Beach evolution monitoring method and device based on multi-source data fusion

Abstract

The invention provides a beach evolution monitoring method and device based on multi-source data fusion, and relates to the technical field of data processing. The method comprises the following steps: collecting airborne LiDAR point cloud data and multi-beam sounding grid data corresponding to a target beach area; based on spatial distribution characteristics in airborne LiDAR point cloud data and multi-beam sounding grid data, carrying out terrain partitioning on a beach area; according to a terrain partitioning result, performing terrain adaptation fusion on the airborne LiDAR point cloud data and the multi-beam sounding grid data by adopting a weighted fusion method to obtain terrain fusion data; constructing a beach evolution prediction model according to the elevation change characteristics and the spatial change characteristics; and according to the beach evolution prediction model, predicting a beach change trend and generating monitoring and early warning information. According to the technical scheme, the monitoring precision and the processing efficiency of the beach area can be improved.

Description

Background Art

[0002] The topographic changes in the coastal area are influenced by both natural factors and human activities, and their dynamic evolution is of great significance to the ecological environment, disaster prevention and mitigation, and infrastructure safety.

[0003] Existing technologies mainly rely on a single measurement method to collect terrain data, such as using a single-beam depth sounder or lidar ranging. However, in complex environments, the applicability of this type of measurement method is limited, and it is difficult to simultaneously meet the measurement accuracy requirements of different areas. For example, although observation methods based on remote sensing can cover a large area, they have certain limitations in local fine measurement, especially in the area where land and water meet. In addition, in terms of real-time monitoring and early warning response, existing methods mainly rely on manual interpretation or static judgment mechanisms based on fixed thresholds, resulting in insufficient perception of sudden changes in the shore and beach, slow response speed, and difficulty in timely and accurate identification of abnormal situations and taking corresponding early warning measures.

[0004] In summary, the accuracy and efficiency of beach evolution monitoring in existing technologies are difficult to guarantee, and cannot meet the needs of high-precision and continuous monitoring of beach terrain changes. Summary of the invention

[0005] The purpose of the embodiments of the present disclosure is to provide a shore evolution monitoring method and system based on multi-source data fusion, thereby improving the monitoring accuracy and processing efficiency of shore evolution monitoring at least to a certain extent.

[0006] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0007] According to a first aspect of an embodiment of the present disclosure, a shore evolution monitoring method based on multi-source data fusion is provided, the method comprising: collecting airborne LiDAR point cloud data and multi-beam bathymetry grid data corresponding to a target shore area; performing terrain zoning on the shore area based on spatial distribution characteristics in the airborne LiDAR point cloud data and the multi-beam bathymetry grid data; according to a result of the terrain zoning, performing terrain adaptation fusion on the airborne LiDAR point cloud data and the multi-beam bathymetry grid data using a weighted fusion method to obtain terrain fusion data; based on the terrain fusion data of different time periods, obtaining elevation change characteristics and spatial change characteristics corresponding to the shore area, and constructing a shore evolution prediction model based on the elevation change characteristics and the spatial change characteristics; according to the shore evolution prediction model, predicting the shore change trend and generating monitoring and early warning information.

[0008] According to a second aspect of an embodiment of the present disclosure, a shore evolution monitoring device based on multi-source data fusion is provided, and the device includes: a data acquisition module, which is used to collect airborne LiDAR point cloud data and multi-beam bathymetry grid data corresponding to the target shore area; a terrain partitioning module, which is used to perform terrain partitioning on the shore area based on the spatial distribution characteristics of the airborne LiDAR point cloud data and the multi-beam bathymetry grid data; a data fusion module, which is used to perform terrain adaptation and fusion on the airborne LiDAR point cloud data and the multi-beam bathymetry grid data according to the result of the terrain partitioning, so as to obtain terrain fusion data; a model construction module, which is used to obtain the elevation change characteristics and spatial change characteristics corresponding to the shore area according to the terrain fusion data at different times, and construct a shore evolution prediction model according to the elevation change characteristics and the spatial change characteristics; a model prediction module, which is used to predict the shore change trend and generate monitoring and early warning information according to the shore evolution prediction model.

[0009] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the shore evolution monitoring method based on multi-source data fusion is implemented.

[0010] According to a fourth aspect of an embodiment of the present disclosure, there is provided a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for monitoring shore evolution based on multi-source data fusion is implemented.

[0011] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects: The shore evolution monitoring method based on multi-source data fusion in the exemplary embodiment of the present disclosure, firstly, can obtain high-precision terrain information in different spatial medium environments through the joint collection of airborne LiDAR point cloud data and multi-beam bathymetric grid data, avoiding the problem of data loss or reduced accuracy that may be caused by a single measurement method in complex shore areas. Terrain zoning is performed based on the spatial distribution characteristics of airborne LiDAR point cloud data and multi-beam bathymetric grid data, so that land areas, water areas and boundary areas can be clearly divided, thereby providing an accurate regional division basis for subsequent data processing, and avoiding the discontinuity problem that may be caused by direct splicing of different measurement data in the boundary area.

[0012] Secondly, on the basis of terrain zoning, the weighted fusion method is used to adapt the airborne LiDAR point cloud data and the multi-beam bathymetric grid data to terrain, which can more accurately characterize the terrain characteristics of the shore area under different environmental conditions. In addition, the elevation change characteristics and spatial change characteristics are extracted based on the terrain fusion data of different periods, so that the dynamic evolution information of the shore area can be displayed in the comparison of multi-period data. The shore evolution prediction model is constructed based on the elevation change characteristics and spatial change characteristics, so that the dynamic change trend of the shore area can be modeled based on historical data, so as to continuously optimize the model parameters in long-term monitoring and improve the prediction accuracy. Based on the shore evolution prediction model, future trends are predicted and monitoring and early warning information is generated, so that the changes in the shore area can not only be monitored in real time, but also be identified in the early stage of trend evolution and reasonable early warning prompts can be given. Therefore, the monitoring accuracy and processing efficiency of the shore area are improved to a certain extent.

[0013] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0015] Figure 1 A schematic flowchart of a shore evolution monitoring method based on multi-source data fusion according to some embodiments of the present disclosure is schematically shown.

[0016] Figure 2 A schematic flow chart of a method for terrain zoning of a beach area according to some embodiments of the present disclosure is schematically shown.

[0017] Figure 3 The flowchart of the method for constructing a shore evolution prediction model according to some embodiments of the present disclosure is schematically shown.

[0018] Figure 4 A block diagram of a shore evolution monitoring device based on multi-source data fusion according to some embodiments of the present disclosure is schematically shown.

[0019] Figure 5 A schematic diagram of the structure of a computer system of an electronic device according to some embodiments of the present disclosure is schematically shown.

[0020] Figure 6A schematic diagram of a computer-readable storage medium according to some embodiments of the present disclosure is schematically shown.

[0021] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts. DETAILED DESCRIPTION

[0022] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this specification. Instead, they are merely examples of devices and methods consistent with some aspects of this specification as detailed in the appended claims.

[0023] The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this specification. The singular forms "a", "the" and "the" used in this specification and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0024] It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this specification, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0025] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more comprehensive and complete and will fully convey the concept of the example embodiments to those skilled in the art.

[0026] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0027] Furthermore, the drawings are only schematic illustrations and are not necessarily drawn to scale. The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0028] The beach topography is affected by natural factors and human activities, and its dynamic changes are crucial to the ecological environment, disaster prevention and mitigation, and infrastructure safety. Relevant technologies mainly rely on a single measurement method, such as a single-beam depth sounder or lidar ranging, but their applicability is limited in complex environments, and it is difficult to simultaneously meet the measurement accuracy requirements of different regions, especially at the interface between land and water. In addition, current monitoring and early warning methods mostly rely on manual interpretation or fixed threshold judgment, resulting in limited perception of sudden changes, delayed response, and difficulty in timely identification and early warning of abnormal situations.

[0029] In order to solve some or all of the above technical problems, the present disclosure provides a shore evolution monitoring method based on multi-source data fusion. Figure 1 The following schematically shows a flow chart of a method for monitoring shore evolution based on multi-source data fusion according to some embodiments of the present disclosure. Figure 1 As shown, the shore evolution monitoring method based on multi-source data fusion may include the following steps: Step S110, collecting airborne LiDAR point cloud data and multi-beam bathymetric grid data corresponding to the target beach area; Step S120, based on the spatial distribution characteristics of the airborne LiDAR point cloud data and the multi-beam bathymetric grid data, terrain zoning of the shore area is performed; Step S130, according to the result of terrain partitioning, a weighted fusion method is used to perform terrain adaptation fusion on the airborne LiDAR point cloud data and the multi-beam bathymetric grid data to obtain terrain fusion data; Step S140, based on the terrain fusion data of different time periods, obtaining the elevation change characteristics and spatial change characteristics corresponding to the shore area, and constructing a shore evolution prediction model according to the elevation change characteristics and spatial change characteristics; Step S150, predicting the beach change trend and generating monitoring and early warning information based on the beach evolution prediction model.

[0030] In the actual operation, the target shore area is first scanned using airborne LiDAR equipment to obtain point cloud data covering the land area, and multi-beam bathymetry equipment is used to collect bathymetry grid data in the water area. After data collection is completed, the spatial distribution characteristics of the airborne LiDAR point cloud data and the multi-beam bathymetry grid data are analyzed. The terrain characteristics of the shore area are determined by calculating the elevation distribution and regional characteristics, and the target area is divided into land area, water area and land-water boundary area. Then, according to the results of terrain zoning, the weighted fusion method is used to perform terrain adaptation fusion on the airborne LiDAR point cloud data and the multi-beam bathymetry grid data. Specifically, in the land area, the airborne LiDAR point cloud data is the main data source, in the water area, the multi-beam bathymetry grid data is the main data source, and in the land-water boundary area, the weights are dynamically adjusted according to the terrain characteristics, and the two types of data are fused. Then, based on the terrain fusion data of different periods, the elevation change characteristics and spatial change characteristics are extracted to build a shore evolution prediction model. During the model training process, it is necessary to optimize the parameter settings based on long-term series data so that the model can adapt to different types of shore environments. Finally, based on the shore evolution prediction model, the shore change trend is predicted and monitoring and early warning information is generated so that relevant personnel can take preventive measures in time.

[0031] In the following, the technical details of the above-mentioned shore evolution monitoring method based on multi-source data fusion will be introduced in detail in other embodiments of the present disclosure.

[0032] In step S110, the airborne LiDAR point cloud data and multi-beam bathymetric grid data corresponding to the target beach area are collected. The target beach area represents the beach range for monitoring and analysis, including land areas, water areas, and land-water boundary areas. The airborne LiDAR point cloud data can represent a three-dimensional spatial coordinate data set obtained by an airborne laser radar (Light Detection and Ranging, LiDAR) device, which is composed of a large number of discontinuous points, and each point can contain elevation information and reflection intensity information. The multi-beam bathymetric grid data can represent the underwater terrain data obtained by a multi-beam echo sounder (Multibeam Echo Sounder, MBES). The data is based on the principle of acoustic echoes, and covers the underwater area through multiple acoustic beams to generate regular or irregular gridded water depth information.

[0033] In some embodiments, collecting airborne LiDAR point cloud data and multi-beam bathymetric grid data corresponding to the target beach area specifically includes the following technical steps: First, the elevation values ​​corresponding to the target shore area are obtained using airborne LiDAR equipment and multi-beam bathymetry equipment, and airborne LiDAR point cloud data and multi-beam bathymetry grid data are constructed based on the elevation values. The elevation value can represent the vertical height information of each measurement point in the target shore area, representing the surface elevation of the land area in airborne LiDAR measurement and the depth of the underwater terrain in multi-beam bathymetry measurement, expressed as a value relative to the reference surface.

[0034] Then, the airborne LiDAR point cloud data and multi-beam bathymetric grid data are denoised and converted into three-dimensional coordinates to unify the data format. Denoising means that during the data processing, the airborne LiDAR point cloud data and multi-beam bathymetric grid data are subjected to noise filtering operations, which may include removing abnormal points, mismeasured points, and non-terrain echo interference to improve the accuracy and reliability of the data. Three-dimensional coordinate conversion can mean a unified spatial transformation of the coordinate systems of the airborne LiDAR point cloud data and multi-beam bathymetric grid data so that they are aligned in the same geographic reference coordinate system to ensure the spatial consistency of different data sources.

[0035] Exemplarily, the airborne LiDAR point cloud data and the multi-beam bathymetric grid data may be denoised and converted into three-dimensional coordinates based on the following technical steps: First, the airborne LiDAR point cloud data is denoised. Denoted as: in, Indicates the first The horizontal coordinates of the points, Indicates the first The vertical coordinates of the points, Indicates the first The elevation value of a point, Indicates the total number of points in the airborne LiDAR point cloud data. , using a denoising method based on density features. Define point neighborhood density for: in, Indicates the The neighborhood area is centered on the point. represents the Euclidean distance between the airborne LiDAR point and other points in the neighborhood, represents the denoising smoothing coefficient. Less than threshold , then the corresponding point is judged as a noise point and Among them, Indicates the critical value of noise judgment.

[0036] Then, the multi-beam bathymetric grid data is denoised. The multi-beam bathymetric grid data is recorded as: in, Indicates the first The horizontal coordinates of the points, Indicates the first The vertical coordinates of the points, Indicates the first The water depth value of the point, Indicates the total number of points in the multibeam bathymetric grid data. , local median filtering is used for denoising, and the depth after filtering is defined as: in, represents the median operation, Indicates the filter window radius. If the original water depth value is The difference is greater than the threshold , then the point is determined to be an abnormal measurement value and is removed. Among them, Indicates the threshold used to remove anomalies in multibeam bathymetry data.

[0037] After the denoising operation is completed, the airborne LiDAR point cloud data and the multi-beam bathymetric grid data are transformed into three-dimensional coordinates. Assume that the airborne LiDAR point cloud data adopts the projection coordinate system , the multi-beam bathymetric grid data adopts the geographic coordinate system ,in, Indicates longitude, Indicates latitude, Indicates the elevation (or depth) relative to the reference base. The coordinate transformation relationship is defined as: in, Represents the horizontal coordinate of the converted multibeam bathymetric grid data in the projection plane, represents the transformed vertical coordinate, Represents the converted elevation value. Represents the transformation matrix from geographic coordinates to projection coordinates. By performing the above transformation on all multi-beam bathymetric grid data points, alignment with the airborne LiDAR point cloud data in the same coordinate system is achieved. Finally, in the unified projection coordinate system In the process, the denoised airborne LiDAR point cloud data and multi-beam bathymetric grid data are obtained, so that the two are consistent in format and coordinate reference.

[0038] In step S120, the beach area is topographically partitioned based on the spatial distribution characteristics of the airborne LiDAR point cloud data and the multi-beam bathymetric grid data. The spatial distribution characteristics represent the spatial distribution law of the airborne LiDAR point cloud data and the multi-beam bathymetric grid data in the target beach area, which may include uniformity, elevation gradient change, regional continuity and spatial correlation.

[0039] In some embodiments, reference Figure 2 As shown in the figure, based on the spatial distribution characteristics of airborne LiDAR point cloud data and multi-beam bathymetric grid data, the terrain zoning of the beach area is carried out, which specifically includes the following technical steps: In the first step, a first elevation function is generated according to the airborne LiDAR point cloud data, and a first complexity index is obtained according to the first elevation function. The first elevation function can represent a mathematical function established based on the airborne LiDAR point cloud data, which can describe the elevation distribution of the land part of the shore area to a certain extent. The first complexity index can represent a quantified index of terrain complexity calculated by the first elevation function.

[0040] In some embodiments, generating a first elevation function according to the airborne LiDAR point cloud data, and obtaining a first complexity index according to the first elevation function specifically include the following technical steps: Generate the first elevation function based on the airborne LiDAR point cloud data ,in, Indicates the plane coordinates of the airborne LiDAR device The elevation value obtained at .

[0041] Based on the first elevation function, by: Determine the first elevation gradient field ,in, Indicates that the airborne LiDAR point cloud data is The rate of change of elevation in a direction, Indicates that the airborne LiDAR point cloud data is The rate of change of elevation in a direction.

[0042] Based on the first elevation gradient field, by: Determine a first complexity index, where Indicates at point The local computation window around it.

[0043] In the second step, a second elevation function is generated according to the multi-beam bathymetric grid data, and a second complexity index is obtained according to the second elevation function. The second elevation function can represent a mathematical function established based on the multi-beam bathymetric grid data, which can describe the underwater terrain elevation distribution of the water body area to a certain extent. The second complexity index can represent a quantified index of terrain complexity calculated by the second elevation function.

[0044] The process of obtaining the second complexity index may include the following technical steps: Generate a second elevation function from multibeam bathymetric data ,in, Indicates the plane coordinates of the multi-beam bathymetric equipment The minus sign is used to convert the water depth into elevation.

[0045] Based on the second elevation function, by: Determine the second elevation gradient field ,in, Indicates that multibeam bathymetric data is The rate of change of elevation in a direction, Indicates that multibeam bathymetric data is The rate of change of elevation in a direction.

[0046] Based on the second elevation gradient field, by: Determine a second complexity index, where Indicates at point The local computation window around it.

[0047] The third step is to determine the elevation difference between the airborne LiDAR elevation value and the multi-beam bathymetry elevation value at different coordinates on the reference plane. The reference plane can represent the base plane used to calculate the elevation difference between the airborne LiDAR point cloud data and the multi-beam bathymetry data. The elevation difference can represent the numerical difference between the airborne LiDAR elevation value and the multi-beam bathymetry elevation value at the same coordinate point, reflecting the topographic changes in the land-water interface area.

[0048] Specifically, the elevation difference Can be based on To determine, Indicates that the airborne LiDAR point cloud data is mapped to the reference plane The elevation value on Indicates the multibeam bathymetric data mapped to the reference plane The elevation value on .

[0049] The fourth step is to divide the beach area into land area, water area and land-water boundary area based on the elevation difference, the first complexity index and the second complexity index through the preset elevation difference threshold and complexity threshold.

[0050] Specifically, the elevation difference threshold and complexity threshold , classify the point set on the beach area, when Existence and ,at the same time Less than When , the corresponding point belongs to the land area; when Existence and ,at the same time Less than When , the corresponding point belongs to the water area; when or and At least one of them is greater than , the corresponding point belongs to the land-water boundary area.

[0051] In step S130, according to the result of terrain zoning, a weighted fusion method is used to perform terrain adaptation fusion on the airborne LiDAR point cloud data and the multi-beam bathymetric grid data to obtain terrain fusion data. Among them, the weighted fusion method can represent the spatial distribution characteristics of the airborne LiDAR point cloud data and the multi-beam bathymetric grid data, dynamically allocate data weights according to regional divisions, and operate on the two types of data to generate unified terrain data. The terrain fusion data can represent a data set processed by the weighted fusion method, which combines the land area advantages of the airborne LiDAR point cloud data and the underwater terrain measurement advantages of the multi-beam bathymetric grid data, and can accurately characterize the terrain change characteristics of the shore area.

[0052] In some embodiments, according to the result of terrain partitioning, a weighted fusion method is used to perform terrain adaptation fusion on the airborne LiDAR point cloud data and the multi-beam bathymetric grid data to obtain terrain fusion data, which specifically includes the following technical steps: First, based on the first elevation gradient field and the second elevation gradient field, by: Determine the first weight and the second weight ,in, represents the weighting parameter that controls the influence of multibeam sounding gradient, represents the weighting parameter that controls the influence of the airborne LiDAR gradient, represents the second elevation gradient field mapped on the reference plane, Represents the first elevation gradient field mapped on the reference plane. When in land area, is 1, when When located in a water area, is 0. This weighting strategy can be adaptively adjusted according to different areas of terrain partitioning, ensuring that the land area mainly relies on airborne LiDAR point cloud data, the water area mainly relies on multi-beam bathymetric data, and the land-water interface area is smoothly transitioned according to the elevation gradient, effectively solving the measurement error problem that may exist in the interface area with different measurement methods. In addition, the weight calculation through the exponential function makes the weighting process smoother, avoids the fusion instability caused by data mutation, and can enhance the continuity of the land-water interface area.

[0053] Then, the airborne LiDAR elevation matrix and the multi-beam bathymetric elevation matrix are fused using the first weight and the second weight to obtain terrain fusion data. It can be defined as: in, Indicates the elevation value of the airborne LiDAR point cloud data at the reference plane coordinate (X, Y). It represents the elevation value of multi-beam bathymetric data at the reference plane coordinate (X, Y), and generates terrain fusion data based on the fused elevation value.

[0054] In addition, in other embodiments of the present disclosure, in order to ensure the continuity and consistency of the transition area, after the fusion is completed, the fused elevation value can also be locally smoothed. With smoothing parameter , define the local smoothing weight , ,in, and Respectively expressed in and The offset in direction, Represents the parameter that controls the degree of smoothness. The following weighted average formula is used to To perform local smoothing: Get terrain fusion data after local smoothing ,in, k Indicates half the size of the smoothing window.

[0055] In step S140, based on the terrain fusion data of different time periods, the elevation change characteristics and spatial change characteristics corresponding to the shore area are obtained, and a shore evolution prediction model is constructed based on the elevation change characteristics and spatial change characteristics. Among them, the elevation change characteristics can represent the spatiotemporal characteristics extracted by the change amount, change rate and change pattern of the elevation value between the terrain fusion data of different time periods. The spatial change characteristics can represent the spatial morphological changes of the shore area in the terrain data of different time periods. The shore evolution prediction model can represent a prediction framework constructed by using statistical methods or machine learning models based on the elevation change characteristics and spatial change characteristics of different time periods, which is used to infer the evolution trend of the shore terrain in the future.

[0056] In some embodiments, based on terrain fusion data of different time periods, the elevation change characteristics and spatial change characteristics corresponding to the beach area are obtained, which specifically includes the following technical steps: In the first step, adjacent observation moments are selected, and the elevation change matrix is ​​obtained based on the terrain fusion data of different time periods. The adjacent observation moments may represent two adjacent time points used to calculate the terrain change. The elevation change matrix may represent a matrix formed by calculating the elevation difference of the same coordinate point between the terrain fusion data of adjacent observation moments.

[0057] Specifically, first, set the discrete time series ,in , in the plane coordinate system Record the terrain fusion data at each moment .

[0058] in, , Indicates at time At coordinates The fused elevation value obtained at Represents the total number of discrete points.

[0059] Then, select adjacent observation times , define the elevation change matrix for: in, Indicates at time The fused elevation value obtained, Indicates at time The obtained fused elevation value.

[0060] The second step is to select the time difference and obtain the rate change matrix according to the elevation change matrix. The rate change matrix can represent a matrix calculated based on the elevation change matrix and the time difference, and its element value represents the elevation change rate of the corresponding coordinate point.

[0061] Specifically, set the time difference , define the rate of change matrix for: Represents the time interval between adjacent observation times.

[0062] The third step is to obtain the centroid coordinates of the change area and generate a spatial centroid matrix based on the elevation change matrix. The spatial centroid matrix can represent a matrix composed of the centroid coordinates of the change area at multiple observation times, and its element values ​​are the centroid coordinate data at different times, which is used to analyze the topographic change trend, morphological migration characteristics and overall evolution direction of the shore area.

[0063] In some embodiments, according to the elevation change matrix, the centroid coordinates of the change area are obtained and the spatial centroid matrix is ​​generated, which specifically includes the following technical steps: According to the elevation change matrix ,pass: Get the coordinates of the centroid of the change area. express The centroid coordinates of the elevation change area in the X-axis direction at the moment, express The centroid coordinates of the elevation change area in the Y-axis direction at the moment, Indicated on the reference plane The coordinate set of the area where the elevation changes. Generate the spatial centroid matrix based on the centroid coordinates of the changed area .

[0064] In some embodiments, reference Figure 3 As shown in the figure, a beach evolution prediction model is constructed based on the elevation change characteristics and spatial change characteristics, which specifically includes the following technical steps: Step S310: construct a time series input sequence based on the elevation change characteristics and spatial change characteristics corresponding to multiple observation times. The time series input sequence can represent terrain change data arranged in time order, including elevation change characteristics and spatial change characteristics, etc., as input data of the prediction model.

[0065] Based on multiple observation times The corresponding elevation change characteristics are integrated with the spatial change characteristics to construct a time series input sequence ,in, Indicates at time The elevation difference, Indicates at time The rate of change of elevation, and Indicates at time The coordinates of the centroid of the changing area.

[0066] Step S320, sequentially input the time series input sequence into the hidden state vector of the prediction model, and generate a prediction output according to the trainable parameters of the model. The hidden state vector may represent the internal state variables of the model used to store the time series information, and is used to memorize and update the historical information. The trainable parameters may represent the parameters in the prediction model that can be optimized through the learning process, including weight matrices, bias terms, etc.

[0067] For example, in order to describe the correlation of beach topography changes in time series, a hidden state vector update chain can be established in the model, which is defined as follows: , Indicated in The hidden state vector obtained after the sequential input is updated is used to store the model’s internal memory in time. Represents the hidden state after the previous time series input, Represent the weight matrices of hidden state, input sequence and output layer respectively, Represent the bias vectors of the hidden layer and the output layer respectively, is the activation function, Represents the predicted output, used to estimate the future time Through this time-series recursive formula, each time a new feature vector is input, it can be combined with the previous hidden state to predict the trend of beach terrain changes in the future.

[0068] Step S330, perform a difference evaluation between the predicted output and the actual fused elevation data at different times, and adjust the trainable parameters of the model according to the difference value. Specifically, the difference evaluation can be performed using a loss function. L It can be defined as: in, Represents plane coordinates The total number of sample points, Indicates that the predicted output is at coordinates The numerical value of Indicates at time The actual fused elevation value obtained. In order to minimize , the trainable parameters are optimized using gradient descent.

[0069] Step S340, after completing the preset number of time series inputs and difference evaluations, outputting a shore evolution prediction model including hidden states and trainable parameters. Specifically, after the training process iterates to a maximum number of cycles or meets a set accuracy threshold, a final shore evolution prediction model is obtained.

[0070] In step S150, the beach change trend is predicted and monitoring and early warning information is generated according to the beach evolution prediction model. Specifically, the above process may include the following technical steps: First, the terrain fusion data collected in real time is input into the shore evolution prediction model to obtain the predicted shore change data. Specifically, the terrain fusion data collected at the current moment is matched with the historical observation data, and the elevation change trend, spatial movement trend and change rate at future moments are calculated based on the hidden state vector and trainable parameters of the model, thereby generating a prediction data matrix.

[0071] Then, according to the pre-set elevation change threshold and rate threshold, it is judged whether the predicted shore change data meets the warning conditions. Specifically, multiple warning thresholds are set, among which the elevation change threshold is used to evaluate the magnitude of terrain change, and the rate threshold is used to measure the change rate, and the predicted data is classified based on different threshold intervals. If the elevation change of the predicted data exceeds the set threshold and the change rate exceeds the safety range, it is determined that there is an abnormal risk in the shore evolution trend.

[0072] Finally, in response to meeting the warning conditions, the corresponding level of monitoring and warning information is generated. Specifically, according to the matching results of the warning threshold, the warning level is determined, and the corresponding monitoring and warning mechanism is automatically triggered. For low-risk warnings, trend information can be recorded and displayed through data storage and visualization means; for medium- and high-risk warnings, alarm signals can be sent to relevant management systems, and visual analysis data of the prediction results can be provided so that managers can take protective measures in advance. In addition, in order to improve the accuracy of the warning information, the current prediction results can be corrected in combination with historical data to ensure the reliability of the monitoring and warning information.

[0073] The shore evolution monitoring method based on multi-source data fusion in the above embodiment improves the accuracy and efficiency of shore evolution monitoring by fusing data from different observation methods. Terrain partitioning is performed based on the spatial distribution characteristics of the data, so that subsequent processing can adopt different strategies for different terrain areas. In the data fusion process, the data of different measurement methods are adapted to the terrain through a weighted fusion method, so that the final terrain fusion data can fully retain the advantages of each data source, reduce measurement errors, and improve the continuity and consistency of terrain data. Especially in the land-water interface area, the data is adaptively weighted using elevation gradient information, which avoids the problems of data mutation or unstable fusion in traditional methods.

[0074] Based on the terrain fusion data of different periods, the elevation change characteristics and spatial change characteristics are extracted, and a shore evolution prediction model is constructed, so that the monitoring system can dynamically analyze the change trend of the shore. By constructing a time series input sequence and combining the hidden state vector and trainable parameters of the prediction model to model the terrain changes at different observation times, the change pattern in the time series data can be effectively mined, and the prediction ability of the long-term evolution trend of the shore can be improved. In addition, the centroid coordinates of the change area are calculated based on the elevation change matrix and the rate change matrix, and the spatial centroid matrix is ​​generated, so that the spatial characteristics of the shore change can be quantitatively described, thereby providing more intuitive terrain evolution information for subsequent analysis. In terms of monitoring and early warning, combined with the output results of the prediction model, the shore evolution trend is classified based on the elevation change threshold and the rate threshold, and monitoring and early warning information is generated according to the change trend of different levels, so as to improve the response ability of the monitoring system. Therefore, the monitoring accuracy and processing efficiency of the shore area are improved to a certain extent.

[0075] It should be noted that, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0076] Secondly, in an exemplary embodiment of the present disclosure, a shore evolution monitoring device based on multi-source data fusion is also provided. Figure 4 As shown, the shore evolution monitoring device 400 based on multi-source data fusion can be composed of a data acquisition module 401, a terrain partitioning module 402, a data fusion module 403, a model building module 404 and a model prediction module 405. Among them, the data acquisition module 401 can be used to collect the airborne LiDAR point cloud data and multi-beam bathymetry grid data corresponding to the target shore area; the terrain partitioning module 402 can be used to perform terrain partitioning on the shore area based on the spatial distribution characteristics of the airborne LiDAR point cloud data and the multi-beam bathymetry grid data; the data fusion module 403 can be used to perform terrain adaptation fusion on the airborne LiDAR point cloud data and the multi-beam bathymetry grid data according to the result of the terrain partitioning, and obtain terrain fusion data by using a weighted fusion method; the model construction module 404 can be used to obtain the elevation change characteristics and spatial change characteristics corresponding to the shore area according to the terrain fusion data at different times, and construct a shore evolution prediction model according to the elevation change characteristics and the spatial change characteristics; the model prediction module 405 can be used to predict the shore change trend and generate monitoring and early warning information according to the shore evolution prediction model.

[0077] It should be noted that the specific details of each part of the above-mentioned shore evolution monitoring device based on multi-source data fusion have been described in detail in the implementation method of the shore evolution monitoring method based on multi-source data fusion. The undisclosed details can be found in the implementation method content of the method part, and will not be repeated here.

[0078] It should be noted that, although several modules or units of the shore evolution monitoring device based on multi-source data fusion are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0079] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above-mentioned shore evolution monitoring method based on multi-source data fusion is also provided.

[0080] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware embodiments, complete software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0081] Refer to the following Figure 5 The electronic device 500 according to the above-mentioned embodiment of the present disclosure is described. Figure 5 The electronic device 500 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0082] like Figure 5 As shown, the electronic device 500 is in the form of a general computing device. The components of the electronic device 500 may include but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different system components (including the storage unit 520 and the processing unit 510), and a display unit 540.

[0083] The storage unit stores program codes, which can be executed by the processing unit 510, so that the processing unit 510 executes the steps according to various exemplary embodiments of the present disclosure described in the above “Exemplary Method” section of this specification. The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 521 and / or a cache storage unit 522, and may further include a read-only storage unit (ROM) 523.

[0084] The storage unit 520 may also include a program / utility 524 having a set (at least one) of program modules 525, such program modules 525 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0085] Bus 530 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0086] The electronic device 500 may also communicate with one or more external devices 570 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 500, and / or communicate with any device that enables the electronic device 500 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 550. In addition, the electronic device 500 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 560. As shown, the network adapter 560 communicates with other modules of the electronic device 500 through a bus 530. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0087] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0088] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible embodiments, various aspects of the present disclosure can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of the present specification.

[0089] refer to Figure 6 As shown, a program product 600 for implementing the above-mentioned shore evolution monitoring method based on multi-source data fusion according to an embodiment of the present disclosure is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0090] The program product may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0091] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0092] The program code contained on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, electromagnetic waves, etc., or any suitable combination of the foregoing.

[0093] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0094] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0095] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0096] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0097] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A shore evolution monitoring method based on multi-source data fusion, characterized in that: include: Collect airborne LiDAR point cloud data and multi-beam bathymetric grid data corresponding to the target beach area; Based on the spatial distribution characteristics of the airborne LiDAR point cloud data and the multi-beam bathymetric grid data, the beach area is topographically zoned; According to the result of terrain zoning, a weighted fusion method is used to perform terrain adaptation fusion on the airborne LiDAR point cloud data and the multi-beam bathymetric grid data to obtain terrain fusion data; Based on the terrain fusion data of different time periods, the elevation change characteristics and spatial change characteristics corresponding to the beach area are obtained, and a beach evolution prediction model is constructed according to the elevation change characteristics and spatial change characteristics; According to the beach evolution prediction model, the beach change trend is predicted and monitoring and early warning information is generated.

2. The method for monitoring shore evolution based on multi-source data fusion according to claim 1 is characterized in that: The airborne LiDAR point cloud data and multi-beam bathymetric grid data corresponding to the target beach area are collected, including: Using an airborne LiDAR device and a multi-beam bathymetric device to obtain an elevation value corresponding to a target shore area, and constructing the airborne LiDAR point cloud data and the multi-beam bathymetric grid data according to the elevation values; The airborne LiDAR point cloud data and the multi-beam bathymetric grid data are denoised and converted into three-dimensional coordinates to unify the data format.

3. The method for monitoring shore evolution based on multi-source data fusion according to claim 1 is characterized in that: The terrain partitioning of the beach area based on the spatial distribution characteristics of the airborne LiDAR point cloud data and the multi-beam bathymetric grid data includes: Generating a first elevation function according to the airborne LiDAR point cloud data, and obtaining a first complexity index according to the first elevation function; generating a second elevation function according to the multibeam bathymetric grid data, and obtaining a second complexity index according to the second elevation function; Determine the elevation difference between the airborne LiDAR elevation value and the multi-beam bathymetric elevation value at different coordinates on the reference plane; Based on the elevation difference, the first complexity index and the second complexity index, the beach area is divided into a land area, a water area and a land-water boundary area through a preset elevation difference threshold and a complexity threshold.

4. The method for monitoring shore evolution based on multi-source data fusion according to claim 3 is characterized in that: Generating a first elevation function according to the airborne LiDAR point cloud data, and obtaining a first complexity index according to the first elevation function, includes: Generate a first elevation function according to the airborne LiDAR point cloud data ,in, Indicates the plane coordinates of the airborne LiDAR device The elevation value obtained at Based on the first elevation function, by: Determine the first elevation gradient field ,in, Indicates that the airborne LiDAR point cloud data is The rate of change of elevation in a direction, Indicates that the airborne LiDAR point cloud data is The rate of change of elevation in a direction; Based on the first elevation gradient field, by: Determine the first complexity index, wherein: Indicates at point The local computation window around it.

5. The method for monitoring shore evolution based on multi-source data fusion according to claim 4 is characterized in that: According to the result of terrain partitioning, a weighted fusion method is used to perform terrain adaptation fusion on the airborne LiDAR point cloud data and the multi-beam bathymetric grid data to obtain terrain fusion data, including: Based on the first elevation gradient field and the second elevation gradient field, by: Determine the first weight and the second weight ,in, represents the weighting parameter that controls the influence of multibeam sounding gradient, represents the weighting parameter that controls the influence of the airborne LiDAR gradient, represents the second elevation gradient field mapped on the reference plane, Represents the first elevation gradient field mapped on the reference plane. When in land area, is 1, when When located in a water area, is 0; The airborne LiDAR elevation matrix and the multi-beam bathymetric elevation matrix are fused using the first weight and the second weight to obtain the terrain fusion data.

6. The method for monitoring shore evolution based on multi-source data fusion according to claim 1 is characterized in that: The obtaining of elevation change characteristics and spatial change characteristics corresponding to the beach area based on the terrain fusion data of different time periods includes: Select adjacent observation moments, and obtain an elevation change matrix based on the terrain fusion data of different time periods; Selecting a time difference, and obtaining a rate change matrix based on the elevation change matrix; According to the elevation change matrix, the centroid coordinates of the change area are obtained and a spatial centroid matrix is ​​generated.

7. The method for monitoring shore evolution based on multi-source data fusion according to claim 6 is characterized in that: The step of obtaining the centroid coordinates of the changed area and generating a spatial centroid matrix according to the elevation change matrix includes: According to the elevation change matrix ,pass: Get the coordinates of the centroid of the change area. express The area with the highest elevation change at any moment is X The coordinates of the center of mass in the axis direction, express The area with the highest elevation change at any moment is Y The coordinates of the center of mass in the axis direction, Indicated on the reference plane The coordinate set of the area where the elevation change occurs; A spatial centroid matrix is ​​generated according to the centroid coordinates of the change area.

8. The method for monitoring shore evolution based on multi-source data fusion according to claim 1 is characterized in that: The method of constructing a beach evolution prediction model according to the elevation change characteristics and the spatial change characteristics includes: Constructing a time series input sequence based on the elevation change characteristics and spatial change characteristics corresponding to multiple observation moments; Inputting the time series input sequence into the hidden state vector of the prediction model in sequence, and generating a prediction output according to the trainable parameters of the model; Performing a difference evaluation between the predicted output and the actual fused elevation data at different times, and adjusting the trainable parameters of the model according to the difference value; After completing a preset number of time series inputs and difference evaluations, the shore evolution prediction model including hidden states and trainable parameters is output.

9. The method for monitoring shore evolution based on multi-source data fusion according to claim 1, characterized in that: The method of predicting the beach change trend and generating monitoring and early warning information according to the beach evolution prediction model includes: Inputting the terrain fusion data collected in real time into the beach evolution prediction model to obtain predicted beach change data; According to the preset elevation change threshold and rate threshold, judging whether the predicted shore change data reaches the warning condition; In response to satisfying the warning condition, monitoring warning information of a corresponding level is generated.

10. A shore evolution monitoring device based on multi-source data fusion, characterized in that: The device comprises: Data acquisition module, used to collect airborne LiDAR point cloud data and multi-beam bathymetric grid data corresponding to the target beach area; A terrain partitioning module, used to perform terrain partitioning on the beach area based on the spatial distribution characteristics of the airborne LiDAR point cloud data and the multi-beam bathymetric grid data; A data fusion module is used to perform terrain adaptation fusion on the airborne LiDAR point cloud data and the multi-beam bathymetric grid data according to the result of terrain zoning by using a weighted fusion method to obtain terrain fusion data; A model building module, used to obtain the elevation change characteristics and spatial change characteristics corresponding to the beach area according to the terrain fusion data at different times, and to build a beach evolution prediction model according to the elevation change characteristics and spatial change characteristics; The model prediction module is used to predict the beach change trend and generate monitoring and early warning information based on the beach evolution prediction model.

Images