabed terrain environments, use the multi-beam bathymetric radar to conduct a series of experiments, and record the measurement errors and the quality of the radar echo signals under each terrain complexity S.

[0073] Through the data collected in the experiment, determine the range of beam angle changes that the system needs to adjust under different S values.

[0074] 2. Data fitting and regression analysis:

[0075] Use the collected experimental data to fit the relationship between the beam angle change Δθ = θ - θ0 and the terrain complexity S. The methods include linear regression or polynomial regression.

[0076] Assume that there is a linear relationship between the two, then the fitted linear regression equation can be expressed as:

[0077] Δθ = k1×S. In this process, the regression analysis will obtain the best fitting coefficient k1, which is the sensitivity of the adjustment angle.

[0078] 3. Verification and optimization:

[0079] Use the obtained k1 value to verify under other terrain conditions to ensure that under different terrain complexities, this coefficient can effectively optimize the beam angle of the radar and ensure the measurement accuracy.

[0080] According to the verification results, it may be necessary to fine-tune k1 to ensure its adaptability under different conditions.

[0081] S12. Transmit frequency adjustment: According to the radar detection depth and the characteristics of the water sound speed, the transmit frequency is dynamically adjusted. High frequencies are used in shallow waters to improve resolution, and low frequencies are used in deep waters to increase the detection depth, thus ensuring the accuracy of the echo signal under different depth conditions. The adjustment of the transmit frequency f is related to the detection depth d. Let the minimum depth of the shallow water area be d min , and the maximum depth of the deep water area be d max . The corresponding transmit frequency range is f max and f min . Then the transmit frequency adjusted according to the detection depth d is:

[0082] where f max and f min are the maximum and minimum values of the transmit frequency respectively, and d min and d max are the minimum and maximum values of the detection depth respectively;

[0083] S13. Through a real-time feedback control loop, continuously monitor the quality of the echo signal. If the quality of the echo signal does not meet the preset accuracy requirements, automatic secondary adjustment will be performed to optimize the beam angle and transmit frequency until a high-precision multi-beam echo signal that meets the requirements is obtained;

[0084] The real-time feedback control loop determines whether secondary adjustment is needed by evaluating the signal quality. The signal quality is represented by the signal-to-noise ratio (SNR). Let the received signal power be P s , and the noise power be P n . Then the signal-to-noise ratio SNR is: If the measured SNR is lower than the preset accuracy threshold SNR min , then the beam angle or transmit frequency needs to be adjusted to improve the signal quality. The specific adjustment method is implemented through an incremental adjustment algorithm:

[0085] Δθ = θ ± δ θ ;

[0086] Δf = f ± δ f ; where δ θ and δ f are small-amplitude angle and frequency adjustment amounts, and positive or negative adjustments are made according to the current SNR situation.

[0087] S2 specifically includes:

[0088] S21. Perform spatial spectrum analysis on the collected multi-beam echo signal, construct a multi-level spectrum analysis model, perform spectrum transformation on the echo signal at different spatial scales, and extract the spectrum information at each spatial scale. The spectrum component corresponding to the spatial scale l i is expressed as:

[0089] Among them, x(t, l i ) is the signal component at different spatial scales, is the Fourier transform, which is used to convert the time-domain signal into the frequency-domain signal;

[0090] S22, Application of sparse representation technology: Based on the spectrum analysis, the sparse representation technology is used to compress the spectrum components, convert the redundant information into sparse features, and the spectrum components corresponding to the spatial scales are sparsely represented as:

[0091] Among them, Φ is an over-complete dictionary matrix, α is a sparse coefficient vector, and data compression is achieved by minimizing the sparsity ||α||0;

[0092] S23, Combination of multi-scale decomposition technology: Based on the multi-scale decomposition technology of wavelet transform, the echo signal is decomposed into multiple scales, and the high-frequency and low-frequency components at different scales are extracted to describe the spatial characteristics of the seabed topography. Assuming the scale of wavelet decomposition is j, the decomposed signal is expressed as: x(t) = ∑ j W j ψ j (t), where W j is the wavelet coefficient corresponding to scale j, and ψ j (t) is the wavelet basis function;

[0093] S24, Generation of spectral feature vectors: Integrate the spectral information after sparse representation and multi-scale decomposition to generate spectral feature vectors for describing the seabed topography. The final spectral feature vector V is expressed as the combination of the sparse representation and multi-scale decomposition results at each scale:

[0094] Among them, are the sparse features at different spatial scales, and W1, W2,..., W m are the wavelet coefficients obtained from multi-scale decomposition.

[0095] Traditional spatial spectrum analysis usually only considers the spectrum information of a single scale, resulting in inaccurate extraction of the characteristics of complex seabed topography. By introducing a multi-level spatial spectrum analysis model, it is possible to extract spectrum information at different spatial scales, covering topographic features from macro to micro; the multi-scale analysis method can capture the diversity of the seabed topography more comprehensively, especially in complex or multi-level structured seabed environments, more accurately reflect the changes and details of the topography, making the generated spectral feature vectors richer and more accurate, providing a more reliable data basis for subsequent topographic modeling.

[0096] The sparse representation technique solves the problems of information redundancy and low computational efficiency in multi-beam bathymetric data by compressing redundant spectral information into sparse feature vectors. Different from traditional compression methods, the sparse representation technique can retain the key information in the signal while compressing; it reduces the requirements for data storage and processing, while retaining the necessary spectral features, making the spectral feature vectors more efficient in calculation and transmission, enhancing the real-time performance and response ability when processing large-scale data sets, especially being able to generate high-quality topographic maps more quickly under complex terrain conditions.

[0097] The multi-scale decomposition technique of wavelet transform further refines the signal representation method. By decomposing the signal into high-frequency and low-frequency components at different scales, it can capture topographic features at different scales, making up for the defects that single-scale analysis may miss details or lose overall features; the multi-scale decomposition enables the generated spectral feature vectors to contain both global features and local details, thus more accurately describing the complex structure of the seabed topography. Especially in complex seabed environments, the application of this technique can significantly improve the mapping accuracy and the reliability of the model.

[0098] Organically combining multi-level spatial spectral analysis, sparse representation technique and multi-scale decomposition technique to generate spectral feature vectors not only solves the limitations of single techniques, but also enhances the expression ability of spectral feature vectors through the synergistic effect of multiple techniques.

[0099] Establishing a seabed topography topological structure model based on the spectral feature vector V in S3 specifically includes:

[0100] S31, defining nodes: Each spectral feature vector V corresponds to a feature point in the seabed topography, and this feature point is defined as a node N in the model i , the spectral feature vector V includes spatial frequency information and the results of sparse representation and multi-scale decomposition, expressed as:

[0101] Among them, V i is the i-th spectral feature vector;

[0102] S32, constructing edges: Connect the nodes of adjacent feature points through an edge E ij , and the weight w ij of the edge is represented by the Euclidean distance d ij between the nodes:

[0103] E ij ={w ij}={d ij};

[0104]

[0105] Among them, and are the spectral feature vector components of two feature points respectively;

[0106] S33, model generation: Connect all nodes N i with their adjacent nodes N j through edges E ij to generate a seabed terrain topological structure model G = (N, E) representing the entire seabed terrain, where N represents nodes and E represents edges. This model represents the spatial distribution and feature relationship of the seabed terrain through the relationship between nodes and edges.

[0107] The dynamic prediction of bathymetric data in complex seabed terrain areas in S3 by combining terrain change trends specifically includes:

[0108] S34, taking the spectral feature vectors V(t), V(t - 1), V(t - 2),..., V(t - n) measured at multiple time points as inputs, constructing a time series analysis model, and using the autoregressive integrated moving average model (ARIMA) time series prediction method to analyze the trend of terrain feature points changing over time, obtaining the change amount ΔV(t) of the spectral feature vector over time: ΔV(t) = ARIMA(V(t), V(t - 1), V(t - 2), …, V(t - n)), where ARIMA represents the autoregressive integrated moving average model;

[0109] Based on historical data and current data, predict the feature vector V(t + Δt) at the future time t + Δt and the position change of the corresponding node : V(t + Δt) = V(t) + ΔV(t);

[0110] S35, according to the predicted terrain changes, conduct dynamic prediction on complex terrain areas and identify potential measurement error areas. Identifying potential measurement error areas is done by comparing the predicted position with the actual measured position to calculate the deviation ∈ i : It also includes the identification of potential measurement error areas. If ∈ i exceeds the preset error threshold ∈ threshold , then mark this node N i and its adjacent area as a potential measurement error area for further error processing.

[0111] S3 also includes correcting the identified potential measurement error areas and optimizing the model. The correction includes correcting the data of the nodes marked as error areas by interpolation. Let N i be the corrected node value then its value is calculated by interpolation from the data of adjacent nodes N j : Among them, λ j is the interpolation weight coefficient, satisfying ∑ j λ j = 1;

[0112] Update the corrected node value to the topological structure model, and recalculate the edge weight w ij of the error region to ensure that the topological structure model accurately reflects the corrected seabed topography.

[0113] More specifically, the ARIMA model combines the concepts of autoregression and moving average, and can consider the differencing operation (I, i.e., the integration operation) in the time series to process non-stationary data. For the spectral feature vector V(t) corresponding to a terrain feature point, let V(t) be the terrain feature value at time t. The ARIMA model is expressed as:

[0114] Among them;

[0115] B is the lag operator, i.e., B k V(t) = V(t - k);

[0116] is the autoregressive polynomial, defined as

[0117] θ(B) is the moving average polynomial, defined as θ(B) = 1 + θ1B + θ2B 2 + … + θ q B q ;

[0118] d is the differencing order, used to process non-stationary data;

[0119] ∈(t) is white noise, assumed to be a random error term with a mean of 0 and a variance of σ 2 .

[0120] In the present invention:

[0121] To make the time series V(t) of the terrain feature point stationary, first perform a differencing operation, and apply (1 - B) d to calculate the incremental change ΔV(t): ΔV(t) = V(t) - V(t - 1);

[0122] The autoregressive part predicts the current value through the previous p lag eigenvalues. If p = 2, the autoregressive part is:

[0123] The moving average part θ(B) corrects the current predicted value based on the previous q lag error terms ∈(t). If q = 2, the moving average part is: θ(B)·∈(t) = ∈(t)+θ1∈(t - 1)+θ2∈(t - 2);

[0124] Finally, combining autoregression and moving average, a complete ARIMA model is obtained for predicting the terrain feature value V(t + Δt) at the future time t + Δt:

[0125] S4 specifically includes:

[0126] S41, fusing the optimized seabed terrain topology structure model G = (N, E) with the spectral feature vector V i generated by the multibeam echo signal. During the fusion, the spatial position of each node N i is matched with its corresponding spectral feature vector V i to obtain a node set {N i , V i}: {N i , V i} = {(N1, V1), (N2, V2), …, (N n , V n ));

[0127] S42, based on the node information in the seabed topology structure model and combining the spectral information in the spectral feature vector, correct the sounding data in the multibeam echo signal. By calculating the spectral feature difference ΔV ij and the sounding difference Δd ij , adjust the sounding data d i :

[0128] where is the adjusted sounding data, and λ ij is the adjustment coefficient, which is determined according to the spatial distance and spectral difference between nodes;

[0129] S43, perform interpolation processing on the corrected sounding data to grid the irregularly distributed sounding data and generate continuous seabed terrain data. Grid the sounding data into regular grid points G x，y , and based on the gridded seabed terrain data G x，y , generate a marine surveying and mapping map. Through methods such as contour line drawing and three-dimensional surface reconstruction, convert the sounding data into a visual seabed topographic map. The generated surveying and mapping map can include isobath maps or three-dimensional topographic maps at different depth levels to intuitively display the seabed terrain structure.

[0130] In the interpolation process in S43, the Kriging interpolation algorithm is used to grid the bathymetric data into regular grid points G x，y , which is expressed as: where λ i is the interpolation weight and depends on the distance between the bathymetric point and the grid point.

[0131] As Figure 2 shown, an ocean mapping system based on a multibeam bathymetric radar for implementing the above-mentioned ocean mapping method based on a multibeam bathymetric radar includes the following modules:

[0132] Multibeam bathymetric radar module: used to emit multiple acoustic beams and receive the echo signals reflected from the seabed to obtain the initial bathymetric data under different depth and terrain conditions;

[0133] Real-time signal acquisition and analysis module: monitors and analyzes the echo signals collected by the bathymetric radar in real time, adjusts the beam emission angle and frequency according to the seabed terrain complexity and detection depth to ensure high-precision echo signal acquisition;

[0134] Multi-level spatial spectrum analysis module: through multi-level spatial spectrum analysis, performs spectrum analysis and data compression on the collected echo signals, generates spectrum feature vectors to reduce data redundancy and retain key spatial frequency information;

[0135] Seabed terrain topological structure model module: constructs a seabed terrain topological structure model based on the spectrum feature vectors, combines time series analysis to dynamically predict terrain changes, and identifies and corrects measurement errors;

[0136] Bathymetric data processing and optimization module: uses the topological structure model and spectrum feature vectors to process and correct the bathymetric data, optimizes the bathymetric results by adjusting coefficients to improve mapping accuracy;

[0137] Ocean mapping chart generation module: interpolates and grids the processed bathymetric data to generate an ocean mapping chart, and displays the terrain structures at different depth levels through a visualization tool to provide accurate terrain information for ocean research and development.

[0138] The present invention covers any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of the present invention. To enable the public to have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, and those skilled in the art can fully understand the present invention without these detailed descriptions. Additionally, well-known methods, processes, procedures, components, and circuits are not described in detail to avoid unnecessary confusion to the essence of the present invention.

[0139] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A marine mapping method based on a multi-beam sounding radar, characterized in that, It includes the following steps: S1. Obtain the echo signals of the multi-beam bathymetric radar in real time. According to the seabed terrain characteristics and the radar detection depth, adjust the beam emission angle and emission frequency to ensure that the radar can obtain multi-beam echo signals under different depths and terrain conditions; S2. Build a multi-level spatial spectrum analysis model to perform spectrum analysis and data compression on the collected multi-beam echo signals. Combine sparse representation and multi-scale decomposition techniques to convert multi-beam data into spectral feature vectors ; S3, Dynamic Prediction and Optimization Based on the Topological Structure of Seabed Topography: Based on the Spectral Feature Vector , establish a seabed topography topological structure model, dynamically predict the bathymetric data in complex seabed topography areas in combination with the terrain change trend, identify areas with potential measurement errors, and optimize the seabed topography topological structure model based on the identified areas with potential measurement errors; S4. According to the optimized seabed terrain topology structure model, combined with the spectral feature vectors of the multi-beam echo signals, process the bathymetric data to generate a marine surveying and mapping map.

2. The marine mapping method based on a multi-beam sounding radar according to claim 1, characterized in that The sound waves emitted by the multi-beam bathymetric radar reflect multi-beam echo signals after encountering the seabed terrain, and record the intensity, time delay of the multi-beam echo signals and their corresponding beam angles; Transmit the collected multi-beam echo signals to the dynamic environment analysis unit, and combine the seabed terrain complexity data and detection depth information at the current measurement position to analyze the seabed terrain characteristics, including seabed slope, protrusion, and depression characteristics.

3. The marine mapping method based on a multi-beam sounding radar according to claim 2, characterized in that, In S1, the adaptive beam control algorithm is used to automatically adjust the beam emission angle and emission frequency of the multi-beam bathymetric radar according to the seabed terrain feature analysis results and the radar detection depth. Specifically, it includes: S11. Beam angle adjustment: Dynamically adjust the beam emission angle according to the slope of the terrain and the seabed undulation to ensure that each beam covers different parts of the seabed; S12. Emission frequency adjustment: Dynamically adjust the emission frequency according to the radar detection depth and the sound velocity characteristics of the water body. Use a high frequency in the shallow water area to improve the resolution, and use a low frequency in the deep water area to increase the detection depth; S13. Through a real-time feedback control loop, continuously monitor the quality of the echo signals. If the quality of the echo signals does not meet the preset accuracy requirements, automatic secondary adjustment will be performed to optimize the beam angle and emission frequency.

4. The marine mapping method based on a multi-beam sounding radar according to claim 1, characterized in that, S2 specifically includes: S21. Perform spatial spectrum analysis on the collected multi-beam echo signals, construct a multi-level spectrum analysis model, extract the spectrum information at each spatial scale by performing spectrum transformation on the echo signals at different spatial scales. The spatial scale The corresponding spectral component is , which is expressed as: , where is the signal component at different spatial scales, is the Fourier transform for converting the time-domain signal into the frequency-domain signal; S22. Application of sparse representation technology: On the basis of spectral analysis, use sparse representation technology to compress the spectral components and convert redundant information into sparse features; S23. Combination of multi-scale decomposition technology: Based on the multi-scale decomposition technology of wavelet transform, perform multi-scale decomposition on the echo signals, and extract the high-frequency and low-frequency components at different scales to describe the spatial characteristics of the seabed terrain; S24, Generation of spectral feature vectors: Integrate the spectral information after sparse representation and multi-scale decomposition to generate spectral feature vectors for describing the seabed topography. The final spectral feature vectors are expressed as a combination of the sparse representation and multi-scale decomposition results at each scale: ; wherein, is the sparse feature at different spatial scales, is the wavelet coefficient obtained by multi-scale decomposition.

5. The marine surveying and mapping method based on a multi-beam sounding radar according to claim 1, characterized in that, The establishment of the seabed terrain topological structure model based on the spectral feature vector in S3 specifically includes: S31. Define nodes: Each spectral feature vector corresponds to a feature point in the seabed terrain, and this feature point is defined as a node in the model , the spectral feature vector includes spatial frequency information and the results of sparse representation and multi-scale decomposition; S32, Build edges: Connect the nodes of adjacent feature points with edges The weight of the edge is represented by the Euclidean distance between the nodes ; S33, Model Generation: All nodes and their adjacent nodes are connected through edges to generate a seabed terrain topological structure model representing the entire seabed terrain. Among them, represents a node, and represents an edge. This model represents the spatial distribution and characteristic relationship of the seabed terrain through the relationship between nodes and edges.

6. The marine mapping method based on a multi-beam bathymetric radar according to claim 5, wherein In S3, the dynamic prediction of the bathymetric data in complex seabed terrain areas is specifically carried out in combination with the terrain change trend, including: S34. Use the spectral feature vectors measured at multiple time points as input to construct a time series analysis model. Adopt the autoregressive moving average model time series prediction method to analyze the trend of topographic feature points changing over time, and obtain the change amount of spectral feature vectors over time ; Predict the feature vector at a future time based on historical data and current data and the corresponding position change of the node ; S35. Dynamically predict complex terrain areas based on predicted terrain changes, and identify potential measurement error areas. The identification of potential measurement error areas is achieved by comparing the predicted positions with the actual measured positions to calculate the deviation . It also includes the identification of potential measurement error areas. If it exceeds the preset error threshold , then mark this node and its adjacent areas as potential measurement error areas.

7. The marine mapping method based on a multi-beam bathymetric radar according to claim 6, characterized in that, S3 also includes correcting and optimizing the model for the identified potential measurement error areas. The correction includes data correction for the nodes marked as error areas by interpolation method; Update the corrected node values to the topology model and recalculate the edge weights of the error region .

8. The marine surveying and mapping method based on a multi-beam bathymetric radar according to claim 6, wherein, S4 specifically includes: S41, the optimized undersea terrain topological structure model is fused with the spectral feature vectors generated by the multi-beam echo signals During the fusion, the spatial position of each node is matched with its corresponding spectral feature vector to obtain a node set including terrain information and spectral features ; S42. Based on the node information in the seabed topology structure model, the bathymetric data in the multibeam echo signal is corrected by combining the spectral information in the spectral feature vector. By calculating the spectral feature differences between nodes and bathymetric differences , the bathymetric data is adjusted : , where is the adjusted sounding data, is the adjustment coefficient; S43. For the corrected sounding data perform interpolation processing to grid the irregularly distributed sounding data and generate continuous seabed terrain data, and grid the sounding data into regular grid points , based on the gridded seabed terrain data , generate a marine surveying and mapping chart.

9. The marine surveying and mapping method based on a multi-beam sounding radar according to claim 8, wherein The interpolation processing in S43 uses the Kriging interpolation algorithm to grid the sounding data into regular grid points , which is expressed as: , where is the interpolation weight.

10. An ocean mapping system based on a multi-beam sounding radar, for implementing the ocean mapping method based on a multi-beam sounding radar according to any one of claims 1-9, characterized in that, It includes the following modules: Multi-beam bathymetric radar module: Used to emit multiple sound beams and receive the echo signals reflected from the seabed, and obtain the initial bathymetric data under different depths and terrain conditions; Real-time signal acquisition and analysis module: Real-time monitor and analyze the echo signals collected by the bathymetric radar, and adjust the beam emission angle and frequency according to the seabed terrain complexity and detection depth to ensure high-precision echo signal acquisition; Multi-level spatial spectrum analysis module: Through multi-level spatial spectrum analysis, perform spectrum analysis and data compression on the collected echo signals to generate spectral feature vectors, so as to reduce data redundancy and retain key spatial frequency information; Submarine Terrain Topological Structure Model Module: Construct a submarine terrain topological structure model based on spectral feature vectors, dynamically predict terrain changes by combining time series analysis, and identify and correct measurement errors; Bathymetric Data Processing and Optimization Module: Use the topological structure model and spectral feature vectors to process and correct bathymetric data, optimize the bathymetric results by adjusting coefficients to improve mapping accuracy; Marine Surveying and Mapping Chart Generation Module: Interpolate and grid the processed bathymetric data to generate marine surveying and mapping charts, and display the terrain structures at different depth levels through visualization tools.

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