Method and system for selecting key defensive area of storm surge disaster based on quantile method

Abstract

The invention discloses a storm surge disaster key defensive area selection method and system based on a quantile method, and relates to the technical field of disaster management, and the method comprises the steps: carrying out the multi-scale simulation based on atmospheric circulation, nearshore hydrodynamic force, regional submergence and building damage for a to-be-managed whole domain, and constructing a multi-scale nested twinborn model; constructing a quantile curved surface based on a quantile dimension, constructing a simulation selector by using the multi-scale nested twin model and the quantile curved surface, performing two-way simulation deduction and quantile decision on a source data set from a plurality of monitoring source ends, determining a defensive area, visualizing the defensive area on a terminal interface, and performing disaster prevention and control early warning management. The method and the system are used for solving the technical problem that an intelligent and flexible disaster deduction decision-making system is lacked in the prior art to solve the technical problem that the emergency response capability of disaster prevention and control is poor due to the fact that the selection and division precision of a defensive area is limited, and the selection and division precision of the defensive area can be effectively improved.

Description

Technical Field

[0001] This invention relates to the field of disaster management technology, specifically to a method and system for selecting key defense zones for storm surge disasters based on the quantile method. Background Technology

[0002] Storm surge disasters are common natural disasters in coastal areas, usually accompanied by strong storms and sea level rise. Therefore, how to accurately predict and prevent storm surge disasters, especially in the selection of key defense zones, has become an important issue in the current prevention and control of marine disasters.

[0003] Traditional storm surge disaster prevention zone delineation typically relies on empirical methods based on historical data or uses only a single simulation model for disaster prediction, which is insufficient in terms of accuracy and timeliness for disaster prevention and early warning. While existing technologies employ various numerical simulation methods for storm surge prediction and assessment, they generally suffer from insufficient detail in the dynamic simulation process and imprecise response measures.

[0004] In summary, existing technologies lack an intelligent and flexible disaster simulation and decision-making system to address the problem of limited accuracy in the selection of defense zones, which leads to poor emergency response capabilities for disaster prevention and control. Summary of the Invention

[0005] This application provides a method and system for selecting key defense zones for storm surge disasters based on the quantile method. It is used to address the technical problem that the existing technology lacks an intelligent and flexible disaster prediction and decision-making system, which limits the accuracy of defense zone selection and results in poor emergency response capabilities for disaster prevention and control.

[0006] In view of the above problems, this application provides a method and system for selecting key defense areas for storm surge disasters based on the quantile method.

[0007] Firstly, this application provides a method for selecting key defense zones for storm surge disasters based on the quantile method. The method includes: conducting multi-scale simulations based on atmospheric circulation, nearshore hydrodynamics, regional inundation, and building damage for the entire area to be managed, and constructing a multi-scale nested twin model; constructing a quantile surface based on the quantile dimension, wherein the quantile dimension includes explicit and implicit classes; constructing a simulation selector using the multi-scale nested twin model and the quantile surface, performing two-way simulation and quantile decision-making on source datasets from multiple monitoring sources to determine defense zones, wherein the two-way simulation includes a one-step continuous rolling simulation based on atmospheric circulation and nearshore hydrodynamics, and a rolling simulation based on the optional triggering of regional inundation and building damage; and visualizing the defense zones on a terminal interface for disaster prevention and early warning management.

[0008] Secondly, this application provides a storm surge disaster key defense zone selection system based on the quantile method. The system includes: a first construction unit: for the entire area to be managed, multi-scale simulation based on atmospheric circulation, nearshore hydrodynamics, regional inundation, and building damage is performed to construct a multi-scale nested twin model; a second construction unit: quantile surfaces based on quantile dimensions are constructed, wherein the quantile dimensions include explicit and implicit classes; a defense zone selection unit: a simulation selector is constructed using the multi-scale nested twin model and the quantile surface to perform two-way simulation and quantile decision-making on source datasets from multiple monitoring sources to determine defense zones, wherein the two-way simulation includes a one-step continuous rolling simulation based on atmospheric circulation and nearshore hydrodynamics, and a rolling simulation based on the optional triggering of regional inundation and building damage; and a prevention and control management unit: the defense zones are visualized on a terminal interface for disaster prevention and early warning management.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages: The storm surge disaster key defense zone selection method based on quantile method provided in this application embodiment conducts multi-scale simulation based on atmospheric circulation, nearshore hydrodynamics, regional inundation, and building damage for the entire area to be managed, and constructs a multi-scale nested twin model; constructs a quantile surface based on the quantile dimension, and uses the multi-scale nested twin model and quantile surface to construct a simulation selector, performs two-way simulation extrapolation and quantile decision on source datasets from multiple monitoring sources, determines the defense zone, and visualizes the defense zone on the terminal interface for disaster prevention and early warning management. This method is used to solve the technical problem of the lack of an intelligent and flexible disaster extrapolation and decision-making system in the prior art, which limits the accuracy of defense zone selection and results in poor emergency response capability for disaster prevention and control. Through flexible extrapolation and prediction based on multi-scale nested twins and decision-making based on quantile surfaces, the accuracy of defense zone selection can be effectively improved. Attached Figure Description

[0010] Figure 1 This application provides a flowchart illustrating the method for selecting key storm surge disaster prevention areas based on the quantile method; Figure 2 This application provides a schematic diagram of the system for delineating key storm surge disaster prevention areas based on the quantile method.

[0011] Explanation of reference numerals in the attached diagram: First building unit 11, Second building unit 12, Defense zone selection unit 13, Prevention and control management unit 14. Detailed Implementation

[0012] This application provides a method and system for selecting key defense zones for storm surge disasters based on the quantile method. This addresses the technical problem of the lack of an intelligent and flexible disaster prediction and decision-making system in the existing technology, which limits the accuracy of defense zone selection and results in poor emergency response capabilities for disaster prevention and control.

[0013] Example 1: As Figure 1 As shown, this application provides a method for selecting key storm surge disaster prevention areas based on the quantile method, the method comprising: S1: For the entire area to be managed, conduct multi-scale simulations based on atmospheric circulation, nearshore hydrodynamics, regional inundation and building damage, and construct a multi-scale nested twin model.

[0014] Furthermore, constructing a multi-scale nested twin model, step S1 of this application includes: Based on the evolution of typhoon path and intensity, atmospheric circulation simulation is performed, and a first twin simulation layer is deployed; based on storm tide level and wave propagation, nearshore hydrodynamic simulation is performed, and a second twin simulation layer is deployed; based on high-resolution DEM and building vector data, regional inundation simulation is performed, and a third twin simulation layer is deployed; based on the fluid-structure coupling of building structures, building damage simulation is performed, and a fourth twin simulation layer is deployed; based on the first, second, third, and fourth twin simulation layers, a multi-scale nested twin model is constructed.

[0015] In this embodiment, during the selection of storm surge disaster prevention zones, for the entire area to be managed—that is, the entire area with disaster prevention needs—multi-scale simulation calculations are first required to ensure that the combined impact of different scales and multiple factors can fully reflect the actual risk of the disaster. Specifically, this application constructs a multi-scale nested twin model for disaster prediction and analysis, forming an accurate disaster simulation system. This step includes four key components: atmospheric circulation simulation, nearshore hydrodynamic simulation, regional inundation simulation, and building damage simulation.

[0016] First, atmospheric circulation simulation is performed based on data on typhoon path and intensity evolution, and a first twin simulation layer is deployed on this basis. The atmospheric circulation simulation is used to model the storm's path and intensity changes, taking into account factors such as storm formation, vortex formation, movement, and intensity variations, as well as their impact on the sea surface and nearshore areas. The establishment of the first twin simulation layer enables real-time tracking of typhoon trends, providing reliable meteorological data for subsequent disaster evolution.

[0017] Simultaneously, based on storm surge levels and wave propagation, nearshore hydrodynamic simulations are performed, and a second twin simulation layer is deployed. The aim is to analyze the impact of storm surges on the coastline and simulate the propagation behavior of ocean waves. This can predict the water level changes and potential destructive effects of storm surges on different coastal areas, serving as a basis for determining dike breaches.

[0018] Simultaneously, regarding regional inundation, simulation analysis is conducted based on high-resolution digital elevation models (DEMs) and building vector data, and a third twin simulation layer is deployed. Specifically, this simulation layer simulates storm surge-induced flooding by meticulously modeling the terrain and building distribution, assessing the inundation severity in different areas. High-resolution DEM data accurately reflects changes in ground elevation, identifies flood-prone areas within the region, and further provides spatial basis for delineating defense zones.

[0019] Simultaneously, based on the fluid-structure coupling principle of building structures, building damage simulation is performed, and a fourth twin simulation layer is deployed. This simulation layer assesses the extent of damage to buildings under storm surge by simulating the interaction between buildings and water flow and wind. Fluid-structure coupling simulation can simulate the specific damage modes and extent of damage to buildings caused by storm surges, especially for risk assessment of low-lying and vulnerable buildings. It can accurately predict the impact of disasters on infrastructure, providing crucial data for post-disaster recovery and emergency response.

[0020] In this application, by constructing the aforementioned four twin simulation layers, comprehensive simulations are conducted from different levels, including meteorology, oceanography, hydrology, and architecture, providing a comprehensive and accurate simulation basis for the selection of disaster prevention zones. These simulation layers are nested and work collaboratively to form a multi-scale nested twin model. Optionally, the above simulations can be built on any visualization simulation platform. This allows for the comprehensive consideration of the influence of different factors during the selection of disaster prevention zones, thereby achieving more scientific and accurate decision support.

[0021] S2: Construct a quantile surface based on the quantile dimension, wherein the quantile dimension includes explicit and implicit classes.

[0022] Furthermore, constructing a quantile surface based on the quantile dimension, step S2 of this application includes: For the entire area to be managed, a non-uniform grid is used to divide it into regional networks; The quantile dimensions are determined, wherein the explicit class of the quantile dimensions is the physical quantile, and the implicit classes include the social vulnerability quantile, the ecological sensitivity quantile, and the economic resilience quantile. For the aforementioned regional network, a joint calculation of the temporal risk value quantiles based on the quantile dimension is performed grid by grid to generate a quantile surface, wherein the quantile surface represents the dynamic risk that changes over time.

[0023] In this application, the construction of a quantile surface based on the quantile dimension is achieved by comprehensively considering multi-dimensional risk information to accurately express and assess the dynamic risk of storm surge disasters. The specific steps include the following key stages.

[0024] First, the entire area to be managed is divided into non-uniform grids. That is, based on the risk characteristics and geographical characteristics of different areas, the entire area to be managed is divided into grids. The internal areas of each grid have similar risk characteristics and geographical characteristics, and can be segmented based on a preset approximation threshold.

[0025] Preferably, a finer mesh is used in higher-risk areas, while a larger mesh size is used in lower-risk areas. This process improves computational efficiency and the effectiveness of subsequent defense zone selection.

[0026] Next, the quantile dimensions are determined. In this application, the quantile dimensions are divided into explicit and implicit classes. The explicit class consists of physical quantiles, which mainly focus on the physical characteristics of the disaster itself, such as wind speed and tide level, which are key factors directly affecting the disaster. The implicit class includes social vulnerability quantiles, ecological sensitivity quantiles, and economic resilience quantiles.

[0027] Specifically, the social vulnerability quantile focuses on the resilience of social groups to disasters, the ecological sensitivity quantile assesses the carrying capacity of the ecological environment, and the economic resilience quantile measures the ease with which economic activities can recover. By integrating these explicit and implicit dimensions, the impact of disasters can be comprehensively assessed, providing multi-dimensional support for the selection of defense zones.

[0028] Then, for each grid point in the aforementioned regional network, a joint calculation of the temporal risk value quantiles based on the quantile dimension is performed. That is, each grid possesses a temporal quantile sequence based on four quantile dimensions. Specifically, within each grid, based on disaster data and risk assessment indicators from different time periods, the risk value for each grid, i.e., the temporal risk value quantile, is calculated. These risk values ​​encompass multiple factors, including physical, social, ecological, and economic aspects. By jointly analyzing the temporal data, a dynamic risk assessment result that changes over time can be obtained, reflecting the evolution trend of risk during disaster occurrence.

[0029] For example, to determine the social vulnerability quantile for a specific grid, taking the calculation of the 50th percentile (median) of social vulnerability in a grid as an example, we first collect data on multiple indicators of the disaster resilience of the social groups within that grid, such as the proportion of vulnerable groups and the average emergency reserves of residents, and integrate them into a comprehensive vulnerability value. We then arrange the comprehensive values ​​for the grid over multiple time periods in chronological order, with n data points. The median position is determined by (n+1)×0.5. If the value is an integer, it is the median; if it is a decimal, the average of two adjacent values ​​is taken as the result.

[0030] Optionally, during the analysis of the time-series risk values, the determination of time-series nodes can be based on 24 hours, 12 hours, 6 hours before login, during login, and after login, in order to ensure the effectiveness of the risk analysis of the data.

[0031] Preferably, the quantile dimension is expandable in this application.

[0032] Finally, using the aforementioned regional grid as a base, the temporal risk quantiles of each grid are written into the base to generate the quantile surface, which clearly displays the risk distribution at different time points. The quantile surface is dynamically updated.

[0033] In summary, the quantile surface not only reflects the risk level of each region at different points in time, but also reveals the risk coupling relationship between different dimensions. This provides a precise risk assessment basis for the selection of storm surge disaster prevention zones, making the delineation of these zones more scientific and reasonable, and enabling effective responses to potential future disaster risks.

[0034] S3: Construct a simulation selector using the multi-scale nested twin model and quantile surface to perform two-way simulation and quantile decision-making on source datasets from multiple monitoring sources to determine the defense zone. The two-way simulation includes a one-step continuous rolling simulation based on atmospheric circulation and nearshore hydrodynamics, and a rolling simulation based on the optional triggering of regional inundation and building damage.

[0035] In this application, a simulated delineator is first constructed using a multi-scale nested twin model and a quantile surface. Specifically, a first simulation node is constructed based on the multi-scale nested twin model, wherein the first simulation node receives real-time monitored multi-source data and performs multi-scale twin simulation deduction; a second planning node is constructed based on the quantile surface, wherein the second planning node updates the quantiles in real time based on the time-series simulation data determined by the first simulation node, and selects grids with high risk based on the quantile data, determining them as defense zones.

[0036] Specifically, the first simulation node and the second planning node are cascaded as the architecture of the simulation selector. Preferably, multi-scale twin simulation rules are formulated for the first simulation node, namely, continuous rolling deduction of the first twin simulation layer and the second twin simulation layer, and selective triggering of rolling deduction of the third twin simulation layer and the fourth twin simulation layer. The selectivity is determined based on the dike control situation. For example, if a dike may collapse and cause large-scale flooding upstream, it needs to be triggered; if there is a high ground that can prevent the flood from spreading inland, the third twin layer and the fourth twin layer are in a dormant state to reasonably plan the simulation process.

[0037] Preferably, for the second planning node, further training based on sample data is performed until the node converges. If the accuracy of the output defense zone selection result meets the preset requirements, the constructed simulation planner is obtained.

[0038] In this application, the simulation selector can comprehensively consider factors such as meteorology, oceanography, regional hydrology, and building damage, providing a comprehensive simulation and decision-making framework.

[0039] Next, the simulation selector performs a two-way simulation based on source datasets from multiple monitoring sources. These multiple monitoring sources include real-time data sources such as satellites, radars, buoys, and shore-based stations, providing real-time and comprehensive information related to storm surge disasters.

[0040] The two-way simulation consists of two main steps: the first step is a continuous rolling simulation based on atmospheric circulation and nearshore hydrodynamics. This simulation method focuses on the impact of storm path and intensity changes on nearshore hydrodynamics, simulating storm surge level changes and wave propagation, and can assess the occurrence of storm surges and their impact on different regions. This process involves continuous rolling simulation, updating the simulation results in real time to keep pace with actual environmental changes.

[0041] If the simulation in the first step shows damage to the dike, for example, the collapse of a dike may cause large-scale flooding upstream, triggering the second step.

[0042] Specifically, the second step involves rolling simulations based on optional triggers of regional inundation and building damage. Building upon the first step, this step further considers the inundation of ground areas and the destructive impact on buildings after a storm surge. Through regional inundation simulation and building damage simulation, the specific impacts of storm surges on different regions and buildings can be predicted, and the delineation of defense zones can be dynamically adjusted according to the degree of inundation and damage.

[0043] Through the aforementioned two-way simulation, disaster scenarios were effectively predicted. Subsequently, quantile-based decision-making was employed, where quantile surfaces were used to measure the dynamic changes in risk and aid in making timely and scientific decisions during real-time simulations. Ultimately, key defense zones for responding to storm surge disasters were accurately delineated, ensuring the effectiveness of disaster prevention and mitigation measures.

[0044] Furthermore, a two-way simulation is performed on the source datasets from multiple monitoring sources. Step S3 of this application includes: By using multi-source monitoring, a source dataset is obtained, wherein the source dataset contains at least real-time observation data from satellites, radars, buoys, and shore base stations; based on the source dataset, a rolling simulation based on the first twin simulation layer and the second twin simulation layer is performed in the multi-scale nested twin model to determine the first rolling simulation data; based on the first rolling simulation data, key nodes in the disaster chain are identified.

[0045] In this application, multi-source monitoring is used to collect data from different monitoring sources in real time, so as to conduct dynamic simulations and real-time decision-making during the disaster.

[0046] First, the source dataset was acquired through multi-source monitoring, including real-time observation data from satellites, radar, buoys, and shore-based stations. These monitoring sources provide real-time data at different levels and in different dimensions. Satellite data provides broad-area meteorological and oceanographic information, radar data is used to track storm paths and intensity changes in real time, buoy data provides real-time sea level and wave information, and shore-based station data reflects specific regional meteorological and oceanographic conditions. These diverse data sources provide comprehensive support for the assessment and early warning of storm surge disasters, ensuring sufficient awareness of the dynamic evolution of disasters.

[0047] Next, based on these source datasets, progressive rolling simulations are performed in a multi-scale nested Siamese model.

[0048] Specifically, the first and second twin simulation layers undertake different simulation tasks. The first twin simulation layer simulates changes in atmospheric circulation and storm paths to predict the evolution and impact range of the storm; the second twin simulation layer, based on nearshore hydrodynamic data and the storm conditions predicted by the first twin simulation layer, simulates the impact of storm surges on the coastline and the wave propagation process. It can assess the dynamic changes of storm surges and their impact on the region in real time. This progressive simulation means that the first simulation layer is executed first, then the simulation progresses to the second twin simulation layer, gradually advancing to more accurate disaster simulation results.

[0049] During the rolling simulation, the initial rolling simulation data provides fundamental information for identifying the disaster chain. It includes key indicators of each stage of disaster evolution, such as wind speed, tide level, wave height, and potential inundation extent. This not only helps determine the development trend of storm surge but also provides accurate forecasting basis for defense decisions.

[0050] Finally, based on the first rolling simulation data, key nodes can be identified through the disaster chain. These key nodes refer to important links in the disaster chain, such as water levels exceeding critical points, indicating the possibility of levee damage. That is, if the predicted storm conditions and water level data may cause levee damage, affecting the entire area under management, further regional flooding and building damage prediction analysis is needed.

[0051] By executing the above steps, this application can accurately identify key aspects of a disaster based on real-time data and dynamic simulation results, and provide solid data support for the precise delineation of defense zones and disaster prevention and control.

[0052] Furthermore, step S3 of this application includes: Based on the probability of breaching near-shore dikes, the key nodes are classified into key amplification nodes and key blocking nodes, wherein the key amplification nodes represent dike breaches and the key blocking nodes represent dike protection; if the key node is the key blocking node, no response is made.

[0053] Furthermore, if the key node is a key amplification node, the third twin simulation layer and the fourth twin simulation layer are triggered and a progressive rolling mode deduction is performed to determine the second rolling deduction data; based on the second rolling deduction data, the quantile surface is updated.

[0054] In the implementation of this application, based on the probability of breach of the near-shore dike, the identified key nodes are further subdivided into key amplification nodes and key blocking nodes in order to take different countermeasures.

[0055] First, critical amplification nodes refer to the key points where levees are likely to breach during a storm surge disaster. When a levee breaches, the storm surge water level will rise rapidly, potentially leading to large-scale flooding and building damage. Therefore, these nodes require close monitoring and rapid response. Conversely, critical blocking nodes indicate that the levee has strong protective capabilities and a low probability of storm surge breach. They are generally considered to have sufficient protective capabilities to withstand the impact of storm surges, and therefore require less intervention.

[0056] Optionally, when the probability is greater than the breakthrough probability, it is classified as a key amplification node; when the probability is less than or equal to the breakthrough probability, it is classified as a key blocking node.

[0057] For critical blocking points, the current storm conditions can be protected without intervention. The existing protective facilities are capable of effectively blocking storm surges, reducing the probability of disasters, and thus avoiding over-response and waste of resources.

[0058] However, if the identified critical node is a critical amplification node, meaning that the current protective facilities may not be able to withstand the current storm surge, then a more detailed disaster simulation is required. At this point, the linkage simulation between the third and fourth twin simulation layers will be triggered.

[0059] The third twin simulation layer primarily performs regional inundation simulations, projecting and predicting the impact of storm surge water levels and wave propagation on the region; the fourth twin simulation layer performs building damage simulations, projecting the potential damage that storm surges may cause to buildings. Here, the first rolling simulation data is used as the basis for the projections.

[0060] By using a two-layer progressive simulation, the impact of storm surge breaching the levee can be predicted more accurately.

[0061] Subsequently, the predicted data from the third and fourth twin simulation layers were used as the second rolling simulation data. This second rolling simulation data contains more accurate disaster impact information after simulations of inundation and building damage. Based on this, the quantile surface was updated to further optimize the dynamic risk assessment of storm surge disasters. The updated quantile surface can more accurately reflect the risk changes in different areas during the disaster evolution process, thus providing more accurate data support for the re-delineation of defense zones and disaster response.

[0062] In summary, the flexible and effective simulation under the multi-scale system makes disaster response more efficient and allows for the adjustment of defense measures in real-time dynamic simulation, thereby reducing disaster losses.

[0063] The first twin simulation layer and the second twin simulation layer adopt a first rolling cycle, and the third twin simulation layer and the fourth twin simulation layer adopt a second rolling cycle.

[0064] In this application, the different settings and running cycles of the twin simulation layers are intended to optimize the efficiency and accuracy of simulation simulation based on the computational requirements and response timeliness of different simulation layers.

[0065] First, the first and second twin simulation layers employ a first rolling cycle. These two layers are primarily responsible for simulating atmospheric circulation and nearshore hydrodynamics. The atmospheric circulation simulation layer focuses on the evolution of storm path and intensity, while the nearshore hydrodynamic simulation layer focuses on storm surge levels and wave propagation. Both simulation layers have relatively long timescales, typically requiring stable simulations during storm formation and movement. Therefore, the first rolling cycle is used to maintain real-time early warning and forecasting capabilities. The first rolling cycle can be quite long, usually measured in hours, ensuring timely updates of critical information such as storm path and water level changes, while minimizing resource waste from frequent calculations.

[0066] Secondly, the third and fourth twin simulation layers employ a second rolling cycle. These two layers are responsible for regional inundation simulation and building damage simulation, respectively. Regional inundation simulation primarily assesses the impact of storm surge-induced water level changes on different regions, while building damage simulation analyzes the extent of storm surge damage to buildings. Because these two simulation layers involve more detailed and complex spatial analyses, such as processing high-resolution DEM data and building vector data, the computational burden is heavy, necessitating the use of a second rolling cycle. The second rolling cycle is shorter than the first rolling cycle to allow for timely responses to the impact of storm surges on the region and the extent of building damage, ensuring that defensive measures can be rapidly adjusted based on real-time data.

[0067] In summary, the time step of the simulation is flexibly adjusted according to the needs of different levels of simulation content, thereby achieving optimal resource allocation and maximizing simulation accuracy. The first and second twin simulation layers use longer rolling cycles to ensure the stability of storm surge prediction and hydrodynamic analysis over long timescales, while the third and fourth twin simulation layers employ shorter rolling cycles to achieve more refined, real-time assessments of inundation and building damage. This enables the simulation system to efficiently and accurately conduct disaster risk assessments and dynamically adjust defense zones.

[0068] Furthermore, quantile decision-making is performed to determine the defense zone. Step S3 of this application includes: Based on the quantile surface, the defense zone is located by identifying risk hotspots; The risk hotspot identification process includes: defining risk-coupled hotspot types, such as high physical and social vulnerability, high physical and ecological sensitivity, and high social vulnerability with low economic resilience; scanning the quantile surface, performing grid positioning and priority ranking based on the risk-coupled hotspot types, and determining the first defense grid sequence; performing comprehensive priority ranking on the remaining grids based on the quantile dimension, and filtering based on a preset quantile threshold to determine the second defense grid sequence; and assigning priority order to the first and second defense grid sequences as the defense zones.

[0069] In this application, the process of identifying risk hotspots based on quantile surfaces aims to determine the defense zones for storm surge disasters by accurately locating risks in different areas.

[0070] First, we define the types of risk coupling hotspots. Risk coupling hotspots refer to areas where multiple risk dimensions are intertwined; these areas typically carry higher risk and require special attention and protection. The defined types of risk coupling hotspots mainly include three categories: High physical and social vulnerability: This refers to areas with high physical disaster risk, such as high storm surge levels and large wave impacts, specifically low-lying areas in old urban areas. These areas also exhibit high social vulnerability, meaning that residents and infrastructure in these areas are more susceptible to disaster impacts. These areas require focused defense, as they are highly vulnerable to severe disasters and difficult to recover from.

[0071] High physical and ecological sensitivity: This refers to areas with high physical disaster risk and simultaneously high ecological sensitivity, meaning the ecological environment of such areas is easily damaged by storm surges, such as damaged coastal protection forests. These areas have weak ecological recovery capabilities, and once damaged, they may have long-term impacts on the ecosystem; therefore, they require priority protection.

[0072] High social vulnerability and low economic resilience: This refers to regions with high social vulnerability and low economic resilience. These regions are easily affected by disasters, and their weak economic resilience means that post-disaster recovery will face more difficulties. Even regions with moderate physical risk require close attention to ensure effective support and recovery after a disaster.

[0073] Next, the quantile surface is scanned, and grid localization and priority ranking based on the aforementioned risk coupling hotspot type are performed. The quantile surface reflects the dynamic risk of different regions over time. By scanning the quantile surface, it is possible to locate which regions have higher risks in different dimensions, and thus determine the defense priority of these regions.

[0074] Specifically, using a preset quantile threshold as the critical value, in the quantile surface, each grid is identified based on the aforementioned coupling conditions. Grids that satisfy the aforementioned coupling conditions are selected, and the coupling quantiles are ordered. The corresponding grids are then used as the first grid defense sequence. Optionally, a weighted calculation of two quantiles, or their mean, is used as the coupling quantile.

[0075] The remaining grids are then comprehensively prioritized based on quantile dimensions. For areas not designated as the first defense grid, further prioritization based on their overall risk is required. This comprehensive risk assessment considers multiple dimensions, including physical risk, social vulnerability, ecological sensitivity, and economic resilience, and incorporates quantitative analysis using quantile surfaces. For example, weighted calculations or mean calculations are performed on the four quantiles of each grid to obtain the comprehensive quantiles. Based on preset quantile thresholds, grids whose comprehensive quantiles meet these thresholds are selected and sequentially ranked to determine the second defense grid sequence. The second defense grid sequence has a relatively lower priority but still requires a certain level of defensive measures.

[0076] Finally, the first and second defense grid sequences are designated as defense zones, with their sequence order indicating defense priority, to minimize losses caused by disasters. Ultimately, the selection of defense zones considers not only the impact of physical disasters but also social, ecological, and economic risk factors, ensuring more accurate and comprehensive selection.

[0077] In summary, risk hotspot identification and defense grid prioritization based on quantile surfaces can scientifically determine key defense areas for storm surge disasters, providing strong support for disaster prevention and mitigation.

[0078] S4: Visualize the defense zone on the terminal interface to perform disaster prevention and early warning management.

[0079] Furthermore, the defense zone is visualized on the terminal interface. Step S4 of this application includes: In the regional network, the defended zone and the non-defended zone are identified by first-order binarization, and the defended zone is identified by second-order priority to generate a regional defense map; the regional defense map is then visualized on a terminal interface.

[0080] In this application, in order to effectively carry out disaster prevention and early warning management, the final defense zone must be displayed in an intuitive and easy-to-operate manner.

[0081] First, in the regional network, defense zones and non-defense zones are identified using a first-order binary representation. The binarization process divides the entire managed area grid into defense and non-defense zones, and then uses binary representation to clearly define the zone category. Optionally, defense zones are marked as 1, and non-defense zones as 0. This simplified identification method makes the zone classification clear and the structure of the regional network explicit, facilitating rapid identification of the disaster preparedness status of each zone.

[0082] Next, a priority-based second-order labeling is applied to the defense zones. Since different defense zones have different priorities, the second-order labeling assigns sequence numbers based on the aforementioned order of the defense zones, such as high priority, secondary priority, etc. This highlights areas that require priority protection and helps decision-makers focus resources and efforts on critical defense areas during a disaster.

[0083] In summary, the generated regional defense map clearly shows the distribution of defended and undefended zones throughout the entire managed area, as well as the priority of each zone within the defended zone. This indicates the overall defense status of the region and which zones require priority response, thus providing precise data support for disaster prevention and emergency response.

[0084] Finally, the regional defense map is visualized via a terminal interface. Optionally, the visualization process transforms the regional defense map into a user-friendly graphical interface, allowing decision-makers and relevant personnel to intuitively view the distribution of defendable and non-defensible zones, as well as the defense priorities of each zone. In the terminal interface, defendable zones are typically displayed using different colors, shapes, or icons, with higher-priority zones prominently marked for rapid location and response in the event of a disaster. Furthermore, the interface can display real-time data, such as storm surge level changes, wind speed, and inundation extent, further enhancing decision-makers' understanding of the disaster situation.

[0085] In summary, this system not only provides a clear understanding of the current defense zone delineation but also allows for real-time monitoring of disaster development trends and risk dynamics. It offers a powerful decision-making tool for disaster prevention, early warning, and management, enabling decision-makers to take timely and targeted emergency response measures to reduce potential disaster risks and losses.

[0086] The method for selecting key storm surge disaster prevention areas based on the quantile method provided in this application has the following technical advantages: A multi-scale nested twin model is constructed, comprising four simulation layers: atmospheric circulation, nearshore hydrodynamics, regional inundation, and building damage. This model fully reproduces the evolution of the storm surge disaster chain, enabling multi-dimensional, full-chain risk simulation and overcoming the limitations of single-scale simulation. The quantile dimension encompasses physical quantiles and implicit quantiles such as social vulnerability and ecological sensitivity. A quantile surface is generated through joint calculation of grid-by-grid time-series risk values, achieving a comprehensive assessment of both explicit and implicit risks and improving the comprehensiveness of risk identification. Based on a two-way rolling simulation—continuous simulation of atmospheric circulation-nearshore hydrodynamics and optional triggering simulation of inundation-building damage—the risk status is dynamically updated using real-time monitoring data. Combined with quantile decision-making and risk-coupled hotspot identification, high-priority defense zones are accurately located, improving the targeting of zone selection. A regional defense map is generated using binarized first-order identifiers and priority second-order identifiers, enabling visualization and hierarchical management of defense zones. This also supports disaster prevention and early warning management, providing a scientific basis for the optimal allocation of storm surge disaster defense resources.

[0087] Example 2: Based on the same inventive concept as the storm surge disaster key defense area selection method based on the quantile method in the previous examples, such as... Figure 2 As shown, this application provides a storm surge disaster key defense area selection system based on the quantile method, the system comprising: First building unit 11: For the entire area to be managed, conduct multi-scale simulations based on atmospheric circulation, nearshore hydrodynamics, regional inundation and building damage, and construct a multi-scale nested twin model; Second construction unit 12: Constructing a quantile surface based on the quantile dimension, wherein the quantile dimension includes explicit and implicit classes; Defense zone selection unit 13: Construct a simulation selector using the multi-scale nested twin model and quantile surface to perform two-way simulation and quantile decision on source datasets from multiple monitoring sources to determine the defense zone. The two-way simulation includes a one-step continuous rolling simulation based on atmospheric circulation and nearshore hydrodynamics, and a rolling simulation based on the optional triggering of regional inundation and building damage. Prevention and control management unit 14: Visualizes the defense zone on the terminal interface and performs disaster prevention and control early warning management.

[0088] Furthermore, the first building unit 11 is also used to perform the following steps: based on the typhoon's path and intensity evolution, perform atmospheric circulation simulation and deploy a first twin simulation layer; based on storm tide level and wave propagation, perform nearshore hydrodynamic simulation and deploy a second twin simulation layer; based on high-resolution DEM and building vector data, perform regional inundation simulation and deploy a third twin simulation layer; based on the fluid-structure coupling of building structures, perform building damage simulation and deploy a fourth twin simulation layer; and construct a multi-scale nested twin model based on the first twin simulation layer, the second twin simulation layer, the third twin simulation layer, and the fourth twin simulation layer.

[0089] Furthermore, the second construction unit 12 is also used to perform the following steps: for the entire area to be managed, perform non-uniform grid division to determine the regional network; determine the quantile dimension, wherein the explicit class in the quantile dimension is the physical quantile, and the implicit class includes the social vulnerability quantile, the ecological sensitivity quantile, and the economic resilience quantile; for the regional network, perform joint calculation of the time-series risk value quantiles based on the quantile dimension on a grid-by-grid basis to generate a quantile surface, wherein the quantile surface represents the dynamic risk that changes over time.

[0090] Furthermore, the defense zone selection unit 13 is also used to perform the following steps: acquiring a source dataset through multi-source monitoring, wherein the source dataset includes at least real-time observation data from satellites, radars, buoys, and shore base stations; based on the source dataset, performing a rolling simulation based on the first twin simulation layer and the second twin simulation layer in the multi-scale nested twin model to determine the first rolling simulation data; and identifying key nodes in the disaster chain based on the first rolling simulation data.

[0091] Furthermore, the defense zone selection unit 13 is also used to perform the following steps: classifying the key nodes into key amplification nodes and key blocking nodes according to the probability of breach of the near-shore dike, wherein the key amplification nodes represent the dike breach category and the key blocking nodes represent the dike protection category; if the key node is the key blocking node, no response is made; if the key node is the key amplification node, the third twin simulation layer and the fourth twin simulation layer are triggered and a layer-by-layer rolling mode deduction is performed to determine the second rolling deduction data; and the quantile surface is updated according to the second rolling deduction data.

[0092] The first twin simulation layer and the second twin simulation layer adopt a first rolling cycle, and the third twin simulation layer and the fourth twin simulation layer adopt a second rolling cycle.

[0093] Furthermore, the defense zone selection unit 13 is also used to perform the following steps: based on the quantile surface, locate the defense zone by identifying risk hotspots; wherein, the risk hotspot identification process includes: defining risk-coupled hotspot types, including high physical and social vulnerability, high physical and ecological sensitivity, and high social vulnerability and low economic resilience; scanning the quantile surface, performing grid positioning and priority sorting based on the risk-coupled hotspot types, and determining the first defense grid sequence; performing comprehensive priority sorting on the remaining grids based on the quantile dimension, filtering based on a preset quantile threshold, and determining the second defense grid sequence; and assigning priority order to the first defense grid sequence and the second defense grid sequence as the defense zone.

[0094] Furthermore, the prevention and control management unit 14 is also used to perform the following steps: in the regional network, perform first-order identification based on binarization on the defense zone and the non-defense zone, perform second-order identification based on priority on the defense zone, and generate a regional defense map; visualize the regional defense map on the terminal interface.

[0095] Through the foregoing detailed description of the method for selecting key defense areas for storm surge disasters based on the quantile method, those skilled in the art can clearly understand the method and system for selecting key defense areas for storm surge disasters based on the quantile method in this embodiment. As for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant parts can be referred to the description in the method section.

[0096] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for selecting key defense zones for storm surge disasters based on the quantile method, characterized in that, The method includes: For the entire area to be managed, multi-scale simulations based on atmospheric circulation, nearshore hydrodynamics, regional inundation and building damage are conducted to construct a multi-scale nested twin model; Construct a quantile surface based on the quantile dimension, wherein the quantile dimension includes explicit and implicit classes; The simulation selector is constructed using the multi-scale nested twin model and quantile surface to perform two-way simulation and quantile decision-making on source datasets from multiple monitoring sources to determine the defense zone. The two-way simulation includes a one-step continuous rolling simulation based on atmospheric circulation and nearshore hydrodynamics, and a rolling simulation based on the optional triggering of regional inundation and building damage. The defense zone is visualized on the terminal interface for disaster prevention and early warning management.

2. The method for selecting key storm surge disaster prevention areas based on the quantile method as described in claim 1, characterized in that, Constructing a multi-scale nested twin model, including: Based on the evolution of typhoon path and intensity, atmospheric circulation simulation is performed, and the first twin simulation layer is deployed. Based on storm tide level and wave propagation, nearshore hydrodynamic simulation is performed, and a second twin simulation layer is deployed. Based on high-resolution DEM and building vector data, regional flooding simulation is performed, and a third twin simulation layer is deployed. Based on the fluid-structure coupling of building structure, building damage simulation is performed, and a fourth twin simulation layer is deployed; A multi-scale nested twin model is constructed based on the first twin simulation layer, the second twin simulation layer, the third twin simulation layer, and the fourth twin simulation layer.

3. The method for selecting key storm surge disaster prevention areas based on the quantile method as described in claim 1, characterized in that, Constructing a quantile surface based on the quantile dimension includes: For the entire area to be managed, a non-uniform grid is used to divide it into regional networks; The quantile dimensions are determined, wherein the explicit class of the quantile dimensions is the physical quantile, and the implicit classes include the social vulnerability quantile, the ecological sensitivity quantile, and the economic resilience quantile. For the aforementioned regional network, a joint calculation of the temporal risk value quantiles based on the quantile dimension is performed grid by grid to generate a quantile surface, wherein the quantile surface represents the dynamic risk that changes over time.

4. The method for selecting key storm surge disaster prevention areas based on the quantile method as described in claim 2, characterized in that, Two-way simulation was performed on source datasets from multiple monitoring sources, including: By using multi-source monitoring, a source dataset is obtained, wherein the source dataset contains at least real-time observation data from satellites, radars, buoys, and shore base stations; Based on the source dataset, in the multi-scale nested twin model, a rolling simulation deduction based on the first twin simulation layer and the second twin simulation layer is performed to determine the first rolling deduction data; Based on the first rolling simulation data, key nodes in the disaster chain are identified.

5. The method for selecting key storm surge disaster prevention areas based on the quantile method as described in claim 4, characterized in that, Based on the probability of breaching near-shore dikes, the key nodes are classified into key amplification nodes and key blocking nodes, wherein the key amplification nodes represent dike breaches and the key blocking nodes represent dike protection. If the critical node is the critical blocking node, no response will be given.

6. The method for selecting key storm surge disaster prevention areas based on the quantile method as described in claim 5, characterized in that, If the key node is a key amplification node, the third twin simulation layer and the fourth twin simulation layer are triggered and a progressive rolling mode deduction is performed to determine the second rolling deduction data; The quantile surface is updated based on the second rolling derivation data.

7. The method for selecting key storm surge disaster prevention areas based on the quantile method as described in claim 6, characterized in that, The first twin simulation layer and the second twin simulation layer adopt a first rolling cycle, and the third twin simulation layer and the fourth twin simulation layer adopt a second rolling cycle.

8. The method for selecting key storm surge disaster prevention areas based on the quantile method as described in claim 7, characterized in that, Perform quantile decisions to determine the defense zone, including: Based on the quantile surface, the defense zone is located by identifying risk hotspots; The risk hotspot identification process includes: Define risk coupling hotspot types, including those with high physical and social vulnerability, high physical and ecological sensitivity, and high social vulnerability with low economic resilience; Scan the quantile surface, perform grid localization and priority sorting based on the risk coupling hotspot type, and determine the first defense grid sequence; The remaining grids are sorted by comprehensive priority based on quantile dimension, and filtered based on preset quantile thresholds to determine the second defense grid sequence; The first defense grid sequence and the second defense grid sequence are assigned a priority order, which is then used as the defense zone.

9. The method for selecting key storm surge disaster prevention areas based on the quantile method as described in claim 3, characterized in that, Visualizing the defense zone on the terminal interface includes: In the regional network, the defended zone and the non-defended zone are identified by first-order binarization, and the defended zone is identified by second-order priority to generate a regional defense map. The regional defense map is visualized on a terminal interface.

10. A storm surge disaster key defense zone selection system based on quantile method, characterized in that, The system is used to execute the storm surge disaster key defense area selection method based on the quantile method as described in any one of claims 1-9, the system comprising: The first building unit: For the entire area to be managed, multi-scale simulation based on atmospheric circulation, nearshore hydrodynamics, regional inundation and building damage is carried out to build a multi-scale nested twin model; The second construction unit: constructs a quantile surface based on the quantile dimension, wherein the quantile dimension includes explicit and implicit classes; Defense zone selection unit: The simulation selector is constructed using the multi-scale nested twin model and quantile surface to perform two-way simulation and quantile decision on the source datasets from multiple monitoring sources to determine the defense zone. The two-way simulation includes a one-step continuous rolling simulation based on atmospheric circulation and nearshore hydrodynamics, and a rolling simulation based on the optional triggering of regional inundation and building damage. Prevention and control management unit: The defense zone is visualized on the terminal interface, and disaster prevention and early warning management is carried out.

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