A multi-dimensional fuzzy flood risk identification method for urban space objects

Abstract

This invention discloses a multi-dimensional fuzzy flood risk identification method for urban spatial objects, belonging to the technical field of urban risk assessment. It includes: classifying urban spatial objects into three categories, assigning each category a unique spatial identifier with coupled coding and a flood status label; analyzing and identifying the relationships between the three categories of spatial objects to construct an object association structure; selecting key associated object pairs from the object association structure and clarifying the risk impact weight of each object in the key associated object pair; constructing a multi-dimensional risk assessment index system by combining the flood status label, the object association structure, and the selection results of key associated object pairs; and using a fuzzy comprehensive evaluation model to calculate the comprehensive risk assessment value of the key associated object pairs based on the multi-dimensional risk assessment index system and mapping it to a flood risk level. Combining the risk impact weight and object value parameters, the corresponding loss level is determined through a loss degree quantification model. This invention solves the problem of fuzzy risk identification caused by the crude classification and chaotic labeling of traditional methods.

Description

Technical Field

[0001] This invention belongs to the technical field of urban risk assessment, specifically relating to a multi-dimensional fuzzy flood risk identification method for urban spatial objects. Background Technology

[0002] As a complex artificial ecosystem, cities have significant differences in the functional attributes, spatial distribution, and relationships of various spatial objects (such as residences, hospitals, roads, pipelines, and waterways). This makes it difficult for traditional flood risk identification methods to accurately adapt to the risk assessment needs of multiple urban scenarios.

[0003] Existing urban flood risk identification technologies suffer from the following core defects: First, object classification is coarse and the labeling is chaotic. Most methods only classify spatial objects into simple categories such as buildings, roads, and water systems, without combining them with urban functional attributes for detailed classification. This makes it difficult to accurately distinguish the risk characteristics of different objects, resulting in low efficiency in subsequent data association and tracing. Second, the risk transmission effect of object relationships is ignored. Traditional methods often use a single object as the assessment unit and analyze its flood risk in isolation, leading to a large deviation between the risk assessment results and the actual disaster scenario. Third, the indicator system is simplistic and lacks dynamism. Existing indicators mostly focus on disaster-causing factors such as inundation depth and duration, without integrating key dimensions such as the inherent disaster resistance attributes of objects and the characteristics of correlation transmission. This makes it impossible to adapt to the dynamic evolution of flood disasters and the functional priority differences of different objects.

[0004] To address the aforementioned issues, there is an urgent need for a multi-dimensional risk identification method that can accurately characterize the classification features, correlation and transmission patterns, and dynamic risk evolution of urban spatial objects, in order to improve the accuracy of flood risk identification and support for emergency decision-making. Summary of the Invention

[0005] To address the technical problems existing in the background art, the present invention provides a method for multi-dimensional fuzzy flood risk identification of urban spatial objects.

[0006] This invention is achieved through the following technical solution: a multi-dimensional fuzzy flood risk identification method for urban spatial objects, comprising the following steps: Based on urban functional attributes and spatial characteristics, urban spatial objects are divided into three categories. Each category of spatial object is assigned a unique spatial identifier with coupled coding and a flood status label, thereby generating a spatial object classification database. Using GIS spatial topology analysis, the relationships between three types of spatial objects are identified, and an object relationship structure is constructed. Key related object pairs are selected from the object relationship structure, and the risk impact weight of each object in the key related object pair is determined. Collect hydraulic simulation data, basic geographic data, and socio-economic data, and combine them with flood status labels, object association structures, and key associated object pairs to construct a multi-dimensional risk assessment indicator system; Based on a multi-dimensional risk assessment index system, a fuzzy comprehensive evaluation model is used to calculate the comprehensive risk assessment value of key related object pairs and map it to flood risk level. Combining risk impact weight and object value parameters, the corresponding loss degree is determined through a loss degree quantification model.

[0007] In a further embodiment, the three types of spatial objects are: target protection objects, risk impact objects, and basic support objects.

[0008] In a further embodiment, the step of assigning the unique spatial identifier of the coupling code is as follows: Based on the base map data, the latitude and longitude coordinates and elevation information of spatial objects are extracted to form three-dimensional spatial data; an adaptive grid indexing system is constructed based on the land feature density and water system distribution characteristics of the target area. The three-dimensional spatial data, the adaptive mesh indexing system, and the category of spatial objects are coupled and encoded to generate a unique spatial identifier for each spatial object.

[0009] In a further embodiment, the process of generating the flood status label is as follows: Based on inundation element data, the inundation depth, inundation duration and flow velocity information of each spatial object are obtained, and the flood status attributes of the spatial objects are formed through normalization processing. By combining flood status attributes with inherent and associated attributes, flood status labels that combine static features with dynamic flood information are generated.

[0010] In a further embodiment, the steps for constructing the object association structure are as follows: Using the unique spatial identifier of a spatial object as the positioning benchmark, and integrating the dynamic information of inundation depth, duration, and flow velocity from the flood status label, three types of relationships are extracted between the three types of spatial objects: adjacency relationship. Includes association Related to flood transmission ; The adjacency association strength is obtained by quantifying and correcting the three types of associations using a differential model. Including correlation strength Correlation strength with flood transmission Synchronous quantization yields the adjacency association propagation efficiency. Including the efficiency of related transmission And the efficiency of flood transmission ; Identifier nodes are bound to unique spatial identifiers, object types, and flood status labels; dynamically associated edges are linked based on adjacency. Includes association Related to flood transmission The system is classified and the corresponding association strength value and transmission efficiency value are marked on the dynamically associated edges. The dynamic associated edges are iterated in real time as the flood status label is updated. Embedding object type-based association priority rules forms an integrated, dynamic object association structure.

[0011] In a further embodiment, the filtering steps for the key associated object pairs are as follows: Taking two sets of spatial objects with an association relationship in the object association structure as the computational objects, the association type of the computational objects is determined and the corresponding association strength is extracted. and transmission efficiency , , ; Based on the correlation strength and transmission efficiency And introduce object type adaptation coefficient The comprehensive evaluation value of the association is obtained by the following calculation. : ;in, These are the weighting coefficients; Given a comprehensive evaluation threshold Filter out The key related object pairs.

[0012] In a further embodiment, the construction process of the multi-dimensional risk assessment indicator system is as follows: Dynamic disaster-causing factors were identified based on hydraulic simulation data and flood status labels. The association transmission factor is obtained based on the object association structure and key associated object pairs; Based on basic geographic data, socioeconomic data, and spatial object classification database, disaster-bearing body characteristic factors are obtained according to object type differences. The dynamic disaster-causing factors, related transmission factors, and disaster-bearing body characteristic factors are standardized, and the combined weights of each factor are determined by the analytic hierarchy process combined with the entropy weight method. This results in the integration of a multi-dimensional risk assessment index system that incorporates dynamic disaster-causing factors, related transmission factors, and disaster-bearing body characteristics.

[0013] In a further embodiment, the process for determining the flood risk level and corresponding loss extent of key associated object pairs is as follows: Based on the standardized factors and combined weights in the multi-dimensional risk assessment indicator system, a fuzzy comprehensive evaluation model is used to calculate the comprehensive risk assessment value of key related object pairs. : ,in, For the first Standardized values ​​of each factor The number of factors participating in the evaluation. For the first The weights of each factor; Set the first threshold for risk level classification and the first threshold ,like If so, it is classified as a high-risk level for flooding; if If it is, then it is at a medium risk level for flooding; if If so, it is considered a low-risk level for flooding.

[0014] In a further embodiment, the quantification process of the degree of loss is as follows: By combining object value parameters, risk impact weights, and historical flood loss statistics from socioeconomic data, a quantitative model for the degree of loss is constructed: ； in, For the extent of flood damage to key related object pairs, and These represent the risk impact weights of the two spatial objects in the key associated object pair. and These represent the value of two spatial objects, respectively.

[0015] The beneficial effects of this invention are as follows: Based on urban functional attributes and spatial characteristics, this invention refines spatial objects into three categories: target protection objects, basic support objects, and risk impact objects, accurately matching the core risk characteristics of different objects; at the same time, a unique spatial identifier is generated through a coupling method, realizing data association and traceability throughout the entire life cycle of the object, solving the problem of vague risk identification caused by the coarse classification and chaotic identification of traditional methods, and laying a data foundation for subsequent accurate assessment.

[0016] This invention identifies three types of relationships between objects—adjacency, inclusion, and flood transmission—through GIS spatial topology analysis, constructs a dynamically updated object relationship structure, and quantifies the relationship strength and transmission efficiency. For the first time, it incorporates the risk transmission effect into the evaluation system. By screening key related object pairs and clarifying the risk impact weight, it accurately captures the evolution process of single object risk, relationship transmission, and group risk.

[0017] This invention constructs a three-dimensional index system of dynamic disaster initiation, correlation transmission, and disaster-bearing body characteristics. It integrates dynamic disaster initiation factors obtained from hydraulic simulation data, introduces transmission factors such as object correlation strength and transmission efficiency, and selects disaster-bearing factors such as inherent disaster resistance attributes according to object type, so that the index system is more in line with the risk assessment needs of different scenarios.

[0018] In summary, this invention solves the problem of the disconnect between static assessment and dynamic evolution of disasters in traditional methods, and provides real-time support for emergency decision-making at different stages of disasters. Attached Figure Description

[0019] Figure 1 This is a flowchart of the multi-dimensional fuzzy flood risk identification method in Example 1.

[0020] Figure 2 This is a schematic diagram of the object association structure. Detailed Implementation

[0021] The present invention will now be further described with reference to the accompanying drawings and embodiments.

[0022] Example 1 like Figure 1 As shown in the figure, this embodiment discloses a multi-dimensional fuzzy flood risk identification method for urban spatial objects, including the following steps: Based on urban functional attributes and spatial characteristics, urban spatial objects are divided into three categories. Each category of spatial object is assigned a unique spatial identifier with coupled coding and a flood status label, thereby generating a spatial object classification database. Using GIS spatial topology analysis, the relationships between three types of spatial objects are identified, and an object relationship structure is constructed. Key related object pairs are selected from the object relationship structure, and the risk impact weight of each object in the key related object pair is determined. Collect hydraulic simulation data, basic geographic data, and socio-economic data, and combine them with flood status labels, object association structures, and key associated object pairs to construct a multi-dimensional risk assessment indicator system; Based on a multi-dimensional risk assessment index system, a fuzzy comprehensive evaluation model is used to calculate the comprehensive risk assessment value of key related object pairs and map it to flood risk level. Combining risk impact weight and object value parameters, the corresponding loss degree is determined through a loss degree quantification model.

[0023] The risk protection targets are categorized into three groups: target protection objects, risk-affected objects, and basic support objects. For example, target protection objects include residential areas and key units (schools, hospitals, etc.). Their core characteristic is that flood risk is directly related to personal and property safety, making them the core priority targets for risk protection. Risk-affected objects include farmland, waterlogged areas, and aging branch pipe networks. Their core characteristic is that they are susceptible to flood stress and easily become sources or transmission nodes of risk, amplifying risks in surrounding areas. Basic support objects include highways, railways, flood control facilities, and main drainage networks. Their core characteristic is ensuring the normal operation of the city and its flood prevention and disaster reduction functions; they are key carriers of risk transmission and the main bodies supporting disaster relief.

[0024] The above three categories of spatial objects differ from existing technologies that classify pipe networks and water pipes into a single category of municipal infrastructure, based solely on the object's physical attributes or a single function. Instead, this approach is guided by the logic of flood risk transmission and combines functional priorities: by clearly defining the position of each type of object in the risk chain, it achieves a precise characterization of risk evolution patterns, solving the problem of unclear risk correlations caused by the ambiguous object classification in traditional methods.

[0025] In a further embodiment, the steps for assigning a unique spatial identifier to the coupled encoding are as follows: Based on the base map data, the latitude and longitude coordinates and elevation information of spatial objects are extracted to form three-dimensional spatial data. In this embodiment, the base map vector data is .shp format layer data containing land cover type, spatial boundary, and category code. Correspondingly, the three-dimensional spatial data includes latitude and longitude coordinates and elevation information, which can be represented as: longitude-latitude-elevation.

[0026] Based on the land cover density and water system distribution characteristics of the target area, an adaptive grid index system is constructed using a quadtree grid partitioning algorithm. For example, for areas with high land cover density or dense water system, the grid partitioning precision is set to 10m×10m; for areas with medium land cover density or relatively dense water system, the grid partitioning precision is set to 50m×50m; and for areas with low land cover density or sparse water system, the grid partitioning precision is set to 100m×100m. Each grid is assigned a unique index code, which can be in the format of: area number - grid row and column number, forming an adaptive grid index system that adapts to the spatial characteristics of the target area.

[0027] The three-dimensional spatial data, the adaptive grid indexing system, and the category of spatial objects are coupled and encoded to generate a unique spatial identifier for each spatial object. Based on the above example, the format of the unique spatial identifier in this embodiment is as follows: Longitude-Latitude-Elevation-Grid Row and Column Number-Spatial Object Type.

[0028] In a further embodiment, the process of generating the flood status label is as follows: Based on inundation element data, inundation depth, inundation duration, and flow velocity information corresponding to each spatial object are obtained, and flood status attributes of the spatial objects are formed through normalization processing; for example, using... min-max The normalization method is used to process the data, which yields the normalized values ​​of the inundation depth, inundation duration, and flow velocity for each spatial object. These values ​​together constitute the flood status attributes of the spatial object, which are used to characterize the degree to which the object is affected by floods in real time.

[0029] By combining flood status attributes with inherent and associated attributes, flood status labels that combine static features with dynamic flood information are generated.

[0030] It should be noted that the inherent attribute of this embodiment is the core static characteristic of spatial objects, which are stable in the long term and do not change dynamically with flood events. It is the key basis for identifying the basic disaster resistance capability and functional positioning of objects. Examples of the inherent attributes of different types of spatial objects are as follows: Target protected objects: object type, building structure type, design flood control standard, number of building floors, and land area.

[0031] Risk-affected objects include: object type, terrain elevation, historical flood-prone frequency, pipeline diameter, construction year, and water storage capacity.

[0032] Basic supporting objects: object type, facility level, design carrying capacity, service scope, and construction standards.

[0033] Further related attributes refer to the correlation characteristics between spatial objects and their external environment (such as water systems and terrain) and other spatial objects. This can be understood as the spatial location correlation, functional dependency correlation, and risk transmission correlation of spatial objects in the transmission of flood risks. Spatial location correlation reflects the spatial layout relationship between the object and its surrounding environment and other objects; functional dependency correlation reflects the functional support or dependency relationship between the spatial object and other facilities; and risk transmission correlation is the risk transmission relationship between the spatial object and other objects.

[0034] Therefore, this embodiment solves the problem of traditional labels focusing only on single-dimensional features and fragmented information by using flood status labels that combine static features and dynamic flood information, thus providing comprehensive data support for subsequent risk assessment.

[0035] Existing spatial object association structures are mostly built based on fixed historical data, making them difficult to update once established. However, in flood disasters, data such as inundation depth and duration change in real time, and the corresponding object associations also change dynamically. For example, when floodwaters rise, two areas that were not directly related may become connected due to water accumulation, forming new flood transmission associations. Traditional static structures cannot reflect these dynamic changes, causing association analysis results to lag behind the actual disaster situation. In this step, the dynamic association edges iterate in real time with the flood status labels, perfectly adapting to the dynamic changes throughout the entire cycle of flood disasters from occurrence to recede.

[0036] Based on the generation of unique spatial identifiers and the creation of flood status tags, this embodiment uses the following method for construction: Using the unique spatial identifier of a spatial object as the positioning benchmark, and integrating the inundation depth (in meters), duration (in hours), and flow velocity dynamic information (in meters per hour) from the flood status label, three types of relationships are extracted between the three types of spatial objects: adjacency relationship. Includes association Related to flood transmission .

[0037] For ease of understanding, the adjacency association described in this embodiment For areas where the overlap length between spatial boundaries is ≥20%, such as secondary arterial roads in cities and surrounding residential areas; including related areas. Defined as an association where more than 90% of the object's spatial scope is nested within a parent object, taking an urban wetland park (parent object) and an ecological monitoring station within the park (child object) as an example; flood transmission association. This can be understood as a relationship where submerged water flows from one object to another with a velocity ≥ 0.1 m / s, such as urban low-lying green space (upstream object) and adjacent kindergarten (downstream object).

[0038] The adjacency association strength is obtained by quantifying and correcting the three types of associations using a differential model. Including correlation strength Correlation strength with flood transmission Synchronous quantization yields the adjacency association propagation efficiency. Including the efficiency of related transmission And the efficiency of flood transmission .

[0039] Among them, the adjacency association strength The calculation formula is as follows: , For correction factor, The length of the boundary coincidence. This is the average of the total length of the boundary. This represents the centroid distance between two spatial objects. Correspondingly, it represents the adjacency propagation efficiency. The calculation formula is: ,in, The submergence velocity (m / s) corresponding to the spatial object. The maximum flooding velocity (m / s) for all spatial objects within the target area. The submerged water depth (m) corresponding to the spatial object. The maximum flood depth (m) for all spatial objects within the target area.

[0040] Includes correlation strength The calculation formula is as follows: ,in, Spatial volume of the object (km) 3 ), The space volume of the parent object (km) 3 ), This is the vertical distance between the centroids of the child and parent objects. This includes the efficiency of correlation propagation. The calculation formula is as follows: .

[0041] Flood Transmission Correlation Strength The calculation formula is as follows: ; Correspondingly, the efficiency of flood transmission and propagation. The calculation formula is: .

[0042] For the essential differences of the three types of correlation, corresponding quantitative formulas are given. Adjacency correlation combines boundary overlap and spatial distance, inclusion correlation focuses on volume ratio and vertical distance, and flood transmission correlation closely follows the core disaster-causing factors of flow velocity and water depth, so as to achieve accurate matching of correlation type, quantitative dimension and model parameters. It should be noted that the correction coefficient described in this embodiment... The value of depends on the flooding duration and the corresponding protection level, therefore it is a dynamic coefficient. This embodiment uses the following formula for calculation: ,in, The protection level of space objects, The inundation duration for spatial objects. Therefore, a correction factor is used in the above. In this case, calculations need to be performed based on the specific parameters of the spatial object. Integrating the real-time impact of floods with the object's own protective capabilities into the correlation strength quantification process avoids the shortcomings of traditional static correlation analysis, which ignores dynamic changes in disasters and individual differences among objects.

[0043] Identifier nodes are bound to unique spatial identifiers, object types, and flood status labels; dynamically associated edges are linked based on adjacency. Includes association Related to flood transmission Classify, such as Figure 2 As shown, adjacency association Use solid lines for illustration, including connections. The dashed lines represent the transmission and correlation of floods. An arrow will be used to illustrate this.

[0044] The dynamic association edges are labeled with corresponding association strength and transmission efficiency values, and the dynamic association edges are iterated in real time as the flood status label is updated. For example, the thickness of the line indicates a positive correlation in association strength, and the size of the arrow indicates the flood conduction efficiency. A positive correlation.

[0045] Dynamically associated edges are bound to flood status labels, and the intensity and transmission efficiency values ​​are updated in real time according to information such as inundation depth, duration, and flow velocity, adapting to the changes in the association relationship throughout the entire cycle of flood occurrence, development, peak, and recede; through the classification diagram of solid lines / dashed lines / solid lines with arrows, and the visual encoding of line width and arrow size, the abstract association types, intensity, and efficiency are transformed into intuitive graphical language, which greatly reduces the information interpretation cost during subsequent risk analysis and decision-making.

[0046] Embedding object type-based association priority rules forms an integrated and dynamic object association structure. Taking three types of spatial objects as an example, the priorities of target protection objects, risk impact objects, and basic support objects tend to decrease. If weight coefficients are used to illustrate the object type association priority rules, it means that the priority of target protection objects is 1.0, the priority of risk impact objects is 0.8, and the priority of basic support objects is 0.6.

[0047] In a further embodiment, the filtering steps for the key associated object pairs are as follows: Taking two sets of spatial objects with an association relationship in the object association structure as the computational objects, the association type of the computational objects is determined and the corresponding association strength is extracted. and transmission efficiency , , ; Based on the correlation strength and transmission efficiency And introduce object type adaptation coefficient The comprehensive evaluation value of the association is obtained by the following calculation. : ;in, This is a weighting coefficient, with a value ranging from 0.5 to 0.7; further, the object type adaptation coefficient in this embodiment... The rules for determining the values ​​and assigning the risk impact weights are shown in Table 1.

[0048] Table 1 Object Type Adaptability Coefficients Rules for determining the weight of risk impact Table 1 uses the target protected object - risk-affected object as an example to explain the meaning of the values ​​0.7 / 0.3: the risk impact weight of the target protected object is 0.7, and the risk impact weight of the risk-affected object is 0.3.

[0049] Given a comprehensive evaluation threshold Filter out The key associated object pairs. Considering that different spatial objects have different risk levels, this embodiment provides a comprehensive evaluation threshold. The value is dynamic, specifically 0.6 for high-risk areas, 0.65 for medium-risk areas, and 0.7 for low-risk areas.

[0050] Based on this, the construction process of the multi-dimensional risk assessment indicator system in this embodiment is as follows: Based on hydraulic simulation data and flood status labels, dynamic disaster-causing factors are identified, such as extracting four dynamic disaster-causing factors: inundation depth, inundation duration, maximum flow velocity, and flood peak arrival time. The data is directly linked to the real-time update information of the flood status labels, reflecting the spatiotemporal variation characteristics of disaster intensity. Based on the object association structure and key associated object pairs, association transmission factors are obtained; for example, three association transmission factors, namely association strength, transmission efficiency, and risk impact weight, are selected to highlight the amplification effect of association on risk transmission and achieve logical connection with the object association structure mentioned above. Based on basic geographic data, socioeconomic data, and spatial object classification databases, disaster-bearing body characteristic factors are obtained according to object type differences. For example, several disaster-bearing body characteristic factors are selected according to object type differences: population density and building protection level are selected for target protection objects; facility importance level and service coverage population are selected for basic support objects; and water storage capacity and number of surrounding sensitive objects are selected for risk impact objects.

[0051] The dynamic disaster-causing factors, related transmission factors, and disaster-bearing body characteristic factors are standardized, and the extreme value method is used to eliminate dimensional differences, normalizing all indicators to a certain value. Interval.

[0052] The weights of each factor combination are determined by combining the analytic hierarchy process (AHP) with the entropy weight method. This weight confirmation method is an existing technology and will not be elaborated here. The results are integrated to form a multi-dimensional risk assessment index system with dynamic disaster-causing, correlation transmission and disaster-bearing body characteristics.

[0053] The process for determining the flood risk level and corresponding loss extent of key related object pairs is as follows: Based on the standardized factors and combined weights in the multi-dimensional risk assessment indicator system, a fuzzy comprehensive evaluation model is used to calculate the comprehensive risk assessment value of key related object pairs. : ,in, For the first The standardized values ​​of each factor, namely the standardized dynamic disaster-causing factor, the associated transmission factor, and the disaster-bearing body characteristic factor mentioned above. The number of factors participating in the evaluation. For the first The weights of each factor (determined based on the analytic hierarchy process combined with the entropy weight method mentioned above). Set the first threshold for risk level classification and the first threshold ,like If so, it is classified as a high-risk level for flooding; if If it is, then it is at a medium risk level for flooding; if This indicates a low risk level for flooding. For example... , , The value can be 0-1.

[0054] The quantification process for the degree of loss is as follows: By combining object value parameters, risk impact weights, and historical flood loss statistics from socioeconomic data, a quantitative model for the degree of loss is constructed: ； in, For the extent of flood damage to key related object pairs, and The risk impact weights for the two spatial objects in the key associated object pair are shown in Table 1. and These are the value values ​​of two spatial objects (which can be extracted directly through their inherent attributes).

[0055] Ultimately, based on the comprehensive risk assessment value and the extent of flood damage Output the flood risk level (e.g., high / medium / low) and corresponding loss level (mild / moderate / severe) of key related object pairs.

Claims

1. A method for multi-dimensional fuzzy flood risk identification of urban spatial objects, characterized in that, Includes the following steps: Based on urban functional attributes and spatial characteristics, urban spatial objects are divided into three categories. Each category of spatial object is assigned a unique spatial identifier with coupled coding and a flood status label, thereby generating a spatial object classification database. Using GIS spatial topology analysis, the relationships between three types of spatial objects are identified, and an object relationship structure is constructed. Key related object pairs are selected from the object relationship structure, and the risk impact weight of each object in the key related object pair is determined. Collect hydraulic simulation data, basic geographic data, and socio-economic data, and combine them with flood status labels, object association structures, and key associated object pairs to construct a multi-dimensional risk assessment indicator system; Based on a multi-dimensional risk assessment index system, a fuzzy comprehensive evaluation model is used to calculate the comprehensive risk assessment value of key related object pairs and map it to flood risk level; By combining risk impact weights and object value parameters, the corresponding degree of loss is determined through a loss degree quantification model.

2. The method for multi-dimensional fuzzy flood risk identification of urban spatial objects according to claim 1, characterized in that, The three types of spatial objects are: target protection objects, risk impact objects, and basic support objects.

3. The method for multi-dimensional fuzzy flood risk identification of urban spatial objects according to claim 1, characterized in that, The steps for assigning the unique spatial identifier of the coupling code are as follows: Based on the base map data, the latitude and longitude coordinates and elevation information of spatial objects are extracted to form three-dimensional spatial data; an adaptive grid indexing system is constructed based on the land feature density and water system distribution characteristics of the target area. The three-dimensional spatial data, the adaptive mesh indexing system, and the category of spatial objects are coupled and encoded to generate a unique spatial identifier for each spatial object.

4. The method for multi-dimensional fuzzy flood risk identification of urban spatial objects according to claim 1, characterized in that, The process of generating the flood status label is as follows: Based on inundation element data, the inundation depth, inundation duration and flow velocity information of each spatial object are obtained, and the flood status attributes of the spatial objects are formed through normalization processing. By combining flood status attributes with inherent and associated attributes, flood status labels that combine static features with dynamic flood information are generated.

5. The method for multi-dimensional fuzzy flood risk identification of urban spatial objects according to claim 1, characterized in that, The steps for constructing the object association structure are as follows: Using the unique spatial identifier of a spatial object as the positioning benchmark, and integrating the dynamic information of inundation depth, duration, and flow velocity from the flood status label, three types of relationships are extracted between the three types of spatial objects: adjacency relationship. Includes association Related to flood transmission ; The adjacency association strength is obtained by quantifying and correcting the three types of associations using a differential model. Including correlation strength Correlation strength with flood transmission Synchronous quantization yields the adjacency association propagation efficiency. Including the efficiency of correlation propagation and the efficiency of flood transmission ; Identifier nodes are bound to unique spatial identifiers, object types, and flood status labels; dynamically associated edges are linked based on adjacency. Includes association Related to flood transmission The system is classified and the corresponding association strength value and transmission efficiency value are marked on the dynamically associated edges. The dynamic associated edges are iterated in real time as the flood status label is updated. Embedding object type-based association priority rules forms an integrated, dynamic object association structure.

6. The method for multi-dimensional fuzzy flood risk identification of urban spatial objects according to claim 1, characterized in that, The steps for filtering the key associated object pairs are as follows: Taking two sets of spatial objects with an association relationship in the object association structure as the computational objects, the association type of the computational objects is determined and the corresponding association strength is extracted. and transmission efficiency , , ; Based on the correlation strength and transmission efficiency And introduce object type adaptation coefficient The comprehensive evaluation value of the association is obtained by the following calculation. : ;in, These are the weighting coefficients; Given a comprehensive evaluation threshold Filter out The key related object pairs.

7. The method for multi-dimensional fuzzy flood risk identification of urban spatial objects according to claim 1, characterized in that, The construction process of the multi-dimensional risk assessment indicator system is as follows: Dynamic disaster-causing factors were identified based on hydraulic simulation data and flood status labels. The association transmission factor is obtained based on the object association structure and key associated object pairs; Based on basic geographic data, socioeconomic data, and spatial object classification database, disaster-bearing body characteristic factors are obtained according to object type differences. The dynamic disaster-causing factors, related transmission factors, and disaster-bearing body characteristic factors are standardized, and the combined weights of each factor are determined by the analytic hierarchy process combined with the entropy weight method. This results in the integration of a multi-dimensional risk assessment index system that incorporates dynamic disaster-causing factors, related transmission factors, and disaster-bearing body characteristics.

8. The method for multi-dimensional fuzzy flood risk identification of urban spatial objects according to claim 1, characterized in that, The process for determining the flood risk level and corresponding loss extent of key related object pairs is as follows: Based on the standardized factors and combined weights in the multi-dimensional risk assessment indicator system, a fuzzy comprehensive evaluation model is used to calculate the comprehensive risk assessment value of key related object pairs. : ,in, For the first Standardized values ​​of each factor The number of factors participating in the evaluation. For the first The weights of each factor; Set the first threshold for risk level classification and the first threshold ,like If so, it is classified as a high-risk level for flooding; if If it is, then it is at a medium risk level for flooding; if If so, it is considered a low-risk level for flooding.

9. A method for multi-dimensional fuzzy flood risk identification of urban spatial objects according to claim 8, characterized in that, The quantification process for the degree of loss is as follows: By combining object value parameters, risk impact weights, and historical flood loss statistics from socioeconomic data, a quantitative model for the degree of loss is constructed: ； in, For the extent of flood damage to key related object pairs, and These represent the risk impact weights of the two spatial objects in the key associated object pair. and These represent the value of two spatial objects, respectively.

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