A method for urban waterlogging resilience assessment and related devices

Abstract

The application belongs to the technical field of urban planning, and discloses a city waterlogging resilience evaluation method and related equipment. Rainfall warning information is obtained, and rainfall event characteristic parameters are obtained by analysis. Dynamic waterlogging resilience evaluation index weights are generated according to the parameters and preset weight adjustment rules, and evaluation is performed based on the weights. Finally, the evaluation result containing weak link identification information is output, effectively solving the problem of weight static and uniform in the existing evaluation method. The evaluation index weights can be dynamically adjusted according to the rainfall event characteristics and city space heterogeneity, so as to realize accurate evaluation of city waterlogging resilience and effectively identify weak links, and provide a scientific basis for accurate scheduling of emergency resources and effective implementation of disaster prevention measures.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban planning, in particular to a method for assessing urban waterlogging resilience and related equipment. BACKGROUND

[0002] Urban waterlogging resilience assessment is an important part of urban disaster prevention and mitigation. When assessing waterlogging resilience, urban management departments usually use an evaluation system that includes multiple indicators, covering drainage pipe networks, pumping stations, storage spaces, emergency response capabilities, and other dimensions. To obtain a comprehensive resilience score, each indicator needs to be assigned a weight to reflect its importance in the overall resilience. Currently, these weight values are determined through subjective weighting methods such as expert meetings and analytic hierarchy process, or by statistical regression analysis of historical disaster data to obtain a fixed weight configuration scheme applicable to the entire city. This static weight system based on expert experience or historical data, while providing macro decision-making basis for urban planning and long-term infrastructure construction, has significant limitations in practical application.

[0003] Firstly, a city is a complex system with high spatial heterogeneity, containing areas with completely different functional attributes, such as high-density central business districts, old built-up areas, emerging industrial areas, and large transportation hub areas. These areas differ significantly in building density, impervious surface ratio, drainage facility standards, population structure, potential risk substances, and economic value, resulting in different vulnerability characteristics when facing waterlogging. Applying a uniform set of indicator weights to all functional areas often masks the real weaknesses of specific areas. For example, for central business districts, the weight of economic loss risk indicators should be much higher than for other areas; for old built-up areas, the weights of drainage facility aging and special population evacuation capacity indicators should be more important. If a city-wide static weight is used, the assessment results may not accurately reveal the true risks of specific areas, leading to disaster prevention and mitigation resources not being accurately targeted at the areas most in need of improvement.

[0004] Secondly, even if different weight schemes are set for different functional areas, this static weight configuration cannot effectively respond to the dynamic risks brought by different types of rainfall events. The disaster-causing rainfall patterns of urban waterlogging are not immutable. For example, short-time strong convective rain (short duration, extremely high intensity) mainly tests the surface runoff convergence speed and the drainage outlet flow capacity, at this time the importance of indicators such as rainwater outlet blockage rate and surface runoff channel smoothness will be significantly improved; while persistent long-duration rainfall (total rainfall is large, rain intensity is gentle) mainly tests the conveying capacity of the entire drainage pipe network system and the pumping station discharge capacity, at this time the importance of indicators such as main pipe network fullness and pump station operation load rate becomes dominant. The existing evaluation system is difficult to adjust the weight of the corresponding region and the corresponding index in the evaluation model in real time and automatically according to specific and dynamic rainfall warning information.

[0005] In summary, the existing urban waterlogging resilience evaluation method has the problems of static and uniformity in setting index weight, and cannot fully consider the complex spatial heterogeneity in the city and the dynamic change characteristics of rainfall events. This leads to the evaluation result may not truly reflect the most vulnerable link of the city under a specific rainfall scenario, thereby affecting the precise scheduling of emergency resources and the effective implementation of disaster prevention measures.

[0006] In view of the above problems, the existing technology needs to be improved. SUMMARY

[0007] The purpose of the present application is to provide a kind of urban waterlogging resilience evaluation method and related equipment, can dynamically adjust the weight of evaluation index according to rainfall event characteristics and city spatial heterogeneity, to realize the precise evaluation of urban waterlogging resilience, effectively identify weak link, provide scientific basis for the precise scheduling of emergency resources and the effective implementation of disaster prevention measures.

[0008] In a first aspect, the present application provides a kind of urban waterlogging resilience evaluation method, the steps of the method include:

[0009] A1.Obtain rainfall warning information, from which the rainfall event characteristic parameters are parsed;

[0010] A2.According to the rainfall event characteristic parameters and the preset weight adjustment rule, generate waterlogging resilience evaluation index weight;

[0011] A3.Using the waterlogging resilience evaluation index weight, execute waterlogging resilience evaluation;

[0012] A4.Output waterlogging resilience evaluation result containing weak link identification information.

[0013] In a second aspect, the present application provides a kind of urban waterlogging resilience evaluation device, the device includes:

[0014] The information acquisition module is configured to acquire rainfall warning information and parse rainfall event characteristic parameters from the rainfall warning information.

[0015] The weight generation module is configured to generate an urban waterlogging resilience evaluation index weight according to the rainfall event characteristic parameters and a preset weight adjustment rule.

[0016] The waterlogging resilience evaluation execution module is configured to perform urban waterlogging resilience evaluation by using the urban waterlogging resilience evaluation index weight.

[0017] The evaluation result output module is configured to output an urban waterlogging resilience evaluation result containing weak link identification information.

[0018] In a third aspect, the present application provides an electronic device including 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 steps of the urban waterlogging resilience evaluation method described above are performed.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the steps of the urban waterlogging resilience evaluation method described above are performed.

[0020] Beneficial effects: The urban waterlogging resilience evaluation method and related device provided by the present application can effectively solve the problems of static and uniform weight in the existing evaluation method by acquiring rainfall warning information and parsing rainfall event characteristic parameters, generating dynamic urban waterlogging resilience evaluation index weight according to the parameters and a preset weight adjustment rule, performing evaluation based on the weight, and finally outputting an evaluation result containing weak link identification information. The evaluation index weight can be dynamically adjusted according to the rainfall event characteristics and urban spatial heterogeneity, thereby realizing accurate evaluation of urban waterlogging resilience and effectively identifying weak links, and providing a scientific basis for accurate scheduling of emergency resources and effective implementation of disaster prevention measures. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 The flowchart of the urban waterlogging resilience evaluation method provided by the embodiments of the present application.

[0022] Figure 2 The structural schematic diagram of the urban waterlogging resilience evaluation device provided by the embodiments of the present application.

[0023] Figure 3 The structural schematic diagram of the electronic device provided by the embodiments of the present application.

[0024] Label explanation: 1, information acquisition module; 2, weight generation module; 3, waterlogging resilience evaluation execution module; 4, evaluation result output module; 301, processor; 302, memory; 303, communication bus. DETAILED DESCRIPTION

[0025] 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.

[0026] 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.

[0027] refer to Figure 1 This application proposes a method for assessing urban waterlogging resilience, which includes the following steps:

[0028] A1. Obtain rainfall warning information and parse it to obtain rainfall event characteristic parameters;

[0029] A2. Generating waterlogging resilience assessment indicator weights based on the rainfall event characteristic parameters and preset weight adjustment rules;

[0030] A3. Conduct a waterlogging resilience assessment using the waterlogging resilience assessment indicator weights.

[0031] A4. Output the waterlogging resilience assessment results including the identification of weak links.

[0032] 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.

[0033] 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.

[0034] The weight adjustment rule refers to a set of pre-set logic or algorithms for guiding the dynamic adjustment of the waterlogging resilience evaluation index weight, which can be implemented in the form of a rule base based on expert experience, a machine learning model, or an optimization algorithm, such as a conditional judgment statement or a decision tree model. It is mainly used to adjust the importance of each evaluation index according to the characteristics of the rainfall event.

[0035] The waterlogging resilience evaluation index weight refers to the relative importance value of each evaluation index in the evaluation of urban waterlogging resilience, which can be represented in the form of a percentage, a score, or a normalized coefficient, such as the weight distribution of the drainage system response capacity, the storage capacity, and the protection capacity of key facilities. It is mainly used to ensure that the evaluation model can highlight the role of the index under specific rainfall scenarios.

[0036] The weak link identification information refers to the areas or system components with high waterlogging risk in the city under specific rainfall scenarios identified in the waterlogging resilience evaluation results, which can be presented in the form of geographic coordinates, regional identifiers, or system component numbers, such as low-lying waterlogging points, drainage pipe network bottlenecks, or infrastructure exposure areas. It is mainly used to provide emergency response and resource deployment basis for city managers.

[0037] The core innovation of the present application lies in the introduction of the acquisition and analysis of rainfall warning information, combined with the pre-set weight adjustment rule, to realize the dynamic generation and adaptive adjustment of the waterlogging resilience evaluation index weight, thereby solving the problem of static weight in existing evaluation methods and the inability to adjust according to the characteristics of dynamic rainfall events, achieving the effect of identifying the weak links of the city under specific rainfall scenarios and optimizing emergency resource scheduling.

[0038] Specifically, the evaluation method of the present application realizes its functions in the following ways: first, the system obtains early warning information of an impending rainfall event, and parses these information to extract rainfall event characteristic parameters. These parameters are the basis for subsequent dynamic adjustment. Next, the system generates a set of waterlogging resilience evaluation index weights for the current specific rainfall scenario according to the rainfall event characteristic parameters obtained by parsing, combined with the pre-set weight adjustment rules. This process ensures that the evaluation model can be configured according to the actual rainfall threat, so that the importance of different evaluation indicators can dynamically reflect its influence in the current scenario. Subsequently, the system uses the dynamically generated waterlogging resilience evaluation index weights to evaluate the waterlogging resilience of the city. The evaluation process fully considers the impact of a specific rainfall event on different regions and infrastructure of the city, thereby obtaining a targeted resilience evaluation result. Finally, the evaluation result is output, which contains the weak link identification information of the city under the current rainfall scenario. These information can guide the city management department to carry out disaster prevention and mitigation and emergency resource scheduling, and ensure that resources are directed to areas or systems that need to be strengthened.

[0039] Through the above scheme, the present application can dynamically adjust the waterlogging resilience evaluation index weight according to the rainfall warning information, thereby overcoming the limitations of static weight in existing methods, which cannot adapt to different rainfall scenarios. This makes the evaluation result accurately reflect the resilience level of the city under a specific rainfall event, and identify potential weak links. Therefore, the present application can provide targeted decision-making basis for city managers, optimize the scheduling of emergency resources and the formulation of disaster prevention and mitigation measures, and improve the response efficiency and accuracy of the city in dealing with waterlogging disasters.

[0040] In some embodiments, step A1 comprises:

[0041] 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;

[0042] A102. Convert the rainfall event type information into a standardized rainfall event type parameter according to a pre-set rainfall event classification rule;

[0043] A103. Convert the forecast rainfall intensity information and the rainfall duration information into a standardized rainfall intensity parameter and a standardized rainfall duration parameter, respectively, according to a pre-set quantitative conversion rule;

[0044] A104. Convert the forecast impact area range information into a structured geographic area parameter according to a pre-set geographic information parsing rule;

[0045] A105. The rainfall event type parameter, the rainfall intensity parameter, the rainfall duration parameter, and the geographical area parameter are taken as the rainfall event characteristic parameters.

[0046] wherein the rainfall event type information refers to the description of the nature of the rainfall event, such as shower, thunder shower, heavy rain, or typhoon rain, etc., which can be represented in the form of text description, code, or label. The forecasted rainfall intensity information refers to the prediction of the rainfall amount per unit time, such as millimeter / hour or centimeter / day, which can be represented in the form of numerical value, level, or interval. The rainfall duration information refers to the prediction of the expected duration of the rainfall process, such as hour, minute, or day, which can be represented in the form of numerical value, time period, or time point. The forecasted impact area range information refers to the geographical area that is expected to be affected by the rainfall event, which can be represented in the form of administrative division name, geographical coordinate range, or polygon area data.

[0047] wherein the preset rainfall event classification rule refers to the logical set used to map the original rainfall event type information to a unified classification system, which can be implemented in the form of lookup table, conditional judgment statement, or machine learning model. The standardized rainfall event type parameter refers to the representation of the rainfall event type after unified classification processing, which has fixed format and meaning, and can be represented in the form of enumeration value, pre-defined code, or unified text label.

[0048] wherein the preset quantization conversion rule refers to the logical set used to convert the descriptive or non-unified unit rainfall intensity and duration information into calculable numerical values, which can be implemented in the form of mathematical formula, piecewise function, or lookup table method. The standardized rainfall intensity parameter and the standardized rainfall duration parameter refer to the rainfall intensity and duration values after unified unit and numerical range processing, which can be directly used for calculation and comparison, and can be represented in the form of unified measurement unit (such as millimeter / hour, hour) and numerical range.

[0049] wherein the preset geographical information analysis rule refers to the logical set used to convert the descriptive or unstructured geographical area information into a data format that can be recognized and processed by a geographic information system, which can be implemented in the form of regular expression, geographic coding service, or spatial analysis algorithm. The structured geographical area parameter refers to the geographical data that can be directly recognized and used for spatial analysis by a geographic information system after analysis, which can be represented in the form of geographical coordinate point set, polygon boundary data, or grid cell identifier.

[0050] The present solution elaborates the specific process of obtaining rainfall warning information and analyzing the 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 necessary data input for subsequent refined analysis. On this basis, in order to ensure that the rainfall type information of different sources or expressions can be accurately identified and utilized by the system, according to the preset rainfall event classification rules, the original rainfall event type information is converted into standardized rainfall event type parameters. This enables the system to uniformly process various rainfall types, providing a unified basis for subsequent weight adjustment based on different rainfall types.

[0051] Further, in order to accurately assess the disaster potential of rainfall events and provide refined quantitative input for subsequent weight adjustment, according to the preset quantitative conversion rules, the forecast rainfall intensity information and rainfall duration information are respectively converted into standardized rainfall intensity parameters and standardized rainfall duration parameters. Through this quantitative conversion, the original descriptive or non-uniform unit information is converted into calculable and comparable numerical parameters. At the same time, in order to enable the evaluation system to accurately identify the specific geographical areas affected, thereby realizing the assessment and resource scheduling of the waterlogging resilience of specific areas, according to the preset geographical information analysis rules, the forecast impact area range information is converted into structured geographical area parameters. This solves the limitation of unified assessment of the whole city, making the assessment results more targeted.

[0052] Finally, the rainfall event type parameters, rainfall intensity parameters, rainfall duration parameters, and geographical area parameters that have been standardized, quantified, and structured are integrated to form a comprehensive, accurate, and easy-to-process rainfall event characteristic parameter set. This set contains the key attributes of the rainfall event and its spatial impact range, providing accurate and multi-dimensional input for generating waterlogging resilience assessment index weights based on rainfall event characteristic parameters in subsequent steps. Through this series of detailed classification, quantification, and structuring, it ensures 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 subsequent dynamic adjustment of assessment index weights and precise assessment, significantly improving the precision and practicality of urban waterlogging resilience assessment.

[0053] In some embodiments, step A2 comprises:

[0054] A201. Obtain city spatial heterogeneity data of the target area represented by the geographical area parameter; the city spatial heterogeneity data includes regional terrain elevation data, drainage pipe network distribution data, and key infrastructure location data;

[0055] A202. Divide the target area into multiple sub-regions according to the urban spatial heterogeneity data;

[0056] A203. Identify the characteristics of each sub-region's waterlogging vulnerability according to the urban spatial heterogeneity data;

[0057] A204. According to the characteristics of the rainfall event, the waterlogging vulnerability characteristics of the sub-region, and the preset weight adjustment rule, generate the corresponding waterlogging resilience assessment index weight for each sub-region.

[0058] Urban spatial heterogeneity data refers to comprehensive information that can reflect the differences in geography, function, infrastructure, etc. of different areas within the city. These data are the basis for understanding the complexity of the city and can include regional terrain elevation data, drainage pipe network distribution data, and key infrastructure location data. They can also include land use type data, population density data, and building density data. The purpose is to provide differentiated information in the spatial dimension for fine assessment.

[0059] Dividing multiple sub-regions means logically or physically dividing the entire target area according to urban spatial heterogeneity data to form several small units with relative consistency or specific functional attributes. This division can be based on the spatial analysis function of geographic information systems (GIS), such as clustering algorithms, grid division, or administrative zoning, functional zoning, etc. The purpose is to divide the complex city into more manageable and assessable local units.

[0060] Identifying waterlogging vulnerability characteristics means analyzing and determining the weak links or sensitivity of each sub-region in the face of waterlogging disasters based on sub-region division. This can involve a comprehensive assessment of the physical characteristics, infrastructure conditions, and socio-economic attributes of the sub-region to reveal its performance in waterlogging risk, such as assessing its water accumulation level, drainage capacity bottlenecks, or exposure risk of important facilities.

[0061] Here, the preset weight adjustment rule is a set of predefined logic or algorithm for guiding the dynamic adjustment of waterlogging resilience assessment index weight. These rules can be based on expert experience, historical data analysis, or model simulation results. The purpose is to quantitatively adjust the importance of assessment indicators according to specific rainfall event characteristics and regional vulnerability, to ensure the relevance and accuracy of the assessment results.

[0062] In the generation of the waterlogging resilience evaluation index weight, first, based on the geographical area parameters obtained in the foregoing steps, the system obtains the urban spatial heterogeneity data of the target region. These data, such as regional terrain elevation data, drainage pipe network distribution data, and key infrastructure location data, are the basis for understanding the complexity and differences within the city and provide information support for subsequent detailed analysis. On this basis, the system divides the target region into multiple sub-regions using these urban spatial heterogeneity data. This division is not simply administrative division, but fully considers the differences in geography, function or risk properties, ensuring that each sub-region has relatively consistent characteristics to some extent, thereby avoiding the evaluation bias caused by treating the entire city as a homogeneous unit. Further, for each divided sub-region, the system further analyzes and identifies its waterlogging vulnerability characteristics using urban spatial heterogeneity data. For example, by analyzing the terrain elevation data, the waterlogging susceptibility can be evaluated, by analyzing the drainage pipe network data, the drainage capacity bottleneck can be determined, and by analyzing the key infrastructure location data, the exposure risk of key facilities (such as hospitals, schools, transportation hubs, power facilities, communication base stations, etc.) can be identified. The identification of these vulnerability characteristics provides a clear understanding of the risk points of each sub-region, providing a detailed basis for subsequent weight adjustment. Finally, the system generates a set of customized waterlogging resilience evaluation index weights for each sub-region by considering the rainfall event characteristic parameters (such as rainfall intensity, duration, etc.) analyzed in the foregoing steps, the waterlogging vulnerability characteristics identified for each sub-region, and the pre-set weight adjustment rules. This generation method makes the evaluation weights no longer static or uniform, but can be adaptively adjusted according to the specific rainfall scenario and the actual vulnerability of the region.

[0063] Through the operation of the above steps, the present scheme can overcome the limitation that the weight setting is too uniform or fixed in the prior art. Specifically, the rainfall event characteristic parameters obtained in the foregoing steps provide dynamic external scenario information, and the present scheme provides fine 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 consider both the dynamics of rainfall events and the spatial heterogeneity within the city. Thus, for different rainfall events and different sub-regions, the system can generate differentiated and targeted evaluation index weights, thereby ensuring that the waterlogging resilience evaluation results accurately reflect the resilience status and weak links of each sub-region under specific rainfall events, providing support for the accurate dispatch of emergency resources and the effective implementation of disaster prevention measures.

[0064] As a preferred embodiment, in generating the weight of the internal flooding resilience assessment indicators, the following steps can be taken: First, when obtaining the urban spatial heterogeneity data of the target area, relevant information can be integrated from multiple data sources using a geographic information system (GIS) platform. For example, regional terrain elevation data can be sourced from a digital elevation model (DEM) and stored in raster or TIN (Triangulated Irregular Network) format; drainage pipe network distribution data can be obtained from the urban underground pipeline survey database, including pipe diameter, material, burial depth, slope, and connection relationship topological information; key infrastructure location data can be obtained from the city planning department or related industry databases, such as hospitals, schools, transportation hubs, power facilities, communication base stations, etc., and represented in the form of points or polygon elements. These data can be stored uniformly in a spatial database to facilitate subsequent spatial analysis and query. Further, when dividing the target area into multiple sub-regions based on urban spatial heterogeneity data, a grid-based division method can be used, such as dividing the entire target area into grid cells with a side length of 500 meters or 1 kilometer. For each grid cell, the internal terrain undulation, pipe network density, impervious area proportion, and the number and type of key infrastructure can be calculated as comprehensive heterogeneity indicators. Subsequently, clustering algorithms such as K-means clustering or DBSCAN clustering can be used to aggregate adjacent grid cells with similar heterogeneity indicators into a sub-region. In addition, administrative divisions or functional zoning (such as commercial, residential, industrial, etc.) can be combined as auxiliary boundaries to adjust the clustering results to form sub-regions that better meet management needs. On this basis, when identifying the internal flooding vulnerability characteristics of each sub-region, the internal urban spatial heterogeneity data of each sub-region can be used for detailed analysis. For example, based on terrain elevation data, the catchment path and potential water accumulation points within the sub-region can be simulated, and the water accumulation depth can be estimated to assess the waterlogging susceptibility. At the same time, combined with the drainage pipe network distribution data, the connectivity of the drainage pipe network within the sub-region, the pipe diameter matching degree, and whether there are bottleneck pipe sections can be analyzed to determine the drainage capacity bottleneck. In addition, by superimposing key infrastructure location data and potential water accumulation areas, key facilities that may be affected by internal flooding can be identified, and their exposure risk can be assessed. These analysis results can be quantified as vulnerability indicators, such as water accumulation area proportion, drainage pipe network load rate, key facility submersion risk level, etc. Finally, when generating the weight of the internal flooding resilience assessment indicators for each sub-region based on the rainfall event characteristic parameters, the internal flooding vulnerability characteristics of the sub-region, and the pre-set weight adjustment rules, a rule-based expert system or machine learning model can be constructed. For example, the pre-set weight adjustment rules can be a series of "if-then" conditional statements, such as "if the rainfall intensity parameter is 'heavy rain' and the 'waterlogging susceptibility' of the sub-region is 'high', then increase the weights of'surface runoff catchment velocity' and 'emergency pumping capacity' indicators by 20%".The system can match and activate corresponding weight adjustment rules from the rule library according to the type and intensity of the current rainfall event and the specific vulnerability characteristics identified for each sub-region. For each sub-region, these activated rules will dynamically adjust the initial baseline weights of the flood resilience assessment indicators within it, thereby generating a set of customized weights that reflect the current scenario and regional characteristics.

[0065] Preferably, step A202 can include:

[0066] According to the urban spatial heterogeneity data, the spatial heterogeneity distribution characteristics of the target region are identified; the spatial heterogeneity distribution characteristics include terrain relief degree, pipe network density distribution, and key infrastructure aggregation degree;

[0067] According to the rainfall event type parameter, the rainfall intensity parameter, and the rainfall duration parameter, the evaluation granularity requirement for this rainfall event is determined;

[0068] According to the spatial heterogeneity distribution characteristics and the evaluation granularity requirement, the target region is adaptively divided into sub-regions, obtaining a plurality of sub-regions.

[0069] Among them, the spatial heterogeneity distribution characteristics refer to the non-uniform distribution mode of geographical, infrastructure or socio-economic attributes in space within the target region, which can be identified and quantified by using the spatial analysis function of geographic information system (GIS) combined with statistical methods or machine learning algorithms.

[0070] Among them, the terrain relief degree refers to the degree of change of the ground elevation within the region, which can be calculated by using digital elevation model (DEM) data (i.e. the region terrain elevation data includes digital elevation model data), for example, by calculating indicators such as slope, slope direction or standard deviation of elevation.

[0071] Among them, the pipe network density distribution refers to the density of the length or connection points of the drainage pipe network per unit area within the region, which can be quantified by using drainage pipe network vector data (i.e. drainage pipe network distribution data includes drainage pipe network vector data) through spatial density analysis or kernel density estimation.

[0072] Among them, the key infrastructure aggregation degree refers to the concentration degree of facilities that have important influence on urban operation and residents' life in space within the region, which can be evaluated by using key infrastructure location data through clustering analysis or spatial weight matrix.

[0073] wherein the evaluation granularity requirement refers to the degree of spatial division required for the internal flooding vulnerability assessment, which can be dynamically determined according to the characteristics of the rainfall event and the evaluation target, and the minimum unit size or level of regional division. The adaptive regional division refers to the process of flexibly adjusting the regional division strategy according to the dynamically changing input conditions and static spatial heterogeneity characteristics, which can be realized by using machine learning-based clustering algorithms, multi-scale grid division or graph theory-based segmentation methods to ensure that the division results can best reflect the risk distribution under the current scenario.

[0074] The operation logic of the present scheme is that firstly, by identifying the spatial heterogeneity distribution characteristics of the target area, a spatial portrait of urban inherent vulnerability is constructed. This includes the influence of terrain on runoff accumulation, the restriction of drainage pipe network on drainage capacity and the concentrated risk of key infrastructure, providing a fine spatial consideration for regional division. On this basis, the scheme introduces dynamic consideration of the upcoming rainfall event, by analyzing the type, intensity and duration parameters of the rainfall event, the required evaluation granularity for this rainfall event is determined. This means that regional division is no longer fixed, but can be dynamically adjusted according to the specific rainfall scenario, ensuring that the degree of evaluation is matched with the actual risk distribution. Finally, the scheme integrates the above static spatial heterogeneity characteristics and dynamic evaluation granularity requirement, and performs adaptive regional division on the target area. This combination makes the divided sub-regions not only reflect the complexity of the city itself, but also respond to the dynamic needs of specific rainfall events. This dynamic and fine regional division provides a more accurate and targeted basis for subsequent internal flooding vulnerability feature identification and internal flooding resilience evaluation index weight generation, thereby significantly improving the accuracy and practical value of the entire internal flooding resilience evaluation method.

[0075] In specific implementation, a terrain relief layer can be first generated by calculating the slope, aspect, and local elevation standard deviation of each grid cell using high-resolution digital elevation model data. Meanwhile, geographic information system vector data of the urban drainage network is imported, and the ratio of network length to area in each region is calculated through kernel density analysis tools to generate a network density distribution layer. In addition, geographic coordinate data of key infrastructure such as hospitals, schools, transportation hubs, and power facilities in the city are obtained, and spatial clustering algorithms are used to identify and quantify the concentration of these facilities in a specific region to generate a key infrastructure concentration layer. These layers can be overlaid and analyzed comprehensively to identify the overall distribution pattern of spatial heterogeneity in the target region. Then, a rule base or lookup table can be established, taking rainfall event type parameters, rainfall intensity parameters, and rainfall duration parameters as inputs to obtain the corresponding evaluation granularity requirements, such as sub-region size requirements. Finally, adaptive region division algorithms based on weighted Voronoi diagrams or quadtree decomposition can be used. The identified spatial heterogeneity distribution characteristics are assigned different weights or priorities, and combined with the evaluation granularity requirements, more detailed division is performed in regions with high heterogeneity, such as further subdividing these regions into smaller grids or irregular polygons. Using a geographic information system platform, the spatial heterogeneity distribution characteristic layers are input, and according to the determined evaluation granularity requirements, the region boundaries are dynamically adjusted through iterative segmentation or merging algorithms until the spatial heterogeneity within each sub-region is relatively uniform and the size of the sub-region meets the evaluation granularity requirements, ultimately obtaining a series of sub-regions of different sizes and shapes that effectively reflect the differences in waterlogging risk.

[0076] Preferably, step A203 can include:

[0077] According to the regional terrain elevation data, determine the catchment path and potential water accumulation depth of each sub-region to assess the low-lying waterlogging degree;

[0078] According to the drainage network distribution data, evaluate the pipe network carrying capacity and drainage path connectivity of each sub-region to determine the drainage capacity bottleneck;

[0079] According to the key infrastructure location data, analyze the geographical relationship between key infrastructure and potential water accumulation area in each sub-region to determine the key facility exposure risk;

[0080] The low-lying waterlogging degree, the drainage capacity bottleneck, and the key facility exposure risk are used as the waterlogging vulnerability characteristics of the sub-region.

[0081] wherein the water accumulation path refers to the direction and route of water flow in the surface runoff process, which can be determined by flow direction analysis algorithms based on terrain elevation data. The water accumulation depth potential refers to the maximum water accumulation depth that a certain area can reach under certain rainfall conditions, which can be determined by hydrological model simulation or terrain-based water storage capacity analysis. The low-lying waterlogging degree refers to the degree to which a region is prone to waterlogging and waterlogging due to topographic conditions, which can be evaluated by the water accumulation path and water accumulation depth potential.

[0082] wherein the pipe network carrying capacity refers to the maximum water volume that the drainage pipe network can transport per unit time, which can be determined by hydraulic calculation models or flow analysis based on pipe network design parameters. The drainage path connectivity refers to the smoothness of water flow transmission between nodes and pipe sections in the drainage pipe network, which can be determined by network topology analysis or connected graph algorithms. The drainage capacity bottleneck refers to the link that limits the overall drainage efficiency of the drainage system when dealing with rainfall due to insufficient pipe network carrying capacity or poor drainage path, which can be evaluated by the pipe network carrying capacity and the drainage path connectivity.

[0083] wherein the geographical relationship between the critical infrastructure and the potential water accumulation area refers to the spatial proximity or overlap between the critical infrastructure and the area where water accumulation may occur, which can be determined by spatial overlay analysis or buffer analysis; wherein the potential water accumulation area can be determined by hydrological model simulation or terrain-based water storage capacity analysis. The critical infrastructure exposure risk refers to the degree to which critical infrastructure may be damaged in a waterlogging event, which can be evaluated by the geographical relationship between the critical infrastructure and the potential water accumulation area.

[0084] wherein the waterlogging vulnerability feature refers to the comprehensive reflection of the vulnerability and weak recovery ability of a sub-region in the face of waterlogging disasters, which can be composed of low-lying waterlogging degree, drainage capacity bottleneck, and critical infrastructure exposure risk.

[0085] The present scheme no longer limits to macroscopic or single-dimensional evaluation when identifying the characteristics of each sub-region's internal flooding vulnerability. Instead, through the deep mining and comprehensive analysis of multi-source urban spatial heterogeneity data, a multi-dimensional vulnerability identification framework is constructed. Specifically, first, the topographic features of each sub-region are systematically analyzed using regional terrain elevation data, including the convergence path of water flow and the potential waterlogging depth. This analysis can directly quantify the low-lying and prone-to-flooding degree of sub-regions due to natural terrain conditions, providing basic geographic information support for identifying internal flooding risks. Second, based on the distribution data of drainage pipe network, the drainage system of each sub-region is finely evaluated, including the actual carrying capacity of the pipe network and the connectivity of the drainage path. Through this evaluation, the weak links of the drainage system in dealing with rainfall, i.e., the drainage capacity bottlenecks, can be accurately identified, thereby revealing the anti-flooding capacity of urban drainage infrastructure. Third, combined with the location data of key infrastructure, the spatial relationship between these important facilities and the previously determined potential waterlogging areas is analyzed, thereby quantifying the exposure risk of key facilities in internal flooding events. This allows the evaluation to focus on the most vulnerable points that have the greatest impact on urban operations and residents' lives. Finally, the three dimensions, i.e., low-lying and prone-to-flooding degree, drainage capacity bottleneck, and key facility exposure risk, are integrated to serve as the internal flooding vulnerability characteristics of sub-regions.

[0086] This comprehensive vulnerability characteristic can comprehensively and accurately reflect the specific weaknesses of sub-regions in terms of topography, drainage system, and key facility protection. Through this detailed vulnerability identification method, the present scheme can overcome the problem of insufficient precision and comprehensiveness in traditional evaluation. It closely links with the steps of obtaining urban spatial heterogeneity data and dividing the target region into multiple sub-regions. Based on the detailed urban spatial heterogeneity data and the fine division of urban regions, the internal vulnerability of each sub-region is further analyzed. This multi-dimensional and fine-grained vulnerability characteristic identification provides a solid data foundation for generating internal flooding resilience evaluation index weights based on the characteristics of rainfall events and the internal flooding vulnerability characteristics of sub-regions, making the generated weights more accurately reflect the actual risk of sub-regions under specific rainfall scenarios, thereby improving the accuracy and relevance of the entire internal flooding resilience evaluation.

[0087] In one specific embodiment, to identify the characteristics of flood vulnerability for each sub-region, high-resolution regional terrain elevation data can be first acquired, such as digital elevation models obtained through aerial photogrammetry or laser radar scanning. Based on this model data, hydrological analysis tools in geographic information system software can be utilized to simulate rainfall runoff processes, thereby determining the catchment paths of water flow within each sub-region and calculating the potential waterlogging depth that can be formed under different rainfall intensities and durations. For example, low-lying points, depressions, and major channels of water flow convergence within the region can be identified, and the maximum water depth that these areas can reach during extreme rainfall events can be quantified to assess the degree of low-lying flood vulnerability. Next, detailed drainage pipe network distribution data can be acquired, including the topological structure, pipe diameter, material, slope, and location and design parameters of facilities such as pumping stations and rainwater inlets. Using these data, professional urban drainage model software such as SWMM can be run to simulate and evaluate the carrying capacity of the pipe network for each sub-region and analyze the connectivity of the drainage path. For example, the full-pipe operation of the pipe network, overflow points, and the transmission efficiency of water flow in the pipe network under specific rainfall scenarios can be simulated to identify potential drainage capacity bottlenecks in the pipe network, such as blockages, backflow, or insufficient capacity. At the same time, location data of key infrastructure, such as hospitals, schools, transportation hubs, power substations, and communication base stations, can be acquired. The location information of these key infrastructure is spatially overlaid and analyzed with the potential waterlogging areas obtained through terrain analysis. For example, the distance from each key infrastructure to the nearest potential waterlogging area can be calculated, or it can be determined whether it is located in or adjacent to an area with high potential waterlogging depth. Through this geographic relationship analysis, the exposure risk that each key facility may face in a flood event can be quantified. Finally, the degree of low-lying flood vulnerability obtained through terrain analysis, the drainage capacity bottlenecks obtained through drainage pipe network analysis, and the exposure risk of key facilities obtained through key infrastructure analysis are integrated. For example, different weights can be assigned to these three dimensions, or a multi-index comprehensive evaluation model can be used to integrate them into a unified flood vulnerability index or a set of multi-dimensional vulnerability feature vectors as the flood vulnerability characteristics of the sub-region. In this way, the vulnerability of each sub-region is no longer a single numerical value, but a comprehensive description containing terrain, drainage, and facility risks.

[0088] Preferably, step A204 can include:

[0089] According to the rainfall event characteristic parameters and the flood vulnerability characteristics of the sub-region, a set of weight adjustment rules applicable to the sub-region in the current rainfall scenario is screened from the pre-set weight adjustment rules;

[0090] In the set of weight adjustment rules, a rule group with conflicting weight adjustment suggestions for the same flood resilience evaluation index is identified;

[0091] When there is a conflict rule group, the conflict rule group is prioritized, and the priority of the rule adjustment suggestion is adjusted according to the priority of the rule adjustment suggestion, and the priority of the rule adjustment suggestion is corrected, and the weight adjustment suggestion after conflict resolution is obtained;

[0092] When there is no conflict rule group, the rule adjustment suggestion in the weight adjustment rule set is taken as the weight adjustment suggestion after conflict resolution;

[0093] According to the weight adjustment suggestion after conflict resolution, on the basis of the preset reference internal flooding resilience evaluation index weight, the corresponding internal flooding resilience evaluation index weight of the sub-region is generated.

[0094] Among them, the weight adjustment rule set refers to a set of rules suitable for the current evaluation scenario selected from the preset rule library according to the characteristics of the specific rainfall event and the characteristics of the sub-region internal flooding vulnerability. Each rule can contain trigger conditions, target evaluation indicators, and corresponding weight adjustment direction and amplitude (target evaluation indicators and corresponding weight adjustment direction and amplitude constitute weight adjustment suggestions). It can be stored and managed in the form of structured data table, rule engine configuration or expert knowledge base, etc.

[0095] Among them, the conflict rule group refers to the case where two or more rules give contradictory or inconsistent weight adjustment suggestions for the same internal flooding resilience evaluation indicator in the weight adjustment rule set. For example, one rule suggests increasing the weight of a certain indicator, while another rule suggests reducing or keeping it unchanged, or the adjustment amplitude is different.

[0096] Among them, the priority sorting refers to determining the importance or execution order of each rule in the identified conflict rule group according to the preset strategy or standard. It can be achieved by using a preset priority list based on expert experience, a rule validity score based on data verification, or a dynamic evaluation based on the applicability of the rule in a specific scenario.

[0097] Among them, the correction of the rule adjustment suggestion with low priority refers to adjusting or covering the rule adjustment suggestion with low priority according to the rule adjustment suggestion with high priority, so as to eliminate the conflict and ensure the consistency of the best weight adjustment suggestion. It can be achieved by using complete coverage, weighted average, incremental adjustment or conditional adjustment logic.

[0098] Among them, the reference internal flooding resilience evaluation index weight refers to the preset internal flooding resilience evaluation index weight as the initial reference when not considering the characteristics of the specific rainfall event and the sub-region vulnerability. It can be determined by using expert consensus, historical data statistical average or default value of general evaluation model, etc.

[0099] The scheme ensures that the generation process of the flood resilience evaluation index weight is both dynamically adapted to specific scenarios and effectively solves rule conflicts, thereby improving the accuracy and guidance of the evaluation results. First, the system receives rainfall event characteristic parameters obtained from rainfall warning information analysis and sub-regional flood vulnerability characteristics identified through city spatial heterogeneity data analysis. These information is the basis for dynamic weight adjustment, which enables subsequent weight adjustment to accurately focus on the specific rainfall risk and regional weak link currently facing. Based on these scenario information, the system selects the relevant rule set from the pre-set weight adjustment rule library according to the logic. This screening process avoids irrelevant rule interference and ensures the relevance of the adjustment.

[0100] Further, within the selected weight adjustment rule set, the system actively identifies whether there is a conflict in the weight adjustment suggestions for the same flood resilience evaluation index. This conflict identification mechanism is an important step in solving the core problem, as it ensures that potential inconsistencies can be clearly identified before weight adjustment. Once a conflict is identified, the system immediately prioritizes the conflicting rule set. This prioritization mechanism allows the system to determine which rules should be prioritized based on pre-set importance or credibility standards. Subsequently, the system adjusts the lower-priority rule adjustment suggestions based on the higher-priority rule adjustment suggestions. This adjustment process ensures that the final weight adjustment suggestion is logically consistent and has a basis, thereby eliminating unreasonable weight or evaluation bias caused by rule conflicts. If there are no conflicts in the selected rule set, the system directly uses these rule adjustment suggestions as the final adjustment basis, simplifying the processing process and ensuring comprehensiveness.

[0101] Finally, the conflict-resolved weight adjustment suggestions are applied to the pre-set baseline flood resilience evaluation index weight. The baseline weight provides the initial importance of the evaluation index, while the adjustment suggestion refines it based on the current situation. In this way, a set of targeted flood resilience evaluation index weights is generated for each sub-region under specific rainfall scenarios. This dynamic, conflict-resolved weight generation mechanism, combined with the pre-step of obtaining rainfall event characteristic parameters and identifying sub-regional flood vulnerability characteristics, enables subsequent flood resilience evaluation to more accurately reflect the city's weak links in the face of specific rainfall events. This not only improves the reliability of the evaluation 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 of inaccurate emergency resource scheduling caused by unreasonable weight or evaluation bias in existing technologies.

[0102] In a specific implementation, the generation of the flood resilience evaluation indicator weight can be conducted as follows. First, the system can filter out a set of weight adjustment rules applicable to the current scenario from a rule base containing a large number of pre-set weight adjustment rules, according to the current obtained rainfall event characteristic parameters, such as rainfall intensity parameter and rainfall duration parameter, and the identified sub-regional flood vulnerability characteristics, such as low-lying flood-prone degree and drainage capacity bottleneck, by matching the triggering conditions of the rules. For example, if the rainfall intensity parameter indicates “heavy rainstorm” and the low-lying flood-prone degree of the sub-region is “high”, the system can filter out all the weight adjustment rules related to “heavy rainstorm” and “high low-lying flood-prone”.

[0103] Subsequently, the system checks, for each flood resilience evaluation indicator in the filtered set of weight adjustment rules, whether there are multiple rules whose weight adjustment suggestions conflict. For example, for the “drainage pipe network carrying capacity” indicator, one rule may suggest increasing its weight by 10%, while another rule may suggest reducing its weight by 5%. When such conflicts are identified, the system will start a priority sorting mechanism. The priority can be pre-set, for example, based on the source of the rule (such as “national standard rule” higher than “local experience rule”), or based on the verification degree of the rule (such as “model-verified rule” higher than “expert experience rule”). The system will adjust the lower-priority rule adjustment suggestion according to the higher-priority rule adjustment suggestion. For example, if the “national standard rule” suggests an increase of 10% and the “local experience rule” suggests a decrease of 5%, the system can adopt the suggestion of the “national standard rule” or adjust in a weighted average manner, but give the “national standard rule” a higher weight. If there are no conflicts in the filtered rule set, the system directly takes all the rule adjustment suggestions as the final adjustment basis.

[0104] Finally, the system applies these conflict-resolved weight adjustment suggestions to the pre-set baseline flood resilience evaluation indicator weights. These baseline weights can be a set of initial weight values determined in general scenarios, such as the default importance of each indicator determined through historical data analysis or expert meetings. The system adjusts the baseline weights incrementally or covers them according to the adjustment suggestions, thereby generating a set of customized flood resilience evaluation indicator weights for each sub-region in the current rainfall scenario. For example, if the weight of “drainage pipe network carrying capacity” in the baseline weight is 0.2, and the conflict-resolved suggestion is to increase by 10%, the final weight of the indicator will be adjusted to 0.22.

[0105] In some embodiments, step A3 comprises:

[0106] A301. Obtain real-time operation state data of the urban drainage system in the target area; the real-time operation state data includes pipe network water level data, pump station load data, and storage space liquid level data;

[0107] A302. According to the real-time operation state data, combined with the urban spatial heterogeneity data, generate real-time pressure-bearing capacity parameters of the urban drainage system in the target area;

[0108] A303. According to the real-time pressure-bearing capacity parameters and the weight of the waterlogging resilience evaluation index, calculate the waterlogging resilience of the target area, and obtain the waterlogging resilience evaluation result.

[0109] Among them, the real-time operation state data refers to the dynamic information of the actual operation of the urban drainage system at a specific time point or time period, which can be obtained by sensor network, Internet of Things device, SCADA system (Supervisory Control And Data Acquisition System) or artificial patrol record. Pipe network water level data refers to the real-time height or fullness information of the water body inside the urban underground drainage pipe network, which can be monitored and collected in real time by ultrasonic water level meter, pressure sensor or float type water level meter. Pump station load data refers to the operation state and working intensity information of the urban drainage pump station at a specific time, which can be obtained by current sensor, power meter or pump station control system log, reflecting the utilization of pump station pumping capacity. The storage space liquid level data refers to the real-time height information of the water body inside the urban storage facilities (such as storage tanks, sunken green spaces, rainwater gardens, etc.) for temporary storage of rainwater, which can be monitored by liquid level sensor, radar water level meter or video monitoring combined with image recognition.

[0110] Among them, the real-time pressure-bearing capacity parameter refers to the actual carrying and assimilation capacity of the urban drainage system in the current real-time operation state to deal with rainfall runoff, which can be obtained by combining real-time operation data and spatial heterogeneity data through methods such as hydraulic model simulation, data-driven model prediction or expert experience evaluation.

[0111] In the execution of the waterlogging resilience assessment, first, the real-time operation state data of the urban drainage system in the target area is obtained. These data include pipe network water level data, pump station load data, and storage space liquid level data, which directly reflect the immediate response and carrying capacity of the drainage system at the current time, making up for the shortcomings of relying solely on static data for assessment. Subsequently, according to these real-time operation state data, combined with urban spatial heterogeneity data, the real-time bearing capacity parameters of the urban drainage system in the target area are generated. This combination integrates dynamic system operation information with inherent regional geographic, pipe network, and infrastructure characteristics, providing a more comprehensive and accurate depiction of the actual carrying capacity and potential weak links of the drainage system in different regions and at different times. Finally, based on these real-time bearing capacity parameters and the previously dynamically generated waterlogging resilience assessment index weights according to the characteristics of rainfall events and sub-regional vulnerability, the waterlogging resilience of the target area is calculated, and the waterlogging resilience assessment result is obtained. By introducing real-time operation state data of the urban drainage system and combining it with urban spatial heterogeneity data and dynamically adjusted assessment index weights, this scheme can overcome the problem of deviation between the assessment results and the actual situation in traditional assessment methods. This integrated processing flow ensures that the waterlogging resilience assessment results not only reflect the current real operation status of the urban drainage system, but also highlight the importance of different assessment indicators under 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. The combination of real-time dynamics and regional and scenario-based weights enables the assessment system to have a more fine-grained and dynamic perception of urban waterlogging risks, thereby supporting more accurate decision-making.

[0112] Preferably, step A302 can include:

[0113] According to the pipe network water level data and the drainage pipe network distribution data, the real-time residual carrying capacity of each pipe segment in the target area is calculated;

[0114] According to the pump station load data, the real-time pumping efficiency of each pump station in the target area is evaluated;

[0115] According to the storage space liquid level data and the regional terrain elevation data, the real-time available storage capacity of each storage space in the target area is determined;

[0116] The real-time residual carrying capacity, the real-time pumping efficiency, and the real-time available storage capacity are taken as the real-time bearing capacity parameters of the urban drainage system in the target area.

[0117] The real-time residual carrying capacity refers to the additional drainage flow or volume that can be carried by each pipe section of the drainage pipe network under the current pipe network water level condition without overflowing or full pipe flow. It can be calculated by subtracting the current actual flow capacity from the designed maximum flow capacity of the pipe section, or by establishing a hydraulic model to simulate the residual transport potential under the current water level.

[0118] The real-time pumping efficiency refers to the ratio of the actual pumping flow to the designed maximum pumping flow of the pump station under the current operating load, reflecting the actual working performance of the pump station. It can be obtained by monitoring the inlet and outlet water flow, motor power consumption, and rotating speed of the pump station, and combining with the designed performance curve of the pump station for real-time calculation.

[0119] The real-time available storage capacity refers to the additional water volume that can be accommodated by the storage space up to the designed overflow elevation or maximum water storage elevation under the current water level condition. It can be obtained by looking up or interpolating the volume-water level curve of the storage space combined with the current real-time water level data.

[0120] The present scheme decomposes the overall pressure-bearing capacity of the urban drainage system into real-time performance evaluation of three core components: pipe network, pump station, and storage space, and comprehensively evaluates these results to provide a more comprehensive and accurate real-time pressure-bearing state of the system. Specifically, after obtaining the real-time operating state data of the urban drainage system in the target area, including pipe network water level data, pump station load data, and storage space water level data, first, the real-time residual carrying capacity of each pipe section in the target area is calculated using the pipe network water level data and drainage pipe network distribution data, which solves the problem that water level data alone cannot directly reflect the overall transport capacity of the pipe network, making the evaluation of the real-time pressure-bearing capacity of the pipe network system more specific and accurate. Second, the real-time pumping efficiency of each pump station in the target area is evaluated using pump station load data and key infrastructure location data, which can more accurately reflect the real-time efficiency of pump stations as key nodes of drainage. Third, the real-time available storage capacity of each storage space in the target area is determined using storage space water level data and regional terrain elevation data, which solves the problem that liquid level data alone cannot intuitively determine the remaining storage potential of the storage space, making the evaluation 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 (pipe network, pump station, and storage space) are combined to form a real-time pressure-bearing capacity parameter, forming a comprehensive and multi-dimensional real-time pressure-bearing capacity parameter. This detailed decomposition and comprehensive method enables the generated pressure-bearing capacity parameter to comprehensively and accurately reflect the overall resilience of the urban drainage system when facing rainfall events. As an input for subsequent waterlogging resilience calculation, this real-time pressure-bearing capacity parameter can significantly improve the accuracy and practicality of the evaluation, providing a solid data foundation for waterlogging resilience assessment.

[0121] Preferably, step 303 can include:

[0122] According to the real-time pressure capacity parameter, the drainage system response capacity, the storage and retention capacity, and the key facility protection capacity of each sub-region in the target region are quantified;

[0123] According to the weight of the waterlogging resilience evaluation index, the drainage system response capacity, the storage and retention capacity, and the key facility protection capacity are comprehensively evaluated to obtain the waterlogging resilience score of each sub-region in the target region;

[0124] The waterlogging resilience scores of the sub-regions are summarized, and the waterlogging resilience evaluation results containing weak link identification information are generated in combination with the waterlogging vulnerability characteristics of the sub-regions.

[0125] The drainage system response capacity refers to the immediate processing efficiency of drainage facilities in the sub-region to rainfall runoff. It can be quantified according to the real-time residual carrying capacity of each pipe section in the sub-region and the real-time pumping efficiency of each pump station in the sub-region. For example, the total real-time residual carrying capacity of all pipe sections and the total real-time pumping efficiency of all pump stations in the sub-region are calculated, and the total real-time residual carrying capacity and the total real-time pumping efficiency are normalized, and then the planning results are weighted and operated to obtain the drainage system response capacity.

[0126] The storage and retention capacity refers to the storage and retention potential of waterlogged water bodies in the sub-region. It can be quantified according to the real-time available storage capacity of the storage and retention space in the sub-region. For example, the total real-time available storage capacity of all storage and retention spaces in the sub-region is calculated, and the total real-time available storage capacity is planning processed, and the normalized total real-time available storage capacity is taken as the storage and retention capacity.

[0127] The key facility protection capacity refers to the safety degree of important infrastructure under the threat of waterlogging. It can be combined with the real-time pressure capacity parameter and the key infrastructure location data to evaluate the degree of protection of the key facility from waterlogging under the current pressure state, so as to quantify the key facility protection capacity. For example, the pressure capacity coefficient of the sub-region can be obtained by looking up the table according to the total real-time pressure capacity parameters of the sub-region (the total real-time pressure capacity parameter refers to the sum of the same kind of real-time pressure capacity parameter, for example, the sum of the real-time pumping efficiency of all pump stations in the sub-region is the total real-time pumping efficiency), and then the type and quantity of the key infrastructure in the sub-region are determined according to the key infrastructure location data, and the initial key facility protection capacity is calculated by the following formula: , wherein is the initial key facility protection capacity, is the pressure capacity coefficient, n is the number of types of key infrastructure in the sub-region, is the number of the i-th type of key infrastructure, preset influence weight of the ith kind of critical infrastructure; and finally, normalizing the initial critical infrastructure protection capability to obtain the final critical infrastructure protection capability.

[0128] The waterlogging resilience score of the target region is calculated by the following formula: wherein, the waterlogging resilience score of the target region is a value obtained by quantitatively evaluating the waterlogging resilience level of each sub-region in the target region, which can be a comprehensive score obtained by weighting and synthesizing various capability indicators. Here, the waterlogging resilience evaluation indicators include the drainage system response capability, the storage and retention capability, and the critical infrastructure protection capability, so the waterlogging resilience evaluation indicator weights include the weights of the three waterlogging resilience evaluation indicators. When calculating the waterlogging resilience score of a sub-region, the quantitatively evaluated drainage system response capability, storage and retention capability, and critical infrastructure protection capability can be multiplied by the corresponding waterlogging resilience evaluation indicator weight, and then the products are summed to obtain the waterlogging resilience score of the sub-region.

[0129] The waterlogging resilience evaluation result containing the weak link identification information can be generated by the following method: sorting or visualizing the waterlogging resilience scores of the sub-regions, and simultaneously superimposing or correlating the vulnerability characteristics of each sub-region, such as the low-lying waterlogging-prone degree, the drainage capacity bottleneck, and the critical infrastructure exposure risk. For example, if the waterlogging resilience score of a sub-region is low (e.g., lower than a preset score threshold), and its vulnerability characteristics show that there is a drainage capacity bottleneck, the evaluation result will clearly indicate that the weak link of this region is the insufficient carrying capacity of the drainage pipe network. This combination is the core of the present scheme, which not only gives a resilience score, but more importantly, by correlating the resilience performance with the vulnerability source, an evaluation result containing weak link identification information can be directly generated. This means that the evaluation report will clearly indicate which areas and aspects are deficient.

[0130] Through the above technical solutions, the present application can reveal the specific factors that cause the waterlogging resilience score, clearly indicate which areas or aspects within the city are the real weak links, so that the evaluation result is no longer limited in guiding precise disaster prevention and mitigation measures and emergency resource scheduling, and can effectively identify and solve the most vulnerable problems of the city under a specific rainfall scenario.

[0131] Reference Figure 2 The present application provides a kind of waterlogging resilience evaluation device of city, which comprises:

[0132] Information acquisition module 1 is used to obtain rainfall warning information, and rainfall event characteristic parameter is obtained from the analysis thereof (specific process can refer to the step A1 of preceding text);

[0133] Weight generation module 2 is used to generate waterlogging resilience evaluation indicator weight according to the rainfall event characteristic parameter and preset weight adjustment rule (specific process can refer to the step A2 of preceding text);

[0134] The waterlogging resilience assessment execution module 3 is configured to execute waterlogging resilience assessment by using the waterlogging resilience assessment index weight (the specific process can refer to step A3 in the foregoing description).

[0135] The assessment result output module 4 is configured to output the waterlogging resilience assessment result containing the weak link identification information (the specific process can refer to step A4 in the foregoing description).

[0136] 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 the processor executes the computer program to perform the steps of the urban waterlogging resilience assessment method as described above.

[0137] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to perform the steps of the urban waterlogging resilience assessment method as described above.

[0138] Please refer to Figure 3 , Figure 3 A structural schematic diagram of an electronic device provided by the embodiments of the present application is provided, and the present application provides an electronic device, comprising a processor 301 and a memory 302, wherein the processor 301 and the memory 302 are interconnected and communicate with each other through a communication bus 303 and / or other forms of connection mechanism (not marked), the memory 302 stores a computer program executable by the processor 301, and when the electronic device is running, the processor 301 executes the computer program to execute the container packing method in any optional implementation manner of the above-mentioned embodiments, so as to realize the following functions: obtaining rainfall warning information, and parsing rainfall event characteristic parameters therefrom; generating waterlogging resilience assessment index weight according to the rainfall event characteristic parameters and a preset weight adjustment rule; executing waterlogging resilience assessment by using the waterlogging resilience assessment index weight; and outputting a waterlogging resilience assessment result containing weak link identification information.

[0139] The embodiment of the application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to execute the container loading method in any optional implementation manner of the above embodiment to realize the following functions: obtaining rainfall warning information, and obtaining rainfall event characteristic parameters by analyzing the rainfall warning information; generating a waterlogging resilience evaluation index weight according to the rainfall event characteristic parameters and a preset weight adjustment rule; performing waterlogging resilience evaluation by using the waterlogging resilience evaluation index weight; and outputting a waterlogging resilience evaluation result containing weak link identification information. The computer readable storage medium can be realized by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk.

[0140] The above merely describes the embodiments of the application, and is not intended to limit the protection scope of the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall be included in the protection scope of the application.

Claims

1. A method for urban waterlogging resilience assessment, characterized in that, The method comprises the following steps: A1. Obtain rainfall warning information, and parse rainfall event characteristic parameters therefrom; A2. Generate an index weight of waterlogging resilience assessment according to the rainfall event characteristic parameters and a preset weight adjustment rule; A3. Perform waterlogging resilience assessment by using the index weight of waterlogging resilience assessment; A4. Output a waterlogging resilience assessment result containing weak link identification information; Step A1 comprises: A101. Obtain rainfall warning information; the rainfall warning information comprises rainfall event type information, forecast rainfall intensity information, rainfall duration information, and forecast impact area range information; A102. Convert the rainfall event type information into a standardized rainfall event type parameter according to a preset rainfall event classification rule; A103. Convert the forecast rainfall intensity information and the rainfall duration information into a standardized rainfall intensity parameter and a standardized rainfall duration parameter, respectively, according to a preset quantitative conversion rule; A104. Convert the forecast impact area range information into a structured geographic area parameter according to a preset geographic information parsing rule; A105. Take the rainfall event type parameter, the rainfall intensity parameter, the rainfall duration parameter, and the geographic area parameter as the rainfall event characteristic parameters; Step A2 comprises: A201. Obtain city spatial heterogeneity data of a target area represented by the geographic area parameter; the city spatial heterogeneity data comprises regional terrain elevation data, drainage pipe network distribution data, and key infrastructure location data; A202. Divide the target area into a plurality of sub-regions according to the city spatial heterogeneity data; A203. Identify the waterlogging vulnerability characteristics of each sub-region according to the city spatial heterogeneity data; A204. Generate a corresponding waterlogging resilience assessment index weight for each sub-region according to the rainfall event characteristic parameters, the waterlogging vulnerability characteristics of the sub-region, and a preset weight adjustment rule; Step A203 comprises: According to the regional terrain elevation data, determine the catchment path and the potential waterlogging depth of each sub-region, so as to evaluate the low-lying and prone-to-flooding degree; According to the drainage pipe network distribution data, evaluate the pipe network carrying capacity and the drainage path connectivity of each sub-region, so as to determine the drainage capacity bottleneck; According to the key infrastructure location data, analyze the geographical relationship between the key infrastructure and the potential waterlogging area of each sub-region, and determine the key facility exposure risk; Take the low-lying and prone-to-flooding degree, the drainage capacity bottleneck, and the key facility exposure risk as the waterlogging vulnerability characteristics of the sub-region.

2. The urban waterlogging resilience assessment method according to claim 1, characterized in that, Step A202 comprises: According to the city spatial heterogeneity data, identify the spatial heterogeneity distribution characteristics of the target area; the spatial heterogeneity distribution characteristics comprise terrain undulation, pipe network density distribution, and key infrastructure aggregation degree; According to the rainfall event type parameter, the rainfall intensity parameter, and the rainfall duration parameter, determine the evaluation granularity requirement for this rainfall event; According to the spatial heterogeneity distribution characteristics and the evaluation granularity requirement, adaptive region division is performed on the target region to obtain a plurality of sub-regions.

3. The urban waterlogging resilience assessment method according to claim 1, characterized in that: Step A204 includes: According to the rainfall event characteristic parameters and the waterlogging vulnerability characteristics of the sub-region, a set of weight adjustment rules suitable for the sub-region in the current rainfall scenario is screened from the preset weight adjustment rules; In the set of weight adjustment rules, a rule group with conflicting weight adjustment suggestions for the same waterlogging resilience evaluation index is identified; When there are conflicting rule groups, the conflicting rule groups are prioritized, and the weight adjustment suggestions with low priority are modified according to the weight adjustment suggestions with high priority to obtain conflict-resolved weight adjustment suggestions; When there are no conflicting rule groups, the weight adjustment suggestions in the set of weight adjustment rules are taken as the conflict-resolved weight adjustment suggestions; According to the conflict-resolved weight adjustment suggestions, the corresponding waterlogging resilience evaluation index weight for the sub-region is generated based on the preset baseline waterlogging resilience evaluation index weight.

4. The urban waterlogging resilience assessment method of claim 1, wherein, Step A3 includes: A301. Obtain real-time operation state data of the urban drainage system in the target region; the real-time operation state data includes pipe network water level data, pump station load data, and storage space liquid level data; A302. According to the real-time operation state data, combined with the urban spatial heterogeneity data, generate real-time pressure-bearing capacity parameters of the urban drainage system in the target region; A303. According to the real-time pressure-bearing capacity parameters and the waterlogging resilience evaluation index weight, calculate the waterlogging resilience of the target region to obtain the waterlogging resilience evaluation result.

5. An urban waterlogging resilience assessment apparatus, characterized by, The device comprises: An information acquisition module for acquiring rainfall warning information and parsing rainfall event characteristic parameters therefrom; A weight generation module for generating waterlogging resilience evaluation index weight according to the rainfall event characteristic parameters and preset weight adjustment rules; A waterlogging resilience evaluation execution module for performing waterlogging resilience evaluation using the waterlogging resilience evaluation index weight; An evaluation result output module for outputting a waterlogging resilience evaluation result containing weak link identification information; When the information acquisition module acquires rainfall warning information and parses rainfall event characteristic parameters therefrom, it performs: A101. Acquire 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, convert the rainfall event type information into standardized rainfall event type parameters; A103. According to the preset quantitative conversion rules, convert the forecast rainfall intensity information and the rainfall duration information into standardized rainfall intensity parameters and standardized rainfall duration parameters, respectively; A104. According to the preset geographic information parsing rules, convert the forecast impact area range information into structured geographic area parameters; A105. taking the rainfall event type parameter, the rainfall intensity parameter, the rainfall duration parameter, and the geographical area parameter as the rainfall event characteristic parameters; The weight generation module generates the waterlogging resilience evaluation index weight according to the rainfall event characteristic parameters and a preset weight adjustment rule, and executes: A201. obtaining urban spatial heterogeneity data of a target area represented by the geographical area parameter; the urban spatial heterogeneity data includes regional terrain elevation data, drainage pipe network distribution data, and key infrastructure location data; A202. dividing the target area into a plurality of sub-regions according to the urban spatial heterogeneity data; A203. identifying the waterlogging vulnerability characteristics of each sub-region according to the urban spatial heterogeneity data; A204. generating a corresponding waterlogging resilience evaluation index weight for each sub-region according to the rainfall event characteristic parameters, the waterlogging vulnerability characteristics of the sub-region, and a preset weight adjustment rule; Step A203 includes: According to the regional terrain elevation data, determine the catchment path and water accumulation depth potential of each sub-region to assess the low-lying waterlogging degree; According to the drainage pipe network distribution data, assess the pipe network carrying capacity and drainage path connectivity of each sub-region to determine the drainage capacity bottleneck; According to the key infrastructure location data, analyze the geographical relationship between the key infrastructure and the potential water accumulation area of each sub-region to determine the key facility exposure risk; The low-lying waterlogging degree, the drainage capacity bottleneck, and the key facility exposure risk are taken as the waterlogging vulnerability characteristics of the sub-region.

6. An electronic device, comprising: A computer program product comprising a processor and a memory storing a computer program executable by the processor, wherein the processor executes the computer program to perform the steps of the urban waterlogging resilience evaluation method according to any one of claims 1-4.

7. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program product comprises a processor and a memory storing a computer program executable by the processor, wherein the processor executes the computer program to perform the steps of the urban waterlogging resilience evaluation method according to any one of claims 1-4.

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