Urban inland inundation toughness evaluation method and related equipment

Abstract

The invention belongs to the technical field of urban planning, and discloses an urban inland inundation toughness evaluation method and related equipment, rainfall event characteristic parameters are obtained through obtaining and analyzing rainfall early warning information, dynamic inland inundation toughness evaluation index weights are generated according to the parameters and a preset weight adjustment rule, and evaluation is executed according to the dynamic inland inundation toughness evaluation index weights. And finally, outputting an evaluation result containing weak link identification information, thereby effectively solving the problems of weight staticization and homogenization in the existing evaluation method. The evaluation index weight can be dynamically adjusted according to rainfall event characteristics and urban spatial heterogeneity, so that accurate evaluation of urban waterlogging toughness is realized, weak links are effectively identified, and a scientific basis is provided for accurate scheduling of emergency resources and effective implementation of disaster prevention measures.

Description

Technical Field

[0001] The present application relates to the field of urban planning technology, and more specifically, to a method for assessing urban waterlogging resilience and related equipment. Background Art

[0002] Assessing urban waterlogging resilience is a crucial component of urban disaster prevention and mitigation. When conducting waterlogging resilience assessments, urban management departments typically employ a multi-indicator evaluation system encompassing drainage networks, pumping stations, storage spaces, emergency response capabilities, and other dimensions. To obtain a comprehensive resilience score, each indicator must be weighted to reflect its importance in overall resilience. Currently, these weights are often determined through subjective weighting methods such as expert meetings and the Analytic Hierarchy Process (AHP), or through statistical regression analysis of historical disaster data to derive a fixed, city-wide weight allocation scheme. While this static weighting system, based on expert experience or historical data, can provide a macro-decision-making basis for urban planning and long-term infrastructure development, it has significant limitations in practical application.

[0003] First, a city is a complex system with a high degree of spatial heterogeneity, encompassing regions with distinct functional attributes, such as high-density central business districts, older built-up areas, emerging industrial zones, and major transportation hubs. These regions differ significantly in building density, proportion of impervious surfaces, drainage infrastructure standards, population structure, potential risk materials, and economic value, resulting in distinct vulnerability characteristics to urban flooding. Applying a uniform set of indicator weights to all functional areas often obscures the true weaknesses of specific regions. For example, for central business districts, the weight of economic loss risk indicators should be much higher than for other areas; whereas for older built-up areas, the weighting of indicators such as the degree of drainage infrastructure degradation and the evacuation capacity of special populations should be more important. If a city-wide, uniform static weight is used, the assessment results may not accurately reveal the true risks of specific areas, resulting in a misalignment of disaster prevention and control resources to the areas most in need of improvement.

[0004] Secondly, even if different weighting schemes are set for different functional areas, this static weighting configuration still cannot effectively address the dynamic risks posed by different types of rainfall events. The rainfall patterns that cause urban flooding are not static. For example, short-term severe convective rainstorms (short duration, extremely intense) primarily test the surface runoff convergence velocity and the flow capacity of drainage outlets. In these cases, indicators such as the blockage rate of stormwater outlets and the smoothness of surface runoff channels become significantly more important. On the other hand, sustained long-duration rainfall (high overall rainfall volume, moderate rainfall intensity) primarily tests the conveying capacity of the entire drainage network system and the discharge capacity of pumping stations. In this case, indicators such as the fullness of the main pipeline network and the operating load rate of pumping stations become more important. Existing assessment systems struggle to automatically adjust the weights of corresponding areas and indicators in the assessment model in real time based on specific and dynamic rainfall warning information.

[0005] In summary, existing urban waterlogging resilience assessment methods suffer from static and homogenous weighting of indicators, failing to fully account for the complex spatial heterogeneity within cities and the dynamic nature of rainfall events. This results in assessments that may not accurately reflect a city's most vulnerable areas under specific rainfall scenarios, hindering the precise deployment of emergency resources and the effective implementation of disaster prevention measures.

[0006] In view of the above problems, the existing technology is in urgent need of improvement. Summary of the Invention

[0007] The purpose of this application is to provide an urban waterlogging resilience assessment method and related equipment, which can dynamically adjust the assessment index weights according to the characteristics of rainfall events and urban spatial heterogeneity, thereby achieving an accurate assessment of urban waterlogging resilience, effectively identifying weak links, and providing a scientific basis for the accurate scheduling of emergency resources and the effective implementation of disaster prevention measures.

[0008] In a first aspect, the present application provides a method for assessing urban waterlogging resilience, the method comprising the following steps: A1. Obtain rainfall warning information and parse it to obtain rainfall event characteristic parameters; A2. Generating waterlogging resilience assessment indicator weights based on the rainfall event characteristic parameters and preset weight adjustment rules; A3. Conduct a waterlogging resilience assessment using the waterlogging resilience assessment indicator weights. A4. Output the waterlogging resilience assessment results including the identification of weak links.

[0009] In a second aspect, the present application provides an urban waterlogging resilience assessment device, which includes: The information acquisition module is used to obtain rainfall warning information and parse it to obtain rainfall event characteristic parameters; A weight generation module, configured to generate weights for waterlogging resilience assessment indicators based on the characteristic parameters of the rainfall event and a preset weight adjustment rule; an urban waterlogging resilience assessment execution module, configured to execute an urban waterlogging resilience assessment using the urban waterlogging resilience assessment indicator weights; The assessment result output module is used to output the waterlogging resilience assessment results including weak link identification information.

[0010] In a third aspect, the present application provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program executable by the processor, and when the processor executes the computer program, it runs the steps in the urban waterlogging resilience assessment method as described above.

[0011] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, runs the steps of the urban waterlogging resilience assessment method as described above.

[0012] Beneficial effects: The present application provides a method for assessing urban waterlogging resilience and related equipment, which obtains rainfall warning information and parses it to obtain rainfall event characteristic parameters, generates dynamic waterlogging resilience assessment indicator weights based on these parameters and preset weight adjustment rules, and performs assessment based on them, and finally outputs assessment results containing weak link identification information, effectively solving the problems of static and uniform weights in existing assessment methods; it can dynamically adjust the assessment indicator weights according to rainfall event characteristics and urban spatial heterogeneity, thereby achieving accurate assessment of urban waterlogging resilience, effectively identifying weak links, and providing a scientific basis for the accurate scheduling of emergency resources and the effective implementation of disaster prevention measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a flow chart of the urban waterlogging resilience assessment method provided in an embodiment of the present application.

[0014] Figure 2 This is a schematic diagram of the structure of the urban waterlogging resilience assessment device provided in an embodiment of the present application.

[0015] Figure 3 A schematic diagram of the structure of an electronic device provided in an application embodiment.

[0016] Explanation of reference numerals: 1. Information acquisition module; 2. Weight generation module; 3. Waterlogging resilience assessment execution module; 4. Assessment result output module; 301. Processor; 302. Memory; 303. Communication bus. DETAILED DESCRIPTION

[0017] The technical model in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. The components of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of this application.

[0018] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0019] refer to Figure 1 This application proposes a method for assessing urban waterlogging resilience, which includes the following steps: A1. Obtain rainfall warning information and parse it to obtain rainfall event characteristic parameters; A2. Generating waterlogging resilience assessment indicator weights based on the rainfall event characteristic parameters and preset weight adjustment rules; A3. Conduct a waterlogging resilience assessment using the waterlogging resilience assessment indicator weights. A4. Output the waterlogging resilience assessment results including the identification of weak links.

[0020] Rainfall warning information refers to forecast data on future rainfall events released by meteorological authorities. This information can be obtained in the form of text reports, numerical model output, or API data streams, for example, through weather radar data or satellite cloud image analysis. Its primary purpose is to provide contextual information about impending rainfall events as input for dynamic assessments.

[0021] Rainfall event characteristic parameters refer to data obtained by parsing rainfall warning information and used to quantitatively describe the attributes of rainfall events. These parameters can be expressed in the form of standardized numerical values, classification labels, or structured geographic data, such as rainfall intensity, duration, or impact area boundaries. Their primary purpose is to transform unstructured warning information into input that can be processed by the assessment model.

[0022] The weight adjustment rules are pre-defined logic or algorithmic sets used to guide the dynamic adjustment of waterlogging resilience indicator weights. These can be implemented using expert rule bases, machine learning models, or optimization algorithms, such as conditional statements or decision tree models. Their primary purpose is to adjust the importance of each assessment indicator based on the characteristics of the rainfall event.

[0023] The weights of waterlogging resilience indicators refer to the relative importance assigned to each indicator when assessing urban waterlogging resilience. These can be expressed as percentages, fractions, or normalized coefficients. For example, weights could be assigned to indicators such as drainage system responsiveness, storage capacity, and the ability to protect key facilities. This is primarily to ensure that the assessment model can prioritize the role of indicators for specific rainfall scenarios.

[0024] Vulnerability identification information refers to areas or system components identified in the flood resilience assessment results as having a higher risk of urban flooding under specific rainfall scenarios. This information can be presented in the form of geographic coordinates, regional identifiers, or system component numbers. For example, it identifies low-lying, flood-prone areas, drainage network bottlenecks, or exposed infrastructure areas. This information is primarily intended to provide city managers with a basis for emergency response and resource deployment.

[0025] The core innovation of this application is that by introducing the acquisition and analysis of rainfall warning information and combining it with preset weight adjustment rules, the weights of urban waterlogging resilience assessment indicators can be dynamically generated and adaptively adjusted, thereby solving the problem of static weights in existing assessment methods and the inability to adjust according to the characteristics of dynamic rainfall events. This achieves the effect of identifying urban weak links under specific rainfall scenarios and optimizing emergency resource scheduling.

[0026] Specifically, the assessment method of this application achieves its function in the following manner: First, the system obtains warning information about impending rainfall events, analyzes this information, and extracts characteristic parameters of the rainfall events. These parameters serve as the basis for subsequent dynamic adjustments. Next, based on these analyzed characteristic parameters of the rainfall events and combined with pre-set weight adjustment rules, the system generates a set of weights for urban waterlogging resilience assessment indicators tailored to the current specific rainfall scenario. This process ensures that the assessment model can be configured based on the actual rainfall threat, so that the importance of different assessment indicators can dynamically reflect their impact in the current scenario. Subsequently, the system uses these dynamically generated urban waterlogging resilience assessment indicator weights to assess the urban waterlogging resilience. The assessment process fully considers the impact of specific rainfall events on different areas and infrastructure in the city, thereby obtaining a targeted resilience assessment result. Finally, the assessment results are output, which include information identifying the city's weak links under the current rainfall scenario. This information can guide urban management departments in disaster prevention and mitigation and emergency resource scheduling, ensuring that resources are allocated to areas or systems that need strengthening.

[0027] Through the above scheme, this application can dynamically adjust the weights of urban waterlogging resilience assessment indicators based on rainfall warning information, thus overcoming the limitations of existing methods, which have static weights and cannot adapt to different rainfall scenarios. This allows the assessment results to accurately reflect the city's resilience level under specific rainfall events and identify potential weak links. Therefore, this application can provide city managers with targeted decision-making basis, optimize the dispatch of emergency resources and the formulation of disaster prevention and mitigation measures, and improve the efficiency and accuracy of urban responses to urban waterlogging disasters.

[0028] In some embodiments, step A1 comprises: A101. Obtain rainfall warning information; the rainfall warning information includes rainfall event type information, forecast rainfall intensity information, rainfall duration information, and forecast impact area range information; A102. According to the preset rainfall event classification rules, the rainfall event type information is converted into standardized rainfall event type parameters; A103. According to a preset quantitative conversion rule, the forecast rainfall intensity information and the rainfall duration information are converted into standardized rainfall intensity parameters and standardized rainfall duration parameters respectively; A104. According to preset geographic information parsing rules, the forecast impact area range information is converted into structured geographic area parameters; A105. Use the rainfall event type parameter, the rainfall intensity parameter, the rainfall duration parameter and the geographical area parameter as the rainfall event characteristic parameters.

[0029] Among them, rainfall event type information refers to the description of the nature of the rainfall event, such as showers, thunderstorms, heavy rain or typhoon rain, etc., which can be expressed in the form of text description, code or label. Forecast rainfall intensity information refers to the prediction of rainfall per unit time, such as millimeters / hour or centimeters / day, which can be expressed in the form of numerical values, levels or intervals. Rainfall duration information refers to the prediction of the expected duration of the rainfall process, such as hours, minutes or days, which can be expressed in the form of numerical values, time periods or time points. Forecast impact area range information refers to the geographical area that the rainfall event is expected to affect, which can be expressed in the form of administrative division names, geographic coordinate ranges or polygonal area data.

[0030] The pre-set rainfall event classification rules are a set of logic used to map raw rainfall event type information to a unified classification system. These can be implemented using lookup tables, conditional statements, or machine learning models. Standardized rainfall event type parameters are representations of rainfall event types with a fixed format and meaning after unified classification. These parameters can be represented using enumerated values, pre-defined codes, or unified text labels.

[0031] The preset quantitative conversion rules are logical sets used to convert rainfall intensity and duration information expressed in descriptive or non-uniform units into calculable values. These can be implemented using mathematical formulas, piecewise functions, or table lookup methods. Standardized rainfall intensity parameters and standardized rainfall duration parameters are rainfall intensity and duration values ​​that have been converted to uniform units and numerical ranges and can be directly used for calculation and comparison. They can be expressed in uniform units (e.g., mm / h, h) and numerical ranges.

[0032] Preset geographic information parsing rules refer to a logical set of rules used to convert descriptive or unstructured geographic area information into a data format that can be recognized and processed by a geographic information system. These rules can be implemented using regular expressions, geocoding services, or spatial analysis algorithms. Structured geographic area parameters refer to geographic data that, after parsing, can be directly recognized by a geographic information system and used for spatial analysis. These parameters can be represented by a set of geographic coordinate points, polygon boundary data, or grid cell identifiers.

[0033] This solution details the specific process of obtaining rainfall warning information and parsing it to obtain rainfall event characteristic parameters. First, by obtaining rainfall warning information, it ensures that the warning information obtained from the source is multi-dimensional and comprehensive, including rainfall event type information, forecast rainfall intensity information, rainfall duration information, and forecast impact area range information, which provides the necessary data input for subsequent refined analysis. On this basis, in order to ensure that rainfall type information from different sources or expressions can be accurately identified and utilized by the system, the original rainfall event type information is converted into standardized rainfall event type parameters according to the preset rainfall event classification rules. This enables the system to uniformly handle various rainfall types and provides a unified basis for subsequent weight adjustments based on different rainfall types.

[0034] Furthermore, to accurately assess the disaster-causing potential of rainfall events and provide refined quantitative input for subsequent weight adjustments, the forecast rainfall intensity and duration information are converted into standardized rainfall intensity and duration parameters, respectively, according to preset quantitative conversion rules. Through this quantitative conversion, the original descriptive or non-uniform unit information is transformed into calculable and comparable numerical parameters. At the same time, to enable the assessment system to accurately identify the specific affected geographic areas, thereby achieving regionally targeted waterlogging resilience assessment and resource scheduling, the forecast impact area range information is converted into structured geographic area parameters according to preset geographic information parsing rules. This overcomes the limitations of a unified city-wide assessment and makes the assessment results more targeted.

[0035] Ultimately, the standardized, quantified, and structured rainfall event type parameters, rainfall intensity parameters, rainfall duration parameters, and geographic area parameters were integrated to form a comprehensive, accurate, and easy-to-process set of rainfall event characteristic parameters. This set includes the key attributes of rainfall events and their spatial impact range, providing precise and multi-dimensional input for generating urban waterlogging resilience assessment indicator weights based on rainfall event characteristic parameters in subsequent steps. Through this series of meticulous classification, quantification, and structured processing, it is ensured that the obtained rainfall event characteristic parameters can comprehensively, accurately, and standardizedly reflect the characteristics of the upcoming rainfall event, thereby providing a solid data foundation for the subsequent dynamic adjustment of assessment indicator weights and accurate assessment, significantly improving the accuracy and practicality of urban waterlogging resilience assessment.

[0036] In some embodiments, step A2 comprises: A201. Obtain urban spatial heterogeneity data of the target area represented by the geographic area parameter; the urban spatial heterogeneity data includes regional terrain elevation data, drainage network distribution data, and key infrastructure location data; A202. Divide the target area into a plurality of sub-areas based on the urban spatial heterogeneity data; A203. Based on the urban spatial heterogeneity data, identify the waterlogging vulnerability characteristics of each sub-region; A204. Based on the characteristic parameters of the rainfall event, the waterlogging vulnerability characteristics of the sub-region and the preset weight adjustment rules, a corresponding waterlogging resilience assessment index weight is generated for each sub-region.

[0037] Urban spatial heterogeneity data refers to comprehensive information that reflects the differences in geography, function, infrastructure, and other aspects between different areas within a city. This data is fundamental to understanding urban complexity and can include regional terrain elevation data, drainage network distribution data, and key infrastructure location data. It can also include data on land use types, population density, and building density. Its purpose is to provide differentiated information on spatial dimensions for detailed assessments.

[0038] Dividing into multiple sub-regions refers to logically or physically dividing the entire target area based on urban spatial heterogeneity data, forming several small units with relatively consistent or specific functional attributes. This division can be based on the spatial analysis functions of the Geographic Information System (GIS), such as clustering algorithms, grid division, or administrative or functional divisions. The goal is to break down the complex urban structure into local units that are easier to manage and evaluate.

[0039] Identifying waterlogging vulnerability characteristics involves analyzing and determining each subregion's potential vulnerabilities or sensitivities to waterlogging hazards based on its subregional division. This can involve a comprehensive assessment of the subregion's physical characteristics, infrastructure, and socioeconomic attributes to reveal its vulnerability to waterlogging. For example, this can include assessing its susceptibility to waterlogging, drainage bottlenecks, or the exposure of critical infrastructure.

[0040] Here, the preset weight adjustment rules are a set of predefined logic or algorithms that guide the dynamic adjustment of the weights of waterlogging resilience assessment indicators. These rules can be based on expert experience, historical data analysis, or model simulation results. Their purpose is to quantitatively adjust the importance of assessment indicators based on the characteristics of specific rainfall events and regional vulnerability, ensuring the relevance and accuracy of the assessment results.

[0041] When generating weights for flood resilience assessment indicators, this solution first uses the geographic parameters obtained in the previous step to obtain urban spatial heterogeneity data for the target area. This data, such as regional terrain elevation data, drainage network distribution data, and key infrastructure location data, is fundamental to understanding the complexity and diversity within a city and provides information support for subsequent detailed analysis. The system then uses this urban spatial heterogeneity data to divide the target area into multiple subregions. This division is not a simple administrative division, but rather fully considers differences in geography, function, or risk attributes, ensuring that each subregion has relatively consistent characteristics to a certain extent, thus avoiding the assessment bias caused by treating the entire city as a homogeneous unit. Furthermore, for each subregion, the system further utilizes urban spatial heterogeneity data to conduct in-depth analysis and identify its flood vulnerability characteristics. For example, terrain elevation data can be used to assess flood susceptibility, drainage network data can be used to identify drainage capacity bottlenecks, and key infrastructure location data can be used to identify the exposure risks of key facilities (referring to those with significant impacts on urban social operations and residents' lives, such as hospitals, schools, transportation hubs, power facilities, and communication base stations). Identifying these vulnerability characteristics provides a clear understanding of the risk points in each subregion, providing a refined basis for subsequent weight adjustments. Ultimately, the system comprehensively considers the rainfall event characteristic parameters analyzed in the previous steps (such as rainfall intensity and duration), the identified waterlogging vulnerability characteristics of each subregion, and combines them with pre-set weight adjustment rules to generate a set of customized waterlogging resilience assessment indicator weights for each subregion. This generation method eliminates static or uniform assessment weights and allows adaptive adjustment based on the specific rainfall scenario and the region's actual vulnerability.

[0042] Through the operation of the above steps, this solution can overcome the limitations of the existing technology in which the weight settings are too uniform or fixed. Specifically, the characteristic parameters of the rainfall event obtained in the above steps provide dynamic external scenario information, and this solution provides detailed internal spatial information by obtaining urban spatial heterogeneity data and refining it into vulnerability characteristics of sub-regions. The combination of these two types of information enables the weight generation process to simultaneously consider the dynamics of rainfall events and the spatial heterogeneity within the city. Therefore, for different rainfall events and different sub-regions, the system can generate different and targeted evaluation index weights, thereby ensuring that the results of the urban waterlogging resilience assessment can accurately reflect the resilience status and weak links of each sub-region under a specific rainfall event, and provide support for the precise scheduling of emergency resources and the effective implementation of disaster prevention and control measures.

[0043] As a preferred implementation, the weights for waterlogging resilience assessment indicators can be generated as follows: First, when obtaining urban spatial heterogeneity data for the target area, a geographic information system (GIS) platform can be used to integrate relevant information from multiple data sources. For example, regional terrain elevation data can be derived from a digital elevation model (DEM) and stored in a raster or TIN (triangulated irregular network) format. Drainage network distribution data can be obtained from a city's underground pipeline survey database, containing topological information such as pipe diameter, material, burial depth, slope, and connectivity. Key infrastructure location data, such as hospitals, schools, transportation hubs, power facilities, and communication base stations, can be obtained from urban planning departments or relevant industry databases and represented as point or polygon features. This data can be stored uniformly in a spatial database to facilitate subsequent spatial analysis and querying. Furthermore, when dividing the target area into multiple subregions based on the urban spatial heterogeneity data, a grid-based partitioning approach can be adopted, for example, dividing the entire target area into grid cells with sides of 500 meters or 1 kilometer. For each grid cell, comprehensive heterogeneity indicators such as topographic relief, pipe network density, proportion of impervious area, and the number and type of key infrastructure can be calculated. Subsequently, clustering algorithms, such as K-means or DBSCAN, can be used to group adjacent grid cells with similar heterogeneity indicators into subregions. Furthermore, administrative or functional zoning (e.g., commercial, residential, or industrial) can be incorporated as auxiliary boundaries to adjust the clustering results and create subregions that better meet management needs. Based on this, detailed analysis of each subregion's vulnerability to flooding can be conducted using its internal urban spatial heterogeneity data. For example, based on topographic elevation data, water catchment paths and potential waterlogging points within the subregion can be simulated, and their waterlogging depths can be estimated to assess flood susceptibility. Furthermore, combined with drainage network distribution data, the connectivity of the drainage network within the subregion, pipe diameter matching, and the presence of bottleneck sections can be analyzed to identify drainage capacity bottlenecks. Furthermore, by overlaying critical infrastructure location data with potential waterlogging areas, key facilities potentially affected by flooding can be identified and their exposure risk assessed. These analysis results can be quantified into vulnerability indicators, such as the percentage of flooded area, drainage network load rate, and the flooding risk level of key facilities. Ultimately, a rule-based expert system or machine learning model can be constructed to generate corresponding waterlogging resilience assessment indicator weights for each subregion based on rainfall event characteristic parameters, the subregion's waterlogging vulnerability characteristics, and preset weight adjustment rules. For example, the preset weight adjustment rules could be a series of "if-then" conditional statements, such as "If the rainfall intensity parameter is 'extremely heavy rain' and the subregion's 'flooding susceptibility' is 'high', then increase the weights of the 'surface runoff convergence velocity' and 'emergency drainage capacity' indicators by 20%."Based on the type and intensity of the current rainfall event and the specific vulnerability characteristics identified for each subregion, the system matches and activates corresponding weight adjustment rules from a rule library. For each subregion, these activated rules dynamically adjust the initial baseline weights of its waterlogging resilience assessment indicators, generating a customized set of weights that reflects the current scenario and regional characteristics.

[0044] Preferably, step A202 may include: Identifying spatial heterogeneity distribution characteristics of the target area based on the urban spatial heterogeneity data; the spatial heterogeneity distribution characteristics include terrain relief, pipeline network density distribution, and key infrastructure concentration; Determining an evaluation granularity requirement for this rainfall event according to the rainfall event type parameter, the rainfall intensity parameter, and the rainfall duration parameter; According to the spatial heterogeneity distribution characteristics and the evaluation granularity requirements, the target area is adaptively divided into regions to obtain multiple sub-regions.

[0045] Among them, spatial heterogeneity distribution characteristics refer to the spatially non-uniform distribution patterns of geography, infrastructure or socio-economic attributes within the target area, which can be identified and quantified by using the spatial analysis function of the geographic information system (GIS) combined with statistical methods or machine learning algorithms.

[0046] Among them, terrain relief refers to the degree of drastic changes in surface elevation within a region, which can be calculated using digital elevation model (DEM) data (that is, regional terrain elevation data includes digital elevation model data), for example, by calculating indicators such as slope, aspect or elevation standard deviation.

[0047] Among them, the pipe network density distribution refers to the length of the drainage pipe network in a unit area or the density of connection points. It can be quantified using drainage pipe network vector data (that is, drainage pipe network distribution data includes drainage pipe network vector data) through methods such as spatial density analysis or kernel density estimation.

[0048] Among them, the concentration of key infrastructure refers to the spatial concentration of facilities in the region that have a significant impact on urban operations and residents' lives. It can be evaluated using key infrastructure location data through cluster analysis or spatial weight matrix methods.

[0049] Assessment granularity refers to the degree of spatial refinement required for waterlogging resilience assessments. This can dynamically determine the minimum unit size or level of regional division based on the characteristics of the rainfall event and the assessment objectives. Adaptive regional division refers to the process of flexibly adjusting regional division strategies based on dynamically changing input conditions and static spatial heterogeneity. This can be achieved using machine learning-based clustering algorithms, multi-scale grid division, or graph-theory-based segmentation methods to ensure that the division results optimally reflect the risk distribution under the current scenario.

[0050] The operational logic of this solution is to first construct a spatial profile of the city's inherent vulnerability by identifying the spatially heterogeneous distribution characteristics of the target area. This includes the impact of topography on runoff collection, the constraints of the drainage network on drainage capacity, and the concentrated risks of critical infrastructure, providing a detailed spatial consideration for zoning. Furthermore, the solution incorporates a dynamic consideration of impending rainfall events, analyzing the type, intensity, and duration of rainfall events to determine the required assessment granularity for that particular rainfall event. This means that zoning is no longer static but can be dynamically adjusted based on specific rainfall scenarios, ensuring that the level of assessment granularity matches the actual risk distribution. Finally, the solution integrates these static spatial heterogeneity characteristics with the dynamic assessment granularity requirements to perform adaptive zoning of the target area. This combination results in sub-regions that reflect the inherent complexity of the city while responding to the dynamic needs of specific rainfall events. This dynamic and detailed zoning provides a more precise and targeted basis for the subsequent identification of urban waterlogging vulnerability characteristics and the generation of weights for waterlogging resilience assessment indicators, significantly improving the accuracy and practical value of the overall waterlogging resilience assessment method.

[0051] In specific implementation, high-resolution digital elevation model data can be first used to generate a terrain relief layer by calculating the slope, aspect, and local elevation standard deviation for each grid cell. Simultaneously, GIS vector data of the urban drainage network can be imported and, using kernel density analysis tools, the ratio of network length to area within each region can be calculated to generate a network density distribution layer. Furthermore, geographic coordinate data for key infrastructure within the city, such as hospitals, schools, transportation hubs, and power facilities, can be obtained. Spatial clustering algorithms can be used to identify and quantify the concentration of these facilities within specific areas, generating a key infrastructure concentration layer. These layers can be overlaid and comprehensively analyzed to identify the overall distribution pattern of spatial heterogeneity within the target area. Next, a rule library or lookup table can be established, taking rainfall event type, rainfall intensity, and rainfall duration parameters as input to derive the corresponding assessment granularity requirements, such as sub-region size requirements. Finally, algorithms based on weighted Voronoi diagrams or quadtree decomposition can be used for adaptive regional segmentation. The identified spatial heterogeneity distribution features are assigned different weights or priorities, and based on the assessment granularity requirements, more detailed divisions are performed in areas of high heterogeneity, such as further subdividing these areas into smaller grids or irregular polygons. Using a geographic information system platform, the spatial heterogeneity distribution feature layer is used as input. Based on the determined assessment granularity requirements, regional boundaries are dynamically adjusted through iterative splitting or merging algorithms until the spatial heterogeneity within each sub-region is relatively uniform and the sub-region size meets the assessment granularity requirements. The result is a series of sub-regions of varying sizes and shapes that effectively reflect the differences in urban flooding risk.

[0052] Preferably, step A203 may include: Based on the terrain elevation data of the area, determine the water catchment path and water accumulation depth potential of each sub-area to assess the degree of flooding in low-lying areas; Based on the drainage network distribution data, evaluate the network carrying capacity and drainage path connectivity of each sub-area to determine drainage capacity bottlenecks; Based on the critical infrastructure location data, analyze the geographical relationship between the critical infrastructure and potential waterlogging areas in each sub-region to determine the exposure risk of the critical facilities; The degree of low-lying flooding, the drainage capacity bottleneck and the exposure risk of key facilities are taken as the waterlogging vulnerability characteristics of the sub-area.

[0053] The catchment path refers to the direction and route of water flow during surface runoff from rainfall, which can be determined through a flow analysis algorithm based on terrain elevation data. The potential for waterlogging depth refers to the maximum waterlogging depth that a region may reach under specific rainfall conditions, which can be determined through hydrological model simulation or terrain-based water storage capacity analysis. The degree of low-lying waterlogging refers to the degree to which an area is prone to waterlogging and inland waterlogging due to terrain conditions, which can be determined through a comprehensive assessment of the catchment path and the potential for waterlogging depth.

[0054] The network's carrying capacity refers to the maximum amount of water that a drainage network can transport per unit time. This can be determined through hydraulic calculation models or flow analysis based on network design parameters. Drainage path connectivity refers to the smoothness of water flow between nodes and pipe sections in the drainage network. This can be determined through network topology analysis or connectivity graph algorithms. The drainage capacity bottleneck refers to the link in the drainage system's overall drainage efficiency when responding to rainfall, which is limited by insufficient network carrying capacity or blocked drainage paths. This bottleneck can be determined through a comprehensive assessment of the network's carrying capacity and drainage path connectivity.

[0055] The geographic relationship between critical infrastructure and potential waterlogging areas refers to the spatial proximity or overlap between critical infrastructure and areas where waterlogging may occur. This can be determined through spatial overlay analysis or buffer zone analysis. Potential waterlogging areas can be identified through hydrological model simulation or terrain-based water storage capacity analysis. The exposure risk of critical facilities refers to the extent of damage that critical infrastructure may suffer in the event of urban flooding. This can be determined through a comprehensive assessment of the geographic relationship between critical infrastructure and potential waterlogging areas.

[0056] Among them, the vulnerability characteristics of urban flooding refer to the comprehensive reflection of the susceptibility to damage and weak links in the recovery capacity of a sub-region when facing urban flooding disasters. It can be composed of the degree of low-lying and flood-prone areas, drainage capacity bottlenecks, and exposure risks of key facilities.

[0057] This approach goes beyond macroscopic or single-dimensional assessments in identifying each subregion's vulnerability to waterlogging. Instead, it constructs a multidimensional vulnerability identification framework through in-depth mining and comprehensive analysis of multi-source urban spatial heterogeneity data. Specifically, first, regional terrain elevation data is used to systematically analyze the topographic characteristics of each subregion, including water flow paths and potential waterlogging depths. This analysis directly quantifies the extent of flooding in low-lying areas caused by natural terrain conditions in each subregion, providing fundamental geographic information support for identifying waterlogging risks. Second, based on drainage network distribution data, a detailed assessment of each subregion's drainage system is conducted, including the actual carrying capacity of the network and the connectivity of drainage pathways. This assessment accurately identifies weak links in the drainage system's response to rainfall, known as drainage capacity bottlenecks, thereby revealing the flood resilience of the city's drainage infrastructure. Furthermore, combined with key infrastructure location data, the spatial relationship between these important facilities and the previously identified potential waterlogging areas is analyzed to quantify the potential exposure of these key facilities to waterlogging events. This allows the assessment to focus on the vulnerabilities that have the greatest impact on urban operations and residents' lives. Finally, the above three dimensions, namely, the degree of low-lying flooding, drainage capacity bottlenecks, and exposure risks of key facilities, are integrated and used as the waterlogging vulnerability characteristics of the sub-region.

[0058] This comprehensive vulnerability feature can fully and accurately reflect the specific weaknesses of the sub-region in terms of terrain, drainage system and protection of key facilities. Through this meticulous vulnerability identification method, this solution can overcome the problem of insufficiently detailed and comprehensive vulnerability identification in traditional assessments. It is closely linked to the steps of obtaining urban spatial heterogeneity data and dividing the target area into multiple sub-regions. On the basis of obtaining detailed urban spatial heterogeneity data and finely dividing the urban area, it further analyzes the inherent vulnerability of each sub-region. This multi-dimensional and refined vulnerability feature identification provides a solid data foundation for the subsequent generation of waterlogging resilience assessment index weights based on rainfall event characteristic parameters and sub-region waterlogging vulnerability characteristics. The generated weights can more accurately reflect the actual risks of the sub-region under specific rainfall scenarios, thereby improving the accuracy and pertinence of the entire waterlogging resilience assessment.

[0059] In one specific embodiment, to identify the flooding vulnerability characteristics of each sub-region, high-resolution regional terrain elevation data can be first obtained, such as a digital elevation model (DEM) obtained through aerial photogrammetry or lidar scanning. Based on this model data, hydrological analysis tools within geographic information system (GIS) software can be used to simulate rainfall-runoff processes, thereby determining the flow paths within each sub-region and calculating the potential depth of accumulated water under varying rainfall intensities and durations. For example, low-lying areas, depressions, and major channels where water converge can be identified within the region, and the maximum water depths these areas could reach during extreme rainfall events can be quantified to assess the flooding vulnerability of these low-lying areas. Next, detailed drainage network distribution data can be obtained, including the network's topology, pipe diameter, material, slope, and the location and design parameters of facilities such as pumping stations and stormwater inlets. Using this data, specialized urban drainage modeling software, such as SWMM, can be used to simulate and assess the carrying capacity of the network in each sub-region and analyze the connectivity of drainage paths. For example, under specific rainfall scenarios, simulations can be conducted to assess pipe network fullness, overflow points, and water flow efficiency within the network, thereby identifying drainage bottlenecks that could lead to blockages, backflow, or insufficient capacity. Furthermore, location data for critical infrastructure, such as hospitals, schools, transportation hubs, power substations, and communication base stations, can be obtained. This infrastructure location information can be spatially overlaid with potential waterlogging areas identified through terrain analysis. For example, the distance from each critical infrastructure facility to the nearest potential waterlogging area can be calculated, or whether it is located in or adjacent to an area with high potential for waterlogging depth can be determined. This geographic relationship analysis quantifies the potential exposure risk of each critical facility to a flooding event. Ultimately, the flooding vulnerability level determined by terrain analysis, the drainage bottlenecks identified by drainage network analysis, and the exposure risk of key facilities identified by critical infrastructure analysis can be integrated. For example, different weights can be assigned to these three dimensions, or a multi-indicator comprehensive evaluation model can be used to integrate them into a unified waterlogging vulnerability index or a set of multi-dimensional vulnerability characteristic vectors, representing the waterlogging vulnerability characteristics of the subregion. In this way, the vulnerability of each sub-region is no longer a single numerical value, but a comprehensive description that includes topography, drainage, and facility risks.

[0060] Preferably, step A204 may include: Based on the rainfall event characteristic parameters and the waterlogging vulnerability characteristics of the sub-region, selecting a set of weight adjustment rules applicable to the sub-region in the current rainfall scenario from the preset weight adjustment rules; In the set of weight adjustment rules, identifying a group of rules having conflicting weight adjustment suggestions for the same waterlogging resilience assessment indicator; When there are conflicting rule groups, the conflicting rule groups are prioritized and, based on the rule adjustment suggestions with higher priorities, the rule adjustment suggestions with lower priorities are modified to obtain weight adjustment suggestions after conflict resolution. When there is no conflicting rule group, taking the rule adjustment suggestions in the weight adjustment rule set as the conflict-resolved weight adjustment suggestions; According to the conflict-resolved weight adjustment suggestions, corresponding waterlogging resilience assessment indicator weights are generated for the sub-region on the basis of preset benchmark waterlogging resilience assessment indicator weights.

[0061] The weight adjustment rule set refers to a set of rules applicable to the current assessment scenario, selected from a pre-set rule base based on the characteristics of a specific rainfall event and the subregion's vulnerability to waterlogging. Each rule can include a trigger condition, a target assessment indicator, and the corresponding weight adjustment direction and magnitude (the target assessment indicator and the corresponding weight adjustment direction and magnitude constitute the weight adjustment recommendation). These rules can be stored and managed using structured data tables, rule engine configurations, or expert knowledge bases.

[0062] A conflicting rule group refers to a situation in which two or more rules within a set of weight adjustment rules provide conflicting or inconsistent weight adjustment recommendations for the same flood resilience assessment indicator. For example, one rule might recommend increasing the weight of an indicator while another recommends decreasing or keeping it unchanged, or the recommended adjustment ranges might differ.

[0063] Prioritization refers to determining the importance or execution order of each rule within an identified conflicting rule group based on pre-set policies or standards. This can be achieved through methods such as a preset priority list based on expert experience, rule source credibility, rule effectiveness scoring based on data verification, or dynamic assessment of rule applicability in specific scenarios.

[0064] Modifying low-priority rule adjustment suggestions involves adjusting or overwriting them based on higher-priority rule adjustment suggestions to eliminate conflicts and ensure consistency in the final weight adjustment suggestions. This can be achieved using logic such as full override, weighted average, incremental adjustment, or conditional adjustment.

[0065] The baseline waterlogging resilience assessment indicator weights are preset, serving as an initial reference, without considering specific rainfall events or subregional vulnerability characteristics. These can be determined through expert consensus, statistical averaging of historical data, or default values ​​from common assessment models.

[0066] Through a series of logically clear steps, this solution ensures that the process of generating weights for urban waterlogging resilience assessment indicators can not only dynamically adapt to specific scenarios but also effectively resolve rule conflicts, thereby improving the accuracy and guidance of the assessment results. First, the system receives rainfall event characteristic parameters obtained from the analysis of rainfall warning information, as well as the sub-regional waterlogging vulnerability characteristics identified through urban spatial heterogeneity data analysis. This information is the basis for dynamic weight adjustment, and it enables subsequent weight adjustments to accurately focus on the specific rainfall risks and regional weaknesses currently faced. Based on this scenario information, the system logically selects a set of rules related to the current sub-region and rainfall scenario from the preset weight adjustment rule library. This screening process avoids interference from irrelevant rules and ensures the targeted nature of the adjustments.

[0067] Furthermore, within the selected set of weight adjustment rules, the system proactively identifies conflicts in weight adjustment recommendations for the same flood resilience assessment indicator. This conflict identification mechanism is a crucial step in resolving the core issue, ensuring that potential inconsistencies are clearly identified before weight adjustments are made. Once a conflict is identified, the system immediately prioritizes these conflicting rule groups. This ranking mechanism allows the system to determine which rule recommendations should be prioritized based on preset importance or credibility criteria. The system then revises lower-priority rule adjustment recommendations based on the higher-priority rule adjustment recommendations. This revision process ensures that the final weight adjustment recommendations are logically consistent and well-founded, eliminating unreasonable weighting or assessment bias caused by rule conflicts. If no conflicts exist within the selected set of rules, the system directly uses these rule adjustment recommendations as the final basis for adjustment, streamlining the process and ensuring comprehensiveness.

[0068] Ultimately, the conflict-resolved weight adjustment recommendations are applied to the preset baseline waterlogging resilience assessment indicator weights. The baseline weights provide the initial importance of the assessment indicators, while the adjustment recommendations refine them based on the current scenario. In this way, a set of highly targeted waterlogging resilience assessment indicator weights is generated for each sub-region under a specific rainfall scenario. This dynamic, conflict-resolving weight generation mechanism is closely integrated with the pre-steps of obtaining rainfall event characteristic parameters and identifying sub-regional waterlogging vulnerability characteristics, enabling subsequent waterlogging resilience assessments to more realistically reflect the city's vulnerable links in the face of specific rainfall events. This not only improves the reliability of the assessment results, but also provides data support for decision makers to accurately identify weak links and optimize the allocation of emergency resources, thereby effectively solving the problem in existing technologies where unreasonable weights or assessment biases affect the accurate scheduling of emergency resources.

[0069] In specific implementations, the weights of waterlogging resilience assessment indicators can be generated as follows. First, the system can filter out a set of weight adjustment rules applicable to the current scenario from a rule library containing a large number of preset weight adjustment rules, based on the currently acquired rainfall event characteristic parameters, such as rainfall intensity parameters and rainfall duration parameters, and the identified sub-region waterlogging vulnerability characteristics, such as the degree of low-lying waterlogging and drainage capacity bottlenecks, by matching the trigger conditions of the rules. For example, if the rainfall intensity parameter indicates "extremely heavy rain" and the low-lying waterlogging degree of the sub-region is "high", the system can filter out all weight adjustment rules related to "extremely heavy rain" and "high-lying waterlogging".

[0070] The system then checks the selected set of weight adjustment rules for each flood resilience assessment indicator to see if there are conflicting weight adjustment recommendations from multiple rule groups. For example, for the "drainage network carrying capacity" indicator, one rule might recommend a 10% increase in its weight, while another might recommend a 5% decrease. When such a conflict is identified, the system initiates a prioritization mechanism. Priorities can be pre-set, for example, based on the source of the rule (e.g., "national standard rules" over "local empirical rules") or the degree of validation (e.g., "model-validated rules over "expert empirical rules"). The system then modifies the lower-priority rule adjustment recommendations based on the higher-priority rule. For example, if the "national standard rules" recommend a 10% increase and the "local empirical rules" recommend a 5% decrease, the system can either adopt the "national standard rules" recommendation or make a weighted average adjustment, assigning a higher weight to the "national standard rules." If there are no conflicts in the selected rule set, the system will directly use all rule adjustment recommendations as the final basis for adjustment.

[0071] Finally, the system applies these resolved weight adjustment suggestions to the preset baseline waterlogging resilience assessment indicator weights. These baseline weights can be a set of initial weight values ​​determined under a common scenario, such as the default importance of each indicator determined through historical data analysis or expert meetings. Based on the adjustment suggestions, the system makes incremental or overriding adjustments to the baseline weights to generate a customized set of waterlogging resilience assessment indicator weights for each sub-region under the current rainfall scenario. For example, if the weight of "drainage network carrying capacity" in the baseline weight is 0.2, and the conflict resolution suggestion is to increase it by 10%, the final weight of this indicator will be adjusted to 0.22.

[0072] In some embodiments, step A3 comprises: A301 obtains real-time operating status data of the urban drainage system within the target area; the real-time operating status data includes pipe water level data, pump station load data and storage space level data; A302. Based on the real-time operating status data and the urban spatial heterogeneity data, generate real-time pressure bearing capacity parameters of the urban drainage system in the target area; A303. Calculate the waterlogging resilience of the target area based on the real-time bearing capacity parameter and the waterlogging resilience assessment index weight to obtain the waterlogging resilience assessment result.

[0073] Real-time operating status data refers to dynamic information on the actual operating status of urban drainage systems at specific points in time or over a specific time period. This data can be obtained through sensor networks, IoT devices, SCADA systems (supervisory control and data acquisition systems), or manual inspection records. Pipeline water level data refers to the real-time height or fullness of water within urban underground drainage networks. This data can be monitored and collected in real time using devices such as ultrasonic water level gauges, pressure sensors, or float-type water level gauges. Pump station load data refers to the operating status and workload of urban drainage pumping stations over a specific period of time. This data can be obtained through current sensors, power meters, or pump station control system logs, reflecting the utilization of the pumping station's pumping capacity. Storage space level data refers to the real-time height of water within urban storage facilities used for temporary rainwater storage (such as storage ponds, sunken green spaces, and rain gardens). This data can be monitored using level sensors, radar water level gauges, or video surveillance combined with image recognition.

[0074] Among them, the real-time bearing capacity parameter refers to the actual carrying and absorbing capacity of the urban drainage system in response to rainfall runoff under the current real-time operating state. It can be obtained by comprehensive calculation based on methods such as hydraulic model simulation, data-driven model prediction or expert experience evaluation, combined with real-time operation data and spatial heterogeneity data.

[0075] This approach first collects real-time operational data on the urban drainage system within the target area when conducting a waterlogging resilience assessment. This data, including pipe network water level data, pump station load data, and storage space liquid level data, directly reflects the drainage system's immediate response and load-bearing capacity, addressing the shortcomings of relying solely on static data. Subsequently, based on this real-time operational data and combined with urban spatial heterogeneity data, real-time pressure bearing capacity parameters for the urban drainage system within the target area are generated. This integration integrates dynamic system operational information with the region's inherent geographic, pipe network, and infrastructure characteristics, providing a more comprehensive and accurate portrayal of the drainage system's actual carrying capacity and potential vulnerabilities in different regions and at different times. Finally, based on these real-time pressure bearing capacity parameters and the waterlogging resilience assessment indicator weights previously generated dynamically based on rainfall event characteristics and sub-regional vulnerability, the target area's waterlogging resilience is calculated, resulting in a waterlogging resilience assessment result. By incorporating real-time operational data on the urban drainage system, combining it with urban spatial heterogeneity data and dynamically adjusted assessment indicator weights, this approach overcomes the discrepancy between assessment results and actual conditions often encountered in traditional assessment methods. This integrated process ensures that waterlogging resilience assessment results not only reflect the current operational status of urban drainage systems but also highlight the importance of different assessment indicators during specific rainfall events. This makes the assessment results more timely and accurate, providing a more reliable basis for identifying weak links and guiding emergency response. This combination of real-time dynamics and regional and contextual weightings enables the assessment system to provide a more refined and dynamic understanding of urban waterlogging risks, thereby supporting more precise decision-making.

[0076] Preferably, step A302 may include: Calculating the real-time residual carrying capacity of each pipe section in the target area based on the pipe network water level data and the drainage pipe network distribution data; Evaluate the real-time pumping efficiency of each pumping station in the target area based on the pumping station load data; Determining the real-time available storage capacity of each storage space in the target area based on the storage space liquid level data and the regional terrain elevation data; The real-time remaining carrying capacity, the real-time pumping efficiency and the real-time available storage capacity are used as the real-time pressure bearing capacity parameters of the urban drainage system in the target area.

[0077] Among them, the real-time residual carrying capacity refers to the additional drainage flow or volume that each section of the drainage network can still carry under the current water level conditions of the network without overflow or full pipe flow. It can be calculated by subtracting the current actual flow capacity from the maximum design flow capacity of the section, or by establishing a hydraulic model to simulate the residual conveying potential under the current water level.

[0078] Among them, real-time pumping efficiency refers to the ratio of the actual pumping flow rate of the pumping station under the current operating load to the designed maximum pumping flow rate, which reflects the actual working performance of the pumping station. It can be obtained by monitoring the inlet and outlet water flow, motor power consumption, speed and other parameters of the pumping station, and combining it with the design performance curve of the pumping station for real-time calculation.

[0079] Among them, the real-time available storage capacity refers to the additional water volume that the storage space can accommodate under the current liquid level conditions until its designed overflow elevation or maximum water storage elevation. It can be obtained by looking up the volume-liquid level curve of the storage space and combining it with the current real-time liquid level data to perform table lookup or interpolation calculation.

[0080] This solution decomposes the overall pressure-bearing capacity of the urban drainage system into real-time performance evaluations of the three core components of the pipeline network, pumping station, and storage space, and integrates these evaluation results to provide a more comprehensive and accurate real-time pressure-bearing status of the system. Specifically, after obtaining the real-time operating status data of the urban drainage system in the target area, including pipeline water level data, pumping station load data, and storage space liquid level data, first, the real-time residual carrying capacity of each pipe section in the target area is calculated using the pipeline water level data and drainage pipeline network distribution data. This solves the problem that water level data alone cannot directly reflect the overall transportation capacity of the pipeline network, making the evaluation of the real-time pressure-bearing capacity of the pipeline network system more specific and accurate. Secondly, the real-time pumping and drainage efficiency of each pumping station in the target area is evaluated using pumping station load data and key infrastructure location data, which can more accurately reflect the real-time performance of the pumping station as a key drainage node. Secondly, using storage space liquid level data and regional terrain elevation data, the real-time available storage capacity of each storage space within the target area is determined. This overcomes the limitation of using liquid level data alone, which prevents intuitive judgment of the remaining storage capacity of the storage space. This makes the assessment of the real-time storage capacity of the storage space more quantitative and reliable. Finally, the real-time performance evaluation results of the three key components (pipeline network, pumping station, and storage space) are combined to form a real-time pressure bearing capacity parameter, forming a holistic, multi-dimensional real-time pressure bearing capacity parameter. This meticulous decomposition and synthesis method enables the generated pressure bearing capacity parameter to comprehensively and accurately reflect the overall resilience of the urban drainage system in the face of rainfall events. As input to the subsequent waterlogging resilience calculation, this real-time pressure bearing capacity parameter can significantly improve the accuracy and practicality of the assessment, providing a solid data foundation for waterlogging resilience assessment.

[0081] Preferably, step 303 may include: quantifying the drainage system response capacity, storage capacity, and key facility protection capacity of each sub-area within the target area based on the real-time pressure bearing capacity parameters; Based on the weights of the waterlogging resilience assessment indicators, the drainage system response capacity, the storage capacity, and the key facility protection capacity are comprehensively considered to obtain a waterlogging resilience score for each sub-region within the target area; The waterlogging resilience scores of the sub-regions are summarized and combined with the waterlogging vulnerability characteristics of the sub-regions to generate the waterlogging resilience assessment result containing weak link identification information.

[0082] The drainage system's responsiveness refers to the immediate efficiency of drainage facilities in a sub-region in handling rainfall runoff. This can be quantified based on the real-time residual carrying capacity of each pipe segment within the sub-region and the real-time drainage efficiency of each pumping station within the sub-region. For example, the total real-time residual carrying capacity of all pipe segments and the total real-time drainage efficiency of all pumping stations within the sub-region are calculated, normalized, and then a weighted sum calculation is performed on the planned results to obtain the drainage system's responsiveness.

[0083] Storage capacity refers to the potential for storing and retaining floodwater within a subregion. This capacity can be quantified based on the real-time available storage capacity of the storage spaces within the subregion. For example, the total real-time available storage capacity of all storage spaces within the subregion can be calculated and planned, with the normalized total real-time available storage capacity serving as the storage capacity.

[0084] Among them, the critical facility protection capacity refers to the safety level of important infrastructure under the threat of urban flooding. The real-time pressure bearing capacity parameters and the location data of critical infrastructure can be combined to evaluate the degree to which critical facilities are protected from urban flooding under the current pressure state, so as to quantify the critical facility protection capacity. For example, the total real-time pressure bearing capacity parameters of the sub-area (the total real-time pressure bearing capacity parameter refers to the sum of the same real-time pressure bearing capacity parameters, such as the sum of the real-time pumping efficiency of all pumping stations in the sub-area, which is the total real-time pumping efficiency) can be used to look up the table to obtain the pressure bearing capacity coefficient of the sub-area. Then, the type and quantity of critical infrastructure in the sub-area can be determined based on the location data of critical infrastructure, and the initial critical facility protection capacity can be calculated using the following formula: ,in For the initial critical facility protection capability, is the bearing capacity coefficient, n is the number of types of key infrastructure in the sub-region, is the number of critical infrastructure of type i, is the preset impact weight of the i-th critical infrastructure; finally, the initial critical facility protection capability is normalized to obtain the final critical facility protection capability.

[0085] The waterlogging resilience score refers to the numerical value obtained by quantifying the waterlogging resilience level of each sub-region within the target area. It can be obtained by weighting and synthesizing various capacity indicators to obtain a comprehensive score. Here, the waterlogging resilience assessment indicators include the drainage system's response capacity, storage capacity, and key facility protection capacity. Therefore, the waterlogging resilience assessment weight includes the weights of these three waterlogging resilience assessment indicators. When calculating the waterlogging resilience score for a sub-region, the quantified drainage system's response capacity, storage capacity, and key facility protection capacity can be multiplied by the corresponding waterlogging resilience assessment indicator weights, and the products are summed to obtain the waterlogging resilience score for that sub-region.

[0086] Generating the waterlogging resilience assessment results, which include identification of weak links, can be achieved by ranking or visualizing the waterlogging resilience scores of each sub-region, while simultaneously overlaying or correlating vulnerability characteristics such as the degree of flooding in low-lying areas, drainage capacity bottlenecks, and exposure risks of key facilities in each sub-region. For example, if a sub-region's waterlogging resilience score is low (e.g., below a preset score threshold) and its vulnerability characteristics indicate a drainage capacity bottleneck, the assessment results will clearly indicate that the weak link in that region is the insufficient carrying capacity of the drainage network. This combination is the core of this solution. It not only provides a resilience score, but more importantly, by correlating resilience performance with the root causes of vulnerability, it can directly generate an assessment result that includes identification of weak links. This means that the assessment report will clearly indicate which areas are deficient in which areas.

[0087] Through the above technical solutions, this application can deeply reveal the specific factors that lead to urban flood resilience scores, and clearly point out which areas or aspects within the city are the real weak links, so that the assessment results no longer have limitations in guiding precise disaster prevention and mitigation measures and emergency resource scheduling, and can effectively identify and solve the city’s most vulnerable problems under specific rainfall scenarios.

[0088] refer to Figure 2 The present application provides an urban waterlogging resilience assessment device, comprising: Information acquisition module 1 is used to obtain rainfall warning information and parse it to obtain rainfall event characteristic parameters (the specific process can be referred to step A1 above); Weight generation module 2, used to generate waterlogging resilience assessment index weights based on the rainfall event characteristic parameters and preset weight adjustment rules (the specific process can be referred to step A2 above); The waterlogging resilience assessment execution module 3 is used to perform waterlogging resilience assessment using the waterlogging resilience assessment indicator weights (the specific process can be referred to step A3 above); The assessment result output module 4 is used to output the waterlogging resilience assessment results including the weak link identification information (the specific process can be found in step A4 above).

[0089] In a third aspect, the present application provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program executable by the processor, and when the processor executes the computer program, it runs the steps in the urban waterlogging resilience assessment method as described above.

[0090] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, runs the steps of the urban waterlogging resilience assessment method as described above.

[0091] Please refer to Figure 3 , Figure 3 This is a structural diagram of an electronic device provided in an embodiment of the present application. The present application provides an electronic device, including: a processor 301 and a memory 302. The processor 301 and the memory 302 are interconnected and communicate with each other via a communication bus 303 and / or other forms of connection mechanisms (not shown). The memory 302 stores a computer program executable by the processor 301. When the electronic device is running, the processor 301 executes the computer program to execute the container packing method in any optional implementation of the above embodiment to achieve the following functions: obtaining rainfall warning information, parsing it to obtain rainfall event characteristic parameters; generating waterlogging resilience assessment indicator weights based on the rainfall event characteristic parameters and preset weight adjustment rules; performing waterlogging resilience assessment using the waterlogging resilience assessment indicator weights; and outputting waterlogging resilience assessment results containing weak link identification information.

[0092] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the container packing method in any optional implementation of the above embodiment is executed to achieve the following functions: obtaining rainfall warning information and parsing it to obtain rainfall event characteristic parameters; generating urban waterlogging resilience assessment index weights based on the rainfall event characteristic parameters and preset weight adjustment rules; performing urban waterlogging resilience assessment using the urban waterlogging resilience assessment index weights; and outputting urban waterlogging resilience assessment results containing weak link identification information. Among them, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0093] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for assessing urban waterlogging resilience, characterized in that: The steps of the method include: A1. Obtain rainfall warning information and parse it to obtain rainfall event characteristic parameters; A2. Generating waterlogging resilience assessment indicator weights based on the rainfall event characteristic parameters and preset weight adjustment rules; A3. Conduct a waterlogging resilience assessment using the waterlogging resilience assessment indicator weights. A4. Output the waterlogging resilience assessment results including the identification of weak links.

2. The urban waterlogging resilience assessment method according to claim 1, characterized in that: Step A1 includes: A101. Obtain rainfall warning information; the rainfall warning information includes rainfall event type information, forecast rainfall intensity information, rainfall duration information, and forecast impact area range information; A102. According to the preset rainfall event classification rules, the rainfall event type information is converted into standardized rainfall event type parameters; A103. According to a preset quantitative conversion rule, the forecast rainfall intensity information and the rainfall duration information are converted into a standardized rainfall intensity parameter and a standardized rainfall duration parameter; A104. According to preset geographic information parsing rules, the forecast impact area range information is converted into structured geographic area parameters; A105. Use the rainfall event type parameter, the rainfall intensity parameter, the rainfall duration parameter and the geographical area parameter as the rainfall event characteristic parameters.

3. The urban waterlogging resilience assessment method according to claim 2, characterized in that: Step A2 includes: A201. Obtain urban spatial heterogeneity data of the target area represented by the geographic area parameter; the urban spatial heterogeneity data includes regional terrain elevation data, drainage network distribution data, and key infrastructure location data; A202. Divide the target area into a plurality of sub-areas based on the urban spatial heterogeneity data; A203. Based on the urban spatial heterogeneity data, identify the waterlogging vulnerability characteristics of each sub-region; A204. Based on the characteristic parameters of the rainfall event, the waterlogging vulnerability characteristics of the sub-region and the preset weight adjustment rules, a corresponding waterlogging resilience assessment index weight is generated for each sub-region.

4. The urban waterlogging resilience assessment method according to claim 3, characterized in that: Step A202 includes: Identifying spatial heterogeneity distribution characteristics of the target area based on the urban spatial heterogeneity data; the spatial heterogeneity distribution characteristics include terrain relief, pipeline network density distribution, and key infrastructure concentration; Determining an evaluation granularity requirement for this rainfall event according to the rainfall event type parameter, the rainfall intensity parameter, and the rainfall duration parameter; According to the spatial heterogeneity distribution characteristics and the evaluation granularity requirements, the target area is adaptively divided into regions to obtain multiple sub-regions.

5. The urban waterlogging resilience assessment method according to claim 3, characterized in that: Step A203 includes: Based on the terrain elevation data of the area, determine the water catchment path and water accumulation depth potential of each sub-area to assess the degree of flooding in low-lying areas; Based on the drainage network distribution data, evaluate the network carrying capacity and drainage path connectivity of each sub-area to determine drainage capacity bottlenecks; Based on the critical infrastructure location data, analyze the geographical relationship between the critical infrastructure and potential waterlogging areas in each sub-region to determine the exposure risk of the critical facilities; The degree of low-lying flooding, the drainage capacity bottleneck and the exposure risk of key facilities are taken as the waterlogging vulnerability characteristics of the sub-area.

6. The urban waterlogging resilience assessment method according to claim 3, characterized in that: Step A204 includes: Based on the rainfall event characteristic parameters and the waterlogging vulnerability characteristics of the sub-region, selecting a set of weight adjustment rules applicable to the sub-region in the current rainfall scenario from the preset weight adjustment rules; In the set of weight adjustment rules, identifying a group of rules having conflicting weight adjustment suggestions for the same waterlogging resilience assessment indicator; When there are conflicting rule groups, the conflicting rule groups are prioritized and, based on the rule adjustment suggestions with higher priorities, the rule adjustment suggestions with lower priorities are modified to obtain weight adjustment suggestions after conflict resolution. When there is no conflicting rule group, taking the rule adjustment suggestions in the weight adjustment rule set as the conflict-resolved weight adjustment suggestions; According to the conflict-resolved weight adjustment suggestions, corresponding waterlogging resilience assessment indicator weights are generated for the sub-region on the basis of preset benchmark waterlogging resilience assessment indicator weights.

7. The urban waterlogging resilience assessment method according to claim 3, characterized in that: Step A3 includes: A301 obtains real-time operating status data of the urban drainage system within the target area; the real-time operating status data includes pipe water level data, pump station load data and storage space level data; A302. Based on the real-time operating status data and the urban spatial heterogeneity data, generate real-time pressure bearing capacity parameters of the urban drainage system in the target area; A303. Calculate the waterlogging resilience of the target area based on the real-time bearing capacity parameter and the waterlogging resilience assessment index weight to obtain the waterlogging resilience assessment result.

8. An urban waterlogging resilience assessment device, characterized in that: The device includes: The information acquisition module is used to obtain rainfall warning information and parse it to obtain rainfall event characteristic parameters; A weight generation module, configured to generate weights for waterlogging resilience assessment indicators based on the characteristic parameters of the rainfall event and a preset weight adjustment rule; an urban waterlogging resilience assessment execution module, configured to execute an urban waterlogging resilience assessment using the urban waterlogging resilience assessment indicator weights; The assessment result output module is used to output the waterlogging resilience assessment results including weak link identification information.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program executable by the processor, and when the processor executes the computer program, the method runs the steps of the urban waterlogging resilience assessment method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the urban waterlogging resilience assessment method according to any one of claims 1 to 7 are executed.

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