A land-sea integrated ecological restoration partition method and management platform system

Abstract

The application discloses a land-sea integrated ecological restoration zoning method and a management platform system, relates to the technical field of coastal ecological restoration zoning, and comprises the following steps: collecting storm tide data and coastline data of a coastal zone region, and first judging whether the coastal zone region is suitable for being divided into an ecological seawall restoration region; performing correlation analysis on the coastal zone region to obtain similar regions of the coastal zone region; predicting changes of the coastline of the coastal zone region to obtain predicted coastline data; performing straightness analysis on the coastline, and secondly judging whether the coastal zone region is suitable for being divided into the ecological seawall restoration region; and the application is used for solving the problem that the existing coastal ecological restoration zoning technology cannot screen similar coastal zone regions when dividing the ecological seawall restoration region in the coastal zone region, and simultaneously predicting future changes of the coastline according to corresponding coastline change data, thereby improving the accuracy of ecological seawall restoration zoning.

Description

Technical Field

[0001] This invention relates to the field of coastal ecological restoration zoning technology, specifically to an integrated land-sea ecological restoration zoning method and management platform system. Background Technology

[0002] Coastal ecological restoration zoning technology refers to a standardized technical system that aims to solve coastal ecological degradation problems, enhance marine disaster prevention capabilities, and coordinate ecological security and economic functions. Based on three dimensions—marine disaster risk characteristics, ecological conditions, and spatial planning requirements of the coastal zone—it uses a systematic technical process to scientifically divide coastal areas into different restoration types through multi-source data collection and quantitative assessment, risk and ecological compatibility screening, spatial unit classification and delineation, and dynamic monitoring and optimization.

[0003] Ecological seawall restoration zones refer to specific coastal spatial units within low- to medium-risk marine disaster zones. These zones aim to replace the single defensive function of traditional rigid seawalls, achieving flood and tide prevention, ecological connectivity, and social services. They utilize technologies such as ecological materials, three-dimensional structural design, and biological community restoration to ecologically transform existing seawalls or construct new ecological seawalls. Their core characteristic is a balance between defensive capabilities and ecological functions. Ecological seawalls are often constructed in coastal areas with relatively straight coastlines. Current coastal ecological restoration zoning techniques often focus on the current coastline condition when defining ecological seawall restoration zones, neglecting changes in the coastline. However, the future straightness of the coastline is a core morphological characteristic of coastline change; if not considered... As coastlines change from straight to winding and fragmented due to erosion and sea-level rise, the core functions of ecological seawalls are directly damaged. Therefore, when delineating ecological seawall restoration zones, it is necessary to predict the future straightness of the coastline to avoid the loss of protective capabilities due to changes in coastline morphology after the construction of ecological seawalls. However, when predicting future changes based on historical changes in the coastline of a coastal zone, the reliability and accuracy of the predictions are often low because there is limited data on coastline changes in a single coastal zone. Therefore, existing coastal ecological restoration zoning technologies cannot select similar coastal zones when delineating ecological seawall restoration zones in coastal zones, and cannot predict future changes in the coastline based on corresponding coastline change data, thereby improving the accuracy of ecological seawall restoration zone delineation. Summary of the Invention

[0004] This invention aims to at least partially address one of the technical problems in the prior art. It collects storm surge data and coastline data from coastal areas, and based on this storm surge data, firstly determines whether the coastal area is suitable for ecological seawall restoration. Then, it performs correlation analysis to identify similar areas within the coastal area. Based on the coastline data, it predicts changes in the coastline of the coastal area, obtaining predicted coastline data. Finally, it performs coastline straightness analysis and makes a second determination to determine whether the coastal area is suitable for ecological seawall restoration. This addresses the problem that existing coastal ecological restoration zoning techniques cannot screen for similar coastal areas when dividing coastal areas into ecological seawall restoration zones, and simultaneously predict future changes in the coastline based on corresponding coastline change data, thereby improving the accuracy of ecological seawall restoration zone delineation.

[0005] To achieve the above objectives, firstly, this application provides a land-sea integrated ecological restoration zoning method, comprising the following steps: Collect storm surge data and coastline data of the coastal zone, and make an initial judgment based on the storm surge data of the coastal zone as a suitable area for ecological seawall restoration. Based on the coastline data of the coastal area, a correlation analysis of the coastal area is performed to obtain similar areas in the coastal zone, which are denoted as similar coastal area data. Based on similar coastal area data and coastal area coastline data, the changes in the coastline of the coastal zone are predicted to obtain the predicted coastline data. Based on the coastal area coastline data and predicted coastline data, a coastline straightness analysis was conducted, and a second assessment was made as to whether the coastal zone is suitable for being designated as an ecological seawall restoration area.

[0006] Further, collecting storm surge data and coastline data of the coastal zone, and making an initial determination based on the storm surge data to determine whether the coastal zone is suitable for ecological seawall restoration, includes the following sub-steps: For any coastal zone to be partitioned, it is denoted as the first coastal zone; the storm surge hazard risk index corresponding to the first coastal zone is obtained, and the corresponding storm surge hazard risk level is obtained, which is denoted as the coastal storm surge data of the first coastal zone; Set the first time period as T1; periodically obtain the average coastline of the first coastal area within the corresponding period according to T1, and record the time span of the corresponding period. Arrange them in chronological order and record them as coastline 1 to coastline n1, which are recorded as the coastline data of the first coastal area. Repeatedly collect storm surge data and coastline data of multiple coastal zones in the area where the first coastal zone is located, and record the collected coastal zones as reference coastal zones. Set thresholds corresponding to the storm surge hazard index and storm surge hazard level, and denote them as k1 and k2 respectively. If the storm surge hazard index of the first coastal area is greater than k1 or the storm surge hazard level of the first coastal area is greater than k2, then the first coastal area is deemed unsuitable as an ecological seawall restoration area; otherwise, the first coastal area is deemed suitable as a candidate area for ecological seawall restoration.

[0007] Furthermore, based on the coastline data, a correlation analysis of the coastal areas is performed to obtain similar areas within the coastal zone, denoted as similar coastal area data. This includes the following sub-steps: If the first coastal area is a candidate area for ecological seawall, then establish a coordinate system; place the coastline 1 to coastline n1 of the first coastal area in the coordinate system according to their relative positional relationship, and make the direction of the coastline 1 to coastline n1 of the first coastal area parallel to the vertical axis of the coordinate system. Select k3 numerical points on the vertical axis of the coordinate system and record them as reference point 1 to reference point v in descending order. Record any one of the reference points as reference point i, where k3 is the number of points set and i is the sequence number. Sequentially obtain the x-axis coordinates of coastlines 1 to n1 with the y-axis coordinate as reference point i, arrange them in the order of the corresponding coastlines, and record them as the coastline sequence of reference point i; repeatedly obtain the coastline sequences corresponding to all reference points, and record them in order as coastline sequence 1 to coastline sequence v; record them as the coastline change data of the first coastal region; repeatedly obtain the coastline change data of all reference coastal regions.

[0008] Furthermore, based on the coastline data, correlation analysis is performed on the coastal areas to obtain similar coastal regions, which are denoted as similar coastal regions. This process also includes the following sub-steps: For any one of the shoreline sequences in the first coastal region, from shoreline sequence 1 to shoreline sequence v, let it be denoted as shoreline sequence i; let any one of the data in shoreline sequence i be denoted as AX(m), where m represents the sequence number; calculate the change difference CX(m) corresponding to AX(m), where CX(m) = AX(m) - AX(m-1); Repeatedly obtain the variation difference corresponding to all data in the shoreline sequence i, and record it as the shoreline variation sequence corresponding to shoreline sequence i; repeatedly obtain the shoreline variation sequences corresponding to shoreline sequence 1 to shoreline sequence v, and record them as shoreline variation sequence A1 to shoreline variation sequence Av respectively; record them as the shoreline variation value data of the first coastal area. Repeatedly acquire shoreline variation data for all reference coastal zones, designate any one of the reference coastal zones as the first reference zone, and designate the shoreline variation sequence of the first reference zone as shoreline variation sequence B1 - shoreline variation sequence Bv. Any one of the shoreline change sequences in the sequence A1 to the sequence Av is denoted as shoreline change sequence Ai; any one of the shoreline change sequences in the sequence B1 to the sequence Bv is denoted as shoreline change sequence Bj; j represents the sequence number.

[0009] Furthermore, based on the coastline data, correlation analysis is performed on the coastal areas to obtain similar coastal regions, which are denoted as similar coastal regions. This process also includes the following sub-steps: Calculate the Pearson correlation coefficient between the shoreline change sequence Ai and the shoreline change sequence Bj, and denote it as the corresponding change correlation coefficient AE; Let DA(m) and DB(m) be the data at any corresponding position in the shoreline change sequence Ai and the shoreline change sequence Bj, respectively; calculate the corresponding relative error DR(m), where DR(m) = |DA(m) - DB(m)| / (|DA(m)| + |DB(m)| + e1); repeat the calculation of the relative error at all corresponding positions in the shoreline change sequence Ai and the shoreline change sequence Bj, and calculate the average water level of all relative errors, which is denoted as the average relative error PR, where e1 is a set minimum constant; Calculate the numerical correlation coefficient BE between the shoreline change sequence Ai and the shoreline change sequence Bj, where BE = 1 / (1+PR); and calculate the average of AE and BE, denoted as the comprehensive correlation coefficient between the shoreline change sequence Ai and the shoreline change sequence Bj. Based on the shoreline change sequence A1-shoreline change sequence Av and the shoreline change sequence B1-shoreline change sequence Bv, repeatedly calculate the comprehensive correlation coefficient of all possible two shoreline change sequences; and arrange them in ascending order, denoted as the comprehensive correlation sequence.

[0010] Furthermore, based on the coastline data, correlation analysis is performed on the coastal areas to obtain similar coastal regions, which are denoted as similar coastal regions. This process also includes the following sub-steps: The maximum value of the composite correlation sequence is denoted as the reference correlation coefficient 1, and the two shoreline change sequences corresponding to the reference correlation coefficient are denoted as sequence 11 and sequence 12, respectively. The composite correlation coefficients involving sequence 11 or sequence 22 in the composite correlation sequence are removed. The maximum value of the remaining composite correlation sequence is denoted as the reference correlation coefficient 2, and the two shoreline change sequences corresponding to the reference correlation coefficient 2 are denoted as sequence 21 and sequence 22, respectively. The composite correlation coefficients involving sequence 21 or sequence 22 in the composite correlation sequence are removed. Repeat the operation until v reference correlation coefficients are obtained, and calculate the average of all reference correlation coefficients, which is denoted as the correlation coefficient between the first coastal region and the first reference region; repeat the calculation of the correlation coefficient between the first coastal region and all reference coastal zones, and denot the reference coastal zones with correlation coefficients greater than k4 as similar coastal zones; obtain all similar coastal zones denoted as the first coastal region, and denot them as similar coastal zone data, where k4 is the set threshold.

[0011] Furthermore, based on similar coastal area data and coastal area coastline data, the changes in the coastline of the coastal zone are predicted, and the predicted coastline data is obtained through the following sub-steps: Acquire shoreline change data for the first coastal region and similar coastal regions, and classify them according to the corresponding coastal zone regions, recording them as basic shoreline data; Using reference point 1 to reference point v as node 1 to node v, the spatiotemporal graph neural network is denoted as the initial prediction model. The model output is set as the x-axis coordinates corresponding to reference point 1 to reference point v. The initial prediction model is trained using basic shoreline data. After completion, the shoreline prediction model is obtained. Based on the shoreline change data of the first coastal area, the x-axis coordinates of reference point 1 to reference point v are predicted for the next k5 first time periods using the shoreline prediction model, thus obtaining the predicted coastline data; where k5 is the number of times the shoreline is set.

[0012] Furthermore, based on the coastal area coastline data and predicted coastline data, a coastline straightness analysis is performed, and a second determination is made as to whether the coastal zone is suitable for being designated as an ecological seawall restoration area. This includes the following sub-steps: In the first coordinate system, obtain the coordinates of reference point 1 to reference point v at the current coastline n1, and record them as coastline coordinates. After completion, obtain the set of coastline coordinates for the current first time period. Perform straight line fitting based on the set of coastline coordinates to obtain the fitted straight line. Obtain the corresponding straight line distance from reference point 1 to reference point v along the fitted straight line, and record it as the reference distance L0. Based on the set of shoreline point coordinates, calculate the Euclidean distance between the shoreline point coordinates of any two adjacent reference points from reference point 1 to reference point v, and sum them up, denoted as the shoreline distance HL. Calculate the curvature WQ1 of the first coastal region in the current first time period, where WQ = HL / L0; Calculate the vertical distance from the coordinates of each shoreline point to the fitted straight line based on the set of shoreline point coordinates, and calculate the average value, denoted as the average deviation distance DL; calculate the smoothness WQ2 of the first coastal area in the current first time period, where WQ2=DL / L0; Based on the predicted coastline data, the curvature and smoothness of the first coastal area are repeatedly calculated for each future first time period.

[0013] Furthermore, the analysis of coastline straightness based on coastal area data and predicted coastline data, and the second determination of whether the coastal zone is suitable for ecological seawall restoration, also includes the following sub-steps: Set the thresholds corresponding to curvature and flatness respectively, and record them as k6 and k7 in order; record the current first time period and all future first time periods in chronological order as period (1) - period (n2); and record any period as period (u), where u represents the position number; The curvature and flatness corresponding to the cycle (u) are denoted as WQ1(u) and WQ2(u) respectively; if WQ1(u) > k6 / E(u) or WQ2(u) > E(u) * k7, then the cycle (u) is deemed unqualified, otherwise the cycle (u) is deemed qualified, where E(u) = min{[1.3 / (k8-1)] * (u-1), 1.3}, where k8 is the set upper limit of the cycle; Repeat the judgment for period (1) to period (n2). If both periods (1) and period (n2) are qualified, the first coastal area is judged to be suitable as an ecological seawall restoration area; otherwise, the first coastal area is judged to be unsuitable as an ecological seawall restoration area.

[0014] Secondly, this application provides a land-sea integrated ecological restoration zoning management platform system, including a data collection module, a correlation analysis module, a shoreline prediction module, and a straightness analysis module; The data collection module includes a collection unit and a first division unit. The collection unit is used to collect coastal storm surge data and coastal coastline data of the coastal zone. The first division unit makes a first determination on whether the coastal zone is suitable to be divided into an ecological seawall restoration area based on the coastal storm surge data. The correlation analysis module performs correlation analysis on the coastal area based on the coastal area coastline data to obtain similar areas in the coastal zone, which are recorded as similar coastal area data. The shoreline prediction module predicts the changes in the coastline of the coastal zone based on similar coastal area data and coastal area coastline data, and obtains predicted coastline data. The straightness analysis module performs a straightness analysis of the coastline based on the coastal area coastline data and the predicted coastline data, and then makes a second determination as to whether the coastal zone is suitable to be classified as an ecological seawall restoration area.

[0015] The beneficial effects of this invention are as follows: This invention collects storm surge data and coastline data of coastal areas, and firstly determines whether the coastal area is suitable for ecological seawall restoration based on the storm surge data; it then performs correlation analysis on the coastline data to obtain similar areas within the coastal area, which are denoted as similar coastal area data; it predicts the changes in the coastline of the coastal area based on the similar coastal area data and the coastline data, obtaining predicted coastline data; and finally, it performs coastline straightness analysis based on the coastline data and the predicted coastline data, and secondly determines whether the coastal area is suitable for ecological seawall restoration. When delineating ecological seawall restoration areas within the coastal area, similar coastal areas can be selected, and future changes in the coastline can be predicted based on the corresponding coastline change data, thereby improving the accuracy of ecological seawall restoration area delineation. This invention calculates the Pearson correlation coefficient, calculates the relative error and converts it into a numerical correlation coefficient, and then takes the average of the two as the comprehensive correlation coefficient. This allows for simultaneous consideration of the similarity of change trends and numerical magnitudes, reducing the misleading effect of a single indicator. By obtaining v reference correlation coefficients and averaging them, the overall correlation coefficient is obtained. Then, a threshold is used to screen out similar regions, which can accurately screen out similar coastal areas and avoid misscreening and omission. After finding similar coastal areas, the shoreline data of these areas can be incorporated into the training set or used as a reference pattern to enhance the sample size and generalization ability of the prediction model and improve prediction accuracy. By adjusting the threshold with the distance of the prediction time through E(u), it reflects a reasonable tolerance for the uncertainty of future predictions, avoids a one-size-fits-all approach, and avoids overly harsh conditions for the division of ecological seawall restoration areas. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the system of the present invention; Figure 2 This is a flowchart illustrating the steps of the method of the present invention; Figure 3 This is a schematic diagram of the first coordinate system of the present invention; Figure 4 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1, please refer to Figure 1As shown, this application provides a land-sea integrated ecological restoration zoning management platform system, including a data collection module, a correlation analysis module, a shoreline prediction module, and a straightness analysis module; The data collection module includes a collection unit and a first division unit. The collection unit is used to collect coastal storm surge data and coastal coastline data of the coastal zone. The first division unit makes an initial determination based on the coastal storm surge data to determine whether the coastal zone is suitable for being classified as an ecological seawall restoration area. The collection unit is configured with a collection strategy, which includes: for any coastal zone to be partitioned, denoted as the first coastal zone; obtaining the storm surge hazard index corresponding to the first coastal zone and the corresponding storm surge hazard level, denoted as the coastal storm surge data of the first coastal zone; the storm surge hazard index is used to quantify the degree of storm surge hazard risk in a specific area; the storm surge hazard level is a classification label based on the numerical range of the storm surge hazard index, with the aim of quickly determining the risk level and guiding early warning and emergency actions; the calculation of the storm surge hazard index and the classification of storm surge hazard levels can be performed with reference to relevant standards. The first time period is set to T1; the average coastline of the first coastal area within the corresponding period is obtained periodically according to T1, and the time span of the corresponding period is recorded and arranged in chronological order, and recorded as coastline 1 to coastline n1, which are recorded as the coastline data of the first coastal area; T1 can be set according to the actual application scenario, and is generally 1 year. In this embodiment, T1=1 year. Repeatedly collect storm surge data and coastline data for multiple coastal zones in the area where the first coastal zone is located, and record the collected coastal zones as reference coastal zones.

[0019] The first division unit is configured with a first division strategy, which includes: setting thresholds corresponding to the storm surge hazard index and the storm surge hazard level, which are denoted as k1 and k2 respectively in order; if the storm surge hazard index of the first coastal area is greater than k1 or the storm surge hazard level of the first coastal area is greater than k2, then the first coastal area is determined to be unsuitable as an ecological seawall restoration area; otherwise, the first coastal area is determined to be suitable as a candidate area for ecological seawall; k1 and k2 can be flexibly set according to the actual application scenario, and the storm surge hazard index and the storm surge hazard level can be selectively used only. In this embodiment, k1=0.75 and k2=level 2; In practical implementation, coastal areas with excessively high storm surge hazard index (i.e., those with excessively high storm surge hazard level) are unsuitable for constructing ecological seawalls. From a defensive perspective, storm surges in high-risk areas far exceed the wave-dissipation capacity of ecological seawalls, and the structural strength of ecological seawalls is lower than that of traditional seawalls. Extreme storm surges can easily cause overtopping and breaches, exacerbating disaster losses. From an ecological perspective, to resist extremely strong storm surges, the seawall structure needs to be strengthened and the vegetation belt in front of the seawall needs to be removed, resulting in the ecological seawall losing its biological attachment and ecological connectivity functions. Furthermore, the disaster recurrence cycle in high-risk areas is short, and the ecological structure restoration cycle cannot keep up with the rate of destruction, making it impossible to form a stable ecosystem. From a site selection perspective, ecological seawalls need to be built in areas with controllable risks, while high-risk areas have uncontrollable risks, easily exceeding the seawall's defense threshold and leading to the destruction of ecological seawalls. The core value of ecological seawall restoration areas lies in achieving a win-win situation for ecology and safety in low- to medium-risk areas, solving the ecological damage problem of traditional seawalls while enhancing disaster buffering capacity through ecological structure. The key premise for its site selection is controllable risk.

[0020] The correlation analysis module performs correlation analysis on the coastal area based on the coastal area data to obtain similar areas in the coastal zone, which are recorded as similar coastal area data. The correlation analysis module is configured with correlation analysis strategies, including: if the first coastal area is a candidate area for ecological seawall, then a plane rectangular coordinate system is established with the vertical axis as the y-axis and the horizontal axis as the x-axis, denoted as the plane coordinate system. The direction pointed to by the horizontal axis is taken as the ocean direction, and the opposite direction pointed to by the horizontal axis is taken as the land direction. The coastlines 1 to n1 of the first coastal region are placed in a planar coordinate system according to their relative positions. The coastlines 1 to n1 are treated as a whole in this coordinate system, and their smallest outer rectangle is obtained, denoted as the reference rectangle. The coastlines 1 to n1 are rotated so that the long side of the reference rectangle is parallel to the y-axis and satisfies the corresponding direction. That is, the direction pointed to by the horizontal axis is taken as the ocean direction, and the opposite direction is taken as the land direction. This avoids errors in subsequent screening of similar coastal areas. The planar coordinate system containing coastlines 1 to n1 is then denoted as the first coordinate system. Coastal segments of arbitrary shapes and orientations are normalized to a unified coordinate reference system to ensure that subsequent reference point extraction and analysis are not biased due to differences in the overall orientation of the coastlines.

[0021] Please see Figure 3As shown, based on the first coordinate system, the range of values ​​for coastline 1 to coastline n1 on the corresponding y-axis is obtained, denoted as the vertical axis range [YA1, YA2]. k3 numerical points are uniformly selected from [YA1, YA2] and sequentially denoted as reference point 1 to reference point v in descending order. Any reference point is denoted as reference point i, where k3 is the set number and i is the sequence number. In this embodiment, k3 = 100, which can be flexibly set according to the actual application scenario. In the first coordinate system, draw a horizontal line with the y-axis equal to that of reference point i, and denote it as horizontal line i; obtain the x-axis coordinates of the intersection points of horizontal line i and coastline 1 - coastline n1, and arrange them according to the order of the corresponding coastlines, and denote them as the coastline sequence of reference point i; arrange the positions of each reference point at different times into a time series for easy subsequent analysis and calculation. Repeatedly acquire the shoreline sequences corresponding to all reference points and record them sequentially as shoreline sequence 1 to shoreline sequence v; record them as the shoreline change data of the first coastal region; repeat the acquisition of shoreline change data of all reference coastal regions.

[0022] For any one of the shoreline sequences in the first coastal region, from shoreline sequence 1 to shoreline sequence v, let it be denoted as shoreline sequence i; let any data in shoreline sequence i be denoted as AX(m), where m represents the sequence number; calculate the variation difference CX(m) corresponding to AX(m), where CX(m) = AX(m) - AX(m-1); the variation difference emphasizes the displacement of the coastline in each cycle rather than the absolute position, thus better capturing the similarity of how it changes and reducing spurious correlations caused by different absolute baseline positions; Repeatedly obtain the variation difference corresponding to all data in the shoreline sequence i, and record it as the shoreline variation sequence corresponding to shoreline sequence i; repeatedly obtain the shoreline variation sequences corresponding to shoreline sequence 1 to shoreline sequence v, and record them as shoreline variation sequence A1 to shoreline variation sequence Av respectively; record them as the shoreline variation value data of the first coastal area. Repeatedly acquire shoreline variation data for all reference coastal zones, designate any one of the reference coastal zones as the first reference zone, and designate the shoreline variation sequence of the first reference zone as shoreline variation sequence B1 - shoreline variation sequence Bv. Any one of the shoreline change sequences in the sequence A1 to the sequence Av is denoted as shoreline change sequence Ai; any one of the shoreline change sequences in the sequence B1 to the sequence Bv is denoted as shoreline change sequence Bj; j represents the sequence number.

[0023] Calculate the Pearson correlation coefficient between the coastline change sequence Ai and the coastline change sequence Bj, denoted as the corresponding correlation coefficient AE. Pearson correlation measures the linear relationship between the two sequences in terms of trend and direction. A positive correlation indicates that the movement direction is consistent within the same time period. It is a statistical indicator for measuring the synchronous changes in coastline morphology and the consistency of coastline change trends. It can detect change patterns with the same frequency and phase, such as simultaneous retreat. Let DA(m) and DB(m) be the data points at any corresponding position in the shoreline change sequence Ai and shoreline change sequence Bj, respectively; calculate the corresponding relative error DR(m), where DR(m) = |DA(m) - DB(m)| / (|DA(m)| + |DB(m)| + e1); repeat the calculation of the relative errors at all corresponding positions in the shoreline change sequence Ai and shoreline change sequence Bj, and calculate the average water level of all relative errors, denoted as the average relative error PR, where e1 is a set minimum constant to avoid the denominator being 0, which can be set by the user; DR(m) measures local differences on a relative scale to avoid masking relative differences when the absolute value is large; Calculate the numerical correlation coefficient BE between the shoreline change series Ai and Bj, where BE = 1 / (1+PR); and calculate the average of AE and BE, denoted as the comprehensive correlation coefficient between the shoreline change series Ai and Bj. Pearson only measures the consistency of trend patterns and is not sensitive to amplitude differences. For example, two series moving in the same direction but with significantly different amplitudes may still have a high correlation coefficient. The numerical correlation coefficient is used to measure amplitude consistency, thus supplementing the blind spot of AE and reducing misjudgments caused by a single indicator. Based on the shoreline change sequence A1-shoreline change sequence Av and the shoreline change sequence B1-shoreline change sequence Bv, the comprehensive correlation coefficient of all possible pairs of shoreline change sequences is repeatedly calculated; and arranged in ascending order, it is denoted as the comprehensive correlation sequence; calculating the comprehensive correlation coefficient of all possible combinations can fully expand the similarity relationship between the two sets of sequences, avoiding the loss of optimal matching due to restricted pairing.

[0024] The maximum value of the composite correlation sequence is denoted as the reference correlation coefficient 1, and the two shoreline change sequences corresponding to the reference correlation coefficient are denoted as sequence 11 and sequence 12, respectively. The composite correlation coefficients involving sequence 11 or sequence 22 in the composite correlation sequence are removed. The maximum value of the remaining composite correlation sequence is denoted as the reference correlation coefficient 2, and the two shoreline change sequences corresponding to the reference correlation coefficient 2 are denoted as sequence 21 and sequence 22, respectively. The composite correlation coefficients involving sequence 21 or sequence 22 in the composite correlation sequence are removed. Repeat the operation until v reference correlation coefficients are obtained, and calculate the average of all reference correlation coefficients, denoted as the correlation coefficient between the first coastal region and the first reference region; repeatedly calculate the correlation coefficient between the first coastal region and all reference coastal zones, and denot the reference coastal zones with correlation coefficients greater than k4 as similar coastal zones; obtain all similar coastal zones denoted as the first coastal region, denoted as similar coastal zone data, where k4 is a set threshold; in this embodiment, k4=0.75, which can be flexibly set; the first coastal region itself has relatively little historical coastline data, and if only its own historical coastline data is used for subsequent prediction, the accuracy cannot be guaranteed, while the data of these similar coastal zones will provide a large amount of reference data for subsequent prediction, improving the accuracy of subsequent prediction; In the specific implementation process, the purpose of screening v reference correlation coefficients is to find v pairs of mutually independent and most representative reference correlation coefficients, so as to avoid a strongly correlated sequence dominating in multiple pairs, thus causing a falsely high overall correlation estimate. By removing the sequences involved, each reference correlation coefficient comes from a different reference point pair, which can better reflect the overall similarity rather than local repetition.

[0025] The shoreline prediction module predicts changes in the coastline of the coastal zone based on similar coastal area data and coastal area coastline data, and obtains predicted coastline data. The shoreline prediction module is configured with a shoreline prediction strategy, which includes: acquiring shoreline change data of the first coastal area and similar coastal areas, and classifying them according to the corresponding coastal zone regions, and recording them as basic shoreline data; Using reference point 1 to reference point v as node 1 to node v, and the spatiotemporal graph neural network as the initial prediction model, the model output is set as the x-axis coordinates corresponding to reference point 1 to reference point v. The initial prediction model is trained using basic shoreline data, and the shoreline prediction model is obtained after the training is completed. Historical data for a single area may be scarce or insufficient. Introducing sample data from similar areas can improve the prediction performance of the model in the target area. Based on the shoreline change data of the first coastal area, the x-axis coordinates of reference point 1 to reference point v in the next k5 first time periods are predicted using the shoreline prediction model to obtain the predicted coastline data; where k5 is the number of settings, which can be set according to the actual application scenario. In the specific implementation process, the predicted coastline data is used to subsequently assess whether the future coastline morphology meets the geometric requirements for the construction of ecological seawalls.

[0026] The straightness analysis module performs a straightness analysis of the coastline based on the coastal area coastline data and the predicted coastline data, and makes a second judgment on whether the coastal zone is suitable to be classified as an ecological seawall restoration area. The straightness analysis module is configured with a straightness analysis strategy, which includes: in the first coordinate system, obtaining the coordinates of reference point 1 to reference point v at the current coastline n1, denoted as coastline point coordinates, and obtaining the set of coastline point coordinates for the current first time period; performing line fitting based on the set of coastline point coordinates to obtain a fitted line, and obtaining the corresponding straight line distance from reference point 1 to reference point v along the fitted line, denoted as the baseline distance L0; the fitted line serves as an idealized straight coastline reference. Based on the set of shoreline point coordinates, calculate the Euclidean distance between the shoreline point coordinates of any two adjacent reference points from reference point 1 to reference point v, and sum them up, denoted as the shoreline distance HL; HL is the sum of the lengths of the broken lines connecting adjacent reference points on the actual shoreline, reflecting the total distance along the actual direction of the shoreline. Calculate the curvature WQ1 of the first coastal region in the current first time period, where WQ = HL / L0; if the coastline is close to straight, WQ is close to 1; if the coastline is curved or has obvious bends, WQ is greater than 1. Calculate the vertical distance from each shoreline point coordinate to the fitted straight line based on the set of shoreline point coordinates, and calculate the average value, denoted as the average deviation distance DL; calculate the smoothness WQ2 of the first coastal area in the current first time period, where WQ2=DL / L0; DL measures the vertical deviation from the fitted straight line and focuses more on local undulations than HL; the larger WQ2 is, the less straight the coastline is. Based on the predicted coastline data, the curvature and smoothness of the first coastal area are repeatedly calculated for each future first time period.

[0027] Set thresholds for curvature and flatness respectively, and record them as k6 and k7 in order; record the current first time period and all future first time periods in chronological order as period (1) - period (n2); and record any period as period (u), where u represents the position number; k6 and k7 can be set according to the actual application scenario. In this embodiment, k6=1.2 and k7=0.1. Let the curvature and flatness corresponding to the period (u) be denoted as WQ1(u) and WQ2(u) respectively. If WQ1(u) > k6 / E(u) or WQ2(u) > E(u)*k7, then the period (u) is judged to be unqualified; otherwise, the period (u) is judged to be qualified. Here, E(u) = min{[1.3 / (k8-1)]*(u-1), 1.3}, where k8 is the set upper limit of the period. When u is greater than or equal to k8, E(u) takes the maximum value, i.e., 1.3. The maximum value can also be flexibly adjusted. E(u) is a coefficient that changes with the first time period. The purpose is to make controllable adjustments to the threshold, that is, to give a more lenient threshold for a longer period, because the model prediction will have deviations, and the deviations will gradually increase as the prediction time increases. Repeat the judgment for period (1) to period (n2). If both period (1) and period (n2) are qualified, the first coastal area is judged to be suitable as an ecological seawall restoration area; otherwise, the first coastal area is judged to be unsuitable as an ecological seawall restoration area. In the specific implementation process, the reason for selecting areas with straight coastlines as ecological seawall restoration areas is that straight coastlines enable ecological seawalls to achieve continuous and uniform disaster resistance. Furthermore, straight coastlines ensure uniform hydrological conditions such as water depth and flow velocity in front of the seawall, which is a prerequisite for building an ecosystem and carrying out ecological restoration. In addition, straight coastlines can reduce the construction and operation and maintenance costs of ecological seawalls. If the coastline changes from straight to tortuous and broken in the future, it will directly destroy the core functions of continuous protection and coordinated wave dissipation of ecological seawalls. For example, protective breaks may occur, and local wave energy may concentrate and scour the seawall. Therefore, it is necessary to predict the straightness of the future coastline to avoid the ecological seawall losing its protective capacity and ecological restoration function due to changes in the coastline morphology after its construction.

[0028] Example 2, please refer to Figure 2 As shown, this application provides a land-sea integrated ecological restoration zoning method, including the following steps: Step S1 involves collecting storm surge data and coastline data for the coastal zone, and using the storm surge data to initially determine whether the coastal zone is suitable for designation as an ecological seawall restoration area. Step S1 includes the following sub-steps: Step S101: For any coastal zone to be partitioned, denoted as the first coastal zone; obtain the storm surge hazard index corresponding to the first coastal zone and obtain the corresponding storm surge hazard level, denoted as the coastal storm surge data of the first coastal zone; Step S102: Set the first time period to T1; periodically obtain the average coastline of the first coastal area within the corresponding period according to T1, and record the time span of the corresponding period. Arrange them in chronological order and record them as coastline 1 to coastline n1, which are recorded as the coastline data of the first coastal area. Step S103: Repeatedly collect storm surge data and coastline data of multiple coastal zones in the area where the first coastal zone is located, and record the collected coastal zones as reference coastal zones. Step S104: Set the thresholds corresponding to the storm surge disaster risk index and storm surge disaster risk level, and record them as k1 and k2 in order; if the storm surge disaster risk index of the first coastal area is greater than k1 or the storm surge disaster risk level of the first coastal area is greater than k2, then it is determined that the first coastal area is not suitable as an ecological seawall restoration area; otherwise, it is determined that the first coastal area is suitable as a candidate area for ecological seawall.

[0029] Step S2 involves performing a correlation analysis of the coastal areas based on the coastline data to obtain similar areas within the coastal zone, which are denoted as similar coastal area data. Step S2 includes the following sub-steps: Step S201: If the first coastal area is a candidate area for ecological seawall, then establish a plane rectangular coordinate system with the vertical axis as the y-axis and the horizontal axis as the x-axis, denoted as the plane coordinate system. The direction pointed to by the horizontal axis is taken as the ocean direction, and the opposite direction pointed to by the horizontal axis is taken as the land direction. Step S202: Place the coastlines 1 to n1 of the first coastal region in a planar coordinate system according to their relative positions. Treat coastlines 1 to n1 as a whole in the planar coordinate system and obtain the smallest outer rectangle of coastlines 1 to n1, which is denoted as the reference rectangle. Rotate coastlines 1 to n1 as a whole so that the long side of the reference rectangle is parallel to the y-axis and satisfies the corresponding direction. The planar coordinate system containing coastlines 1 to n1 at this time is denoted as the first coordinate system. Step S203, based on the first coordinate system; obtain the value range of coastline 1 to coastline n1 on the corresponding y-axis, denoted as the vertical axis range [YA1, YA2], uniformly select k3 numerical points from [YA1, YA2], and denot them as reference point 1 to reference point v in descending order, and denot any reference point as reference point i, where k3 is the number set and i is the sequence number; Step S204: Draw a horizontal line in the first coordinate system with the y-axis equal to that of the reference point i, and denote it as horizontal line i; obtain the x-axis coordinates of the intersection of horizontal line i and coastline 1 - coastline n1, and arrange them in the order of the corresponding coastlines, and denote them as the coastline sequence of the reference point i. Step S205: Repeatedly acquire the shoreline sequences corresponding to all reference points, and record them sequentially as shoreline sequence 1 - shoreline sequence v; record them as the shoreline change data of the first coastal area; repeat the acquisition of shoreline change data of all reference coastal areas.

[0030] Step S206: For any one of the shoreline sequences in the shoreline sequence 1-shoreline sequence v of the first coastal region, denoted as shoreline sequence i; any one of the data in shoreline sequence i is denoted as AX(m), where m represents the sequence number; calculate the change difference CX(m) corresponding to AX(m), where CX(m) = AX(m) - AX(m-1). Step S207: Repeatedly obtain the change difference value corresponding to all data in the shoreline sequence i, and record it as the shoreline change sequence corresponding to the shoreline sequence i; Repeatedly obtain the shoreline change sequence corresponding to the shoreline sequence 1 to the shoreline sequence v, and record them as the shoreline change sequence A1 to the shoreline change sequence Av respectively; Record them as the shoreline variation value data of the first coastal area. Step S208: Repeatedly acquire shoreline variation data for all reference coastal zones, designate any one of the reference coastal zones as the first reference zone, and designate the shoreline variation sequence of the first reference zone as shoreline variation sequence B1 - shoreline variation sequence Bv. Step S209: Denote any one of the shoreline change sequences in the shoreline change sequence A1 to shoreline change sequence Av as shoreline change sequence Ai; denote any one of the shoreline change sequences in the shoreline change sequence B1 to shoreline change sequence Bv as shoreline change sequence Bj; j represents the sequence number.

[0031] Step S210: Calculate the Pearson correlation coefficient between the shoreline change sequence Ai and the shoreline change sequence Bj, and denot it as the corresponding change correlation coefficient AE; Step S211: Denote the data at any corresponding position in the shoreline change sequence Ai and the shoreline change sequence Bj as DA(m) and DB(m) respectively; calculate the corresponding relative error DR(m), where DR(m) = |DA(m) - DB(m)| / (|DA(m)| + |DB(m)| + e1); repeat the calculation of the relative error corresponding to all corresponding positions in the shoreline change sequence Ai and the shoreline change sequence Bj, and calculate the average water level of all relative errors, which is denoted as the average relative error PR, where e1 is a set minimum constant; Step S212: Calculate the numerical correlation coefficient BE between the shoreline change sequence Ai and the shoreline change sequence Bj, where BE = 1 / (1+PR); and calculate the average value of AE and BE, which is denoted as the comprehensive correlation coefficient between the shoreline change sequence Ai and the shoreline change sequence Bj. Step S213: Based on the shoreline change sequence A1-shoreline change sequence Av and the shoreline change sequence B1-shoreline change sequence Bv, repeatedly calculate the comprehensive correlation coefficient of all possible two shoreline change sequences; and arrange them in ascending order, denoted as the comprehensive correlation sequence.

[0032] Step S214: The maximum value of the composite correlation sequence is denoted as the reference correlation coefficient 1, and the two shoreline change sequences corresponding to the reference correlation coefficient are denoted as sequence 11 and sequence 12, respectively; the composite correlation coefficients involving sequence 11 or sequence 22 in the composite correlation sequence are removed; the maximum value of the remaining composite correlation sequence is denoted as the reference correlation coefficient 2, and the two shoreline change sequences corresponding to the reference correlation coefficient 2 are denoted as sequence 21 and sequence 22, respectively; the composite correlation coefficients involving sequence 21 or sequence 22 in the composite correlation sequence are removed; Step S215: Repeat the operation until v reference correlation coefficients are obtained, and calculate the average value of all reference correlation coefficients, which is denoted as the correlation coefficient between the first coastal region and the first reference region; repeat the calculation of the correlation coefficient between the first coastal region and all reference coastal zones, and denot the reference coastal zones with correlation coefficients greater than k4 as similar coastal zones; obtain all similar coastal zones denoted as the first coastal region, and denot them as similar coastal zone data, where k4 is the set threshold.

[0033] Step S3 involves predicting the changes in the coastline of the coastal zone based on similar coastal area data and coastal area coastline data, thereby obtaining predicted coastline data. Step S3 includes the following sub-steps: Step S301: Obtain shoreline change data for the first coastal region and similar coastal regions, and classify them according to the corresponding coastal zone regions, recording them as basic shoreline data. Step S302: Take reference point 1-reference point v as node 1-node v, denote the spatiotemporal graph neural network as the initial prediction model, set the model output as the x-axis coordinates corresponding to reference point 1-reference point v, and use the basic shoreline data to train the initial prediction model. After completion, the shoreline prediction model is obtained. Step S303: Based on the shoreline change data of the first coastal area, the shoreline prediction model is used to predict the x-axis coordinates of reference point 1 to reference point v in the next k5 first time periods to obtain the predicted coastline data; where k5 is the number set.

[0034] Step S4 involves performing a coastline straightness analysis based on the existing coastline data and predicted coastline data, and then making a second determination as to whether the coastal zone is suitable for ecological seawall restoration. Step S4 includes the following sub-steps: Step S401: In the first coordinate system, obtain the coordinates of reference point 1 to reference point v at the current coastline n1, and record them as coastline coordinates. After completion, obtain the set of coastline coordinates for the current first time period. Perform straight line fitting based on the set of coastline coordinates to obtain the fitted straight line. Obtain the corresponding straight line distance from reference point 1 to reference point v along the fitted straight line, and record it as the reference distance L0. Step S401: Based on the set of shoreline point coordinates, calculate the Euclidean distance between the shoreline point coordinates of any two adjacent reference points in reference point 1 to reference point v, and sum them up, denoted as the shoreline distance HL. Step S402, calculate the curvature WQ1 of the first coastal region in the current first time period, where WQ=HL / L0; Step S403: Calculate the vertical distance from the coordinates of each shoreline point to the fitted straight line based on the set of shoreline point coordinates, and calculate the average value, denoted as the average deviation distance DL; calculate the flatness WQ2 of the first coastal area in the current first time period, where WQ2=DL / L0; Step S404: Based on the predicted coastline data, repeatedly calculate the curvature and smoothness of the first coastal area in each future first time period.

[0035] Step S405: Set the thresholds corresponding to curvature and flatness respectively, and record them as k6 and k7 in order; record the current first time period and all future first time periods in chronological order as period (1) - period (n2); and record any period as period (u), where u represents the position number; Step S406: Record the curvature and flatness corresponding to the cycle (u) as WQ1(u) and WQ2(u) respectively; if WQ1(u) > k6 / E(u) or WQ2(u) > E(u)*k7, then the cycle (u) is deemed unqualified; otherwise, the cycle (u) is deemed qualified. Here, E(u) = min{[1.3 / (k8-1)]*(u-1), 1.3}, where k8 is the set upper limit of the cycle. Step S407: Repeat the judgment on period (1) to period (n2). If both period (1) and period (n2) are qualified, the first coastal area is judged to be suitable as an ecological seawall restoration area; otherwise, the first coastal area is judged to be unsuitable as an ecological seawall restoration area.

[0036] Example 3, please refer to Figure 4 As shown, Figure 4 A schematic diagram of an electronic device is provided, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions, and the processor can call these instructions. When the processor executes a computer-readable instruction, it performs steps as described in a land-sea integrated ecological restoration zoning method to achieve the following functions: collecting storm surge data and coastline data of the coastal zone; determining, for the first time, whether the coastal zone is suitable for ecological seawall restoration based on the storm surge data; performing correlation analysis of the coastline data to obtain similar areas of the coastal zone, denoted as similar coastal area data; predicting coastline changes in the coastal zone based on the similar coastal area data and the coastline data, obtaining predicted coastline data; and performing coastline straightness analysis based on the coastline data and the predicted coastline data, and determining, for the second time, whether the coastal zone is suitable for ecological seawall restoration.

[0037] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0038] Example 4: This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program performs the steps of a land-sea integrated ecological restoration zoning method to achieve the following functions: collecting coastal storm surge data and coastal coastline data of the coastal zone, and determining for the first time whether the coastal zone is suitable for being classified as an ecological seawall restoration area based on the coastal storm surge data; performing coastal correlation analysis based on the coastal coastline data to obtain similar areas of the coastal zone, denoted as similar coastal area data; predicting the changes in the coastline of the coastal zone based on the similar coastal area data and the coastal coastline data to obtain predicted coastline data; and performing coastline straightness analysis based on the coastal coastline data and the predicted coastline data, and determining for the second time whether the coastal zone is suitable for being classified as an ecological seawall restoration area.

[0039] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0040] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0041] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A land-sea integrated ecological restoration zoning method, characterized in that, The steps include the following: Collect storm surge data and coastline data of the coastal zone, and make an initial judgment based on the storm surge data of the coastal zone as a suitable area for ecological seawall restoration. Based on the coastline data of the coastal area, a correlation analysis of the coastal area is performed to obtain similar areas in the coastal zone, which are denoted as similar coastal area data. Based on similar coastal area data and coastal area coastline data, the changes in the coastline of the coastal zone are predicted to obtain the predicted coastline data. Based on the coastal area coastline data and predicted coastline data, a coastline straightness analysis was conducted, and a second assessment was made as to whether the coastal zone is suitable for being designated as an ecological seawall restoration area. Collecting storm surge data and coastline data for the coastal zone, and making an initial determination based on the storm surge data to determine whether the coastal zone is suitable for ecological seawall restoration, includes the following sub-steps: For any coastal zone to be partitioned, it is denoted as the first coastal zone; the storm surge hazard risk index corresponding to the first coastal zone is obtained, and the corresponding storm surge hazard risk level is obtained, which is denoted as the coastal storm surge data of the first coastal zone; Set the first time period to T1; The average coastline of the first coastal region within the corresponding period is obtained periodically according to T1, and the time span of the corresponding period is recorded. The coastlines are arranged in chronological order and recorded as coastline 1 to coastline n1, which are recorded as the coastline data of the first coastal region. Repeatedly collect storm surge data and coastline data of multiple coastal zones in the area where the first coastal zone is located, and record the collected coastal zones as reference coastal zones. Set thresholds for storm surge hazard index and storm surge hazard level, and denote them as k1 and k2 respectively. If the storm surge hazard index of the first coastal area is greater than k1 or the storm surge hazard level of the first coastal area is greater than k2, then the first coastal area is deemed unsuitable as an ecological seawall restoration area. Otherwise, the first coastal area is deemed suitable as a candidate area for an ecological seawall. Based on the coastline data, correlation analysis is performed on the coastal areas to obtain similar coastal regions, which are denoted as similar coastal regions. The data includes the following sub-steps: If the first coastal area is a candidate area for ecological seawall, then establish a plane rectangular coordinate system with the vertical axis as the y-axis and the horizontal axis as the x-axis, denoted as the plane coordinate system. The direction pointed to by the horizontal axis is taken as the ocean direction, and the opposite direction pointed to by the horizontal axis is taken as the land direction. Place the coastlines 1 to n1 of the first coastal region in a planar coordinate system according to their relative positions. Treat coastlines 1 to n1 as a whole in the planar coordinate system and obtain the smallest outer rectangle of coastlines 1 to n1, which is denoted as the reference rectangle. Rotate coastlines 1 to n1 as a whole so that the long side of the reference rectangle is parallel to the y-axis and satisfies the corresponding direction. The planar coordinate system containing coastlines 1 to n1 at this time is denoted as the first coordinate system. Based on the first coordinate system; obtain the value range of coastline 1 to coastline n1 on the corresponding y-axis, denoted as the vertical axis range [YA1, YA2], uniformly select k3 numerical points from [YA1, YA2], and denot them as reference point 1 to reference point v in descending order, and denot any reference point as reference point i, where k3 is the number set and i is the sequence number; In the first coordinate system, draw a horizontal line with the y-axis equal to that of the reference point i, and denote it as horizontal line i; obtain the x-axis coordinates of the intersection points of horizontal line i and coastline 1 - coastline n1, and arrange them in the order of the corresponding coastlines, and denote them as the coastline sequence of the reference point i. Repeatedly acquire the shoreline sequences corresponding to all reference points and record them sequentially as shoreline sequence 1 to shoreline sequence v; record them as the shoreline change data of the first coastal region; repeat the acquisition of shoreline change data of all reference coastal regions.

2. The land-sea integrated ecological restoration zoning method according to claim 1, characterized in that, Based on the coastline data, correlation analysis of coastal areas is performed to obtain similar coastal areas, which are denoted as similar coastal areas. This process also includes the following sub-steps: For any one of the shoreline sequences in the first coastal region's shoreline sequence 1-shoreline sequence v, let it be denoted as shoreline sequence i; let any one of the data in shoreline sequence i be denoted as AX(m), where m represents the sequence number; Calculate the change difference CX(m) corresponding to AX(m), where CX(m) = AX(m) - AX(m-1); Repeatedly obtain the variation difference corresponding to all data in the shoreline sequence i, and record it as the shoreline variation sequence corresponding to shoreline sequence i; repeatedly obtain the shoreline variation sequences corresponding to shoreline sequence 1 to shoreline sequence v, and record them as shoreline variation sequence A1 to shoreline variation sequence Av respectively; record them as the shoreline variation value data of the first coastal area. Repeatedly acquire shoreline variation data for all reference coastal zones, designate any one of the reference coastal zones as the first reference zone, and designate the shoreline variation sequence of the first reference zone as shoreline variation sequence B1 - shoreline variation sequence Bv. Any one of the shoreline change sequences in the sequence A1 to the sequence Av is denoted as shoreline change sequence Ai; any one of the shoreline change sequences in the sequence B1 to the sequence Bv is denoted as shoreline change sequence Bj; j represents the sequence number.

3. The land-sea integrated ecological restoration zoning method according to claim 2, characterized in that, Based on the coastline data, correlation analysis of coastal areas is performed to obtain similar coastal areas, which are denoted as similar coastal areas. This process also includes the following sub-steps: Calculate the Pearson correlation coefficient between the shoreline change sequence Ai and the shoreline change sequence Bj, and denote it as the corresponding change correlation coefficient AE; Let DA(m) and DB(m) be the data at any corresponding position in the shoreline change sequence Ai and the shoreline change sequence Bj, respectively; calculate the corresponding relative error DR(m), where DR(m) = |DA(m) - DB(m)| / (|DA(m)| + |DB(m)| + e1); repeat the calculation of the relative error at all corresponding positions in the shoreline change sequence Ai and the shoreline change sequence Bj, and calculate the average water level of all relative errors, which is denoted as the average relative error PR, where e1 is a set minimum constant; Calculate the numerical correlation coefficient BE between the shoreline change sequence Ai and the shoreline change sequence Bj, where BE = 1 / (1+PR); and calculate the average of AE and BE, denoted as the comprehensive correlation coefficient between the shoreline change sequence Ai and the shoreline change sequence Bj. Based on the shoreline change sequence A1-shoreline change sequence Av and the shoreline change sequence B1-shoreline change sequence Bv, repeatedly calculate the comprehensive correlation coefficient of all possible two shoreline change sequences; and arrange them in ascending order, denoted as the comprehensive correlation sequence.

4. The land-sea integrated ecological restoration zoning method according to claim 3, characterized in that, Based on the coastline data, correlation analysis of coastal areas is performed to obtain similar coastal areas, which are denoted as similar coastal areas. This process also includes the following sub-steps: The maximum value of the composite correlation sequence is denoted as the reference correlation coefficient 1, and the two shoreline change sequences corresponding to the reference correlation coefficient are denoted as sequence 11 and sequence 12, respectively. The composite correlation coefficients involving sequence 11 or sequence 22 in the composite correlation sequence are removed. The maximum value of the remaining composite correlation sequence is denoted as the reference correlation coefficient 2, and the two shoreline change sequences corresponding to the reference correlation coefficient 2 are denoted as sequence 21 and sequence 22, respectively. The composite correlation coefficients involving sequence 21 or sequence 22 in the composite correlation sequence are removed. Repeat the operation until v reference correlation coefficients are obtained, and calculate the average of all reference correlation coefficients, which is denoted as the correlation coefficient between the first coastal region and the first reference region. Repeat the calculation of the correlation coefficient between the first coastal region and all reference coastal zones, and denot the reference coastal zones with correlation coefficients greater than k4 as similar coastal zones. Obtain all similar coastal zones denoted as the first coastal region, and denot them as similar coastal zone data, where k4 is the set threshold.

5. The land-sea integrated ecological restoration zoning method according to claim 4, characterized in that, Predicting changes in the coastline of a coastal zone based on similar coastal area data and coastal area coastline data involves the following sub-steps: Acquire shoreline change data for the first coastal region and similar coastal regions, and classify them according to the corresponding coastal zone regions, recording them as basic shoreline data; Using reference point 1 to reference point v as node 1 to node v, the spatiotemporal graph neural network is denoted as the initial prediction model. The model output is set as the x-axis coordinates corresponding to reference point 1 to reference point v. The initial prediction model is trained using basic shoreline data. After completion, the shoreline prediction model is obtained. Based on the shoreline change data of the first coastal area, the x-axis coordinates of reference point 1 to reference point v are predicted for the next k5 first time periods using the shoreline prediction model, thus obtaining the predicted coastline data; where k5 is the number of times the shoreline is set.

6. The land-sea integrated ecological restoration zoning method according to claim 5, characterized in that, Based on the coastline data and predicted coastline data, a coastline straightness analysis is performed, and a second determination is made as to whether the coastal zone is suitable for ecological seawall restoration. This includes the following sub-steps: In the first coordinate system, obtain the coordinates of reference point 1 to reference point v at the current coastline n1, and record them as coastline coordinates. After completion, obtain the set of coastline coordinates for the current first time period. Perform straight line fitting based on the set of coastline coordinates to obtain the fitted straight line. Obtain the corresponding straight line distance from reference point 1 to reference point v along the fitted straight line, and record it as the reference distance L0. Based on the set of shoreline point coordinates, calculate the Euclidean distance between the shoreline point coordinates of any two adjacent reference points from reference point 1 to reference point v, and sum them up, denoted as the shoreline distance HL. Calculate the curvature WQ1 of the first coastal region in the current first time period, where WQ = HL / L0; Calculate the vertical distance from the coordinates of each shoreline point to the fitted line based on the set of shoreline point coordinates, and calculate the average value, denoted as the average deviation distance DL; Calculate the smoothness WQ2 of the first coastal area in the current first time period, where WQ2 = DL / L0; Based on the predicted coastline data, the curvature and smoothness of the first coastal area are repeatedly calculated for each future first time period.

7. The land-sea integrated ecological restoration zoning method according to claim 6, characterized in that, The analysis of coastline straightness based on coastal area data and predicted coastline data, and the second determination of whether the coastal zone is suitable for ecological seawall restoration, also includes the following sub-steps: Set the thresholds corresponding to curvature and flatness respectively, and record them as k6 and k7 in order; record the current first time period and all future first time periods in chronological order as period (1) - period (n2); and record any period as period (u), where u represents the position number; The curvature and flatness corresponding to the cycle (u) are denoted as WQ1(u) and WQ2(u) respectively; if WQ1(u) > k6 / E(u) or WQ2(u) > E(u) * k7, then the cycle (u) is deemed unqualified, otherwise the cycle (u) is deemed qualified, where E(u) = min{[(1.3 / (k8-1)] * (u-1), 1.3}, where k8 is the set upper limit of the cycle; Repeat the judgment for cycle (1) to cycle (n2). If both cycle (1) and cycle (n2) are qualified, then the first coastal area is deemed suitable as an ecological seawall restoration area. Otherwise, the first coastal area is deemed unsuitable as an area for ecological seawall restoration.

8. A land-sea integrated ecological restoration zoning management platform system, applicable to the land-sea integrated ecological restoration zoning method described in any one of claims 1-7, characterized in that, It includes a data collection module, a correlation analysis module, a shoreline prediction module, and a straightness analysis module; The data collection module includes a collection unit and a first division unit. The collection unit is used to collect coastal storm surge data and coastal coastline data of the coastal zone. The first division unit makes a first judgment on whether the coastal zone is suitable to be divided into an ecological seawall restoration area based on the coastal storm surge data. The correlation analysis module performs correlation analysis on the coastal area based on the coastal area coastline data to obtain similar areas in the coastal zone, which are recorded as similar coastal area data. The shoreline prediction module predicts the changes in the coastline of the coastal zone based on similar coastal area data and coastal area coastline data, and obtains predicted coastline data. The straightness analysis module performs a straightness analysis of the coastline based on the coastal area coastline data and the predicted coastline data, and then makes a second determination as to whether the coastal zone is suitable to be classified as an ecological seawall restoration area.

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