Sediment deposition monitoring method and system based on underwater acoustic wave detection

Abstract

The present application relates to the technical field of acoustic wave detection, and more particularly to a silt accumulation monitoring method and system based on underwater acoustic wave detection. The method obtains underwater environment sensor data of a target area, fuses sonar echo signals and water flow velocity vectors, and establishes an underwater topographic model. A particle filtering algorithm is used to process uncertain factors, update the propagation path, extract key node coordinates, and determine beam width adjustment parameters. According to the width adjustment scheme and the turbulence intensity index, a gradient descent algorithm is used to optimize the beam width configuration, generate acoustic wave emission control signals, obtain echo feedback data, and update the underwater topographic model. The corrected environmental representation is used to re-predict the propagation path, generate a navigation control sequence, and ultimately generate an acoustic wave detection instruction set based on the sequence to guide the acoustic wave emission device to accurately monitor the target silt layer. Through multi-step collaborative optimization, the real-time performance, stability and accuracy of underwater silt accumulation monitoring are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of acoustic detection technology, and in particular to a method and system for monitoring sediment deposition based on underwater acoustic detection. Background Technology

[0002] Underwater sediment deposition monitoring is of great significance in environmental research and resource management of rivers, lakes, and oceans. Sediment accumulation not only affects the flow of waterways but can also lead to ecological changes and navigation safety issues. Therefore, accurate and real-time monitoring of the distribution and changes in underwater sediment layers is a crucial task in hydrological environmental research and water conservancy project management.

[0003] Traditional methods for monitoring sediment deposition typically rely on underwater sampling and manual detection, which are often time-consuming and labor-intensive, resulting in low timeliness and spatial resolution. With the development of sonar technology and underwater detection equipment, acoustic detection methods are increasingly being applied to underwater sediment monitoring. Underwater acoustic detection, by emitting sound pulses and receiving echo signals, can acquire real-time information about the bottom of the water body without direct contact. However, existing technologies still face challenges such as low path prediction accuracy and limited detection precision when dealing with complex environmental factors such as water flow disturbances, changes in turbulence intensity, and topographic obstruction.

[0004] Therefore, overcoming these problems and improving the robustness and real-time performance of underwater acoustic detection systems through accurate path estimation and beamwidth adjustment techniques has become a key issue in the development of underwater sediment deposition monitoring methods. This invention proposes a sediment deposition monitoring method based on underwater acoustic detection. By fusing multiple real-time data sources and employing advanced particle filtering and gradient descent algorithms, it addresses uncertainties in the underwater environment, optimizes the prediction accuracy of the acoustic propagation path, and improves the beamwidth configuration, thereby enhancing the accuracy and efficiency of underwater sediment layer detection. Summary of the Invention

[0005] This invention provides a method and system for monitoring sediment deposition based on underwater acoustic detection, which is used to ensure the accuracy and efficiency of underwater sediment detection.

[0006] In a first aspect, the present invention provides a method for monitoring sediment deposition based on underwater acoustic detection, the method comprising:

[0007] Step S1: Acquire underwater environmental sensor data of the target area, fuse sonar echo signals and water flow velocity vectors, determine turbulence intensity index and terrain obstruction distribution, and obtain underwater terrain model;

[0008] Step S2: Calculate the propagation path of the sound wave for the underwater terrain model, use the particle filter algorithm to handle uncertainties, and update the propagation path estimate; extract the coordinates of key nodes from the propagation path estimate, determine the beamwidth adjustment parameters of the sound wave, and obtain the beamwidth adjustment scheme;

[0009] Step S3: Based on the width adjustment scheme, the water flow velocity vector and turbulence intensity index are fused, and the beamwidth configuration is optimized using a gradient descent algorithm; through the beamwidth configuration and the propagation path estimation, an acoustic wave emission control signal is generated, and echo feedback data is obtained; the underwater terrain model is updated based on the echo feedback data to obtain the corrected environmental representation.

[0010] Step S4: Obtain the updated turbulence intensity index and terrain occlusion data from the corrected environmental representation, re-predict the propagation path, and obtain the navigation control sequence;

[0011] Step S5: Generate an acoustic wave detection command set according to the navigation control sequence and the beamwidth configuration, and control the acoustic wave emitting device based on the command set to monitor the target sediment layer.

[0012] As a preferred embodiment of the present invention, step S1, obtaining an underwater terrain model, includes:

[0013] Sonar echo signals are acquired from sonar equipment to determine the sound wave propagation time and intensity; water flow velocity vectors are acquired from water flow sensors to calculate the water flow direction and velocity components; based on the sound wave propagation time and intensity, combined with the water flow direction and velocity components, a turbulence intensity index is calculated; terrain reflection characteristics are analyzed using the sonar echo signals to determine the terrain obstruction distribution; the turbulence intensity index and the terrain obstruction distribution are fused to construct a real-time underwater terrain model, wherein the underwater terrain model includes terrain height distribution and turbulence region distribution.

[0014] As a preferred embodiment of the present invention, step S2 involves using a particle filter algorithm to handle uncertainties and update the propagation path estimate, including:

[0015] Initialize a particle set, which represents the possible states of the propagation path; calculate the weight of each particle according to the underwater terrain model; if the predicted trajectory deviation of the particle exceeds a preset threshold, adjust the weight distribution of the particle; resample the particle set according to the adjusted weight distribution and update the predicted trajectory of the propagation path; obtain an optimized propagation path estimate by iteratively updating the particle set, wherein the propagation path estimate includes path coordinates and direction vectors.

[0016] As a preferred embodiment of the present invention, step S2 yields a width adjustment scheme, including:

[0017] Key node coordinates are extracted from the propagation path estimation, where the key node coordinates represent turning points on the propagation path; beamwidth requirement indicators at the corresponding nodes are obtained based on the key node coordinates; energy concentration areas are determined by calculating the energy density distribution; beamwidth adjustment parameters are calculated by fusing the energy density distribution and density gradient distribution; and a preliminary beamwidth adjustment scheme is generated based on the beamwidth adjustment parameters, wherein the beamwidth adjustment scheme includes the beamwidth value for each key node.

[0018] As a preferred embodiment of the present invention, step S3 involves optimizing the beamwidth configuration using a gradient descent algorithm, including:

[0019] According to the beamwidth adjustment scheme, the initial beamwidth parameters are obtained; the water flow velocity vector and turbulence intensity index are fused to calculate the gradient direction for beamwidth adjustment; the beamwidth parameters are iteratively updated using a gradient descent algorithm; if the energy balance index after iteration does not meet the standard, the step size and convergence threshold of gradient descent are adjusted; through multiple iterations, the final beamwidth configuration is obtained, wherein the beamwidth configuration includes optimized beamwidth values ​​and energy distribution parameters.

[0020] As a preferred embodiment of the present invention, step S3 involves updating the underwater terrain model to obtain a corrected environmental representation, including:

[0021] Based on the beamwidth configuration and the propagation path estimation, an acoustic wave emission control signal is generated; the acoustic wave emission control signal is sent through an acoustic wave emission device to obtain echo feedback data; based on the echo feedback data, the matching degree with the target sediment layer is calculated; if the matching degree is lower than a preset threshold, deviation features are extracted from the echo feedback data; based on the deviation features, the local details of the underwater topography model are updated to obtain a corrected environmental representation.

[0022] As a preferred embodiment of the present invention, step S4, obtaining the navigation control sequence, includes:

[0023] The updated turbulence intensity index and terrain occlusion data are extracted from the corrected environmental representation; a new particle set is initialized based on the updated turbulence intensity index and terrain occlusion data; a new propagation path trajectory is predicted using a particle filtering algorithm; the propagation path trajectory is adjusted through path deviation correction; and an enhanced navigation control sequence is generated based on the corrected propagation path trajectory and combined with sequence time updates, wherein the navigation control sequence includes path direction and speed commands.

[0024] As a preferred embodiment of the present invention, step S5, controlling the acoustic wave emitting device based on the instruction set to monitor the target sediment layer, includes:

[0025] By integrating the navigation control sequence and the beamwidth configuration, a sound wave detection command set is generated; the sound wave transmitting device is driven to transmit detection signals through the sound wave detection command set; the coverage deviation of the target sediment layer is calculated based on the echo data after transmission; if the coverage deviation exceeds a preset threshold, trajectory smoothing data is extracted from the coordinates of key nodes; the sound wave detection command set is updated based on the trajectory smoothing data to achieve monitoring of the target sediment layer.

[0026] Secondly, the present invention also provides a sediment deposition monitoring system based on underwater acoustic detection for implementing the above-mentioned method, the system comprising:

[0027] The environmental data acquisition unit is used to acquire underwater environmental sensor data of the target area, fuse sonar echo signals and water flow velocity vectors, determine turbulence intensity indicators and terrain obstruction distribution, and obtain an underwater terrain model.

[0028] The propagation path estimation unit is used to calculate the propagation path of the sound wave for the underwater terrain model, and uses a particle filtering algorithm to handle uncertainties and update the propagation path estimation; it extracts the coordinates of key nodes from the propagation path estimation, determines the beamwidth adjustment parameters of the sound wave, and obtains the beamwidth adjustment scheme.

[0029] The beam configuration optimization unit is used to optimize the beam width configuration by fusing the water flow velocity vector and turbulence intensity index according to the width adjustment scheme and using a gradient descent algorithm; it generates an acoustic wave emission control signal by means of the beam width configuration and the propagation path estimation, and obtains echo feedback data; it updates the underwater terrain model according to the echo feedback data to obtain a corrected environmental representation.

[0030] The path correction prediction unit is used to obtain updated turbulence intensity indices and terrain occlusion data from the corrected environmental representation, re-predict the propagation path, and obtain the navigation control sequence.

[0031] The detection command control unit is used to generate a sound wave detection command set according to the navigation control sequence and the beamwidth configuration, and control the sound wave emitting device to monitor the target sediment layer based on the command set.

[0032] Thirdly, the present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the above-described method.

[0033] The beneficial effects of this invention are as follows:

[0034] This invention extracts updated turbulence intensity indices and terrain occlusion data from a corrected underwater topography model, forming a fundamental dataset that accurately reflects the current underwater environment. This provides strong data support for subsequent path prediction and beamwidth adjustment. Using a particle filter algorithm, a particle set is initialized based on this updated data to predict new propagation paths. Furthermore, through state transition models and likelihood calculations, the particle filter algorithm effectively handles the influence of dynamic factors such as turbulence, improving the robustness and accuracy of path prediction. When there is a deviation between the predicted trajectory and the actual echo data, the path deviation correction step uses methods such as least squares to finely adjust the trajectory, ensuring path accuracy. The corrected path will generate a precise navigation control sequence through a time-series update mechanism. The path direction and velocity requirements at each time point are dynamically adjusted to adapt to changes in water flow, ensuring... Acoustic signals can propagate along the optimal path, thereby improving the coverage accuracy of sediment layers. During the transmission of acoustic signals, the generated acoustic detection command set activates the control module of the transmitting device to transmit signals, and the target sediment layer is detected in real time by receiving echo data. If the coverage deviation exceeds a preset threshold, the path and beamwidth are adjusted based on deviation feature extraction, Kalman filtering smoothing, and turbulence intensity correction techniques to ensure that the detection signal accurately covers the target area. Through the cooperation of the above technical solutions, namely, path prediction through particle filtering algorithm, path optimization through deviation correction, ensuring navigation accuracy through time-series updates, and adapting to the dynamic underwater environment by adjusting beamwidth and path trajectory in real time, the stability, accuracy, and real-time performance of underwater acoustic detection are greatly improved, providing reliable technical support for sediment deposition monitoring. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart of the sediment deposition monitoring method based on underwater acoustic detection in the embodiment.

[0037] Figure 2 This is a flowchart of the beamwidth configuration optimization method in the embodiment;

[0038] Figure 3 This is a structural diagram of the sediment deposition monitoring system based on underwater acoustic detection in the embodiment. Detailed Implementation

[0039] This invention provides a method and system for monitoring sediment deposition based on underwater acoustic detection. The terms "first," "second," "third," "fourth," etc. (if applicable) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0040] For ease of understanding, the specific process of the embodiments of the present invention will be described below, such as... Figure 1 As shown, the sediment deposition monitoring method based on underwater acoustic detection in this embodiment of the invention includes:

[0041] Step S1: Acquire underwater environmental sensor data for the target area, fuse sonar echo signals and water flow velocity vectors, determine turbulence intensity indices and topographic obstruction distribution, and obtain an underwater topographic model; specifically including:

[0042] The sonar echo signal corresponding to the target area is obtained from the sonar device to determine the sound wave propagation time and intensity; the water flow velocity vector is obtained from the water flow sensor to calculate the water flow direction and velocity components; based on the sound wave propagation time and intensity, combined with the water flow direction and velocity components, the turbulence intensity index is calculated; the terrain reflection characteristics are analyzed through the sonar echo signal to determine the terrain obstruction distribution; the turbulence intensity index and the terrain obstruction distribution are fused to construct a real-time data representation of the underwater terrain model, wherein the underwater terrain model includes terrain height distribution and turbulence region distribution.

[0043] Specifically, in one embodiment, preliminary perception of the underwater environment is achieved by acquiring sonar echo signals corresponding to the target area from a sonar device and determining the sound wave propagation time and intensity. Specifically, the sonar device emits sound wave pulses to the target area and receives the returned echo signals. The propagation time of the sound waves is calculated by measuring the time interval between transmission and reception, and the signal intensity is determined by measuring the intensity of the echo signals. Simultaneously, a water flow velocity vector is acquired using a water flow sensor. The direction and velocity components of the water flow are calculated from this velocity vector. The water flow velocity vector data is acquired using a water flow sensor, such as an acoustic Doppler current meter. After vector decomposition, the velocity components and direction angles along the x, y, and z axes are obtained, providing crucial data for subsequent calculations of turbulence intensity and ensuring that the influence of flow in different directions and velocities on sound wave propagation is fully considered. Combined with the sound wave propagation... The study analyzes time and intensity, as well as the direction and velocity components of the water flow. Furthermore, it calculates turbulence intensity indices by temporally aligning propagation time and intensity data to form a fused dataset. The Reynolds average method is then used to calculate turbulent kinetic energy, which decomposes the velocity into mean and fluctuating components. The variance of the fluctuating components is used to estimate the turbulent kinetic energy, serving as an indicator of turbulence intensity. Additionally, the direction of the water flow is introduced to perform vector correction on the turbulent kinetic energy, ensuring that the calculated turbulence intensity accurately reflects the distribution of turbulence in three-dimensional space, thus providing effective support for the accurate construction of underwater terrain models. Finally, the study analyzes terrain reflection characteristics using sonar echo signals to determine terrain occlusion distribution. Specifically, by performing Fourier transform on the echo signals and extracting frequency features, steep terrain areas corresponding to high-frequency reflections can be identified, thus determining the terrain occlusion distribution and providing detailed occlusion feature data for terrain modeling.

[0044] After acquiring turbulence intensity indices and topographic occlusion distribution, a real-time underwater topographic model is constructed by fusing these data. This model comprises two main components: topographic height distribution and turbulence region distribution. During model construction, turbulence intensity indices are first mapped onto grid coordinates to form a distribution map of turbulent regions. Then, a weighted average method is used to fuse the topographic occlusion distribution and the turbulence map to calculate the topographic height distribution. The weighted average is based on a linear combination of the weights of each indices, ensuring that the underwater topographic model accurately reflects changes in water flow and their impact on the sound wave propagation path, while also improving the model's real-time update capability. This technical solution effectively addresses common problems in underwater detection, such as complex terrain and high turbulence intensity, thus providing accurate and reliable technical support for monitoring sediment deposition through underwater acoustic detection.

[0045] Step S2: Calculate the propagation path of the sound wave for the underwater terrain model, use the particle filter algorithm to handle uncertainties, and update the propagation path estimate; extract the coordinates of key nodes from the propagation path estimate, determine the beamwidth adjustment parameters of the sound wave, and obtain the beamwidth adjustment scheme;

[0046] In step S2, the particle filter algorithm is used to handle uncertainties and update the propagation path estimate, including:

[0047] Initialize a particle set, which represents the possible states of the propagation path; calculate the weight of each particle according to the underwater terrain model; if the predicted trajectory deviation of the particle exceeds a preset threshold, adjust the weight distribution of the particle; resample the particle set according to the adjusted weight distribution and update the predicted trajectory of the propagation path; obtain an optimized propagation path estimate by iteratively updating the particle set, wherein the propagation path estimate includes path coordinates and direction vectors.

[0048] Specifically, in one embodiment, a particle filtering algorithm is used to handle uncertainties in underwater propagation path estimation. First, a particle ensemble is initialized, with each particle representing a possible state of the sound wave propagation path. Then, using real-time data from the underwater terrain model, such as sonar echo signals and water velocity vectors, the position and direction of each particle are randomly sampled to form an initial path prediction. This initialization of the particle ensemble ensures coverage of multiple possible propagation path states, providing diverse initial assumptions for subsequent path estimation. Furthermore, based on the acquired sonar echo signals, current velocity data, and terrain model, the weight of each particle is calculated. The weight calculation is based on the degree of matching between the particle path and the model data, and a likelihood function is used to measure the particle path... The path estimation is determined by the consistency between the predicted path and the environmental data, such as turbulence intensity and terrain occlusion characteristics. Particle weights are then derived to effectively select the most reliable path hypotheses. When the deviation between the predicted path and the actual propagation path of some particles exceeds a preset threshold, the particle weight distribution is adjusted for correction. Specifically, if the path deviation exceeds the threshold, the weight of particles with larger deviations is reduced, while the weight of particles that match the actual situation better is increased. This makes the particle set more accurately reflect the probability of the true path, ensuring that the path estimation can be dynamically adjusted based on real-time data in complex underwater environments. This avoids the impact of error accumulation on the accuracy of path estimation, optimizes the path estimation process, and improves the accuracy of subsequent optimizations.

[0049] To further improve the accuracy of path estimation, based on the adjusted particle weight distribution, an importance sampling method is used to resample the particle set, updating the particle positions and orientations to obtain a more accurate propagation path prediction. Through multiple iterations, the particle set gradually converges to the true propagation path. The optimized path includes accurate path coordinates and direction vectors, forming a stable propagation path. The update process of path coordinates and direction vectors is based on the continuous optimization of the particle filtering algorithm. By iteratively correcting deviations, the path estimation gradually approximates the actual situation, ultimately providing accurate path planning data for the navigation control system. The above technical solution, through iterative updating of the particle set, yields a propagation path estimate with high stability and accuracy. It can effectively cope with complex factors in the underwater environment, such as turbulence interference and terrain obstruction, providing high-precision path control for the detection of sediment deposition. This enables the underwater acoustic detection process to efficiently and accurately monitor the distribution of target sediment layers.

[0050] Further, in step S2, a width adjustment scheme is obtained, including:

[0051] Key node coordinates are extracted from the propagation path estimation, where the key node coordinates represent turning points on the propagation path; beamwidth requirement indicators at the corresponding nodes are obtained based on the key node coordinates; energy concentration areas are determined by calculating the energy density distribution; beamwidth adjustment parameters are calculated by fusing the energy density distribution and density gradient distribution; and a preliminary beamwidth adjustment scheme is generated based on the beamwidth adjustment parameters, wherein the beamwidth adjustment scheme includes the beamwidth value for each key node.

[0052] Specifically, in one embodiment, key node coordinates are extracted from the propagation path estimation to determine turning points on the path. These key nodes provide crucial positioning data for underwater detection. Specifically, by analyzing the propagation path trajectory, points with significant changes in path curvature are identified as turning points, thereby extracting the corresponding key node coordinates. These key node coordinates provide important information about the path, enabling accurate calculation and adjustment of subsequent beamwidth requirements. Based on these key node coordinates, relevant turbulence intensity and terrain obstruction data are further obtained from the underwater terrain model. Combined with information such as water flow velocity vectors, the beamwidth requirements at each node are calculated, providing data support for beam configuration. Furthermore, the beamwidth requirements on the propagation path are calculated... The energy density distribution is used to determine the energy concentration area. Specifically, by analyzing the distribution of acoustic energy along the path using energy density calculation methods, areas with high energy concentration, such as those with high turbulence intensity or significant topographic changes, are identified. These areas are typically the focus of attention, helping to optimize acoustic wave propagation and detection coverage. Based on this, energy density distribution and density gradient distribution are fused, and weighted averaging and other calculation methods are used to obtain parameters for beamwidth adjustment. This ensures that beamwidth adjustment can adapt to different underwater environments and detection needs. The density gradient distribution, by calculating the rate of change of energy density, identifies density changes in areas with high turbulence intensity, and, combined with energy density information, further adjusts the beam configuration.

[0053] Based on the fused energy density distribution and density gradient distribution, a preliminary beamwidth adjustment scheme is generated. This involves applying beamwidth adjustment parameters to assign appropriate beamwidth values ​​to each key node, ensuring that the propagation of sound waves more accurately covers the target sediment layer during underwater detection. This avoids energy waste or detection blind spots caused by excessively wide or narrow beams. This technical solution allows the beamwidth adjustment scheme to better adapt to complex underwater environments, optimizing the coverage and accuracy of sound wave detection. This provides accurate data support for subsequent sediment deposition monitoring, overcoming problems such as inaccurate path estimation and unreasonable beamwidth adjustment in traditional underwater detection methods.

[0054] Step S3: Based on the width adjustment scheme, the water flow velocity vector and turbulence intensity index are fused, and the beamwidth configuration is optimized using a gradient descent algorithm; through the beamwidth configuration and the propagation path estimation, an acoustic wave emission control signal is generated, and echo feedback data is obtained; the underwater terrain model is updated based on the echo feedback data to obtain the corrected environmental representation.

[0055] In step S3, the gradient descent algorithm is used to optimize the beamwidth configuration, such as... Figure 2 As shown, it includes:

[0056] According to the beamwidth adjustment scheme, the initial beamwidth parameters are obtained; the water flow velocity vector and turbulence intensity index are fused to calculate the gradient direction for beamwidth adjustment; the beamwidth parameters are iteratively updated using a gradient descent algorithm; if the energy balance index after iteration does not meet the standard, the step size and convergence threshold of gradient descent are adjusted; through multiple iterations, the final beamwidth configuration is obtained, wherein the beamwidth configuration includes optimized beamwidth values ​​and energy distribution parameters.

[0057] Specifically, in one embodiment, a gradient descent algorithm is used to optimize the beamwidth configuration. First, based on the beamwidth adjustment scheme, initial beamwidth parameters are obtained. These parameters are directly determined by the key node coordinates extracted from the optimized propagation path estimation and used as initial values ​​for subsequent calculations. The key node coordinates reflect the inflection points on the path, helping to further determine the corresponding beamwidth requirements. The gradient direction for beamwidth adjustment is calculated by fusing the water flow velocity vector and turbulence intensity index. The water flow velocity vector and turbulence intensity index are obtained by fusing data from a real-time underwater terrain model. The water flow velocity vector is decomposed into horizontal and vertical components, while the turbulence intensity index reflects the impact of water flow disturbance on beam propagation. These data are used together to calculate a comprehensive interference factor, representing the effect of water flow changes on beamwidth. The effects of the adjustment are analyzed to derive the gradient direction. Using this gradient direction and the gradient descent algorithm, the beamwidth parameter is iteratively updated. In each iteration, the beamwidth parameter is adjusted based on the calculated gradient direction, gradually moving along the negative gradient direction to optimize the beamwidth configuration. This ensures the beamwidth configuration can adapt to complex underwater environments and reduces beam energy loss. If the energy balance index after iteration does not meet the predetermined standard, further optimization is achieved by adjusting the step size and convergence threshold of the gradient descent. The step size and convergence threshold are adaptively adjusted based on the degree of deviation in the energy balance index. If the energy balance is insufficient, the step size is increased to accelerate convergence, or the convergence threshold is decreased to improve accuracy. This ensures that the beamwidth configuration can converge stably during the iteration process in complex underwater environments.

[0058] Through multiple iterations, the final beamwidth configuration includes optimized beamwidth values ​​and energy distribution parameters, reflecting the adaptability of beamwidth adjustment to underwater topography and the optimization of energy distribution. This enables precise coverage of the target area, especially for the detection of sediment layers. Furthermore, the final generated beamwidth configuration will be used to generate acoustic wave transmission control signals, ensuring the effective propagation of acoustic waves in the underwater environment and improving the accuracy and efficiency of underwater detection. The above technical solution, through beamwidth optimization, not only effectively reduces energy loss but also enhances the robustness and real-time performance of the detection system under complex environments such as different water flow velocities, turbulence intensities, and terrain obstructions, meeting the high-precision requirements of sediment deposition monitoring.

[0059] Further, in step S3, the underwater terrain model is updated to obtain a corrected environmental representation, including:

[0060] Based on the beamwidth configuration and the propagation path estimation, an acoustic wave emission control signal is generated; the acoustic wave emission control signal is sent through an acoustic wave emission device to obtain echo feedback data; based on the echo feedback data, the matching degree with the target sediment layer is calculated; if the matching degree is lower than a preset threshold, deviation features are extracted from the echo feedback data; based on the deviation features, the local details of the underwater topography model are updated to obtain a corrected environmental representation.

[0061] Specifically, in one embodiment, an acoustic wave transmission control signal is generated based on beamwidth configuration and propagation path estimation. Specifically, the beamwidth adjustment parameters in the beamwidth configuration and the key node coordinates in the propagation path estimation are first fused to calculate the initial amplitude and phase distribution of the acoustic signal. The initial amplitude is determined using an energy density distribution formula, while the phase is based on the temporal update of the path trajectory, thus ensuring that the generated acoustic signal can adapt to dynamic changes in complex underwater environments and improving detection accuracy. The generated control signal is received by an acoustic wave transmitting device and converted into an electroacoustic signal, which is then transmitted underwater. The receiving array captures the reflected echoes, forming a feedback data sequence. After obtaining the echo feedback data, the matching degree with the target sediment layer is calculated based on this data. Intensity and time delay features are extracted from the echo feedback data, where intensity is calculated by integrating the echo energy, and time delay is obtained based on the signal propagation time difference. The extracted features are then correlated with reference features of a pre-stored sediment layer model to obtain a matching degree value, specifically using the Pearson correlation coefficient formula. The similarity between echo data and the target sediment layer is calculated to effectively identify the impact of topographic occlusion in complex underwater environments and improve matching accuracy, especially when encountering dynamic factors such as water flow and turbulence. This allows for real-time quantification of deviations, avoiding invalid detections. The pre-stored sediment layer model is trained based on historical echo data and sediment layer-related features from underwater detection. If the calculated matching degree is lower than a preset threshold, deviation features are further extracted from the echo feedback data. These deviation features include intensity deviation and time delay deviation. Intensity deviation is calculated using the difference, while time delay deviation is obtained through time series alignment. These deviation features reveal the actual differences between the echo signal and the target sediment layer, especially in deep water environments with high turbulence, helping to capture subtle changes caused by topographic occlusion. A particle filtering algorithm is then used to adjust the weights of the deviation features. Through iterative updates of the state transition model and observation model, the accuracy of the deviation features is optimized, ensuring that feature extraction remains efficient and robust even in environments with significant water flow variations.

[0062] Based on the extracted deviation features, the local details of the underwater terrain model are updated to obtain a corrected environmental representation. Specifically, this involves mapping the deviation feature vector to the corresponding node positions of the underwater terrain model and using coordinate transformation to ensure that the application of the deviation is accurately aligned with specific terrain locations. The model parameters are iteratively adjusted using a gradient descent algorithm, calculating the gradient of the loss function and updating the parameters until convergence, to optimize terrain occlusion data and improve the representation of local details. Through multiple iterations, updated turbulence intensity indices and terrain distribution are generated, forming the corrected environmental representation. This provides more accurate terrain information for subsequent underwater exploration. The corrected environmental representation considers water flow velocity, turbulence intensity, terrain occlusion, and deviation features, ensuring that the underwater terrain model can adapt to dynamic changes in real time and provide accurate path planning and detection commands. This significantly improves the model's accuracy, especially in monitoring sediment deposition in complex underwater environments. It effectively reduces the impact of uncertainty on path estimation and beamwidth adjustment, ensuring accurate coverage of acoustic detection, reducing detection bias, and improving the system's real-time response capability.

[0063] Step S4: Obtain updated turbulence intensity indices and terrain occlusion data from the corrected environmental representation, re-predict the propagation path, and obtain the navigation control sequence; specifically including:

[0064] The updated turbulence intensity index and terrain occlusion data are extracted from the corrected environmental representation; a new particle set is initialized based on the updated turbulence intensity index and terrain occlusion data; a new propagation path trajectory is predicted using a particle filtering algorithm; the propagation path trajectory is adjusted through path deviation correction; and an enhanced navigation control sequence is generated based on the corrected propagation path trajectory and combined with sequence time updates, wherein the navigation control sequence includes path direction and speed commands.

[0065] Specifically, by extracting updated turbulence intensity indices and terrain occlusion data from the corrected environmental representation, the prediction and navigation control of underwater propagation paths are further optimized based on these updated indices and data. First, turbulence intensity indices and terrain occlusion data are obtained from the corrected environmental representation. Then, the data grid is traversed to identify changes in turbulence intensity and extract terrain occlusion data at corresponding coordinates. This data combination provides real-time feedback on the underwater environment, offering crucial information for subsequent path prediction. Based on the updated turbulence intensity indices and terrain occlusion data, a new particle set is initialized. This initial particle set is generated by randomly sampling using the turbulence intensity indices as weighting factors. Each particle represents a potential propagation path. The initial state of the path is determined to ensure that the particle swarm reflects the diversity of the path and takes into account the impact of turbulence intensity and terrain occlusion on the propagation path. In addition, by fusing terrain occlusion data, the initial distribution of the particle swarm is adjusted to avoid particles concentrating in high occlusion areas, thereby improving the accuracy and robustness of the prediction. The particle filtering algorithm updates the state of each particle based on the state transition model, iteratively calculates the likelihood value of each particle, combines the observed turbulence intensity and terrain occlusion data, resamples the particle swarm, and removes low-weight particles, finally giving a new estimate of the propagation path trajectory. This ensures that the path prediction can adapt to complex underwater environments and overcome the effects of turbulence and terrain occlusion. Bayesian filtering is also used to optimize the path trajectory and improve the accuracy of the path prediction.

[0066] To further optimize the propagation path, path deviation correction is performed. This involves calculating the deviation vector between the predicted path and the actual observed path, fitting the deviation curve using the least squares method, and iteratively correcting the trajectory point coordinates based on the deviation vector until the deviation falls below a set threshold, ensuring the path matches the actual environment. By comparing the actual path with the echo feedback data, the trajectory is dynamically adjusted to overcome changes and uncertainties in the underwater environment and improve sediment coverage efficiency. Combining the corrected propagation path trajectory with a time-series update mechanism, an enhanced navigation control sequence is generated. This involves decomposing the corrected propagation path trajectory into multiple time segments, calculating the path direction and velocity requirements for each segment, and ensuring path control... The navigation control sequence can adapt to changes in water flow in real time and accurately guide acoustic detection. It transforms the directional deviation and velocity requirements at each time point into specific instructions and continuously updates them using a recursive method. This ensures that the generated navigation control sequence can adapt to changes in complex underwater environments and provides stable navigation support, thereby improving the accuracy and efficiency of underwater sediment layer detection. The above technical solution, through multiple iterations to optimize the propagation path estimation, combined with dynamic adjustment and real-time updates, can not only effectively cope with the influence of water flow and turbulence, but also handle uncertainties such as terrain obstruction and path deviation. Ultimately, it generates an accurate navigation control sequence, ensuring path stability and accuracy during acoustic detection, thereby achieving precise sediment deposition monitoring.

[0067] Step S5: Based on the navigation control sequence and the beamwidth configuration, generate a sound wave detection command set, and control the sound wave emitting device based on the command set to monitor the target sediment layer; specifically including:

[0068] By integrating the navigation control sequence and the beamwidth configuration, a sound wave detection command set is generated; the sound wave transmitting device is driven to transmit detection signals through the sound wave detection command set; the coverage deviation of the target sediment layer is calculated based on the echo data after transmission; if the coverage deviation exceeds a preset threshold, trajectory smoothing data is extracted from the coordinates of key nodes; the sound wave detection command set is updated based on the trajectory smoothing data to achieve monitoring of the target sediment layer.

[0069] Specifically, an acoustic detection command set is generated by fusing navigation control sequences and beamwidth configurations. Path trajectory data is extracted from the navigation control sequences and combined with the beamwidth parameter in the beamwidth configuration. The path trajectory data and the beamwidth parameter are then vector-superimposed to generate a preliminary command sequence, thereby fusing the path trajectory and beamwidth configuration data to ensure the detection signal propagates along an optimized path and makes corresponding adjustments to the coverage area. Based on the initial command sequence, a time synchronization marker is incorporated to form a complete acoustic detection command set, enabling more precise signal transmission guidance during underwater detection and achieving preliminary positioning of the target sediment layer. The generated acoustic detection command set is then input into the control module of the acoustic transmitting device, activating the transmitting circuit and transmitting the acoustic signal into the underwater environment. The echo data after transmission is collected and used to calculate the coverage deviation of the target sediment layer. The process involves extracting intensity distribution and time delay characteristics from echo data, calculating the difference between echo intensity and the expected sediment layer reflection model, and quantifying the coverage deviation using the Euclidean distance formula if the difference exceeds a preset threshold. This process calculates the geometric distance between the echo point set and the target layer boundary point set, and obtains the corresponding deviation value. The deviation calculation result is then corrected using the turbulence intensity index to obtain the final coverage deviation, thereby optimizing the detection accuracy.

[0070] If the calculated coverage deviation exceeds a preset threshold, trajectory smoothing data is extracted from the key node coordinates. Specifically, it first determines whether the coverage deviation exceeds a set value, such as 5%. If so, a sequence of key node coordinates in the path is selected, and the selected coordinate sequence is smoothed using a Kalman filter algorithm. The estimation error is minimized through a recursive estimation method to obtain the smoothed trajectory, thereby effectively reducing path jitter, avoiding errors caused by turbulence and other factors, and improving path stability, especially in environments with significant water flow variations. A deviation correction vector is extracted from the smoothed trajectory as trajectory smoothing data, used to update the acoustic detection command set, thus achieving the target mud... Precise coverage of sand layers is achieved by integrating the corrected trajectory data into the original instruction set, adjusting the path trajectory parameters, recalculating the beamwidth configuration, and generating an updated instruction set. The updated instruction set drives the transmitting device to transmit, ensuring that the coverage deviation is less than a preset threshold, thereby achieving precise detection of sediment layers. This technical solution not only improves the real-time performance and accuracy of detection but also effectively reduces detection deviations caused by path errors or inappropriate beamwidth. Especially in complex underwater environments, such as riverbeds and lake bottoms with significant topographic changes, the acoustic detection system can adaptively adjust the path and beamwidth to ensure stable and reliable completion of sediment deposition monitoring tasks.

[0071] This invention also provides a sediment deposition monitoring system based on underwater acoustic detection, used to implement the above-mentioned method, such as... Figure 3 As shown, the system includes:

[0072] The environmental data acquisition unit is used to acquire underwater environmental sensor data of the target area, fuse sonar echo signals and water flow velocity vectors, determine turbulence intensity indicators and terrain obstruction distribution, and obtain an underwater terrain model.

[0073] The propagation path estimation unit is used to calculate the propagation path of the sound wave for the underwater terrain model, and uses a particle filtering algorithm to handle uncertainties and update the propagation path estimation; it extracts the coordinates of key nodes from the propagation path estimation, determines the beamwidth adjustment parameters of the sound wave, and obtains the beamwidth adjustment scheme.

[0074] The beam configuration optimization unit is used to optimize the beam width configuration by fusing the water flow velocity vector and turbulence intensity index according to the width adjustment scheme and using a gradient descent algorithm; it generates an acoustic wave emission control signal by means of the beam width configuration and the propagation path estimation, and obtains echo feedback data; it updates the underwater terrain model according to the echo feedback data to obtain a corrected environmental representation.

[0075] The path correction prediction unit is used to obtain updated turbulence intensity indices and terrain occlusion data from the corrected environmental representation, re-predict the propagation path, and obtain the navigation control sequence.

[0076] The detection command control unit is used to generate a sound wave detection command set according to the navigation control sequence and the beamwidth configuration, and control the sound wave emitting device to monitor the target sediment layer based on the command set.

[0077] The present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the above-described method.

[0078] In summary, this invention extracts updated turbulence intensity indices and terrain occlusion data from the corrected underwater terrain model, forming a fundamental dataset that accurately reflects the current underwater environment. This provides strong data support for subsequent path prediction and beamwidth adjustment. Using a particle filtering algorithm, a particle set is initialized based on this updated data to predict new propagation paths. Furthermore, through state transition models and likelihood calculations, the particle filtering algorithm effectively handles the influence of dynamic factors such as turbulence, improving the robustness and accuracy of path prediction. When there is a deviation between the predicted trajectory and the actual echo data, the path deviation correction step uses methods such as least squares to finely adjust the trajectory, ensuring path accuracy. The corrected path will then be updated through a time-series update mechanism to generate a precise navigation control sequence. In this system, the path direction and velocity requirements at each time point are dynamically adjusted to adapt to changes in water flow, ensuring that the acoustic signal can propagate along the optimal path, thereby improving the coverage accuracy of the sediment layer. During the transmission of the acoustic signal, the generated acoustic detection command set activates the control module of the transmitting device to transmit the signal, and the target sediment layer is detected in real time by receiving echo data. If the coverage deviation exceeds a preset threshold, the path and beamwidth are adjusted based on deviation feature extraction from the echo feedback data, Kalman filtering smoothing, and turbulence intensity correction techniques to ensure that the detection signal accurately covers the target area. Through the cooperation of the above technical solutions, the stability, accuracy, and real-time performance of underwater acoustic detection are greatly improved, providing a reliable technical guarantee for sediment deposition monitoring.

[0079] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0080] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0081] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring sediment deposition based on underwater acoustic wave detection, characterized by, The method comprises: Step S1: obtaining underwater environment sensor data of a target area, fusing sonar echo signals and flow velocity vectors, determining turbulence intensity indicators and terrain shelter distribution, and obtaining an underwater terrain model; Step S2: calculating a propagation path of a sound wave for the underwater terrain model, processing uncertain factors by using a particle filtering algorithm, updating the propagation path estimation, extracting key node coordinates from the propagation path estimation, determining a beam width adjustment parameter of the sound wave, and obtaining a width adjustment scheme; Step S3: according to the width adjustment scheme, fusing the flow velocity vectors and the turbulence intensity indicators, optimizing the beam width configuration by using a gradient descent algorithm, generating a sound wave emission control signal through the beam width configuration and the propagation path estimation, and obtaining echo feedback data, updating the underwater terrain model according to the echo feedback data, and obtaining a corrected environment representation; Step S4: obtaining updated turbulence intensity indicators and terrain shelter data from the corrected environment representation, re-predicting the propagation path, and obtaining a navigation control sequence; Step S5: generating a sound wave detection instruction set according to the navigation control sequence and the beam width configuration, and controlling a sound wave emission device based on the instruction set to realize monitoring of a target silt layer; In step S2, the particle filtering algorithm is used to process uncertain factors and update the propagation path estimation, which includes: initializing a particle set, the particle set representing possible states of the propagation path; calculating the weight of each particle according to the underwater terrain model; if the predicted trajectory deviation of the particle exceeds a preset threshold, adjusting the weight distribution of the particle; resampling the particle set according to the adjusted weight distribution, updating the predicted trajectory of the propagation path; and updating the particle set through iteration to obtain an optimized propagation path estimation, wherein the propagation path estimation includes path coordinates and direction vectors; In step S2, the width adjustment scheme is obtained, which includes: extracting key node coordinates from the propagation path estimation, the key node coordinates representing turning points on the propagation path; obtaining beam width demand indicators at corresponding nodes according to the key node coordinates; determining energy concentration regions by calculating energy density distribution; fusing the energy density distribution and density gradient distribution to calculate beam width adjustment parameters; and generating a preliminary width adjustment scheme according to the beam width adjustment parameters, wherein the width adjustment scheme includes beam width values of each key node; In step S3, the gradient descent algorithm is used to optimize the beam width configuration, which includes: obtaining initial beam width parameters according to the width adjustment scheme; fusing the flow velocity vectors and the turbulence intensity indicators to calculate the gradient direction of beam width adjustment; iteratively updating the beam width parameters by using the gradient descent algorithm; if the energy balance indicator after iteration does not meet the standard, adjusting the step size and convergence threshold of the gradient descent; and through multiple iterations, obtaining a final beam width configuration, wherein the beam width configuration includes optimized beam width values and energy distribution parameters.

2. The method of claim 1, wherein, In step S1, the underwater terrain model is obtained, which includes: The sonar echo signal is obtained from the sonar device to determine the sound wave propagation time and intensity; the water flow velocity vector is obtained from the water flow sensor to calculate the water flow direction and velocity component; the turbulence intensity index is calculated according to the sound wave propagation time and intensity, combined with the water flow direction and velocity component; the terrain reflection characteristics are analyzed through the sonar echo signal to determine the terrain shielding distribution; the turbulence intensity index and the terrain shielding distribution are fused to construct the underwater terrain model represented by real-time data, wherein the underwater terrain model includes the terrain height distribution and the turbulence region distribution.

3. The method of claim 1, wherein, In step S3, the underwater terrain model is updated to obtain a corrected environment representation, including: According to the beam width configuration and the propagation path estimation, a sound wave emission control signal is generated; the sound wave emission control signal is sent through the sound wave emission device to obtain echo feedback data; according to the echo feedback data, the matching degree with the target sediment layer is calculated; if the matching degree is lower than a preset threshold, the deviation features are extracted from the echo feedback data; according to the deviation features, the local details of the underwater terrain model are updated to obtain a corrected environment representation.

4. The method of claim 1, wherein, In step S4, a navigation control sequence is obtained, including: The updated turbulence intensity index and terrain shielding data are extracted from the corrected environment representation; according to the updated turbulence intensity index and terrain shielding data, a new particle set is initialized; a particle filtering algorithm is used to predict a new propagation path trajectory; the propagation path trajectory is adjusted through path deviation correction; according to the corrected propagation path trajectory, combined with sequence timing update, an enhanced navigation control sequence is generated, wherein the navigation control sequence includes path direction and speed instructions.

5. The method of claim 1, wherein, In step S5, the sound wave emission device is controlled based on the instruction set to monitor the target sediment layer, including: The navigation control sequence and the beam width configuration are fused to generate a sound wave detection instruction set; the sound wave detection instruction set is used to drive the sound wave emission device to emit a detection signal; according to the echo data after emission, the coverage deviation of the target sediment layer is calculated; if the coverage deviation exceeds a preset threshold, the trajectory smoothing processing data is extracted from the key node coordinates; according to the trajectory smoothing processing data, the sound wave detection instruction set is updated to monitor the target sediment layer.

6. A sedimentation monitoring system based on acoustic underwater detection for implementing the method according to any one of claims 1 to 5, characterized in that, The system includes: An environment data acquisition unit is configured to obtain underwater environment sensor data of a target area, fuse sonar echo signals and water flow velocity vectors, determine turbulence intensity index and terrain shielding distribution, and obtain an underwater terrain model; A propagation path estimation unit is configured to calculate the propagation path of sound waves for the underwater terrain model, process uncertainty factors using a particle filtering algorithm, update the propagation path estimation, extract key node coordinates from the propagation path estimation, determine the beam width adjustment parameters of sound waves, and obtain a width adjustment scheme; A beam configuration optimization unit is configured to fuse the water flow velocity vector and the turbulence intensity indicator according to the width adjustment scheme, and optimize the beam width configuration using a gradient descent algorithm; generate a sound wave emission control signal through the beam width configuration and the propagation path estimation, and obtain echo feedback data, update the underwater terrain model according to the echo feedback data, and obtain a corrected environment representation; A path correction and prediction unit is configured to obtain updated turbulence intensity indicators and terrain occlusion data from the corrected environment representation, re-predict a propagation path, and obtain a navigation control sequence; A detection instruction control unit is configured to generate a sound wave detection instruction set according to the navigation control sequence and the beam width configuration, and control the sound wave emission device to monitor the target sediment layer based on the instruction set.

7. A computer-readable storage medium having stored thereon instructions, the computer-readable storage medium comprising: The instructions are executed by the processor to implement the method of any one of claims 1-5.

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