Urban rainstorm waterlogging risk assessment method, device and equipment and storage medium
By acquiring multi-source data and constructing a structured evaluation index system using the analytic hierarchy process, the limitations of traditional evaluation methods are overcome, enabling accurate quantification of urban rainstorm and flood risk and providing scientific decision support.
Patent Information
- Application Number
- CN202511790008.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-01
- Publication Date
- 2026-03-17
AI Technical Summary
Traditional urban rainstorm and flood disaster risk assessments rely on a single data source and simple models, which cannot fully reflect the complex urban environment, resulting in inaccurate and untimely assessment results, making it difficult to support the scientific decision-making of urban emergency management departments.
A structured assessment index system was constructed by acquiring multi-source data and using the analytic hierarchy process (AHP). Through weighted calculation and a risk matrix model, the risk level was quantified by comprehensively considering disaster-causing factors and the vulnerability of disaster-bearing bodies.
It enables precise assessment of urban flooding risks caused by rainstorms, improves the comprehensiveness and accuracy of the assessment, and provides a scientific basis for urban flood control and disaster reduction.
Smart Images
Figure CN121684618A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of risk assessment, specifically relating to a method, apparatus, equipment, and storage medium for assessing urban rainstorm and flooding risks. Background Technology
[0002] Floods, as a natural disaster, cause severe damage to human life, agricultural production, and the ecological environment. In recent years, with the increase in global climate change and extreme weather events, urban flooding has become more frequent and its destructive power has continued to intensify. Therefore, timely and accurate monitoring of severely flooded urban areas and accurate assessment of urban flooding risks during rainstorms are of great significance for flood prevention and disaster reduction, disaster emergency response, and post-disaster reconstruction.
[0003] Traditional urban stormwater flooding risk assessments primarily rely on single data sources or simple assessment models. For example, they often depend solely on rainfall data provided by meteorological departments, combined with simple empirical formulas to determine disaster risk. While these methods have played a role in the past, their limitations have become increasingly apparent with the expansion of urban scale and the increasing complexity of the environment. Urban development has led to more complex population and infrastructure distributions, making it impossible for a single data source to comprehensively reflect the risk status of urban stormwater flooding. Furthermore, simple assessment models struggle to comprehensively consider multiple factors, including disaster-causing factors, the disaster-prone environment, disaster-bearing structures, and disaster prevention and mitigation capabilities. This results in inaccurate and untimely assessments, failing to provide scientific and effective decision-making support for urban emergency management departments and failing to meet the actual needs of cities in responding to stormwater flooding disasters. Summary of the Invention
[0004] To address the aforementioned problems, this invention provides a method, apparatus, equipment, and storage medium for assessing urban stormwater flooding risks.
[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for assessing urban flooding risk during rainstorms, the method comprising: Acquire multi-source data from different areas of the target city, including rainfall data, drainage network data, topographic elevation data, population distribution data, and regional economic data; Based on the analytic hierarchy process, two risk factors, namely disaster-causing factors and vulnerability of disaster-bearing bodies, are used as criteria to screen and evaluate indicators in order to construct a structured evaluation indicator system and assign weights to each evaluation indicator in the evaluation indicator system. Using the structured evaluation index system and its weights, the multi-source data of each region are weighted and calculated to obtain the comprehensive evaluation value of each region in terms of the probability of occurrence and the degree of impact of rainstorm flooding risk. The comprehensive evaluation values of each region in terms of probability of occurrence and degree of impact are input into a predefined risk matrix model, and the quantitative risk level of each region is obtained through matrix mapping.
[0006] Optionally, after acquiring the multi-source data, the multi-source data is further preprocessed, including: The range normalization algorithm is used to map the original values of the multi-source data to the [0,1] interval to obtain standardized index data.
[0007] Optionally, constructing a structured evaluation index system includes: A three-level system consisting of an objective layer, a criterion layer, and an indicator layer is constructed based on the analytic hierarchy process. The criteria layer includes disaster-causing factors and the vulnerability of disaster-bearing bodies; The indicators under the disaster-causing factor criterion layer include rainfall intensity, drainage capacity, and topographic elevation; The indicators under the vulnerability criteria layer for disaster-bearing bodies include population density and GDP density.
[0008] Optionally, assigning weights to each indicator in the evaluation indicator system includes: For each indicator under the disaster-causing factor criterion layer, its weight is determined by constructing a judgment matrix and performing a consistency test. For each indicator under the vulnerability criterion layer of the disaster-bearing body, a variety of preset weight configuration schemes are provided, including economic priority, balanced, or population priority, which can be selected according to the characteristics of the assessment area.
[0009] Optionally, the step of inputting the comprehensive evaluation value of each region into a predefined risk matrix model, and calculating the quantitative risk level result of each basic assessment unit through matrix mapping includes: Define a 5×5 risk matrix; The comprehensive evaluation value of the probability of occurrence dimension is divided into five levels: extremely low, low, medium, high, and extremely high. The comprehensive evaluation value of the aforementioned impact level dimension is divided into five levels: minor, small, moderate, severe, and catastrophic. The comprehensive evaluation value of each region is mapped to the corresponding position in the matrix to determine its final risk level.
[0010] Optionally, after quantifying the risk level results, the method further includes: The quantitative risk level results are spatially rendered and graphically displayed in an interactive visualization interface, and multi-layer switching and risk composition analysis functions are provided.
[0011] Optionally, the spatial rendering and graphical display include: Based on geographic information systems, a gradient color scheme from light green to red is used to render areas with different risk levels on an electronic map, generating risk zoning maps. Provide pie charts or stacked bar charts to dynamically display the contribution ratio of each risk factor to the total risk; Obtain the area selected by the user, generate and display a time-series trend chart of the evaluation indicators within that area.
[0012] An urban stormwater flooding risk assessment device, the device comprising: The acquisition module is used to acquire multi-source data from different areas of the target city, including rainfall data, drainage network data, topographic elevation data, population distribution data, and regional economic data. A module is constructed to use the Analytic Hierarchy Process (AHP) to select evaluation indicators as criteria layer based on two risk factors: disaster-causing factors and vulnerability of disaster-bearing bodies, in order to construct a structured evaluation indicator system and assign weights to each evaluation indicator in the evaluation indicator system. The calculation module is used to perform weighted calculations on the multi-source data of each region using the structured evaluation index system and its weights, to obtain a comprehensive evaluation value for each region in terms of the probability of occurrence and the degree of impact of rainstorm flooding risk. The assessment module is used to input the comprehensive evaluation values of each region in terms of probability of occurrence and degree of impact into a predefined risk matrix model, and obtain the quantitative risk level result of each region through matrix mapping.
[0013] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for assessing urban stormwater flooding risk.
[0014] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the aforementioned method for assessing urban flooding risk during rainstorms.
[0015] The urban rainstorm flood risk assessment method provided by this invention has the following beneficial effects: This invention acquires multi-source data from different areas of a target city, such as rainfall, drainage networks, topography, population distribution, and regional economic data, to comprehensively capture various information affecting urban rainstorm flooding risks from multiple dimensions, effectively overcoming the limitations of single data sources. Based on the analytic hierarchy process (AHP), a structured assessment index system is constructed and weights are assigned, systematically integrating multiple factors such as disaster-causing factors and the vulnerability of disaster-bearing bodies. This ensures that the assessment model comprehensively considers multiple key aspects, including disaster-causing factors, the disaster-prone environment, disaster-bearing bodies, and disaster prevention and mitigation capabilities. By weighting the multi-source data, a comprehensive evaluation value for the probability and impact of rainstorm flooding risks in each region is obtained, enabling more accurate quantification of the risk characteristics of different regions. Finally, these two comprehensive evaluation values are input into a risk matrix model to obtain the quantitative risk level results for each region, providing a reliable basis for refined assessment and scientific decision-making regarding urban rainstorm flooding risks, significantly improving the comprehensiveness and accuracy of risk assessment. Attached Figure Description
[0016] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 This is a flowchart illustrating an urban rainstorm flood risk assessment method provided by the present invention according to an exemplary embodiment.
[0018] Figure 2 This is a modular block diagram of an urban rainstorm flood risk assessment method provided by the present invention according to an exemplary embodiment.
[0019] Figure 3 This is a schematic diagram illustrating the execution process of an urban rainstorm flood risk assessment method provided by the present invention according to an exemplary embodiment.
[0020] Figure 4 This is a block diagram of an urban rainstorm flood risk assessment device provided by the present invention according to an exemplary embodiment. Detailed Implementation
[0021] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.
[0022] This invention provides an urban rainstorm flood risk early warning assessment and visualization tool. Through a technical chain of "multi-dimensional assessment model construction - multi-source data collaborative processing - interactive visualization presentation", it achieves accurate quantification, dynamic updating and intuitive display of flood risk.
[0023] The technical solutions provided by the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0024] First, this invention provides a method for assessing urban flooding risk during rainstorms, specifically as follows: Figure 1 As shown, it includes the following steps: S101. Obtain multi-source data from different areas of the target city.
[0025] The multi-source data includes rainfall data, drainage network data, topographic elevation data, population distribution data, and regional economic data. After acquiring the multi-source data, a range normalization algorithm is used to map the original values of the multi-source data to the [0,1] interval to obtain standardized index data.
[0026] Furthermore, to further implement the technical solution of this invention, this invention also provides a modular structure for implementing the method proposed in this invention. For example... Figure 2 As shown, the modular structure comprises three core components: a multi-dimensional risk assessment module, a multi-source data processing module, and a visualization module. Each module works collaboratively through standardized interfaces to form a complete technical loop from data input to decision output.
[0027] This step corresponds to the data processing module, which enables standardized access and efficient management of multi-type and multi-source data, providing high-quality data support for risk assessment. Specific functions include: data import, data processing, and data storage.
[0028] ① Data import.
[0029] Supports batch import of various file formats (such as CSV, Excel, Shapefile, tif), automatically parsing spatial and attribute data; connects to databases and API interfaces to achieve real-time dynamic data access, ensuring data source updates are synchronized. Supports single file upload and batch import; provides a visual upload interface, supporting drag-and-drop upload and format validation; automatically parses data metadata (such as coordinate system, field structure) and generates previews.
[0030] ② Data processing.
[0031] Data cleaning: Automatically identify and process missing values, outliers, and duplicate records; perform geometric validity checks and repairs on spatial data (such as closed polygons and invalid coordinates).
[0032] Data transformation: Unify the coordinate system to ensure consistency of spatial data overlay; unify the units for non-spatial attribute data.
[0033] Data standardization: The range method is used to normalize each indicator and map it to the [0,1] interval to eliminate the influence of dimensions.
[0034] ③ Data storage.
[0035] Database design: SQLite is used as a lightweight database, which supports expansion to PostgreSQL / MySQL; a multi-table structure is designed, including spatial_data, attribute_data, user_config, etc.
[0036] Data management functions: Supports searching, updating, and deleting datasets; provides data version management and supports historical data backtracking.
[0037] S102. Based on the analytic hierarchy process, two risk factors, namely disaster-causing factors and vulnerability of disaster-bearing bodies, are used as criteria to screen evaluation indicators to construct a structured evaluation indicator system. Weights are assigned to each evaluation indicator in the evaluation indicator system. Using the structured evaluation indicator system and its weights, the multi-source data of each region are weighted and calculated to obtain the comprehensive evaluation value of each region in terms of the probability of occurrence and the degree of impact of rainstorm and urban flooding risk.
[0038] In this step, a three-tiered system is constructed based on the Analytic Hierarchy Process (AHP), comprising a target layer, a criterion layer, and an indicator layer. The criterion layer includes disaster-causing factors and the vulnerability of disaster-bearing bodies. The indicators under the disaster-causing factor criterion layer include rainfall intensity, drainage capacity, and topographic elevation. The indicators under the disaster-bearing body vulnerability criterion layer include population density and GDP density. For each indicator under the disaster-causing factor criterion layer, a judgment matrix is constructed and a consistency test is performed to determine its weight. For each indicator under the disaster-bearing body vulnerability criterion layer, multiple preset weight configuration schemes are provided, including economic priority, balanced, or population priority schemes, selected according to the characteristics of the assessment area.
[0039] S103. Input the comprehensive evaluation value of each region into a predefined risk matrix model, and calculate the quantitative risk level result of each basic assessment unit through matrix mapping.
[0040] In this step, a 5×5 risk matrix is defined; the comprehensive evaluation value of the probability of occurrence dimension is divided into five levels: extremely low, low, medium, high, and extremely high; the comprehensive evaluation value of the impact dimension is divided into five levels: slight, minor, medium, severe, and catastrophic; the comprehensive evaluation value of each region is mapped to the corresponding position in the matrix to determine its final risk level.
[0041] Based on the above description, this step provides a risk assessment module, which is the core of the tool. By integrating the Analytic Hierarchy Process (AHP) and a 5×5 risk matrix, it constructs a two-dimensional assessment model of "probability of occurrence - degree of impact" to achieve quantitative classification of risk.
[0042] ①Indicator system construction: From the perspective of "flood-causing factors - vulnerability of disaster-bearing bodies", five core assessment indicators were selected to form a three-level system of "target layer - criterion layer - indicator layer".
[0043] ② Weight Calculation: Construct a judgment matrix. Through expert scoring, compare the importance of the three indicators (rainfall, drainage, and topography) within the probability of occurrence criterion layer pairwise to determine the judgment matrix, and calculate the largest eigenvalue λ of the judgment matrix. max The consistency index CI, combined with the random consistency index RI, yields the consistency ratio CR, which verifies the rationality of the weight allocation. The weights of the two indicators (population and GDP) within the influence degree criterion layer can be selected from three preset schemes: "economic priority", "balanced", and "population priority" according to regional needs, and the weight allocation of population and GDP is based on experience.
[0044] ③ Risk level calculation: Perform risk matrix mapping on the standardized data. Based on the numerical range of P and I, correspond to 5 levels of occurrence probability (extremely low, low, medium, high, extremely high) and 5 levels of impact (slight, minor, medium, severe, catastrophic). Determine the final risk level through a 5×5 risk matrix.
[0045] In this way, by integrating a multi-dimensional indicator system with the AHP-risk matrix model, the five core factors of rainfall, drainage, topography, population, and economy are comprehensively considered, avoiding the limitations of single-factor assessment. Moreover, through flexible weight configuration and multi-source data adaptation capabilities, it can meet the needs of cities with different climates and topography: for plain-type urban flooding, the weight of "drainage pipe density" can be increased to focus on assessing the risk of urban flooding caused by pipe network siltation; for coastal typhoon-induced urban flooding, the weight of "rainfall" can be adjusted to adapt to the short-duration heavy rainfall characteristics brought by typhoons.
[0046] After obtaining the risk level results for each region, to make the assessment results more intuitive, this invention also provides a visualization module for spatial rendering and charting the quantified risk level results in an interactive visualization interface. It provides multi-layer switching and risk composition analysis functions, and can also use a gradient color scheme from light green to red, based on a geographic information system, to render regions with different risk levels on an electronic map, generating a risk zoning map. It provides pie charts or stacked bar charts to dynamically display the contribution ratio of each risk factor to the total risk; and it obtains the user-selected area and generates and displays a time-series trend chart of the assessment indicators within that area.
[0047] For example, this module transforms risk assessment results into intuitive spatial information, supports multi-dimensional interactive operations, and enables spatial visualization of risk.
[0048] ① Map display function: Integrates multiple online map services such as Gaode Map as the base map; supports basic interactive operations such as base map switching, zooming, panning, and resetting.
[0049] ② Risk level visualization: Based on the output of the risk matrix, the region is divided into five risk levels: "extremely low, low, medium, high and extremely high". Using the principles of color psychology, a gradient color scheme from light green (extremely low risk) to red (extremely high risk) is used for rendering.
[0050] ③ Multi-layer switching: Supports switching between multiple layers such as risk zoning map, land use type map, and administrative division map.
[0051] ④ Risk composition analysis: Provide pie charts or stacked bar charts to show the contribution ratio of each risk factor to the total risk.
[0052] ⑤ Chart Analysis: Through line charts, bar charts, and other charts, the annual changing trends of key indicators such as regional rainfall, drainage pipe density, and population density are displayed intuitively.
[0053] Thus, this invention adopts a spatialized, interactive, and visual design, breaking through the limitations of traditional static table output: the risk zoning map intuitively presents spatial distribution differences through color grading, allowing decision-makers to quickly locate key prevention and control areas; it supports switching between multiple layers, allowing users to view the land use types of risk areas, and assisting in the formulation of targeted prevention and control plans.
[0054] Based on the above steps, the present invention also provides an embodiment. Taking a risk assessment of urban flooding in a certain province (Pingyuan area) as an example, the execution process is as follows: Figure 3 As shown, the specific steps are as follows:
[0055] 1. Implementation preparation.
[0056] ① Data preparation (sourced from a provincial statistical yearbook): Rainfall data: Annual precipitation in 18 prefecture-level cities of a certain province in 2023.
[0057] Drainage data: The length of drainage pipes in each city, combined with the area of the urban built-up area, is used to calculate the density of drainage pipes.
[0058] Topographic data: Average elevation values for each city (sourced from Geospatial Data Cloud DEM data).
[0059] Socioeconomic data: total population and GDP of each city, combined with the built-up area to calculate population density and GDP density.
[0060] ② Equipment and Environment: Hardware: CPU Intel Core i7-12700H, RAM 16GB, Hard Drive 512GB SSD.
[0061] Software: Operating system Windows 10, browser Chrome 120.0, tool developed based on Flask 2.2.5 framework, dependent on libraries such as GDAL 3.4.3, GeoPandas, Leaflet.js, etc.
[0062] 2. Implementation steps.
[0063] ① Data import and processing: Log in to the tool, go to the "Data Management - Data Import" interface, and upload the above-mentioned rainfall and socio-economic data in Excel format, administrative division vector data in Shapefile format, and DEM data in GeoTIFF format.
[0064] Complete data cleaning, projection transformation, and standardization.
[0065] ② Risk Calculation: Select the risk assessment module, click "Provincial Risk Management", and import the data.
[0066] Start the calculation and check the results.
[0067] ③Result visualization: Enter the "Visualization Module - Map Display" interface and load a risk zoning map of a certain province.
[0068] View the "Chart Analysis" interface and select the area to view.
[0069] This invention acquires multi-source data from different areas of a target city, such as rainfall, drainage networks, topography, population distribution, and regional economic data, to comprehensively capture various information affecting urban rainstorm flooding risks from multiple dimensions, effectively overcoming the limitations of single data sources. Based on the analytic hierarchy process (AHP), a structured assessment index system is constructed and weights are assigned, systematically integrating multiple factors such as disaster-causing factors and the vulnerability of disaster-bearing bodies. This ensures that the assessment model comprehensively considers multiple key aspects, including disaster-causing factors, the disaster-prone environment, disaster-bearing bodies, and disaster prevention and mitigation capabilities. By weighting the multi-source data, a comprehensive evaluation value for the probability and impact of rainstorm flooding risks in each region is obtained, enabling more accurate quantification of the risk characteristics of different regions. Finally, these comprehensive evaluation values are input into a risk matrix model to obtain the quantitative risk level results for each basic assessment unit, providing a reliable basis for refined assessment and scientific decision-making regarding urban rainstorm flooding risks, significantly improving the comprehensiveness and accuracy of risk assessment.
[0070] Secondly, the present invention also provides an urban rainstorm flood risk assessment device, such as... Figure 4 As shown, it includes: The acquisition module 201 is used to acquire multi-source data from different areas of the target city, including rainfall data, drainage network data, topographic elevation data, population distribution data, and regional economic data.
[0071] Module 202 is used to construct a structured evaluation index system by using the analytic hierarchy process (AHP) to select evaluation indicators as two risk factors: disaster-causing factors and vulnerability of disaster-bearing bodies, and to assign weights to each evaluation indicator in the evaluation index system.
[0072] The calculation module 203 is used to perform weighted calculations on the multi-source data of each region using the structured evaluation index system and its weights, so as to obtain the comprehensive evaluation value of each region in terms of the probability of occurrence and the degree of impact of rainstorm and urban flooding risk.
[0073] The assessment module 204 is used to input the comprehensive evaluation value of each region in terms of the probability of occurrence and the degree of impact into a predefined risk matrix model, and obtain the quantitative risk level result of each region through matrix mapping.
[0074] The present invention also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 The steps of the provided urban rainstorm flood risk assessment method.
[0075] This invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, memory, and non-volatile memory, and may also include other hardware required for various operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then executes it to achieve the above-mentioned functions. Figure 1 The steps of the provided urban rainstorm flood risk assessment method.
[0076] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0077] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0078] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0079] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0080] It should be noted that the specific embodiments described above enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail in this specification, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered within the protection scope of the patent of the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.
Claims
1. A method for urban stormwater flooding risk assessment, characterized in that, The method comprises: obtaining multi-source data of different regions of a target city, including rainfall data, drainage network data, terrain elevation data, population distribution data and regional economic data; based on the analytic hierarchy process, taking the disaster-causing factor and the disaster-bearing body vulnerability as the criterion layer to screen and evaluate the indexes, so as to construct a structured evaluation index system, and assigning weights to each evaluation index in the evaluation index system; using the structured evaluation index system and its weights, the multi-source data of each region is weighted and calculated to obtain the comprehensive evaluation value of each region in the occurrence probability dimension and the influence degree dimension of the rainstorm waterlogging risk; inputting the comprehensive evaluation value of each region in the occurrence probability dimension and the influence degree dimension into a predefined risk matrix model to obtain the quantitative risk level result of each region through matrix mapping.
2. The method of claim 1, wherein, After obtaining the multi-source data, the multi-source data is also preprocessed, including: using the range normalization algorithm to map the original values of the multi-source data to the [0, 1] interval to obtain standardized index data.
3. The method of claim 1, wherein, The construction of a structured evaluation index system comprises: based on the analytic hierarchy process, a three-level system including a target layer, a criterion layer and an index layer is constructed; the criterion layer includes a disaster-causing factor and a disaster-bearing body vulnerability; the indexes under the disaster-causing factor criterion layer include rainfall intensity, drainage capacity and terrain elevation; the indexes under the disaster-bearing body vulnerability criterion layer include population density and GDP density.
4. The method of claim 1, wherein, Assigning weights to each index in the evaluation index system comprises: for each index under the disaster-causing factor criterion layer, the weight is determined by constructing a judgment matrix and performing consistency check; for each index under the disaster-bearing body vulnerability criterion layer, a plurality of preset weight configuration schemes are provided, including economic priority type, balanced type or population priority type, and the selection is made according to the characteristics of the evaluation region.
5. The method of claim 1, wherein, The inputting of the comprehensive evaluation value of each region into a predefined risk matrix model through matrix mapping to calculate the quantitative risk level result of each basic evaluation unit comprises: defining a 5x5 risk matrix; dividing the comprehensive evaluation value of the occurrence probability dimension into five levels of extremely low, low, medium, high and extremely high; dividing the comprehensive evaluation value of the influence degree dimension into five levels of slight, small, medium, severe and disastrous; mapping the comprehensive evaluation value of each region to the corresponding position of the matrix to determine the final risk level.
6. The method of claim 1, wherein, After quantifying the risk level result, the method further comprises: spatial rendering and charting display of the quantitative risk level result in an interactive visualization interface, and providing multi-layer switching and risk composition analysis functions.
7. The method of claim 6, wherein, The spatial rendering and charting display comprises: using a gradient color system from light green to red based on geographic information system to render the regions of different risk levels on the electronic map to generate a risk zoning map; providing a pie chart or a stacked column chart to dynamically display the contribution proportion of each risk factor to the total risk; obtaining a user-selected region to generate and display a time series trend chart of the evaluation indexes in the region.
8. An urban rainstorm waterlogging risk assessment device, characterized in that, The device comprises: An acquisition module is configured to acquire multi-source data of different regions in a target city, including rainfall data, drainage pipe network data, terrain elevation data, population distribution data and regional economic data; An establishment module is configured to filter and evaluate indexes based on an analytic hierarchy process (AHP), taking two risk factors of a disaster-causing factor and a disaster-bearing body vulnerability as a criterion layer, to establish a structured evaluation index system, and assign weights to each evaluation index in the evaluation index system; A calculation module is configured to perform weighted calculation on the multi-source data of each region by using the structured evaluation index system and the weights, to obtain a comprehensive evaluation value of each region in a probability dimension and an influence degree dimension of a rainstorm waterlogging risk; An evaluation module is configured to input the comprehensive evaluation value of each region in the probability dimension and the influence degree dimension into a predefined risk matrix model, to obtain a quantitative risk level result of each region by matrix mapping.
9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program is executed by the processor to implement the method in any one of claims 1-7.
10. A computer device, comprising: The computer program is stored in the memory and executable on the processor, and the processor implements the method in any one of claims 1-7 when executing the program.