A method for improving the toughness and long-term maintenance of a maintained and repaired beach

Abstract

The present application relates to a kind of beach maintenance and repair flexibility promotion and long-acting method for maintenance, utilize unmanned aerial vehicle remote sensing, shore-based sensor and other means to obtain beach topography and environmental data, erosion risk under different storm scenarios and sea level rise conditions is evaluated by improved model;Based on the key indicators such as shoreline recession, beach shoulder elevation, vegetation coverage, the effective period and failure threshold of each repair measure are calculated;Using multi-objective optimization algorithm to balance ecological protection, protection efficiency, economic cost and regulatory restrictions, the measures are deployed in space and time by zoning and staging;Through periodic or real-time monitoring threshold comparison, subsequent scheme is iteratively corrected in time.The present application realizes the continuous repair and high flexibility maintenance of beach in uncertain environment through real-time or periodic coastal dynamics monitoring, multi-dimensional risk assessment, multi-objective optimization and rolling update mechanism.

Description

Technical Field

[0001] This invention relates to a method for improving the resilience and long-term management of beaches, belonging to the field of coastal ecological restoration and management technology. Background Technology

[0002] Beaches not only serve multiple functions, such as protecting coastlines, mitigating wave impact, and maintaining biodiversity, but also occupy an important position in coastal tourism and socio-economic development. Currently, beach restoration and management, as crucial means of coastal ecological protection and disaster prevention, face numerous challenges against the backdrop of global climate change and intensified human activities.

[0003] Traditional beach restoration techniques often employ one-off or short-term engineering measures, such as reinforcing dikes or embankments in localized areas, or adding small amounts of sand. These approaches frequently overlook dynamic environmental factors such as sea-level rise, changes in storm surge intensity, and human disturbance, making it difficult to maintain restoration effects over the long term. They fail to effectively address the risks posed by environmental uncertainties and struggle to achieve stable and resilient beach functions. Secondly, existing technologies typically focus on a single indicator or objective, such as protective efficiency or ecological protection, lacking a comprehensive consideration of the multifaceted needs of ecological protection, protective efficiency, economic costs, and regulatory constraints. This limitation makes it difficult to balance the natural ecosystem, shoreline safety, and socio-economic benefits, resulting in low overall efficiency and feasibility of restoration projects, and even resource waste or ecological damage. Furthermore, traditional beach management lacks differentiated analysis of the characteristics of different beach areas, failing to fully consider the differences in topographic features, erosion risks, and ecological sensitivities of each zone. The "one-size-fits-all" restoration strategy leads to irrational resource allocation, resulting in both ineffective restoration and potential waste of funds and resources due to over-restoration, failing to achieve efficient resource utilization.

[0004] Therefore, there is an urgent need for a long-term beach management and resilience enhancement technology that can achieve dynamic adaptability, take into account the needs of multiple parties, provide differentiated management by region, flexibly respond to extreme events, and improve decision-making transparency and traceability, so as to form a truly meaningful "long-term management" and achieve the sustainable development goal of beach management. Summary of the Invention

[0005] A method for enhancing the resilience and long-term management of beaches during conservation and restoration includes the following steps: Step S1: Set up several monitoring sections or monitoring stations along the beach line, periodically acquire raw data of information along the beach line, preprocess the raw data to form a basic dataset. Step S2: Simulate future changes in beach morphology based on beach topography evolution model, divide the beach into regions and discrete units, conduct risk assessment on the indicators output by simulation, and identify beach erosion hotspots and vulnerable areas. Step S3: Analyze the simulated output indicators and estimate the effective period and failure threshold of maintenance and repair measures based on the toughness index. The specific steps are as follows: Step S31, define the beach failure event. F And define the measurement method; Set beach failure event F The measurement method, namely beach failure events F The defining formula is: (1) In the formula, The shoreline setback distance. Allowable shoreline setback distance ， For the elevation of the beach shoulder ， This is the critical elevation of the beach shoulder. For volume loss, The threshold for volume loss; probability of failure of beach failure event P The expression is: (2) In the formula, As an indicator, it represents the distance the shoreline has retreated. Or changes in beach shoulder elevation Or changes in beach volume , for The probability density function; Step S32: Based on the simulation results of the beach topography evolution model, perform statistical analysis on the indicators and calculate the failure probability of the beach failure event. Step S33, Define the loss function C ( F Determine the expected loss; Step S34: By combining the beach failure probability with the loss function, determine whether maintenance and restoration measures are needed to improve beach resilience. Step S35: Based on different maintenance and restoration measures, several resilience indicators are set under different working conditions to measure the beach resilience from different perspectives and obtain a comprehensive resilience value. A totalThe overall resilience of the beach under maintenance and restoration is assessed by using a comprehensive resilience value. This involves normalizing the set resilience indicators and assigning weights to them to obtain the comprehensive resilience value. A total ; Step S36: By assuming simulation scenarios under different working conditions, obtain the curve of the comprehensive toughness value evolving over time, and continue to obtain the expected effective period of maintenance and repair measures under multiple scenarios. Step S37, the measure failure threshold is defined as follows: when simulating the index, if the input external driving condition is... Oh crit The effectiveness of maintenance and repair measures decreases sharply over time; Step S4: Based on the beach zoning in Step S2, clarify the resilience restoration measures and feasible measures for each zone, and use a multi-objective optimization algorithm to balance ecological protection, protection efficiency, economic cost and regulatory restrictions. Using the validity period and failure threshold estimated in step S3 as the basis for judgment, if the algorithm data meets the requirements of the validity period and failure threshold, the toughness repair measures are maintained; otherwise, the next step is carried out. Step S5: Continuously conduct rolling monitoring, trigger update conditions when remediation measures fail to meet requirements, adjust remediation measures based on update results, and optimize the predictive capability of the beach topography evolution model and the adaptability of remediation strategies;

[0006] Furthermore, in step S1, raw data is collected using UAV remote sensing, shore-based sensors, or buoy observation equipment. The raw data obtained includes the shoreline location, elevation, wave and tide elements, and vegetation cover of the beach. The raw data is cleaned to remove random noise, aligned in time and space, outliers are removed, and interpolation or merging operations are performed to improve the integrity and usability of the raw data. The preprocessed raw data is imported into a geographic information system or a dedicated database to form a basic dataset for visualization.

[0007] Furthermore, in step S2, the set external driving conditions are input into the beach topography evolution model to simulate the future changes in beach morphology and output indicators, including shoreline setback distance. Changes in beach shoulder elevation and changes in beach volume ; By statistically analyzing the indicators output by the beach topography evolution model within each discrete unit, areas with significant topographic changes are identified using intuitive threshold comparisons or standardized methods, thus determining potential erosion hotspots. The deformation process at each time step within a potential erosion hotspot is tracked and simulated using a beach topography evolution model. If erosion continues for several time points or exceeds a threshold, the area is identified as a vulnerable zone.

[0008] Furthermore, in step S32, there are three ways to obtain the failure probability of the beach failure event: direct statistical method, fitted distribution method, and multi-scenario synthesis method. In step S33, the expression for the expected loss is defined as follows: (5) In the formula, The degree of economic loss, reduction in ecosystem service value, or social impact. The failure probability of a beach failure event; In step S35, assuming different maintenance and repair measures { m 1, m 2,… m k}, Introducing different operating conditions { Oh 1, Oh 2,…, Oh m}, outputting several quantified resilience indicators { a 1, a 2,…, a n The resilience index is normalized using the following formula: (6) In the formula, As a resilience indicator The maximum value that appears in the sample. As a resilience indicator The minimum value that appears in the sample. As a resilience indicator, ; Continue to assign weights to each resilience indicator. w 1, w 2,… w n The weighting coefficients satisfy: (7) In the formula, As the weight of the resilience index, ; Finally, the overall toughness value is obtained: (8) In the formula, To determine the overall toughness value, As the weight of the resilience index, As a resilience indicator, ; like A total (t)> A crit Then it is determined that the maintenance and repair measures are in place. t If the maintenance and repair measures have not expired, then the maintenance and repair measures are deemed to be in effect. t Expiration at any time; In step S36, it is assumed that under the operating conditions... Oh s The simulation yielded a time-varying curve. The curve indicates a certain maintenance and repair measure m j Validity period T j,s Defined as: (9) In the formula, In the context Oh s The curve showing the evolution of the overall toughness value over time obtained from the simulation. , For maintenance and repair measures m j The validity period, ; If we consider multiple scenarios and give their probabilities p s Therefore, the expected lifespan of this maintenance and repair measure under multiple scenarios is: (10) In the formula, For maintenance and repair measures m j The validity period, ; In step S37, when the external driving condition is Oh crit If the maintenance and repair measures show a significant decline in effectiveness, then the failure threshold for these measures is set as follows: Oh crit , Oh crit ∈{ Oh 1, Oh 2,…, Oh m}; When operating conditions Oh s Down, Maintenance and repair measures m j If the failure probability is less than 5%, then the maintenance and repair measures are deemed effective under the current driving conditions. Ohs Safety measures, such as maintenance and repair measures m j If the failure probability is ≥5%, then the maintenance and repair measures are deemed to have exceeded the failure threshold, requiring replacement or supplementation of the maintenance and repair measures. m j The failure probability is defined as p fail ,satisfy: (11);

[0009] Furthermore, in step S32, the methods for obtaining the failure probability of the beach failure event are the direct statistical method, the fitted distribution method, and the multi-scenario synthesis method. Direct statistical methods refer to statistical indicators obtained through extensive Monte Carlo simulations or repeated numerical experiments under multiple scenarios. The distribution of the probability density function, or its mean and variance, can be approximately estimated when the sample size is sufficient. Numerically, this can be approximated by calculating the transcendental frequency after sorting the results. P ( F If the sample size is N, and M of the samples satisfy... The failure probability is approximately: P ( F ) = M / N; The fitted distribution method refers to fitting the index G to a specific distribution based on the actual working conditions, using maximum likelihood estimation to obtain the distribution parameters μ and σ, and then calculating the failure probability according to the cumulative distribution function. The calculation method is as follows: (3) In formula (3), The cumulative distribution function of the standard normal distribution; The multi-scenario synthesis method refers to the process of determining the failure probability under different scenarios if the beach morphology behaves differently. P i (F) Perform a weighted summation: (4) In formula (4), The failure probability of failure events under different scenarios;

[0010] Furthermore, in step S4, based on the partitioning of the beach in step S2, a multi-objective optimization form is defined, namely... (12) In formula (12), For decision variables regarding the conservation and restoration measures, scale, and implementation sequence in beach zones, For economic costs, Due to erosion risk or inadequate coastal protection, For ecological indicators;

[0011] Furthermore, in step S4, the maintenance and repair measures obtained in step S36 are... m j Validity period T j,s and the measure failure threshold obtained in step S37 Oh crit By combining multi-objective optimization methods, the activation sequence and connection method of maintenance and repair measures are determined in stages;

[0012] Furthermore, in step S5, the monitoring trigger update conditions are set, and steps S1-S4 are repeated to implement local adjustments to the partitioned beach or to optimize the beach as a whole. Archive rolling monitoring and update information to build a beach management system that is constantly learning and upgrading.

[0013] By employing the above technical solutions, the present invention has the following beneficial effects compared to the prior art: 1. The method for improving the resilience and long-term management of beaches provided by this invention assesses the topographic features, erosion risks and ecological sensitivity of each zone of the beach, and sets different restoration combinations and implementation sequences in a targeted manner, which greatly improves resource utilization efficiency and reduces waste caused by ineffective or excessive restoration. 2. The method for improving the resilience and long-term management of beaches provided by this invention incorporates ecological protection, protection efficiency, economic costs and regulatory restrictions into a multi-objective optimization framework, comprehensively balancing the natural ecosystem, shoreline safety and socio-economic benefits, avoiding the limitations of traditional single indicators or single objectives, and improving the comprehensive benefits and feasibility of restoration solutions. 3. The beach maintenance and restoration resilience enhancement and long-term management method provided by this invention is no longer limited to one-time or short-term restoration measures. Instead, through rolling monitoring and adaptive updates, it can adjust the management strategy in real time according to changes in uncertain factors such as sea level rise, storm wave intensity, and human activity interference, so as to maintain the stability and high resilience of beach function over a longer time scale. Attached Figure Description

[0014] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0015] Figure 1 This is a schematic diagram of the process for improving the resilience and long-term maintenance of beaches provided by the present invention. Detailed Implementation

[0016] The present invention will now be described in further detail with reference to the accompanying drawings. The specific dimensions used in this embodiment are merely illustrative and do not limit the scope of protection of the present invention.

[0017] As described in the background section, traditional beach management methods do not take into account future changes in sea level rise rate and storm surge intensity, resulting in a lack of flexibility in response plans. At the same time, traditional technologies do not comprehensively consider various needs, making their feasibility and effectiveness questionable.

[0018] To address the aforementioned issues, this application provides a method for enhancing the resilience and long-term management of beaches during conservation and restoration. Figure 1 As shown, the process mainly includes five steps. First, multi-source data is collected and preprocessed to capture the dynamic changes in the beach environment. Preprocessing improves data quality, ensuring the accuracy of subsequent analysis and simulation, and avoiding erroneous decisions due to data bias. Next, a beach evolution model is used to simulate future morphology and conduct risk assessments. The main purpose is to accurately locate beach erosion hotspots and vulnerable areas, improve resource utilization efficiency, and reduce beach losses caused by erosion. Second, resilience indicators and restoration measures are analyzed to provide a quantitative basis for the sustainable management of restoration measures. This basis includes key indicators, estimated measure validity periods, and failure thresholds. Third, multi-objective optimization design is carried out, mainly seeking a balance between ecology, protection, cost, and relevant regulations. Simultaneously, a combination of zoning and phased approaches is used to ensure that the restoration plan meets ecological needs while also possessing economic feasibility and policy compliance, thereby improving the overall benefits and long-term sustainability of the restoration plan. Finally, implementing a rolling monitoring and triggering update mechanism allows restoration measures to be dynamically adjusted according to the actual situation. When restoration measures fail to meet the requirements, they can be updated in a timely manner, enabling a rapid response to changes in the beach environment, such as the impact of extreme storms and sea-level rise. Optimizing the predictive capabilities of the beach topography evolution model can improve the accuracy of predicting future changes in the beach, further enhance the adaptability of restoration strategies, and achieve dynamic optimization of beach management.

[0019] The following is a detailed explanation of each step. Step S1 involves setting up several monitoring sections or stations along the beach and collecting raw data using UAV remote sensing, shore-based sensors, or buoy observation equipment. The raw data acquired periodically includes the beach's shoreline location, elevation, wave and tide parameters, and vegetation cover. Next, the raw data is cleaned to remove random noise, align the data temporally and spatially, remove outliers, and perform interpolation or merging operations. The preprocessed raw data is then imported into a Geographic Information System (GIS) or a dedicated database to form the basic dataset required for visualization and subsequent numerical simulations. Step S2: Simulate future changes in beach morphology based on a beach topography evolution model. The preferred model is the CROSPE model. Combining factors such as wind, waves, tides, sediment transport, and sea-level rise, a set of representative external driving conditions (such as different storm intensities, sea-level rise rates, and tide combinations) are selected as input scenarios to simulate future changes in beach morphology. Output indicators include shoreline retreat distance R and changes in beach shoulder elevation. and changes in beach volume .

[0020] The three indicators mentioned above can be used for risk measurement at different levels. For example, shoreline retreat exceeding a certain threshold may lead to infrastructure damage, a decrease in beach shoulder elevation increases the risk of storm surge inundation, and volume loss is related to a weakening of the overall protective function of the beach. By setting allowable ranges for each indicator, simulation results can be distinguished as "qualified / failed" or "normal / exceeding limits," thereby conducting quantitative risk analysis.

[0021] To identify beach erosion hotspots and vulnerable areas, the beach is divided into discrete units (grids or profiles). Risk assessment is then performed based on the simulated output indicators, which can be achieved through intuitive threshold comparisons or standardized methods (such as...). (Z-score processing) is used to identify the sections with the most drastic changes, which exceed the average level by a certain multiple. Large topographical changes indicate the most drastic changes, and these can be identified as potential erosion hotspots.

[0022] For specific sections or units, beach topographic evolution models are used to track and simulate the deformation process at each time step within potential erosion hotspots. If rapid erosion occurs continuously or quickly exceeds a threshold within a certain period, it is identified as a vulnerable area. A vulnerable area is not necessarily the region with the highest erosion; it could also be a "highly responsive area" that is particularly sensitive to even minor storms. In beach management, vulnerable areas often overlap with engineering or ecological value, such as proximity to man-made facilities or key ecological protection areas. A comprehensive assessment of regional priority is conducted using multi-source data (such as socioeconomic loss assessments and vegetation degradation rates), leading to focused attention in restoration and management strategies.

[0023] Step S3 involves analyzing the simulated output indicators and estimating the effective period and failure threshold of maintenance and repair measures based on the toughness index. This step is the core of the entire plan, and its specific steps are as follows: Step S31: Define a beach failure event as one in which "the beach morphology or function no longer meets design or ecological requirements". F The application specifies three preferred forms of measurement, namely, beach failure events. F The defining formula is: (1) In the formula, The shoreline setback distance. Allowable shoreline setback distance ， For the elevation of the beach shoulder ， This is the critical elevation of the beach shoulder. For volume loss, This is the threshold for volume loss.

[0024] Based on the numerical simulation results, statistical analysis can be performed on key indicators (such as G) to calculate the failure probability. It is assumed that G exhibits certain distribution characteristics under different external conditions. If indicator G is considered a random variable, the failure probability P of the beach failure event is expressed as: (2) In the formula, As an indicator, it represents the distance the shoreline has retreated. Or changes in beach shoulder elevation Or changes in beach volume , for The probability density function.

[0025] Step S32: Based on the simulation results of the beach topography evolution model, perform statistical analysis on the indicators and calculate the failure probability of the beach failure event; this application provides three methods for obtaining the failure probability of the beach failure event, namely, direct statistical method, fitted distribution method and multi-scenario synthesis method. Direct statistical methods refer to statistical indicators obtained through extensive Monte Carlo simulations or repeated numerical experiments under multiple scenarios. The distribution of the probability density function, or its mean and variance, can be approximately estimated when the sample size is sufficient. Numerically, this can be approximated by calculating the transcendental frequency after sorting the results. P ( F If the sample size is N, and M of the samples satisfy... The failure probability is approximately: P ( F ) = M / N; The fitted distribution method refers to fitting the index G to a specific distribution based on the actual working conditions, using maximum likelihood estimation to obtain the distribution parameters μ and σ, and then calculating the failure probability according to the cumulative distribution function. The calculation method is as follows: (3) In formula (3), The cumulative distribution function of the standard normal distribution; The multi-scenario synthesis method refers to the process of determining the failure probability under different scenarios if the beach morphology behaves differently. P i (F) Perform a weighted summation: (4) In formula (4), This represents the failure probability of failure events under different scenarios.

[0026] Step S33: Based on obtaining the failure probability P(F), practical applications often require combining it with the "consequences" to measure risk. Therefore, a loss function can be defined. C ( F Determine the expected loss; (5) In the formula, The degree of economic loss, reduction in ecosystem service value, or social impact. This represents the probability of beach failure events. For erosion hotspots and vulnerable areas, higher weighting or a separate risk list can be added to prioritize the allocation of restoration or protection resources.

[0027] Step S34: By combining the beach failure probability with the loss function, determine whether maintenance and restoration measures are needed to improve beach resilience.

[0028] Specifically, in step S35, based on actual working conditions, when the probability of beach failure is high, maintenance and repair measures are usually required to improve the beach's resilience.

[0029] Assuming different maintenance and repair measures { m 1, m 2,… m k}, Introducing different operating conditions { Oh 1, Oh 2,…, Oh m}, outputting several quantified resilience indicators { a 1, a 2,…, a n Beach resilience can be measured from various perspectives (including protective capacity, ecological function, and sustainability), such as shoreline stability, beach shoulder elevation, vegetation cover, underwater topographic slope, and socioeconomic indicators. These indicators have different dimensions and large numerical differences, therefore, resilience indicators need to be normalized. Common methods include range standardization and Z-score standardization. Range standardization is preferred as an example, with the following formula: (6) In the formula, As a resilience indicator The maximum value that appears in the sample. As a resilience indicator The minimum value that appears in the sample. As a resilience indicator, After this processing, all indicators are between 0 and 1.

[0030] Continue to assign weights to each resilience indicator. w 1, w 2,… w n The weighting coefficients satisfy: (7) In the formula, As the weight of the resilience index, ; Weighting coefficients can be obtained through the analytic hierarchy process (AHP) or direct scoring by decision-makers, and the normalized indicators will be... With weighting coefficients Multiply and sum them to obtain the overall resilience value of the beach at a certain moment or under a certain scenario: (8) In the formula, To determine the overall toughness value, As the weight of the resilience index, As a resilience indicator, Obviously, when A total The larger the value, the better the overall resilience of the beach under current conditions.

[0031] The above formula assumes different maintenance and repair measures. m 1, m 2,… m k Examples include supratidal sand replenishment, underwater sandbar construction, vegetation restoration, and sand barrier installation. For each measure... m j Introducing different external scenarios into numerical models or experimental analyses Oh 1, Oh 2,…, Oh m The simulation includes beach evolution modeling. External scenarios include storm intensity, sea-level rise rate, and human activities. During the simulation period (or after multiple storms), resilience indices at different times are output. Based on this, the overall toughness is calculated. A total .

[0032] To ensure that the resilience of restored beaches meets the required standards, a critical threshold is typically set for the overall resilience value. A crit ,like A total (t)> A critThen it is determined that the maintenance and repair measures are in place. t If the maintenance and repair measures are still effective, then the maintenance and repair measures are deemed to be ineffective. t It is always "invalid" or "requires upgrade / replacement";

[0033] Step S36: In order to calculate the effective period of toughness repair, it is assumed that under the working conditions... Oh s The simulation yielded a time-varying curve. The curve indicates a certain maintenance and repair measure m j Validity period T j,s Defined as: (9) In the formula, In the context Oh s The curve showing the evolution of the overall toughness value over time obtained from the simulation. , For maintenance and repair measures m j The validity period, ; If we consider multiple scenarios and give their probabilities p s Therefore, the expected lifespan of this maintenance and repair measure under multiple scenarios is: (10) In the formula, For maintenance and repair measures m j The validity period, .

[0034] Step S37: In order to determine the applicable scenario of a maintenance and repair measure, when a certain maintenance and repair measure... m j If the effective period under the external scenario Ωs is greater than a certain reference value (such as 5 years or a typical planning cycle), then it can be considered that... m j This applies to the given scenario. By traversing all scenarios, the applicability distribution of each measure under different sea-level rise rates, storm frequencies, and sand source supply conditions can be determined.

[0035] Failure of toughness repair measures typically refers to the toughness index no longer meeting basic requirements or exceeding extreme limits. When external driving conditions are... Oh crit If the maintenance and repair measures show a significant decline in effectiveness, then the failure threshold for these measures is set as follows: Oh crit , Oh crit∈{ Oh 1, Oh 2,…, Oh m}; When operating conditions Oh s Down, Maintenance and repair measures m j If the failure probability is less than 5%, then the maintenance and repair measures are deemed effective under the current driving conditions. Oh s Safety measures, such as maintenance and repair measures m j If the failure probability is ≥5%, then the maintenance and repair measures are deemed to have exceeded the failure threshold, requiring replacement or supplementation of the maintenance and repair measures. m j The failure probability is defined as p fail ,satisfy: (11) p fail This represents the failure probability of the maintenance and repair measures. If it falls within a certain range... p fail If the threshold is less than 5%, the maintenance and repair measures can be considered relatively safe in this environment; if it exceeds the threshold, alternative or supplementary measures need to be planned in advance.

[0036] Step S4, based on the beach zoning in Step S2, clarifies the resilience restoration needs and feasible measures for each zone according to the zone classification (e.g., high-risk zone, buffer zone, stable zone), and uses a multi-objective optimization algorithm to balance ecological protection, protection efficiency, economic cost, and regulatory constraints. These objectives mainly include: ecological protection goals, protection efficiency goals, economic cost goals, and regulatory constraints. The multi-objective optimization form is defined as follows: (12) In formula (12), For decision variables regarding the conservation and restoration measures, scale, and implementation sequence in beach zones, For economic costs (the lower the better, based on what can be obtained from the construction unit, including the manpower and material resources for repairs). The risk of erosion or the degree of inadequacy of coastal protection (the lower the better, which can be obtained from the data collected in step S1). For ecological indicators, vegetation cover, habitat integrity, etc., can be used (because maximizing ecological indicators is equivalent to minimizing them). ).

[0037] Of course, based on regulatory restrictions, mandatory constraints can be added, such as... This means that the intertidal zone and subtidal zone must not be altered. ≥ h min This means ensuring the beach shoulder elevation is greater than a certain value. Other boundary constraints can also be considered, such as construction period and approval regulations. The above equation can be solved using a weighted method or a multi-objective genetic algorithm. Each solution corresponds to a "combination of zoning measures + implementation sequence," and its performance in terms of ecology, protection, and cost can be calculated separately.

[0038] Next, using the validity period and failure threshold estimated in step S3 as the basis for judgment, the activation sequence and connection method of maintenance and restoration are determined in stages. Combining the sea-level rise rate predicted by the model with the storm cycle, the entire management period is divided into several stages, and then matched according to the validity period and failure threshold of each maintenance and restoration (for example, in the first stage, priority is given to deploying supratidal sand replenishment measures; if the storm intensity increases or vegetation growth stabilizes after the second stage, sand barriers or underwater sandbars are added in vulnerable sections). One or more early warning thresholds are set for each type of maintenance and restoration to determine whether to proceed to the subsequent plan or switch to the alternative path. If the monitoring data shows that there is still a safety margin before the critical threshold, the existing maintenance and restoration can continue; otherwise, the next stage of measures is initiated ahead of schedule or as scheduled.

[0039] Another innovation of this application is that it is no longer limited to one-off or short-term restoration measures, but through rolling monitoring and adaptive updates, it can adjust the management strategy in real time according to changes in uncertain factors such as sea level rise, changes in storm wave intensity and human activity interference, so as to maintain the stability and high resilience of beach function over a longer time scale.

[0040] Step S5 involves continuous rolling monitoring, installing or deploying observation equipment in different beach zones, including shore-based video surveillance, drone aerial patrols, and wave / tide level sensors, and integrating this data with numerical forecast data (such as storm paths and tidal conditions). Periodic (e.g., weekly or monthly) or focused patrols are conducted after extreme events (such as typhoon landfall) to comprehensively understand beach morphology, vegetation cover, and the actual effects of maintenance and restoration measures. Monitoring data is compared with pre-set thresholds, such as shoreline retreat, beach shoulder elevation, vegetation degradation rate, or changes in underwater topography. When any indicator approaches or exceeds a safety threshold, the system issues an alert, alerting managers or automatically triggering subsequent decision-making processes. Geographic Information System (GIS) or cloud databases are used to record monitoring results in real time, creating a digital beach archive. A visual interface intuitively presents the spatiotemporal evolution of key indicators, providing a basis for subsequent adaptive updates.

[0041] When the deviation between measured data and predictions exceeds a preset threshold, an automatic or manual "path iteration" process is triggered to optimize and update subsequent restoration plans in terms of spatial layout and implementation sequence. Conditions triggering updates include: monitoring indicators exceeding or about to exceed thresholds, significant deviations from model predictions, drastic changes in beach morphology caused by extreme storms or unforeseen events, and new regulations, changes in funding, or technological advancements rendering the original plan no longer optimal or compliant. If the problem is concentrated in certain areas (e.g., localized erosion), the restoration plan for the corresponding section can be adjusted locally. If there is a systemic deviation in overall conditions (e.g., sea-level rise significantly higher than expected), a comprehensive reassessment and optimization of the entire beach is required.

[0042] The implementation sequence is adjusted based on the updated results, such as activating a measure ahead of schedule or replacing it with an alternative technology. If extreme situations are detected (e.g., excessively strong storm surges), emergency plans are activated (e.g., emergency sand replenishment, sandbar reinforcement, temporary beach closure, etc.), and the response process is recorded. After each update, the effects of the new measures are compared with actual observation results. If the error is too large, it indicates that the model parameters or assumptions are inaccurate, and the numerical model needs to be recalibrated. Through rolling iterations, the model's predictive ability and the applicability of the remediation strategies can be continuously improved.

[0043] Finally, all information (data, decisions, and effectiveness assessments) from rolling monitoring and each adaptive update is archived to build a sustainable learning and upgrading beach management system, forming a closed-loop management model of "monitoring-assessment-updating-remonitoring". This approach can flexibly respond to extreme events. Based on phased path planning and monitoring thresholds, combined with emergency plans, restoration measures can be quickly switched or upgraded in situations such as extreme storms, major erosion, and changes in regulatory policies. This significantly reduces the vulnerability of beaches to sudden impacts and reduces the high costs and ecological losses of reactive remediation.

[0044] Finally, this application provides two embodiments. Embodiment 1 highlights the advantages of phased beach restoration, while Embodiment 2 highlights a beach restoration demonstration that balances tourism and ecology. Example

[0045] A stretch of beach near a coastal city is frequently affected by monsoons and tropical storms. Over the past three years, the coastline has receded significantly, and the drop in beach shoulder elevation has weakened its wave-resistant capabilities. Local marine ecological red lines strictly prohibit the construction of rigid dikes in the intertidal zone, but allow for sand replenishment and flexible protection in the supratidal zone.

[0046] Based on the beach conservation and restoration resilience enhancement and long-term management method provided in this application, step 1: multi-source data acquisition and initialization, including: installing shore-based video surveillance and tide gauges; taking aerial photos by drones every quarter to extract the shoreline location and elevation distribution and establish a geographic information database; and statistically analyzing the maximum storm tide level and extreme wave height in the region based on storm records of the past ten years and incorporating them into the numerical simulation scenario.

[0047] Step 2: Beach evolution simulation and risk assessment, including: using the CROSPE model, setting tropical storm scenarios with different return periods, and finding (assuming) a 20-year return period storm occurs in the next 5 years, the coastline could retreat by up to 40 m, and the beach shoulder elevation could decrease by up to 0.8 m. Combining monitoring data and model output, two key erosion hotspots were identified.

[0048] Step 3: Develop resilience indicators and remediation measures, including: setting resilience indicators as shoreline retreat rate, beach shoulder elevation, and vegetation cover. Remediation measures include supratidal sand replenishment (estimated effectiveness of approximately 3 years), underwater artificial sandbars (estimated effectiveness of approximately 5 years), and sand barrier dikes (enhancing vegetation survival, approximately 4 years). Numerical simulations show that (assuming) under different storm scenarios, single or combined measures can maintain shoreline retreat of less than 20m for 3-6 years.

[0049] Step 4: Resilience enhancement design, including: In the first stage, in the most severely eroded sections, concentrated deployment of supratidal sand replenishment, coupled with sand barriers to aid vegetation development; in other sections, small-scale sand replenishment will be implemented as appropriate; In the second stage, if monitoring reveals that vegetation coverage is below 50% or the beach shoulder height has decreased by more than 0.6m, underwater artificial sandbars will be added to that section as soon as possible; In the third stage, if monitoring shows that storm surge intensity continues to increase or shoreline retreat accelerates, or the effectiveness of sand replenishment is nearing expiration, sand barriers will be added to key sections in advance to reinforce and slow down the retreat trend.

[0050] Step 5: Rolling monitoring and adaptive updates, including: monitoring the beach shoulder elevation and shoreline retreat of each zone every quarter; when it is found that the retreat of a hot spot section has exceeded 25m after the third year, triggering the "critical threshold of 30m" warning, the second phase of measures will be accelerated; based on real-time data, the parameters of the multi-objective optimization algorithm will be updated to prepare for the third phase to expand the sand barrier or increase the scale of sand replenishment, forming a path iteration. Example

[0051] A coastal tourist resort area has a gentle beach but a large number of tourists; human trampling and improper development have led to vegetation degradation and localized erosion. During typhoon season, small-scale erosion and water accumulation in low-lying areas are common. Regulations stipulate that large-scale hard alterations to the intertidal zone are prohibited without authorization, and both aesthetic appeal and tourist safety must be taken into account.

[0052] Based on the beach conservation and restoration resilience enhancement and long-term management method provided in this application, step 1: multi-source data and initial survey, including: periodically taking drone photos of densely populated tourist areas to count the extent of damage to vegetation caused by trampling; installing sensors to monitor daily changes in wind, waves and tides; and integrating data such as vegetation coverage, tourist flow, and shoreline deformation into a GIS platform.

[0053] Step 2: Beach evolution simulation and human interference analysis, including: combining beach evolution models such as XBeach to set extreme values ​​of typhoon waves and high tides; analyzing the impact of the superposition of "peak holiday trampling + typhoon waves" on beach morphology; the results show (assuming) that in several high-flow inlet areas, the average height of the beach shoulder decreases by about 0.3m per year, which can easily cause flooding or erosion during storm surge season.

[0054] Step 3: Calculation of resilience indicators and the validity period of measures, including setting resilience indicators as beach shoulder elevation, vegetation coverage, and visitor carrying capacity; specific resilience enhancement measures include: local sand replenishment (valid for 2 years), sand barrier boardwalks (protecting vegetation, valid for 5 years), and underwater sandbars (damping against typhoons, valid for 5-7 years). Through numerical simulation and scenario analysis, if continuous high trampling is uncontrolled, the validity period of simple sand replenishment will be significantly shortened.

[0055] Step 4: Prioritize the construction of sand barrier boardwalks in the main tourist access area to reduce trampling damage and provide a buffer for vegetation reconstruction; if the vegetation coverage is found to decrease by more than 20% during the holiday season, or the beach shoulder elevation drops below 0.5m, replenish sand in advance and add underwater sandbars to defend against storm erosion; implement in three phases (each phase lasting about 3 years) and make minor adjustments based on monitoring data before and after the peak tourist season each year.

[0056] Step 5: Rolling monitoring and adaptive updates, including: after the peak holiday season, a concentrated check of the elevation and vegetation changes in trampled areas is conducted, and the data is compared with model predictions before and after the typhoon season; if the trampling damage rate is severe, sand replenishment and sand barrier expansion are completed in advance in the early part of the next holiday season; when the storm extreme value exceeds the initial design wave height by 20%, the stability of the underwater sandbar is automatically assessed and an emergency reinforcement plan is prepared.

[0057] In summary, compared with traditional one-off or fixed beach restoration solutions, this application has significantly improved in terms of adaptive response to uncertain environments, optimal allocation of restoration resources, and scientific operation and maintenance in the later stages, truly achieving a balance between the long-term resilience of beach management and multiple benefits.

[0058] Those skilled in the art will understand that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.

[0059] The meaning of "and / or" as used in this application includes situations where each exists alone or both exist simultaneously.

[0060] The term "connection" as used in this application can mean a direct connection between components or an indirect connection between components through other components.

[0061] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for enhancing the resilience and long-term management of beaches, characterized by: Specifically, the following steps are included: Step S1: Set up several monitoring sections or monitoring stations along the beach line, periodically acquire raw data of information along the beach line, preprocess the raw data to form a basic dataset. Step S2: Simulate future changes in beach morphology based on beach topography evolution model, divide the beach into regions and discrete units, conduct risk assessment on the indicators output by simulation, and identify beach erosion hotspots and vulnerable areas. Step S3: Analyze the simulated output indicators and estimate the effective period and failure threshold of maintenance and repair measures based on the toughness index. The specific steps are as follows: Step S31, define the beach failure event. F And define the measurement method; Set beach failure event F The measurement method, namely beach failure events F The defining formula is: (1) In the formula, The shoreline setback distance. Allowable shoreline setback distance ， For the elevation of the beach shoulder ， This is the critical elevation of the beach shoulder. For volume loss, The threshold for volume loss; probability of failure of beach failure event P The expression is: (2) In the formula, As an indicator, it represents the distance the shoreline has retreated. Or changes in beach shoulder elevation Or changes in beach volume , for The probability density function; Step S32: Based on the simulation results of the beach topography evolution model, perform statistical analysis on the indicators and calculate the failure probability of the beach failure event. Step S33, Define the loss function C ( F Determine the expected loss; Step S34: By combining the beach failure probability with the loss function, determine whether maintenance and restoration measures are needed to improve beach resilience. Step S35: Based on different maintenance and restoration measures, several resilience indicators are set under different working conditions to measure the beach resilience from different perspectives and obtain a comprehensive resilience value. A total The overall resilience of the beach under maintenance and restoration is assessed by using a comprehensive resilience value. This involves normalizing the set resilience indicators and assigning weights to them to obtain the comprehensive resilience value. A total ; Step S36: By assuming simulation scenarios under different working conditions, obtain the curve of the comprehensive toughness value evolving over time, and continue to obtain the expected effective period of maintenance and repair measures under multiple scenarios. Step S37, the measure failure threshold is defined as follows: when simulating the index, if the input external driving condition is... Ω crit The effectiveness of maintenance and repair measures decreases sharply over time; Step S4: Based on the beach zoning in Step S2, clarify the resilience restoration measures and feasible measures for each zone, and use a multi-objective optimization algorithm to balance ecological protection, protection efficiency, economic cost and regulatory restrictions. Using the validity period and failure threshold estimated in step S3 as the basis for judgment, if the algorithm data meets the requirements of validity period and failure threshold, the toughness repair measures are maintained; otherwise, the next step is carried out. Step S5 involves continuous rolling monitoring. When the remediation measures fail to meet the requirements, update conditions are triggered. Based on the update results, the remediation measures are adjusted to optimize the predictive capabilities of the beach topography evolution model and the adaptability of the remediation strategy.

2. The method for improving the resilience and long-term management of beaches according to claim 1, characterized in that: In step S1, raw data is collected using UAV remote sensing, shore-based sensors, or buoy observation equipment. The raw data includes the shoreline location, elevation, wave and tide elements, and vegetation cover of the beach. The raw data is cleaned to remove random noise, aligned in time and space, outliers are removed, and interpolation or merging operations are performed to improve the integrity and usability of the raw data. The preprocessed raw data is imported into a geographic information system or a dedicated database to form a basic dataset for visualization.

3. The method for improving the resilience and long-term management of beaches according to claim 2, characterized in that: In step S2, the set external driving conditions are input into the beach topography evolution model to simulate the future changes in beach morphology and output indicators, including shoreline setback distance. Changes in beach shoulder elevation and changes in beach volume ; By statistically analyzing the indicators output by the beach topography evolution model in each discrete unit, areas with significant topographic changes are identified using intuitive threshold comparisons or standardized methods, and these areas are determined as potential erosion hotspots. By using a beach topography evolution model to track and simulate the deformation process at each time step within a potential erosion hotspot, areas that continue to erode or exceed a threshold within a certain number of moments are identified as vulnerable zones.

4. The method for improving the resilience and long-term management of beaches according to claim 1, characterized in that: In step S32, there are three ways to obtain the failure probability of the beach failure event: direct statistical method, fitted distribution method, and multi-scenario synthesis method. In step S33, the expression for the expected loss is defined as follows: (5) In the formula, The degree of economic loss, reduction in ecosystem service value, or social impact. The failure probability of a beach failure event; In step S35, assuming different maintenance and repair measures { m 1, m 2,… m k }, Introducing different operating conditions { Ω 1, Ω 2,…, Ω m }, outputting several quantified resilience indicators { a 1, a 2,…, a n The resilience index is normalized using the following formula: (6) In the formula, As a resilience indicator The maximum value that appears in the sample. As a resilience indicator The minimum value that appears in the sample. As a resilience indicator, ; Continue to assign weights to each resilience indicator. w 1, w 2,… w n The weighting coefficients satisfy: (7) In the formula, As the weight of the resilience index, ; Finally, the overall toughness value is obtained: (8) In the formula, To calculate the overall toughness value, As the weight of the resilience index, As a resilience indicator, ; like A total (t)> A crit Then it is determined that the maintenance and repair measures are in place. t If the maintenance and repair measures have not expired, then the maintenance and repair measures are deemed to be in effect. t Expiration at any time; In step S36, it is assumed that under the operating conditions... Ω s The simulation yielded a time-varying curve. The curve indicates a certain maintenance and repair measure m j Validity period T j,s Defined as: (9) In the formula, In the context Ω s The curve showing the evolution of the overall toughness value over time obtained from the simulation. , For maintenance and repair measures m j The validity period, ; If we consider multiple scenarios and give their probabilities p s Therefore, the expected lifespan of this maintenance and repair measure under multiple scenarios is: (10) In the formula, For maintenance and repair measures m j The validity period, ; In step S37, when the external driving condition is Ω crit If the maintenance and repair measures show a significant decline in effectiveness, then the failure threshold for these measures is set as follows: Ω crit , Ω crit ∈{ Ω 1, Ω 2,…, Ω m }; When operating conditions Ω s Down, Maintenance and repair measures m j If the failure probability is less than 5%, then the maintenance and repair measures are deemed effective under the current driving conditions. Ω s Safety measures, such as maintenance and repair measures m j If the failure probability is ≥5%, the maintenance and repair measures are deemed to have exceeded the failure threshold, requiring replacement or supplementation. m j The failure probability is defined as p fail ,satisfy: (11)。 5. The method for improving the resilience and long-term management of beaches according to claim 4, characterized in that: In step S32, the methods for obtaining the failure probability of the beach failure event are the direct statistical method, the fitted distribution method, and the multi-scenario synthesis method. Direct statistical methods refer to statistical indicators obtained through extensive Monte Carlo simulations or repeated numerical experiments under multiple scenarios. The distribution of the probability density function, or its mean and variance, can be approximately estimated when the sample size is sufficient. Numerically, this can be approximated by calculating the transcendental frequency after sorting the results. P ( F If the sample size is N, and M of the samples satisfy... The failure probability is approximately: P ( F ) = M / N; The fitted distribution method refers to fitting the index G to a specific distribution based on the actual working conditions, using maximum likelihood estimation to obtain the distribution parameters μ and σ, and then calculating the failure probability according to the cumulative distribution function. The calculation method is as follows: (3) In formula (3), The cumulative distribution function of the standard normal distribution; The multi-scenario synthesis method refers to the process of determining the failure probability under different scenarios if the beach morphology behaves differently. P i (F) Perform a weighted summation: (4) In formula (4), This represents the failure probability of failure events under different scenarios.

6. The method for improving the resilience and long-term management of beaches according to claim 1, characterized in that: In step S4, based on the partitioning of the beach in step S2, a multi-objective optimization form is defined, namely... (12) In formula (12), For decision variables regarding the conservation and restoration measures, scale, and implementation sequence in beach zones, For economic costs, Due to erosion risk or inadequate coastal protection, For ecological indicators.

7. The method for improving the resilience and long-term management of beaches according to claim 6, characterized in that: In step S4, the maintenance and repair measures obtained in step S36 are implemented. m j Validity period T j,s and the measure failure threshold obtained in step S37 Ω crit By combining multi-objective optimization, the activation sequence and connection method of maintenance and repair measures are determined in stages.

8. The method for improving the resilience and long-term management of beaches according to claim 1, characterized in that: In step S5, the monitoring trigger update conditions are set, and steps S1-S4 are repeated to implement local adjustments to the partitioned beach or optimize the beach as a whole. Archive rolling monitoring and update information to build a beach management system that is constantly learning and upgrading.

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