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

Abstract

The invention relates to the technical field of sound wave detection, in particular to a sediment deposition monitoring method and system based on sound wave underwater detection. According to the method, underwater environment sensor data of a target area is obtained, sonar echo signals and water flow velocity vectors are fused, and an underwater terrain model is established; processing uncertain factors by adopting a particle filtering algorithm, updating a propagation path, extracting coordinates of key nodes, and determining beam width adjustment parameters; according to the width adjustment scheme and the turbulence intensity index, a gradient descent algorithm is adopted to optimize beam width configuration, a sound wave emission control signal is generated, echo feedback data is obtained, and the underwater terrain model is updated; a propagation path is predicted again according to the corrected environment representation, a navigation control sequence is generated, finally, a sound wave detection instruction set is generated based on the sequence, and a sound wave emission device is guided to achieve accurate monitoring of a target sediment layer. Through multi-step collaborative optimization, the real-time performance, the stability and the accuracy of underwater sediment deposition monitoring are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of acoustic wave detection, and in particular to a sediment deposition monitoring method and system based on underwater acoustic wave detection. BACKGROUND

[0002] Underwater sediment deposition monitoring is of great significance in the environmental research and resource management of water bodies such as rivers, lakes and oceans. The accumulation of sediment not only affects the dredging of water flow channels, but also may cause problems in ecological environment change, shipping safety, etc. Therefore, accurately and timely monitoring the distribution and changes of underwater sediment layer is a key task in hydrological environmental research and water conservancy engineering management.

[0003] Traditional sediment deposition monitoring methods usually rely on underwater sampling and manual detection, which are often time-consuming and labor-intensive, and have low timeliness and spatial resolution. With the development of sonar technology and underwater detection equipment, methods based on acoustic wave detection have been gradually applied to underwater sediment monitoring. Underwater acoustic wave detection can obtain real-time information of the bottom of the water body by emitting acoustic wave pulses and receiving echo signals without direct contact with the bottom. However, existing technologies still face challenges such as low path prediction accuracy and low detection accuracy in dealing with complex environmental factors such as flow disturbance, turbulence intensity changes and terrain shielding.

[0004] Therefore, how to overcome these problems, improve the robustness and real-time performance of the underwater acoustic wave detection system through accurate path estimation and beam width adjustment technology, has become a key problem in the development of underwater sediment deposition monitoring methods. The present application proposes a sediment deposition monitoring method based on underwater acoustic wave detection, which solves the uncertainty factors in the underwater environment by fusing multiple real-time data and using advanced particle filtering algorithm and gradient descent algorithm, optimizes the prediction accuracy of acoustic wave propagation path and the configuration of beam width, thereby improving the accuracy and efficiency of underwater sediment layer detection. SUMMARY

[0005] The present application provides a sediment deposition monitoring method and system based on underwater acoustic wave detection, which is used to ensure the accuracy and detection efficiency of the underwater sediment detection process.

[0006] In a first aspect, the present application provides a sediment deposition monitoring method based on underwater acoustic wave detection, which comprises: Step S1: Obtain underwater environmental sensor data of the target area, fuse sonar echo signals and flow velocity vectors, determine turbulence intensity indicators and terrain shielding distribution, and obtain an underwater terrain model; Step S2: Calculate the propagation path of the sound wave for the underwater terrain model, use the particle filter algorithm to process the uncertainty factor, update the propagation path estimate; extract the key node coordinates from the propagation path estimate, determine the beam width adjustment parameter of the sound wave, and obtain the width adjustment scheme; Step S3: According to the width adjustment scheme, fuse the flow velocity vector and the turbulence intensity index, and use the gradient descent algorithm to optimize the beam width configuration; generate the sound wave emission control signal through the beam width configuration and the propagation path estimate, and obtain the echo feedback data, update the underwater terrain model according to the echo feedback data, and obtain the corrected environment representation; Step S4: Obtain the updated turbulence intensity index and terrain shielding data from the corrected environment representation, re-predict the propagation path, and obtain the navigation control sequence; Step S5: According to the navigation control sequence and the beam width configuration, generate a set of sound wave detection instructions, and control the sound wave emission device based on the instructions to realize the monitoring of the target silt layer.

[0007] As a preferred technical solution of the present application, step S1, obtaining the underwater terrain model, comprises: Obtain the sonar echo signal from the sonar device, determine the sound wave propagation time and intensity; obtain the flow velocity vector from the flow sensor, calculate the flow direction and velocity component; calculate the turbulence intensity index according to the sound wave propagation time and intensity, combined with the flow direction and velocity component; analyze the terrain reflection characteristics through the sonar echo signal, determine the terrain shielding distribution; fuse the turbulence intensity index and the terrain shielding distribution, and construct the underwater terrain model represented by real-time data, wherein the underwater terrain model includes terrain height distribution and turbulence region distribution.

[0008] As a preferred technical solution of the present application, in step S2, the particle filter algorithm is used to process the uncertainty factor and update the propagation path estimate, comprising: Initialize the particle set, the particle set represents the possible state 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 the preset threshold, adjust the weight distribution of the particle; resample the particle set according to the adjusted weight distribution, update the predicted trajectory of the propagation path; update the particle set through iteration to obtain the optimized propagation path estimate, wherein the propagation path estimate includes path coordinates and direction vector.

[0009] As a preferred technical solution of the present application, in step S2, the width adjustment scheme is obtained, comprising: extracting a key node coordinate from the propagation path estimation, the key node coordinate representing a turning point on the propagation path; obtaining a beam width requirement index at a corresponding node according to the key node coordinate; determining an energy concentration region by calculating an energy density distribution; fusing the energy density distribution and a density gradient distribution to calculate a beam width adjustment parameter; and generating a preliminary width adjustment scheme according to the beam width adjustment parameter, wherein the width adjustment scheme comprises a beam width value of each key node.

[0010] As a preferred technical solution of the present application, in step S3, the gradient descent algorithm is used to optimize the beam width configuration, comprising: According to the width adjustment scheme, an initial beam width parameter is obtained; a gradient direction of beam width adjustment is calculated by fusing the water flow velocity vector and the turbulence intensity index; the gradient descent algorithm is used to iteratively update the beam width parameter; if the energy balance index after iteration does not meet the standard, the step size and convergence threshold of gradient descent are adjusted; and through multiple iterations, the final beam width configuration is obtained, wherein the beam width configuration comprises an optimized beam width value and an energy distribution parameter.

[0011] As a preferred technical solution of the present application, in step S3, the underwater terrain model is updated to obtain a revised environmental representation, comprising: 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 a sound wave emission device to obtain echo feedback data; a matching degree with the target sediment layer is calculated according to the echo feedback data; if the matching degree is lower than a preset threshold, a deviation feature is extracted from the echo feedback data; and the local details of the underwater terrain model are updated according to the deviation feature to obtain a revised environmental representation.

[0012] As a preferred technical solution of the present application, in step S4, a navigation control sequence is obtained, comprising: From the revised environmental representation, updated turbulence intensity index and terrain occlusion data are extracted; a new particle set is initialized according to 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 according to the corrected propagation path trajectory combined with sequence timing update, wherein the navigation control sequence comprises a path direction and a speed instruction.

[0013] As a preferred technical solution of the present application, in step S5, the sound wave emission device is controlled based on the instruction set to monitor the target sediment layer, comprising: 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 a sound wave emission device to emit a detection signal; a coverage deviation of a target silt layer is calculated according to echo data after emission; if the coverage deviation exceeds a preset threshold, trajectory smoothing processing data is extracted from the key node coordinates; the sound wave detection instruction set is updated according to the trajectory smoothing processing data, so that the target silt layer is monitored.

[0014] In a second aspect, the present application further provides a silt accumulation monitoring system based on underwater sound wave detection, which is used to implement the above method, and the system comprises: An environmental data acquisition unit is configured to acquire underwater environmental sensor data of a target area, fuse sonar echo signals and flow velocity vectors, determine a turbulence intensity index and a terrain sheltering distribution, and obtain an underwater terrain model. A propagation path estimation unit is configured to calculate a propagation path of a sound wave according to the underwater terrain model, process uncertain factors by using a particle filtering algorithm, update the propagation path estimation, extract key node coordinates from the propagation path estimation, determine a beam width adjustment parameter of the sound wave, and obtain a width adjustment scheme. A beam configuration optimization unit is configured to fuse the flow velocity vectors and the turbulence intensity index according to the width adjustment scheme, optimize the beam width configuration by using a gradient descent algorithm, generate a sound wave emission control signal through the beam width configuration and the propagation path estimation, acquire echo feedback data, update the underwater terrain model according to the echo feedback data, and obtain a revised environmental representation. A path correction and prediction unit is configured to acquire updated turbulence intensity index and terrain sheltering data from the revised environmental 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 a sound wave emission device to monitor a target silt layer based on the instruction set.

[0015] In a third aspect, the present application further provides a computer readable storage medium, which stores instructions, and the instructions are executed by a processor to implement the above method.

[0016] The present application has the following advantages: The application extracts updated turbulence intensity indicators and terrain shelter data from the corrected underwater terrain model, forms a basic data set that accurately reflects the current underwater environment, and provides strong data support for subsequent path prediction and beam width adjustment; using the particle filtering algorithm, the particle set is initialized based on the updated data and the new propagation path trajectory is predicted, and through the state transition model and the likelihood value calculation, the particle filtering algorithm can effectively handle the influence of dynamic factors such as turbulence, improve the robustness and accuracy of path prediction, and when there is a deviation between the predicted trajectory and the actual echo data, the path deviation correction step adjusts the trajectory through the least square method and the like to ensure the accuracy of the path; the corrected path will generate accurate navigation control sequences through the time sequence updating mechanism, and the path direction and speed requirement of each time sequence point is dynamically adjusted to adapt to the change of the water flow, ensuring that the sound wave signal can propagate along the optimal path, thereby improving the coverage accuracy of the silt layer; during the emission of the sound wave signal, the generated sound wave detection instruction set emits the signal by activating the control module of the emission device, and detects the target silt layer in real time through the received echo data; if the coverage deviation exceeds the preset threshold, the path and the beam width are adjusted based on the deviation feature extraction, Kalman filter smoothing processing and turbulence intensity correction of the echo feedback data, to ensure that the detection signal accurately covers the target area; through the mutual cooperation between the above technical solutions, that is, through the particle filtering algorithm for path prediction, through the deviation correction to optimize the path, through the time sequence updating to ensure the accuracy of navigation, and through the real-time adjustment of the beam width and the path trajectory to adapt to the dynamic underwater environment, the stability, accuracy and real-time performance of underwater sound wave detection are greatly improved, providing reliable technical support for silt accumulation monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor based on these drawings.

[0018] Figure 1 The flow chart of the silt accumulation monitoring method based on underwater sound wave detection in the embodiment; Figure 2 The flow chart of the beam width configuration optimization method in the embodiment; Figure 3 The structure diagram of the silt accumulation monitoring system based on underwater sound wave detection in the embodiment. DETAILED DESCRIPTION

[0019] The embodiment of the present application provides a silt accumulation monitoring method and system based on underwater acoustic wave detection. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" or "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0020] For ease of understanding, the specific flow of the embodiment of the present application is described as follows, as shown in the figure, the silt accumulation monitoring method based on underwater acoustic wave detection in the embodiment of the present application comprises: Figure 1 Step S1: obtaining underwater environment sensor data of a target area, fusing sonar echo signals and water flow velocity vectors, determining turbulence intensity indicators and terrain shelter distribution, and obtaining an underwater terrain model; specifically comprising: obtaining sonar echo signals corresponding to the target area from a sonar device, determining sound wave propagation time and intensity; obtaining water flow velocity vectors from a water flow sensor, calculating water flow direction and velocity component; calculating turbulence intensity indicators according to the sound wave propagation time and intensity, combining the water flow direction and velocity component; analyzing terrain reflection characteristics through the sonar echo signals, determining terrain shelter distribution; fusing the turbulence intensity indicators and the terrain shelter distribution, and constructing an underwater terrain model represented by real-time data, wherein the underwater terrain model comprises terrain height distribution and turbulence area distribution.

[0021] ​Specifically, in one embodiment, the preliminary perception of the underwater environment is achieved by obtaining the sonar echo signals corresponding to the target area from the sonar device, and determining the sound wave propagation time and intensity; that is, the sonar device transmits a sound wave pulse to the target area and receives the returned echo signal, the propagation time of the sound wave is calculated by measuring the time interval between transmission and reception, and the intensity of the signal is determined by measuring the intensity of the echo signal; at the same time, the flow velocity vector is obtained by the flow sensor, and the direction and velocity component of the flow are calculated by the flow velocity vector; wherein the flow velocity vector data is collected by the flow sensor, such as an acoustic Doppler current profiler, and after vector decomposition, the velocity components along the x, y, and z axes and the direction angle are obtained, thereby providing key data for subsequent calculation of the turbulence intensity, ensuring that the influence of flow in different directions and velocities on the propagation of sound waves is fully considered; the sound wave propagation time and intensity and the flow direction and velocity component are combined; the turbulence intensity index is further calculated, specifically by time series alignment of the propagation time and intensity data to form a fusion data set, and the Reynolds average method is used to calculate the turbulent kinetic energy, wherein the Reynolds average method decomposes the flow velocity into average and fluctuation components, and estimates the turbulent kinetic energy from the variance of the fluctuation component as an index of turbulence intensity; in addition, the flow direction is introduced to vector correct the turbulent kinetic energy, thereby ensuring that the calculated turbulence intensity can accurately reflect the distribution of turbulence in three-dimensional space, and further providing effective support for the accurate construction of the underwater terrain model; the terrain reflection characteristics are analyzed by the sonar echo signal, and the terrain occlusion distribution is determined, that is, by Fourier transform of the echo signal, the frequency characteristics are extracted, and the steep terrain area corresponding to the high-frequency reflection is identified, thereby determining the terrain occlusion distribution and providing detailed occlusion feature data for terrain modeling.

[0022] After obtaining the turbulence intensity index and the terrain occlusion distribution, the underwater terrain model represented by real-time data is constructed by fusing the above data, wherein the underwater terrain model includes two main components: terrain height distribution and turbulence region distribution; during model construction, the turbulence intensity index is first mapped to the grid coordinates to form a turbulence region distribution map; then, the terrain occlusion distribution and the turbulence map are fused by weighted average method to calculate the height distribution of the terrain, wherein the weighted average is linearly combined based on the weight of each index, thereby ensuring that the underwater terrain model can accurately reflect the changes of the flow and its influence on the sound wave propagation path, and at the same time improving the real-time updating capability of the model; the above technical solution can effectively solve the problems of complex terrain and high turbulence intensity commonly encountered in underwater exploration, thereby providing accurate and reliable technical support for monitoring sediment deposition by underwater acoustic exploration.

[0023] Step S2: calculating the propagation path of the sound wave for the underwater terrain model, processing the uncertainty factors by using a particle filtering algorithm to update the propagation path estimation; extracting the key node coordinates from the propagation path estimation to determine the beam width adjustment parameters of the sound wave, and obtaining the width adjustment scheme; In step S2, the particle filtering algorithm is used to process the uncertainty factors to update the propagation path estimation, including: Initializing a particle set, which represents the 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 to update the predicted trajectory of the propagation path; and updating the optimized propagation path estimation by iteratively updating the particle set, wherein the propagation path estimation includes path coordinates and direction vectors.

[0024] Specifically, in an embodiment, the particle filtering algorithm is used to process the uncertainty factors in the underwater propagation path estimation. Specifically, the particle set is initialized, each particle representing a possible state of the sound wave propagation path, and the position and direction of each particle are randomly sampled based on real-time data in the underwater terrain model, such as sonar echo signals and flow velocity vectors, to form an initial path prediction. The initialization of the particle set ensures that a variety of possible states of the propagation path are covered, providing diverse initial assumptions for subsequent path estimation. The weight of each particle is also calculated based on the obtained sonar echo signals, flow velocity data, and terrain model. The weight calculation is based on the matching degree of the particle path and the model data. The degree of conformity between the particle path and the environment data, such as turbulence intensity indicators and terrain obstruction characteristics, is measured using a likelihood function, thereby obtaining the particle weight. This effectively filters out the most reliable path assumption. When the predicted path of some particles deviates from the actual propagation path by more than a preset threshold, the particle weight distribution is adjusted for correction. If the path deviation exceeds the threshold, the weight of the particle with large deviation is reduced, and the weight of the particle that better matches the actual situation is increased, so that the particle set more accurately reflects the possibility of the true path. This ensures that the path estimation can always be dynamically adjusted according to real-time data in complex underwater environments, avoiding the influence of error accumulation on the accuracy of path estimation, optimizing the path estimation process, and improving the accuracy of subsequent optimization.

[0025] In order to further improve the accuracy of path estimation, according to the adjusted particle weight distribution, the importance sampling method is used to resample the particle set, update the position and direction of the particle, so as to obtain more accurate propagation path prediction. Through multiple iterations, the particle set is updated and 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 the result of continuous optimization based on the particle filtering algorithm. Through iterative correction of deviation, the path estimation gradually approaches the true situation, and finally provides accurate path planning data for the navigation control system. The above technical scheme updates the particle set through iteration, and the obtained propagation path estimation has high stability and accuracy, which can effectively cope with complex factors in the underwater environment, such as turbulent interference and terrain obstruction. It provides high-precision path control for the detection of sediment deposition, so that the acoustic underwater detection process can efficiently and accurately monitor the distribution of the target sediment layer.

[0026] Further, in step S2, the width adjustment scheme is obtained, including: extracting key node coordinates from the propagation path estimation, the key node coordinates representing turning points on the propagation path; according to the key node coordinates, obtaining the beam width requirement index at the corresponding node; determining the energy concentration area by calculating the energy density distribution; fusing the energy density distribution and the density gradient distribution to calculate the beam width adjustment parameter; generating a preliminary width adjustment scheme according to the beam width adjustment parameter, wherein the width adjustment scheme includes the beam width value of each key node.

[0027] Specifically, in an embodiment, by extracting key node coordinates from the propagation path estimation, the turning points on the path are determined, and the key nodes provide important positioning data for underwater exploration; specifically, by analyzing the propagation path trajectory, points with large changes in path curvature are identified as turning point identifiers, and the corresponding key node coordinates are extracted, which provide important information on the path, enabling accurate calculation and adjustment of subsequent beam width requirement indicators; based on the key node coordinates, relevant turbulence intensity and terrain occlusion data are further obtained from the underwater terrain model, combined with information such as flow velocity vectors, to calculate the beam width requirement indicators at each node, providing data support for beam configuration; the energy density distribution on the propagation path is also calculated to determine the energy concentration area; wherein the energy density calculation method is used to analyze the distribution of sound wave energy on the path, identify areas with high turbulence intensity or large terrain changes, and parts with high energy concentration, which are usually areas that need to be focused on, helping to optimize the propagation and detection coverage of sound waves; on this basis, the energy density distribution and density gradient distribution are fused, and the beam width adjustment parameters are obtained through weighted averaging and other calculation methods, ensuring that the beam width adjustment can adapt to different underwater environments and detection requirements, wherein the density gradient distribution identifies the density changes in areas with high turbulence intensity by calculating the rate of change of energy density, and further adjusts the beam configuration in combination with energy density information.

[0028] According to the fused energy density distribution and density gradient distribution, a preliminary beam width adjustment scheme is generated, i.e., by applying the beam width adjustment parameters, appropriate beam width values are assigned to each key node, thereby ensuring that during underwater exploration, the propagation of sound waves can more accurately cover the target sediment layer, avoiding energy waste or detection blind spots caused by excessively wide or narrow beams; through the above technical solution, the beam width adjustment scheme can better adapt to complex underwater environments, ensuring that the coverage and accuracy of sound wave detection are optimized, thereby providing accurate data support for subsequent sediment deposition monitoring, overcoming the problems of inaccurate path estimation and unreasonable beam width adjustment in traditional underwater exploration.

[0029] Step S3: According to the width adjustment scheme, fuse the flow velocity vector and turbulence intensity indicators, and use the gradient descent algorithm to optimize the beam width configuration; generate a sound wave emission control signal and obtain echo feedback data through the beam width configuration and the propagation path estimation, update the underwater terrain model according to the echo feedback data, and obtain a corrected environment representation; In step S3, the gradient descent algorithm is used to optimize the beam width configuration, as shown in Figure 2 , including: According to the width adjustment scheme, an initial beam width parameter is obtained; the flow velocity vector and the turbulence intensity index are fused to calculate a gradient direction of beam width adjustment; a gradient descent algorithm is used to iteratively update the beam width parameter; if the energy balance index after iteration does not meet the standard, the step size and the convergence threshold of the gradient descent are adjusted; and through multiple iterations, a final beam width configuration is obtained, wherein the beam width configuration includes an optimized beam width value and an energy distribution parameter.

[0030] Specifically, in an embodiment, the beam width configuration is optimized by a gradient descent algorithm. Specifically, according to the width adjustment scheme, an initial beam width parameter is obtained. The beam width parameter is directly determined by the key node coordinates extracted from the optimized propagation path estimation and is applied as an initial value to subsequent calculations. The key node coordinates reflect the turning point positions on the path and help to further determine the corresponding beam width requirement. Through the fusion of the flow velocity vector and the turbulence intensity index, the gradient direction of beam width adjustment is calculated. The flow velocity vector and the turbulence intensity index are obtained by fusing data in the real-time underwater terrain model. The flow velocity vector is decomposed into horizontal and vertical components. The turbulence intensity index reflects the influence of flow disturbance on beam propagation. The above data jointly calculate a comprehensive interference factor representing the influence of flow changes on beam width adjustment, thereby deriving the gradient direction of adjustment. Through the gradient direction, the beam width parameter is iteratively updated by combining the gradient descent algorithm. In each iteration, the beam width parameter is adjusted according to the calculated gradient direction, and is gradually adjusted along the negative gradient direction to optimize the beam width configuration, thereby ensuring that the beam width configuration can adapt to complex underwater environments and reduce beam energy loss. If the energy balance index after iteration does not meet the predetermined standard, the step size and the convergence threshold of the gradient descent are adjusted to further optimize. The adjustment of the step size and the convergence threshold is adaptively changed according to the deviation degree of the energy balance index. If the energy balance is insufficient, the step size is increased to accelerate convergence, or the convergence threshold is reduced to improve the accuracy requirement, thereby ensuring that the beam width configuration can stably converge in the iteration process in complex underwater environments.

[0031] Through multiple iterations, the final beam width configuration includes an optimized beam width value and an energy distribution parameter, reflecting the adaptability of beam width adjustment to underwater terrain and the optimization of energy distribution, thereby accurately covering the target area, especially the detection of silt layers. The final generated beam width configuration will be used to generate acoustic wave emission control signals to ensure effective propagation of acoustic waves in underwater environments and improve the accuracy and efficiency of underwater detection. The above technical scheme not only effectively reduces energy loss but also enhances the robustness and real-time performance of the detection system in complex environments such as different flow velocities, turbulence intensities, and terrain obstructions, meeting the high-precision requirements of silt accumulation monitoring.

[0032] Further, 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 by a sound wave emission device to obtain echo feedback data; according to the echo feedback data, a matching degree with the target sediment layer is calculated; if the matching degree is lower than a preset threshold, a deviation feature is extracted from the echo feedback data; according to the deviation feature, local details of the underwater terrain model are updated to obtain a corrected environment representation.

[0033] Specifically, in an embodiment, a sound wave emission control signal is generated according to a beam width configuration and a propagation path estimation; specifically, the width adjustment parameter in the beam width configuration and the key node coordinates in the propagation path estimation are first fused to calculate the initial amplitude and phase distribution of the sound wave signal, the initial amplitude is determined by the energy density distribution formula, and the phase is updated based on the time sequence of the path trajectory, so as to ensure that the generated sound wave signal can adapt to the dynamic changes in the complex underwater environment and improve the detection accuracy; the generated control signal is received by a sound wave emission device and converted into an electro-acoustic signal, which is then emitted underwater, and the receiving array captures the reflected echo to form a feedback data sequence; after obtaining the echo feedback data, the matching degree with the target sediment layer is calculated according to the data, and the intensity and time delay features are extracted from the echo feedback data, wherein the intensity is calculated by integrating the echo energy, and the time delay is calculated based on the signal propagation time difference; the matching degree value is obtained by calculating the correlation between the extracted features and the reference features of the pre-stored sediment layer model, specifically, the Pearson correlation coefficient formula is used to calculate the similarity between the echo data and the target sediment layer, so as to effectively identify the influence of terrain shielding in the complex underwater environment and improve the matching accuracy, especially when encountering dynamic factors such as water flow and turbulence, the deviation can be quantified in real time to avoid invalid detection, wherein the pre-stored sediment layer model is trained based on historical echo data and features related to the sediment layer in the underwater detection process; if the calculated matching degree is lower than a preset threshold, further deviation features are extracted from the echo feedback data, wherein the deviation features include intensity deviation and time delay deviation, wherein the intensity deviation is calculated by difference, and the time delay deviation is calculated by time sequence alignment; the deviation features can reveal the actual difference between the echo signal and the target sediment layer, especially in deep water environments with high turbulence intensity, which helps to capture subtle changes caused by terrain shielding; further, the particle filtering algorithm is used to adjust the weight of the deviation features, and the state transition model and the observation model are iteratively updated to optimize the accuracy of the deviation features, ensuring that the feature extraction remains efficient and robust in environments with large changes in water flow.

[0034] According to the extracted deviation features, the local details of the underwater terrain model are updated to obtain a corrected environment representation; specifically, the deviation feature vector is mapped to the corresponding node position of the underwater terrain model, and coordinate transformation is performed to ensure that the application of the deviation can be accurately connected to the specific terrain position; the model parameters are iteratively adjusted through the gradient descent algorithm, the gradient of the loss function is calculated and the parameters are updated until convergence, to optimize the terrain occlusion data and improve the representation of local details; through multiple iterations, the updated turbulence intensity index and terrain distribution are generated to form the corrected environment representation, which provides more accurate terrain information for subsequent underwater exploration, wherein the corrected environment representation considers the water flow velocity, turbulence intensity, terrain occlusion and deviation features, thereby ensuring that the underwater terrain model can adapt to dynamic changes in real time and provide accurate path planning and exploration instructions, significantly improving the accuracy of the model, especially in complex underwater environments such as silt accumulation monitoring, which can effectively reduce the influence of uncertainty on path estimation and beam width adjustment, ensure accurate coverage of sound wave exploration, reduce exploration deviation, and improve the real-time response capability of the system.

[0035] Step S4: Obtain the updated turbulence intensity index and terrain occlusion data from the corrected environment representation, re-predict the propagation path, and obtain the navigation control sequence; specifically including: Extract the updated turbulence intensity index and terrain occlusion data from the corrected environment representation; initialize a new particle set according to the updated turbulence intensity index and terrain occlusion data; predict a new propagation path trajectory using a particle filtering algorithm; adjust the propagation path trajectory through path deviation correction; generate an enhanced navigation control sequence according to the corrected propagation path trajectory combined with sequence timing updates, wherein the navigation control sequence includes path direction and speed instructions.

[0036] Specifically, by extracting the updated turbulence intensity index and terrain occlusion data from the corrected environment representation, the prediction and navigation control of the underwater propagation path are further optimized based on the above-mentioned updated turbulence intensity index and terrain occlusion data. Specifically, the turbulence intensity index and terrain occlusion data are obtained from the corrected environment representation, and the data grid is traversed to identify the change of turbulence intensity and extract the terrain occlusion data at the corresponding coordinates. The above data form a real-time feedback of the underwater environment, providing important information for subsequent path prediction; according to the updated turbulence intensity index and terrain occlusion data, a new particle set is initialized, wherein the initialization of the particle set is performed by random sampling with the turbulence intensity index as a weight factor to generate an initial particle set, each particle representing the initial state of the potential propagation path, thereby ensuring that the particle set can reflect the diversity of the path and taking into account the influence of turbulence intensity and terrain occlusion on the propagation path; in addition, the initial distribution of the particle set is adjusted by fusing the terrain occlusion data, avoiding the concentration of particles in the high occlusion area, 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, calculates the likelihood value of each particle through iterative calculation, combines the observed turbulence intensity and terrain occlusion data, resamples the particle set, and eliminates low-weight particles, finally giving the estimation of the new propagation path trajectory, ensuring that the path prediction can adapt to the complex underwater environment and overcome the influence of turbulence and terrain occlusion; the Bayesian filtering method is also used to optimize the path trajectory, improving the accuracy of path prediction.

[0037] In order to further optimize the path trajectory, correction is made through path deviation, that is, a deviation vector between the predicted trajectory and the actual observed path is calculated, a deviation curve is fitted by using the least square method, and according to the deviation vector, the coordinates of the trajectory points are iteratively corrected until the deviation is lower than a set threshold, so as to ensure that the path conforms to the actual environment, dynamically adjust the trajectory by comparing with the actual path in the echo feedback data, overcome the changes and uncertainties in the underwater environment, and improve the coverage efficiency of the sediment layer; in combination with the corrected propagation path trajectory and the timing update mechanism, an enhanced navigation control sequence is generated, that is, the corrected propagation path trajectory is decomposed into multiple timing segments, the path direction and speed requirement are calculated for each timing segment, so as to ensure that the path control sequence can adapt to the change of water flow in real time and accurately guide the sound wave detection, the direction deviation and speed requirement of each timing point are converted into specific instructions, which are continuously updated through the recursive method, so as to ensure that the generated navigation control sequence can adapt to the change of the complex underwater environment and provide stable navigation support, thereby improving the accuracy and efficiency of the underwater sediment layer detection; the above technical scheme can effectively deal with the influence of water flow and turbulence, and can also deal with uncertain factors such as terrain shielding and path deviation, so as to finally generate an accurate navigation control sequence and ensure the path stability and accuracy in the sound wave detection process, thereby realizing accurate sediment deposition monitoring.

[0038] Step S5: generating a sound wave detection instruction set according to the navigation control sequence and the beam width configuration, and controlling the sound wave emitting device to realize monitoring of the target sediment layer based on the instruction set; specifically including: Fusing the navigation control sequence and the beam width configuration, a sound wave detection instruction set is generated; the sound wave emitting device is driven to emit a detection signal through the sound wave detection instruction set; 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, trajectory smoothing processing data is extracted from the key node coordinates; the sound wave detection instruction set is updated according to the trajectory smoothing processing data, so as to realize monitoring of the target sediment layer.

[0039] Specifically, the sound wave detection instruction set is generated by fusing the navigation control sequence and the beam width configuration, specifically, the path trajectory data is extracted from the above navigation control sequence, and the path trajectory data is vector superimposed with the width parameter in the beam width configuration to generate a preliminary instruction sequence, so that the path trajectory and the data of the width configuration are fused with each other, the propagation of the detection signal along the optimized path is ensured, and the coverage range is adjusted accordingly; on the basis of the initial instruction sequence, the time synchronization mark is fused to form a complete sound wave detection instruction set, so as to more accurately guide the signal emission in underwater detection and realize the preliminary positioning of the target silt layer; the generated sound wave detection instruction set is then input to the control module of the sound wave emission device to activate the emission circuit, and the sound wave signal is emitted into the underwater environment, and the echo data after emission is collected and used to calculate the coverage deviation of the target silt layer. Among them, the intensity distribution and time delay characteristics are extracted from the echo data, and the difference between the echo intensity and the expected silt layer reflection model is calculated, if the difference value exceeds the preset threshold value, the coverage deviation is quantified by the Euclidean distance formula, the geometric distance between the echo point set and the target layer boundary point set is calculated, and the corresponding deviation value is obtained; the deviation calculation result is corrected by the turbulence intensity index to obtain the final coverage deviation, so as to optimize the detection accuracy.

[0040] If the calculated coverage deviation exceeds the preset threshold value, the trajectory smoothing processing data is extracted from the key node coordinates; that is, whether the coverage deviation exceeds the set value, such as 5%, is first judged, if yes, the key node coordinate sequence in the path is selected, and the selected coordinate sequence is smoothed by the Kalman filtering algorithm, the estimation error is minimized by the recursive estimation method, and the smoothed trajectory is obtained, so as to effectively reduce the path jitter, avoid the error caused by turbulence and the like, and improve the path stability, especially in the environment with large water flow change; the deviation correction vector is extracted from the smoothed trajectory as the trajectory smoothing processing data, which is used to update the sound wave detection instruction set, so as to realize the accurate coverage of the target silt layer; the corrected trajectory data is specifically fused into the original instruction set, the path trajectory parameter is adjusted, the beam width configuration is recalculated, and the updated instruction set is generated; the emission device is driven by the updated instruction set to emit, so as to ensure that the coverage deviation is less than the preset threshold value, thereby realizing the accurate detection of the silt layer; the above technical scheme not only improves the real-time and accuracy of the detection, but also effectively reduces the detection deviation caused by path error or improper beam width, especially in complex underwater environments, such as areas with significant changes in riverbed, lake bottom and the like, so that the sound wave detection system can adaptively adjust the path and the beam width to ensure that the silt deposition monitoring task is stably and reliably completed.

[0041] The application also provides a silt deposition monitoring system based on sound wave underwater detection, which is used to realize the above method, as shown in the figure, the system comprises: Figure 3 ​ An environmental data acquisition unit is configured to acquire underwater environmental sensor data of a target area, fuse sonar echo signals and water flow velocity vectors, determine a turbulence intensity index and a terrain shelter distribution, and obtain an underwater terrain model; A propagation path estimation unit is configured to calculate a propagation path of a sound wave based on the underwater terrain model, process uncertainty factors by using a particle filter algorithm, update the propagation path estimation, extract key node coordinates from the propagation path estimation, determine a beam width adjustment parameter of the sound wave, and obtain a width adjustment scheme; A beam configuration optimization unit is configured to fuse the water flow velocity vectors and the turbulence intensity index based on the width adjustment scheme, optimize a beam width configuration by using a gradient descent algorithm, generate a sound wave emission control signal based on the beam width configuration and the propagation path estimation, acquire echo feedback data, update the underwater terrain model based on the echo feedback data, and obtain a corrected environmental representation; A path correction and prediction unit is configured to acquire updated turbulence intensity index and terrain shelter data from the corrected environmental representation, re-predict a propagation path, and obtain a navigation control sequence; A detection instruction control unit is configured to generate a set of sound wave detection instructions based on the navigation control sequence and the beam width configuration, and control a sound wave emission device to monitor a target silt layer based on the set of instructions.

[0042] The application further provides a computer readable storage medium, wherein the computer readable storage medium stores instructions, and the instructions are executed by a processor to implement the method.

[0043] In summary, the application extracts updated turbulence intensity indicators and terrain shelter data from the corrected underwater terrain model to form a basic data set that accurately reflects the current underwater environment, providing strong data support for subsequent path prediction and beam width adjustment. Using the particle filter algorithm, the particle set is initialized based on the updated data and the new propagation path trajectory is predicted. Through state transition modeling and likelihood value calculation, the particle filter algorithm can effectively handle the effects 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 fine-tune the trajectory to ensure accuracy. The corrected path will be updated in time sequence to generate accurate navigation control sequences, with the path direction and speed requirements at each time sequence point being dynamically adjusted to adapt to changes in water flow, ensuring that the sound wave signal can propagate along the optimal path, thereby improving the coverage accuracy of the sediment layer. During the emission of the sound wave signal, the generated sound wave detection instruction set is emitted by activating the control module of the emission device, and the target sediment layer is detected in real time by receiving the echo data. If the coverage deviation exceeds the preset threshold, the path and beam width are adjusted based on techniques such as echo feedback data deviation feature extraction, Kalman filter smoothing processing, and turbulence intensity correction to ensure accurate coverage of the target area by the detection signal. Through the mutual cooperation between the above technical solutions, the stability, accuracy, and real-time performance of underwater sound wave detection are greatly improved, providing reliable technical support for sediment deposition monitoring.

[0044] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, system and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0045] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0046] The above-described embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalent replacements; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for monitoring sediment deposition based on underwater acoustic detection, characterized in that, The method includes: 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; 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; 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. 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; 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.

2. The method as described in claim 1, characterized in that, Step S1, obtaining the underwater terrain model, including: 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.

3. The method as described in claim 1, characterized in that, In step S2, the particle filter algorithm is used to handle uncertainties and update the propagation path estimate, including: 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.

4. The method as described in claim 3, characterized in that, In step S2, a width adjustment scheme is obtained, including: 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.

5. The method as described in claim 1, characterized in that, In step S3, the gradient descent algorithm is used to optimize the beamwidth configuration, including: 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.

6. The method as described in claim 5, characterized in that, In step S3, the underwater terrain model is updated to obtain a corrected environmental representation, including: 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.

7. The method as described in claim 1, characterized in that, In step S4, the navigation control sequence is obtained, including: 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.

8. The method as described in claim 1, characterized in that, In step S5, the acoustic wave emitting device is controlled based on the instruction set to monitor the target sediment layer, including: 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.

9. A sediment deposition monitoring system based on underwater acoustic detection, used to implement the method as described in any one of claims 1-8, characterized in that, The system includes: 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. 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. 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 using 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. 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. 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.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the method as described in any one of claims 1-8.

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