A shoreline evolution early warning method based on deformation characteristics of beach cross section

Abstract

The present application relates to the technical field of shoreline evolution, and specifically relates to a shoreline evolution early warning method based on beach cross-section deformation characteristics, comprising multi-source data fusion processing: integrating historical and real-time monitoring data of the target shoreline, including water depth data and wave data, constructing a multi-period beach cross-section topographic data set, the data set comprising a cross-section elevation point set and plane coordinates; deformation feature vectorization: based on the cross-section elevation point set, identifying deformation feature points with an elevation change rate exceeding a dynamic threshold, and outputting a deformation feature vector set comprising plane displacement, vertical scouring and silting amount, and displacement direction; engineering-level early warning decision: performing time-space aggregation analysis on the deformation feature vector set, and generating three-level engineering early warning signals according to the vector space density and direction consistency. The present application constructs a multi-source monitoring data fusion processing flow, realizes the time-space unification and wave-driven correction of the beach cross-section elevation point set, and significantly improves the spatial accuracy and time response capability of shoreline evolution monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of shoreline evolution, and in particular to a shoreline evolution early warning method based on shore cross-sectional deformation characteristics. Background Art

[0002] In recent years, multi-source data acquisition technology has gradually matured, providing the possibility for high-precision modeling of shoreline evolution processes. However, how to integrate multi-period terrain data with temporal and spatial heterogeneity, wave-driven information, and engineering response logic into a complete data processing and early warning decision-making chain still lacks systematic method support. In particular, in terms of deformation feature recognition and early warning level classification, a closed-loop mechanism with dynamic thresholds, adaptive directional analysis, and engineering response linkage as the core has not yet been established. Therefore, it is urgent to propose a shoreline evolution early warning method for engineering applications with high-resolution dynamic monitoring and response capabilities to achieve intelligent support for the entire process from data fusion to risk response. Summary of the Invention

[0003] Based on the above objectives, the present invention provides a shoreline evolution early warning method based on the deformation characteristics of the shore section.

[0004] A shoreline evolution early warning method based on shore cross-sectional deformation characteristics comprises the following steps:

[0005] S1, multi-source data fusion processing: integrating historical and real-time monitoring data of the target shoreline, including water depth data and wave data, to construct a multi-period shore cross-section topography dataset, which includes a cross-section elevation point set and plane coordinates;

[0006] S2, deformation feature vectorization: Based on the cross-section elevation point set, identify deformation feature points whose elevation change rate exceeds the dynamic threshold, and output a deformation feature vector set including plane displacement, vertical scouring and deposition, and displacement direction;

[0007] S3, engineering-level early warning decision: Perform spatiotemporal aggregation analysis on the deformation feature vector set and generate a three-level engineering early warning signal based on the vector space density and direction consistency.

[0008] Furthermore, the S1 includes:

[0009] S11, data spatiotemporal alignment: historical water depth, real-time water depth and wave data are spatiotemporally matched to establish a unified spatiotemporal coordinate system;

[0010] S12, wave-terrain coupling correction: using the wave refraction-diffraction model, the original elevation data is corrected according to parameters such as wave height, wave direction and shoreline direction;

[0011] S13, multi-source data fusion: Based on the accuracy differences of each measurement data, a weighted Bayesian method is used to fuse multi-source elevation data;

[0012] S14, cross-section data generation: Construct a high-density cross-section sequence in the normal direction of the shoreline, collect multi-period fused elevation data, and form a cross-section data set that meets the needs of time series analysis.

[0013] Furthermore, the S12 includes:

[0014] S121, Calculation of wave impact: Calculate the impact of waves on the original elevation data based on the measured significant wave height, wave incident angle, shoreline normal angle, and the type of beach bottom.

[0015] S122, Corrected elevation calculation: Based on the original elevation data, the wave impact is superimposed and an exponential attenuation factor is introduced to obtain the corrected elevation value.

[0016] Furthermore, the S14 includes:

[0017] S141, cross-section layout construction: Arrange a sequence of equally spaced cross sections along the normal direction of the coastline, and determine the spatial position and number of each cross section;

[0018] S142, multi-period data collection and organization: high-density sampling points are deployed on each section, and the fused elevation data of each time node are collected to construct a section dataset including spatial position and temporal evolution information.

[0019] Furthermore, the S2 includes:

[0020] S21, elevation change rate calculation: Calculate the elevation change rate of a section point between two adjacent monitoring times to assess the local scouring and deposition rate;

[0021] S22, dynamic threshold calibration: Calculate the standard deviation of the elevation change rate based on historical data, and introduce a sensitivity coefficient to set the dynamic threshold;

[0022] S23, feature point identification: select points whose elevation change rate exceeds the dynamic threshold as deformation feature points to identify significant erosion and deposition areas on the beach;

[0023] S24, displacement vector generation: calculate the displacement and direction angle of the feature point in the horizontal and vertical directions, and correct the direction offset caused by wave propagation;

[0024] S25, vector set construction: Summarize the three-dimensional displacement information of all feature points to form a feature vector set.

[0025] Furthermore, the S22 includes:

[0026] S221, calculation of standard deviation of elevation change rate: During the historical monitoring period, the change rate data of the elevation points of the cross section are collected and their standard deviation is calculated;

[0027] S222, dynamic threshold setting: based on the calculated standard deviation, a sensitivity coefficient is introduced to determine the dynamic threshold for feature point screening.

[0028] Furthermore, the S24 includes:

[0029] S241, Calculation of Planar and Vertical Displacement: Calculate the planar displacement of the deformation feature point in the x and y directions and the vertical change in the elevation direction between two adjacent monitoring times to obtain the actual movement amplitude in the three-dimensional space;

[0030] S242, displacement direction angle calculation: based on the directional relationship of the plane displacement, calculate the initial displacement direction angle of the feature point;

[0031] S243, wave direction offset correction: Combine wave height and incident angle parameters to estimate the disturbance offset of the displacement direction caused by wave propagation, and correct the initial direction angle to obtain the true direction of movement.

[0032] Furthermore, the S3 includes:

[0033] S31, space-time grid division: using the model grid as the spatial unit, divide the time into windows of 30 days to construct space-time units;

[0034] S32, vector density calculation: count the number of abnormal points and the total number of points in each spatiotemporal unit, calculate the coverage rate, and measure the spatial aggregation degree of local deformation;

[0035] S33, Directional consistency analysis: Calculate the consistency of deformation direction using the circular statistics method of direction angles;

[0036] S34, warning signal generation: Generate red, yellow, and blue warning levels based on density and direction consistency indicators;

[0037] S35, engineering response trigger: Response measures are linked according to the warning level, including red warning to start sand replenishment, yellow warning to set up submerged dikes, and blue warning to increase monitoring frequency.

[0038] Furthermore, the S31 includes:

[0039] S311, spatial grid setting: Based on the existing spatial grid in the shore evolution model, it is used as the basic analysis unit;

[0040] S312, time window setting: setting a fixed time length to divide the time axis into continuous non-overlapping windows;

[0041] S313, Spatiotemporal Unit Aggregation: Combine spatial grids and time windows to construct spatiotemporal analysis units.

[0042] Furthermore, the S33 includes:

[0043] S331, direction angle decomposition: decompose the direction angles of all deformation feature vectors in the spatiotemporal unit into cosine components and sine components, which represent the projections in the horizontal and vertical directions respectively;

[0044] S332, Circular Statistics Consistency Calculation: Using the circular statistics method, calculate the mean of the decomposed directional components and synthesize the directional consistency index.

[0045] Beneficial effects of the present invention:

[0046] The present invention realizes the spatiotemporal unification and wave-driven correction of the shore section elevation point set by constructing a multi-source monitoring data fusion processing flow, significantly improving the spatial accuracy and temporal response capability of shoreline evolution monitoring. Compared with the traditional method that relies on fixed sections and static elevation difference criteria, the present invention introduces a weighted Bayesian fusion algorithm and a wave-terrain coupling correction mechanism, which can dynamically restore the true section evolution state under heterogeneous data conditions, providing a more stable data basis for subsequent deformation analysis and engineering intervention.

[0047] This invention is the first to construct an engineering-level early warning criterion system with deformation feature vector sets as the core. Combined with dynamic threshold selection, directional consistency circle statistical analysis and spatiotemporal grid aggregation mechanism, it can achieve accurate identification of unstable beach evolution areas and early warning level classification; at the same time, the early warning results can directly link engineering response measures such as artificial sand replenishment, submerged dike layout and monitoring density adjustment, opening up the "perception-assessment-intervention" closed loop, and has significant engineering practical value and intelligent application potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0050] Figure 2 A graph is constructed for features of an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0052] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0053] like Figure 1-Figure 2 As shown, a shoreline evolution early warning method based on shore cross-sectional deformation characteristics includes the following steps:

[0054] S1, multi-source data fusion processing: integrating historical and real-time monitoring data of the target coastline, including water depth data and wave data, to construct a multi-period shore cross-sectional topographic dataset, which includes cross-sectional elevation point sets and plane coordinates;

[0055] S1 specifically includes:

[0056] S11, Data time-space alignment: To achieve consistent processing of multi-source monitoring data, it is first necessary to unify the water depth and wave data from different times and sources to the same time-space reference, construct a four-dimensional data structure including spatial coordinates and plane elevation, and represent the historical water depth data separately. , real-time water depth data Establish a unified time-space coordinate system with the wave data W, expressed as:

[0057] ;

[0058] ;

[0059] ;

[0060] Among them, i is the historical wave data index, is the plane coordinate of the i-th historical water depth point (unit: m), is the elevation value of the i-th historical water depth point (unit: m), is the collection time of the i-th historical water depth data, H is the significant wave height (unit: m), is the wave incident angle (unit: ), Is the historical monitoring period, the time span usually meets Coastline evolution is a slow-changing process, and at least five years of historical data are needed to reflect medium- and long-term trends and cyclical changes, to ensure the stability and statistical significance of deformation trend analysis. is the plane coordinate of the jth real-time water depth point (unit: m), j is the real-time wave data index, is the elevation value of the jth real-time water depth point (unit: m), is the jth real-time water depth data acquisition time, It is the real-time monitoring period, which is usually 30 days can capture short-term sudden changes in scouring and deposition or abnormal erosion events, while 30 days can cover short-period disturbances such as typical storm surge processes and monthly periodic waves. is the coordinate of the kth wave point, is the effective wave height at the kth wave point, with a value range of 0.2-3.5. It is used to capture short-term sudden changes in scouring and deposition or abnormal erosion events. 30 days can cover short-period disturbances such as typical storm surge processes and monthly periodic waves. is the kth wave incident angle, ranging from , is the direction angle, defined within the circumference, covering any wave propagation direction, and correcting the propagation direction with the shoreline normal angle. is the wave data collection time, is the wave detection time set, and The value depends on the coastline topography and ranges from ,Generally, the elevation of the coastal zone measurement area is above and below the intertidal zone, covering the underwater scour area and the bank slope accumulation area. The specific range depends on the region;

[0061] S12, wave-terrain coupling correction: Since wave action can cause water fluctuations and measurement disturbances, wave effect correction needs to be performed on the original water depth or terrain data. Based on the wave refraction-diffraction model, the elevation correction under actual wave conditions is calculated, and the wave height attenuation factor is introduced for adjustment, which is expressed as:

[0062] ;

[0063] in, is the original water depth or elevation data (unit: m), is the elevation data after wave correction (unit: m), is the wave effect correction amount (unit: m), , k is the bottom coefficient, ranging from 0.15 to 0.25, which characterizes the attenuation ability of wave energy on different bottoms (such as sand, gravel, and clay). Sandy coastlines have a moderate response to wave action, and the empirical value is usually set in this range to balance the incident wave energy and terrain disturbance. H is the effective wave height, ranging from 0.2 to 3.5. The effective wave height is used to characterize the intensity of wave interference on measurement. The annual wave height in the project area is usually 0.5–1.5m, and extreme storm waves can reach 3.5m. When the wave height is lower than 0.2m, the wave impact can be ignored, so the lower limit is set to 0.2m. Is the shoreline normal direction angle (unit: ), It is the base wave height, which is the average value of the project area in recent years (unit: m), with a value range of 0.5-2.0. It represents the benchmark scale of the typical wave energy level in the region. The annual average wave height of the project area is usually taken. 0.5m represents a relatively gentle shore section, and 2.0m can be used for high-energy shore sections. It is used to adjust the attenuation amplitude of wave impact as the intensity changes. , The visual inspection area is located in the intertidal zone or underwater terrain, generally covering scour troughs, bank slopes and accumulation areas, and is suitable for conventional application ranges of laser radar and water depth detection. ;

[0064] S13, multi-source data fusion: Due to the differences in spatial density, accuracy and time interval between different measurement methods, fusion processing is required to improve the robustness of the cross-section elevation data. Based on the corrected multi-source data, a weighted Bayesian fusion model is used to construct a fused elevation dataset, which is expressed as:

[0065] ;

[0066] in, is the fused elevation value, s is the data source index (such as unmanned survey ship, airborne LiDAR, historical archives, etc.), is the sth data source at location The original elevation value, is the fusion weight of the sth data source, which is inversely proportional to the measurement uncertainty. The smaller it is, the more credible it is. It is the measurement standard deviation of the sth data source, reflecting the measurement accuracy of various data sources and the core basis for Bayesian fusion weight allocation. The airborne LiDAR system has high accuracy, with a standard deviation range of 0.02–0.05m. The multi-beam bathymetry system is affected by environmental interference and attitude accuracy, with a standard deviation of about 0.10–0.15m. The error of manual interpretation, historical maps, or underwater image measurement is usually not less than 0.20m due to its non-real-time and subjective nature. It is a smoothing factor used to avoid division by zero or extremely small values ​​during the weight calculation process, ensuring numerical stability and preventing division by zero errors. It is fixed at 0.01, which is small enough not to interfere with the actual weight distribution. At the same time, it effectively suppresses the risk of overfitting caused by misjudging a data source as extremely high accuracy. N is the number of data sources, ranging from 2 to 5, covering typical shore monitoring methods such as unmanned survey ships, airborne lidar, historical archives, and wave condition inversion. In actual projects, more than five data sources often lead to an exponential increase in computational complexity, while marginal accuracy improvements are not significant. The introduction of low-quality data may even reduce the overall fusion effect. Therefore, maintaining a moderate number of representative data sources is an important factor in improving fusion efficiency and quality.

[0067] S14, cross-section data set generation: To meet the requirements of shoreline deformation feature identification and time series analysis, it is necessary to construct a high-density, equally spaced cross-section grid in the shoreline normal direction. Each cross-section consists of multi-period fused elevation data, expressed as:

[0068] ;

[0069] Where m is the section number, p is the index of the sampling point within the section, and P is the sampling density of each section (no less than 200 points / 100 meters). The section sampling density P determines the spatial resolution of the beach profile deformation. Sampling at an average interval of 0.5 meters can better capture local erosion and deposition changes, step evolution, and micro-geomorphological transition characteristics. If the sampling density is too low, the section morphology may be blurred, unable to support accurate elevation change rate analysis and slope extraction, and affecting the accuracy of subsequent deformation identification. is the nth monitoring time point, is point p at time The fusion elevation range is , corresponding to geomorphological characteristic zones such as shallow water scour troughs, intertidal zones and slope transition zones. This range not only reflects the common dynamic response areas of beaches, but also matches the observation capabilities of mainstream bathymetry and laser measurement systems, which is conducive to ensuring full data coverage and supporting complete cross-section analysis.

[0070] S2, deformation feature vectorization: Based on the cross-section elevation point set, identify deformation feature points whose elevation change rate exceeds the dynamic threshold, and output a deformation feature vector set including plane displacement, vertical scouring and deposition, and displacement direction;

[0071] S2 specifically includes:

[0072] S21, Elevation change rate calculation: In order to identify the terrain evolution trend of the beach at different time nodes, the output cross-section elevation point set , calculate adjacent periods and The elevation change rate of , which reflects the local scouring and deposition speed, is an important basis for subsequent feature point screening and is expressed as:

[0073] ;

[0074] Among them, x and y are the plane coordinate positions of the elevation point, indicating the spatial position of a point in the cross section in the plane. The coordinate range is determined by the geographical location of the specific bank section. It is generally the measured coordinates under a unified projected coordinate system. There is no fixed numerical range limit, but it must be consistent with the cross section layout. for point In time The fused elevation value of for point In time The fusion elevation value of , which covers the shallow sea area, intertidal zone and bank slope, and the elevation result after fusion of multi-source data usually falls within this range, which can effectively characterize the changes of scour trough, beach surface and accumulation body. It refers to two adjacent observation time points, typically with an interval of 1 to 90 days. The time interval depends on the monitoring period. If it is used for long-term evolution trend analysis, monthly sampling is recommended; if storm surge processes or rapid erosion are of concern, daily sampling is recommended. The time difference between the two periods cannot be zero, and the unit is uniformly day. Yes The elevation change rate has no fixed value and the range is , with the unit of m / year, reflects the intensity of erosion and deposition of local landforms. Positive values ​​indicate deposition, while negative values ​​indicate erosion. The value is affected by factors such as waves, tides, and sediment supply. Under normal circumstances, the annual variation will not exceed ±5 meters, but may be greater in extreme events.

[0075] S22, dynamic threshold calibration: Due to the significant differences in scouring and sedimentation fluctuation intensity in different bank sections and time periods, a fixed threshold may lead to a decrease in recognition accuracy. Therefore, a dynamic threshold mechanism is introduced. Based on the historical sediment scouring and sedimentation simulation results, the standard deviation of the elevation change rate is calculated. , and through the sensitivity coefficient Set the dynamic threshold for filtering, expressed as:

[0076] ;

[0077] in, During the historical monitoring period The standard deviation of the internal elevation change rate is used to characterize the intensity of erosion and deposition fluctuations in the area. It is a discrete measure of the rate of change, ranging from 0.05 to 1.0, and is used to characterize the uncertainty of historical scouring and deposition intensity. Smaller, exposed shore sections or areas affected by strong waves Larger, Is the sensitivity coefficient, which is used to adjust the magnification of the dynamic threshold relative to the standard deviation. The recommended value range is 1.5-2.0. The screening intensity of the dynamic threshold is controlled. A smaller sensitivity coefficient is suitable for sensitive shore sections and can identify more scouring and silting areas. A larger sensitivity coefficient is suitable for stable shore sections and emphasizes the exclusion of small disturbances. The recommended value range can effectively balance the relationship between "false alarms and missed alarms" and has strong engineering adaptability. It is a dynamic threshold used to identify points with significantly abnormal elevation change rates. It is the basis for subsequent feature point extraction and has a value range of 0.1-2.0. The numerical range of fluctuates with the bank section, sampling accuracy and simulation results, and usually corresponds to the range of change speed of significant scouring and silting points. The dynamic setting method can avoid the distortion problem caused by a unified fixed value.

[0078] S23, feature point identification: Mark the measurement points whose change rate exceeds the dynamic threshold as deformation feature points to ensure that the identified area has a significant erosion and deposition trend. The feature point screening criterion is expressed as:

[0079] ;

[0080] Only when the elevation change rate exceeds the dynamic threshold When , it is considered that there is significant scouring and deposition change at this point;

[0081] S24, displacement vector generation: To further characterize the three-dimensional deformation trend of the feature point, its plane displacement, vertical change, and displacement direction angle need to be calculated. The direction angle calculation takes into account the disturbance effect of wave propagation on the true direction. The overall vector generation process is expressed as:

[0082] ;

[0083] in, is the directional deviation caused by the wave incident angle, is the corresponding wave parameter, H is the effective wave height, which indicates the wave energy intensity, , the significant wave height determines the intensity of wave action, affects the degree of terrain disturbance and the amplitude of directional deviation correction, is the wave incident angle, relative to the shoreline normal, The incident angle determines the direction of the wave impact on the shoreline and is one of the input variables of the direction correction model. It is necessary to consider the omnidirectional propagation scenario, so the full circumference range is used. , the oblique propagation of waves will cause coastal currents or lateral impacts, causing the actual displacement direction to deviate from the elevation gradient direction. According to the measured and simulated results, the deviation angle is generally within ± Within, depending on the wave energy and shoreline shape, The characteristic point at two adjacent moments and The displacement in the x and y planes ranges from 0 to 10. The plane displacement reflects the horizontal advancement, retreat or lateral expansion of the beach. The annual displacement of most shore sections is within a few meters. Extreme erosion or accumulation areas (such as gullies and port entrances) may exceed 10 meters. Therefore, this range is set to cover both conventional and key areas. is the change in the vertical direction (elevation) of the feature point, and its value range is , represents the amount of local scouring or silting, with positive values ​​representing accumulation and negative values ​​representing erosion. Limited by the rate of landform evolution on the bank slope, this range can reflect the typical deformation degree within a year and covers most scenarios of bank scouring and silting. Is the plane displacement direction angle, indicating the feature point from arrive The true direction of movement is in the range of To fully express the two-dimensional displacement direction, the angle value must cover the entire polar coordinate space. The direction angle is expressed by It is obtained by calculation and corrected by wave influence, which is suitable for describing the deformation trend of the shore in any direction.

[0084] S25, vector set construction: The three-dimensional displacement information of all deformation feature points is organized into a vector set, including plane displacement, vertical scouring and deposition, and direction angle after wave correction. The final deformation feature vector set is expressed as:

[0085] ;

[0086] Among them, V is the deformation feature vector set, which represents the three-dimensional displacement information set of all identified deformation feature points, which is used for engineering early warning input or risk clustering modeling. f is the feature point index, and the output result will serve as the input basis of the engineering early warning model. is the displacement of the feature point f in the x direction, The displacement of the feature point f in the y direction, It is the change in the vertical direction (elevation) of the feature point f, which is used to characterize the local scouring and deposition intensity. It is the displacement direction angle of the characteristic point f after wave disturbance correction.

[0087] S3, engineering-level early warning decision: Perform spatiotemporal aggregation analysis on the deformation feature vector set and generate a three-level engineering early warning signal based on the vector space density and direction consistency.

[0088] S3 specifically includes:

[0089] S31, Spatiotemporal grid division: To achieve regional clustering and temporal dynamic analysis of deformation characteristics, the spatial grid in the shore evolution model is used as the basic unit and the fixed time length is used. The time window is divided into days, and the spatiotemporal analysis unit is constructed to collect all deformation feature vectors of the area within the time period, which is expressed as:

[0090] ;

[0091] in, is the deformation feature vector set, is the spatial grid index, b is the time window index, It is The spatial grid and the space-time unit under the b-th time window, The 30-day time scale takes into account both the evolution of the deformation process and the controllability of engineering responses: on the one hand, beach deformation under natural conditions usually shows a cumulative change trend on a monthly scale, and the 30-day window can effectively capture typical evolutionary patterns such as local scouring and silting, and slope foot retreat. On the other hand, from the perspective of management practice, the monthly period is a common cycle for engineering inspections, sand replenishment decisions, and operation and maintenance scheduling, which has good engineering compatibility and response efficiency. Therefore, choosing 30 days as the time window is consistent with the natural time sequence of landform evolution and is also convenient for subsequent dynamic early warning linkage and engineering intervention strategy formulation.

[0092] S32, vector space density calculation: To measure the degree of aggregation of deformation features in a spatiotemporal unit, the ratio of deformation feature points in each unit to the total sampling points is calculated. This ratio reflects the density of deformation events in space and is an important reference indicator for early warning classification. The coverage rate of deformation points in each spatiotemporal unit is calculated as:

[0093] ;

[0094] in, It is The number of deformation feature points within the space-time unit, is the total number of sampling points in the corresponding spatial grid, It is Deformation point coverage of space-time unit;

[0095] S33, Directional consistency analysis: To determine whether the deformation is directional consistent, the circular statistics method is used to perform cosine-sine vector averaging on the directional angles of all feature points in each unit and calculate the concentration of its directional distribution. The closer this value is to 1, the more uniform the deformation trend in the region is, expressed as:

[0096] ;

[0097] in, is the true displacement direction angle, It is Directional consistency index of spatiotemporal units;

[0098] S34, Early Warning Signal Generation: Based on Spatial Density Indicators Consistency with direction , generating a three-level warning level. This rule comprehensively considers the intensity and directional convergence of local scouring and silting activities, which helps to accurately identify high-risk shore sections. The warning classification rule is expressed as:

[0099] ;

[0100] in, Calculated using the circular statistics method, the value range is 0-1. Indicates that most displacement vectors tend to be consistent and have strong directional aggregation, which usually corresponds to the overall advancement of the shoreline or severe local scouring, which is easy to cause stress concentration in engineering structures. Therefore, it is used as the lower limit of the red warning trigger. When the directional convergence is at a medium level, it indicates that the shoreline deformation has a certain dominant direction, but is greatly affected by local disturbances. It is common in boundary fluctuation areas such as coastal oblique erosion and local high tide intrusion. This range is suitable for yellow warning, which is used to indicate secondary risks. When the directional dispersion is large, it usually means that the scouring and deposition process has obvious local disturbance or staggered trend, and does not have the characteristics of continuous advancement. However, the spatial variation may imply potential instability, so it is set as the blue warning range, suggesting that the monitoring frequency needs to be increased; when When , it means that nearly half of the sampling points in the space-time grid have undergone significant deformation, indicating that the local area has evolved violently and has high-risk aggregation. Therefore, 40% is regarded as the critical threshold of space fusion and is used for red warning judgment. When , it means that the deformation points begin to have regional clustering characteristics, but it is not enough to form an obvious destructive trend. Therefore, 30% is used as the lower limit of the yellow warning spatial density to capture the potential risk evolution earlier. When , it means that the distribution of small-scale deformation feature points is scattered but there is a tendency for continuity. Although the risk level is low, it is still necessary to trigger a blue warning to guide the deployment of intensified monitoring and avoid risk omissions.

[0101] S35, Engineering Response Trigger: Based on the warning level, the corresponding engineering response strategy is automatically matched to form a risk-response closed-loop mechanism. The red level indicates severe erosion or accumulation trends, requiring immediate intervention. Yellow indicates moderate risk, and physical protection measures are recommended. Blue indicates early warning, and increased monitoring density is recommended. The warning signal is mapped to the corresponding engineering measures as follows:

[0102] .

[0103] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present invention is limited to these examples. Within the scope of the present invention, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity.

Claims

1. A shoreline evolution early warning method based on shore cross-sectional deformation characteristics, characterized by: The following steps are involved: S1, multi-source data fusion processing: Integrate historical and real-time monitoring data of the target shoreline, including water depth data and wave data, to construct a multi-period shore cross-section topography dataset. The shore cross-section topography dataset includes a cross-section elevation point set and plane coordinates; specifically, it includes: S11, data spatiotemporal alignment: historical water depth, real-time water depth and wave data are spatiotemporally matched to establish a unified spatiotemporal coordinate system; S12, wave-terrain coupling correction: using the wave refraction-diffraction model, the original elevation data is corrected according to parameters such as wave height, wave direction and shoreline direction; S13, multi-source data fusion: Based on the accuracy differences of each measurement data, a weighted Bayesian method is used to fuse multi-source elevation data; S14, cross-section data generation: construct a high-density cross-section sequence in the direction of the shoreline normal, collect multi-period fusion elevation data, and form a cross-section data set that meets the needs of time series analysis; S2, deformation feature vectorization: Based on the cross-section elevation point set, identify deformation feature points whose elevation change rate exceeds the dynamic threshold, and output a deformation feature vector set including plane displacement, vertical scouring and deposition, and displacement direction; S3, engineering-level early warning decision-making: Perform spatiotemporal aggregation analysis on the deformation feature vector set and generate a three-level engineering early warning signal based on the vector space density and direction consistency; specifically, it includes: S31, space-time grid division: using the model grid as the spatial unit, divide the time into windows of 30 days to construct space-time units; S32, vector density calculation: count the number of abnormal points and the total number of points in each spatiotemporal unit, calculate the coverage rate, and measure the spatial aggregation degree of local deformation; S33, Directional consistency analysis: Calculate the consistency of deformation direction using the circular statistics method of direction angles; S34, warning signal generation: Generate red, yellow, and blue warning levels based on density and direction consistency indicators; S35, engineering response trigger: Response measures are linked according to the warning level, including red warning to start sand replenishment, yellow warning to set up submerged dikes, and blue warning to increase monitoring frequency.

2. The shoreline evolution early warning method based on shore cross-sectional deformation characteristics according to claim 1 is characterized in that: The S12 includes: S121, Calculation of wave impact: Calculate the impact of waves on the original elevation data based on the measured significant wave height, wave incident angle, shoreline normal angle, and the type of beach bottom. S122, Corrected elevation calculation: Based on the original elevation data, the wave impact is superimposed and an exponential attenuation factor is introduced to obtain the corrected elevation value.

3. The shoreline evolution early warning method based on shore cross-sectional deformation characteristics according to claim 1 is characterized in that: The S14 includes: S141, cross-section layout construction: Arrange a sequence of equally spaced cross sections along the normal direction of the coastline, and determine the spatial position and number of each cross section; S142, multi-period data collection and organization: high-density sampling points are deployed on each section, and the fused elevation data of each time node are collected to construct a section dataset including spatial position and temporal evolution information.

4. The shoreline evolution early warning method based on shore cross-sectional deformation characteristics according to claim 3 is characterized in that: The S2 includes: S21, elevation change rate calculation: Calculate the elevation change rate of a section point between two adjacent monitoring times to assess the local scouring and deposition rate; S22, dynamic threshold calibration: Calculate the standard deviation of the elevation change rate based on historical data, and introduce a sensitivity coefficient to set the dynamic threshold; S23, feature point identification: select points whose elevation change rate exceeds the dynamic threshold as deformation feature points to identify significant erosion and deposition areas on the beach; S24, displacement vector generation: calculate the displacement and direction angle of the feature point in the horizontal and vertical directions, and correct the direction offset caused by wave propagation; S25, vector set construction: Summarize the three-dimensional displacement information of all feature points to form a feature vector set.

5. The shoreline evolution early warning method based on shore cross-sectional deformation characteristics according to claim 4 is characterized in that: The S22 includes: S221, calculation of standard deviation of elevation change rate: During the historical monitoring period, the change rate data of the elevation points of the cross section are collected and their standard deviation is calculated; S222, dynamic threshold setting: based on the calculated standard deviation, a sensitivity coefficient is introduced to determine the dynamic threshold for feature point screening.

6. The shoreline evolution early warning method based on shore cross-sectional deformation characteristics according to claim 4 is characterized in that: The S24 includes: S241, Calculation of Planar and Vertical Displacement: Calculate the planar displacement of the deformation feature point in the x and y directions and the vertical change in the elevation direction between two adjacent monitoring times to obtain the actual movement amplitude in the three-dimensional space; S242, displacement direction angle calculation: based on the directional relationship of the plane displacement, calculate the initial displacement direction angle of the feature point; S243, wave direction offset correction: Combine wave height and incident angle parameters to estimate the disturbance offset of the displacement direction caused by wave propagation, and correct the initial direction angle to obtain the true direction of movement.

7. The shoreline evolution early warning method based on shore cross-sectional deformation characteristics according to claim 1 is characterized in that: The S31 includes: S311, spatial grid setting: Based on the existing spatial grid in the shore evolution model, it is used as the basic analysis unit; S312, time window setting: setting a fixed time length to divide the time axis into continuous non-overlapping windows; S313, Spatiotemporal Unit Aggregation: Combine spatial grids and time windows to construct spatiotemporal analysis units.

8. The shoreline evolution early warning method based on shore cross-sectional deformation characteristics according to claim 1 is characterized in that: The S33 includes: S331, direction angle decomposition: decompose the direction angles of all deformation feature vectors in the spatiotemporal unit into cosine components and sine components, which represent the projections in the horizontal and vertical directions respectively; S332, Circular Statistics Consistency Calculation: Using the circular statistics method, calculate the mean of the decomposed directional components and synthesize the directional consistency index.

Images