Method and system for evaluating security resilience of urban community

Abstract

The application discloses a kind of safety and resilience evaluation methods of urban community, it is characterized in that, including the following steps: obtaining the multi-source map data of the region where research community is located, the distance between each grid center and surrounding hazard source is calculated;Calculate the spatial exposure index of each grid;The building vulnerability index of each building is calculated, and the average value of the building vulnerability index of each building is obtained, to obtain the building vulnerability index of grid;The spatial vulnerability index of grid is calculated using the building vulnerability index and population vulnerability index of each grid;The accessibility of grid to obtain emergency service is calculated;The resilience of road network is calculated, and the initial resilience index is corrected to obtain the modified resilience index of grid;Screening high safety and resilience aggregation area and low safety and resilience aggregation area of research community, locate the area of weak safety and resilience, and generate the safety and resilience distribution map of research community, guide precise disaster prevention resource allocation.

Description

Technical Field

[0001] This invention relates to the field of urban community safety management, and specifically to a method and system for evaluating the safety resilience of urban communities. Background Technology

[0002] As urbanization continues, urban operating systems are becoming increasingly complex, and urban safety development faces new challenges. This necessitates evolving and refining the concept of a safe city to adapt to the characteristics of this new era. The concept of a "safe and resilient city" has emerged as the latest interpretation of a safe city and represents a new paradigm for urban safety development.

[0003] Urban community safety resilience assessment is crucial for ensuring redundancy in daily safety responses within a region. Through this assessment, a comprehensive understanding of the community's current emergency response capabilities can be achieved, ensuring a timely response during disasters and maintaining the community at a high level of safety protection. However, urban community safety resilience assessment relies on detailed spatial information. Currently, there is no publicly available technology for evaluating urban community safety resilience using multi-source map data. Existing technologies face significant challenges in fully utilizing information such as road networks, facility distribution, and the spatial radiation of hazard sources within map data. This leads to assessments of emergency response capabilities and exposure levels being detached from real spatial relationships, making it impossible to accurately evaluate the safety resilience of urban communities and hindering the management of urban community safety emergency responses. Summary of the Invention

[0004] To address the aforementioned shortcomings of existing technologies, this invention provides a method and system for evaluating the safety resilience of urban communities. Based on multi-source map data, it precisely characterizes the spatial differentiation of safety resilience to support precise planning and emergency resource allocation in urban communities.

[0005] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: A method for evaluating the safety resilience of urban communities is provided, which includes the following steps: S1: Obtain multi-source map data of the area where the research community is located, divide it into uniform grids, and calculate the distance between the center of each grid and the surrounding hazard sources; S2: Introduce a state correction factor, use the hazard source index to calculate the comprehensive hazard coefficient of the hazard source, and combine the distance between the grid center and the surrounding hazard sources to calculate the spatial exposure index of each grid. S3: Calculate the building vulnerability index of each building based on its structural type, construction year, and number of floors within the grid, and take the average of the building vulnerability indices of each building to obtain the building vulnerability index of the grid. S4: Extract the population density of each grid from the population distribution raster, calculate the population vulnerability index of each grid; and introduce structural vulnerability weights, using the building vulnerability index and population vulnerability index of each grid to calculate the spatial vulnerability index of the grid. S5: By taking all fire stations, medical points and refuge sites in the research community as emergency service points, a distance attenuation function for emergency service capacity is introduced to calculate the supply-demand ratio of emergency services in the grid, and then the accessibility of the grid to emergency services is calculated. S6: The initial resilience index of the grid is obtained by weighted summation of the grid's accessibility to emergency services, spatial vulnerability index, and spatial exposure index; random disaster simulation is performed on the road network to calculate the road network's resilience, and the initial resilience index is corrected to obtain the grid's corrected resilience index. S7: Calculate the safety resilience hotspot index for each grid based on the average variance of the corrected resilience index of all grids. Based on the sign of the safety resilience hotspot index and the magnitude of the corrected resilience index, screen the high safety resilience clusters and low safety resilience clusters in the study community, locate areas with weak safety resilience, and generate a safety resilience distribution map of the study community to guide the precise allocation of disaster prevention resources.

[0006] Furthermore, the spatial exposure index for each grid is calculated. The method is as follows: ; ; in, i For grid numbering, j Number the hazards around the grid. The distance between the grid and the hazard source. J The number of hazards around the grid. Hazardous source j The overall risk factor, Hazardous source j The maximum danger impact distance, This refers to the spatial diffusion bandwidth of the impact of this type of hazard source. As a sensitivity modulator, Hazardous source j Inherent risk index This serves as the baseline hazard index threshold for this type of hazard source. State correction factor The state correction factor Calculated based on the cumulative dimensions of obstacles between the grid and the hazard source; ; in, Hazardous sourcej With grid i The vertically accumulated thickness of the obstacles between them Hazardous source j With grid i The lateral width of the obstacles between them Hazardous source j With grid i Maximum height of obstacles between them Hazardous source j With grid i The cumulative height of obstacles between them These are the weighting coefficients for the obstacle's thickness, width, and height, respectively.

[0007] Furthermore, the method for calculating the building vulnerability index of each building is as follows: Calculate the foundation vulnerability coefficient based on the structural type, construction year, and number of stories of each building within the grid. Degeneration coefficient Layer-height effect coefficient and reinforcement reduction factor Then, the building vulnerability index of each building is calculated. ; ; in, m Number the buildings within the grid.

[0008] Further, step S4 includes: S41: Extract population density for each grid cell from the population distribution raster. Calculate the population vulnerability index for each grid. ; ; in, The minimum population density across all grids. This represents the maximum population density across all grid cells. S42: Introduce structural vulnerability weights and utilize the population vulnerability index. Building vulnerability index Calculate the spatial vulnerability index of the grid ; ; in, This represents the structural vulnerability weight.

[0009] Further, step S5 includes: S51: All fire stations, medical points, and shelters within the research community area will be designated as emergency service points. The emergency service supply-demand ratio of the grid will be calculated based on the emergency service capacity of each emergency service point and the population within the grid. ; S52: Combining the supply and demand ratio of emergency services and distance decay function Accessibility of emergency services to computational grids ; .

[0010] in, u This is the number of the emergency service point. The distance from the grid to the emergency service point is the road network distance. K The number of emergency service points.

[0011] Further, step S6 includes: S61: Accessibility of emergency services to the grid Space Vulnerability Index and spatial exposure index The initial toughness index of the mesh is obtained by performing a weighted summation. ; S62: Abstract the road network of the area where the research community is located into a graph. , N For road intersections and The number of connection points between roads and grids L Calculate the global efficiency of the road network given the number of road segments. ; S63: Simulate a random disaster on a road network, cut off road segments from the network, remove the cut segments, and calculate the efficiency of the road network after removal. Improve road network efficiency With overall efficiency The proportion serves as the resilience of the regional road network. ; S64: Utilizing toughness Multiply by the initial toughness index The initial toughness index of the mesh After making corrections, the corrected toughness index is obtained. .

[0012] Further, step S7 includes: S71: Modified toughness index based on all grids within the research community's area. variance and average Calculate the safety resilience hotspot index for each grid. ; S72: Based on the safety resilience hotspot index The positive and negative values ​​of the index and the magnitude of the modified resilience index are used to screen high-safety-resilience clusters and low-safety-resilience clusters in the study community, locate areas with weak safety resilience, and generate a safety resilience distribution map of the study community to guide the precise allocation of disaster prevention resources. when and At that time, the grid i If the security resilience hotspot index of a grid is higher than the global mean, and the security resilience hotspot indexes of its neighboring grids are also generally higher than the global mean, then the grid is considered to be... i Located within a high-safety, resilient cluster area; when and At that time, the grid i If the security resilience hotspot index of a grid is lower than the global mean, and the security resilience hotspot index of its neighboring grids is also generally lower than the global mean, then the grid is considered to be... i Located within a low-safety-resilience cluster area; Otherwise, determine the grid. i These are anomalies in the safety and resilience space.

[0013] A safety resilience assessment system for urban communities is provided for performing the aforementioned safety resilience assessment method for urban communities, comprising: a computer and a processor; The processor is equipped with a data acquisition module and a data processing module. The data acquisition module is used to collect multi-source map data of the area where the research community is located. The data processing module is used to filter out high-security-resilience clusters and low-security-resilience clusters of the research community based on the multi-source map data, and to construct a security-resilience distribution map. The computer is equipped with a map display module, which is used to display the constructed security resilience distribution map and to view the high security resilience clusters and low security resilience clusters of the research community.

[0014] The beneficial effects of this invention are as follows: Based on map grid division and multi-source map data, this invention realizes fine-grained spatial assessment of safety resilience, and the evaluation results can be accurate to each building group in the community; the autocorrelation hotspot detection of community space can automatically divide safety resilience early warning zones, providing an intuitive decision-making basis for precise disaster prevention planning in the community. Attached Figure Description

[0015] Figure 1 A flowchart for evaluating the safety resilience of urban communities. Detailed Implementation

[0016] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0017] like Figure 1 As shown, a method for evaluating the safety resilience of urban communities includes the following steps: S1: Obtain multi-source map data of the area where the research community is located, divide it into uniform grids, and calculate the distance between the center of each grid and the surrounding hazard sources.

[0018] Step S1 specifically includes: S11: Obtain multi-source map data of the area where the study community is located, including building vectors, road network, fire station coordinates, medical point coordinates, refuge coordinates, hazard source coordinates (gas station, hazardous chemical warehouse), population distribution raster (100m resolution), and DEM digital elevation model; S12: Divide the study area into a uniform grid and obtain the center coordinates of each grid. , i Assign grid numbers based on the coordinates of hazard sources surrounding the grid. Calculate the distance between the grid and the hazard source. ; .

[0019] S2: Introduce a state correction factor, calculate the comprehensive hazard coefficient of the hazard source using hazard source indicators, and combine it with distance. Calculate the spatial exposure index for each grid cell. ; ; ; in, j Number the hazards around the grid. J The number of hazards around the grid. Hazardous source j The overall risk factor, the overall risk factor The value ranges from (0,1]. When the calculated comprehensive risk factor is greater than 1, it is taken as 1, where 1 represents extreme risk. Hazardous source j The maximum danger impact distance, This refers to the spatial diffusion bandwidth of the impact of this type of hazard source. As a sensitivity modulator, it is usually taken as... , For transition bandwidth, Hazardous source j Inherent risk index This serves as the baseline hazard index threshold for this type of hazard source. This is the state correction factor, calculated based on the cumulative size of obstacles between the grid and the hazard source; ; in, Hazardous source j With grid i The vertically accumulated thickness of the obstacles between them Hazardous source j With grid i The lateral width of the obstacles between them Hazardous source j With grid i Maximum height of obstacles between them Hazardous source j With grid i The cumulative height of obstacles between them These are the weighting coefficients for the obstacle's thickness, width, and height, respectively. State correction factor This embodiment is used to describe the degree to which obstacles between a hazard source and a grid mitigate the impact of the hazard; it meets the following requirements. The weighting coefficients for obstacle thickness, width, and height are appropriately selected based on the different types of hazard sources. For example, for a gas station, the thicker the obstacle between the hazard source and the grid, the more it can absorb most of the impact during an explosion. For hazardous chemical warehouses, during the release of toxic gases, the wider and higher the obstacles, the more difficult it is for the gases to diffuse into the corresponding grid. .

[0020] This invention calculates the comprehensive risk factor based on the risk potential energy theory. It characterizes the inherent severity of consequences, which is jointly determined by the "severity of the consequences of the accident" and the "possibility of exposure to the consequences," avoiding the risk of overestimating the risks at long distances or underestimating the risks at close distances due to simple linear superposition.

[0021] S3: Calculate the foundation vulnerability coefficient based on the structural type, construction year, and number of stories of each building within the grid. Degeneration coefficient Layer-height effect coefficient and reinforcement reduction factor Then, the building vulnerability index of each building is calculated. ; ; in,m Number the buildings within the grid.

[0022] Based on the "Standard for Seismic Design of Buildings" (GB50011) and the actual seismic intensity of the community, this embodiment classifies building structures into the following categories, with foundation vulnerability coefficients... The values ​​are shown in Table 1 below: Table 1 Basic Vulnerability Coefficient Value table

[0023] Depreciation coefficient over time The decline in structural resistance caused by building material aging and code iteration is calculated using an exponential decay model: ; in, The material degradation sensitivity coefficient is set at 0.5 for brick-concrete structures, 0.3 for concrete structures, and 0.2 for steel structures, depending on the building structure type. For the current year, Year of construction The structural design reference period; using the age decay coefficient. This characterizes the physical fact that the vulnerability of buildings exceeding their service life within the grid increases rapidly.

[0024] The layer-height effect coefficient in this embodiment Taking into account the increased risk of collapse due to height and the possible earthquake whiplash effect, but excluding the high reliability brought about by the strict design of modern high-rise buildings, a piecewise function is used for value selection; ; in, This refers to the number of floors in the building.

[0025] Layer-height effect coefficient Using piecewise functions to select appropriate values ​​reflects the objective law of "low-level safety - mid-level vulnerability - high-level slightly less severe". In particular, it provides peak vulnerability for 5-7 story brick-concrete self-built houses commonly found in urban villages, which is consistent with actual earthquake damage statistics.

[0026] Calculate the reinforcement reduction factor based on the reinforcement methods and reinforcement area of ​​each building. ; ; in, The effective reinforcement coefficient is 0.6 for carbon fiber cloth, 0.7 for steel plate reinforcement, and 0.9 for seismic isolation bearings. To reinforce the affected area, This refers to the area of ​​the building's base.

[0027] reinforcement reduction factor This allows the evaluation to dynamically respond to urban renewal and old community renovation projects. A new reinforcement as-built drawing can trigger vulnerability reduction, greatly improving the practicality of the project.

[0028] Take the building vulnerability index of each building within the grid. The average value is used to obtain the building vulnerability index for each grid. ; ; M This represents the number of buildings within the grid.

[0029] This embodiment takes the calculation of building vulnerability index within a typical grid of a riverside community as an example. Relevant data are retrieved from urban building vector map data and real estate data, and the relevant parameters for calculating the building vulnerability index are shown in Table 2 below. Table 2 Calculation Table of Building Vulnerability Index

[0030] Current year Based on 2026 calculations, although Building B4 is an old brick and wood building, it is extremely fragile, with a high building vulnerability index. The building vulnerability index can significantly amplify the most dangerous "building vulnerabilities" in the area, providing a physical data foundation for the construction of community resilience maps.

[0031] S4: Extract the population density of each grid from the population distribution raster, calculate the population vulnerability index of each grid; and introduce structural vulnerability weights to calculate the spatial vulnerability index of each grid using the building vulnerability index and population vulnerability index of each grid.

[0032] Step S4 specifically includes: S41: Extract population density for each grid cell from the population distribution raster. Calculate the population vulnerability index for each grid. ; ; in, The minimum population density across all grids. This represents the maximum population density across all grid cells. S42: Introduce structural vulnerability weights and utilize the population vulnerability index. Building vulnerability index Calculate the spatial vulnerability index of the grid ; ; in, As the structural vulnerability weight, this embodiment takes... ; S5: By taking all fire stations, medical points, and shelters within the research community as emergency service points, a distance attenuation function for emergency service capacity is introduced to calculate the supply-demand ratio of emergency services in the grid, and then the accessibility of the grid to emergency services is calculated.

[0033] Step S5 specifically includes: S51: All fire stations, medical points, and shelters within the research community area will be designated as emergency service points. The emergency service supply-demand ratio of the grid will be calculated based on the emergency service capacity of each emergency service point and the population within the grid. ; ; in, u This is the number of the emergency service point. The service capacity of emergency service points (characterized by the number of beds, fire trucks, and the area of ​​the refuge area). The population count for the grid (extracted from the population distribution raster). The distance from the grid to the emergency service point is the road network distance. The service distance threshold for emergency service points. Distance attenuation function for emergency service capability; Road network distance from grid to emergency service point The shortest path is obtained by calculating the road network of the research community.

[0034] Distance decay function By introducing attenuation parameters The specific method for retrieving values ​​is as follows: ; S52: Combining the supply and demand ratio of emergency services and distance decay function Accessibility of emergency services to computational grids ; ; in, K The number of emergency service points.

[0035] S6: The initial resilience index of the grid is obtained by weighted summation of the grid's accessibility to emergency services, spatial vulnerability index, and spatial exposure index; random disaster simulation is performed on the road network to calculate the road network's resilience, and the initial resilience index is corrected to obtain the grid's corrected resilience index.

[0036] Step S6 specifically includes: S61: Accessibility of emergency services to the grid Space Vulnerability Index and spatial exposure index The initial toughness index of the mesh is obtained by performing a weighted summation. ; ; in, These represent the weights of the impact of emergency services, inter-space vulnerability, and spatial exposure on security resilience, respectively. This embodiment uses... ; The comprehensive index calculation results of a certain grid in a riverside community in this embodiment are shown in Table 3 below; Table 3. Calculation Table of Comprehensive Indicators for Some Grids in Binhe Community (Example)

[0037] This shows that grid 10231 has a high spatial exposure index due to its proximity to a gas station and its old buildings; although its emergency response capability is moderate, its initial resilience is 0.596. In contrast, grid 14578 has a lower exposure and a stronger emergency response capability. Older residential areas along the river and low-lying areas are prone to forming significant low-safety-resilience "depressions," while newly built high-rise residential areas have higher safety resilience and are more likely to form high-safety-resilience clusters.

[0038] S62: Abstract the road network of the area where the research community is located into a graph. , N For road intersections and The number of connection points (set of points) between the road and the grid. L Given the number of road segments (with road segments as edges in the graph), calculate the global efficiency of the road network. ; ; in, Let be the number of any two connection points. The shortest path length between two connection points; S63: Perform random disaster simulation on the road network, cut off road segments (e.g., flood-prone sections or bridges, road collapses, burials, etc.), remove the cut road segments from the road network, and calculate the efficiency of the road network after removal. Then, the resilience of the road network within the research community is calculated. ; ; The closer the value is to 1, the higher the redundancy of the road network. S64: Utilizing toughness Multiply by the initial toughness index The initial toughness index of the mesh After making corrections, the corrected toughness index is obtained. .

[0039] S7: Calculate the safety resilience hotspot index for each grid based on the average variance of the corrected resilience index of all grids. Based on the sign of the safety resilience hotspot index and the magnitude of the corrected resilience index, screen the high safety resilience clusters and low safety resilience clusters in the study community, locate the areas with weak safety resilience, and generate a safety resilience distribution map of the study community to guide the precise allocation of disaster prevention resources.

[0040] Step S7 specifically includes: S71: Modified toughness index based on all grids within the research community's area. variance and average Calculate the safety resilience hotspot index for each grid. ; ; in, o For grid i The surrounding neighborhood grid numbers, O For grid i The number of surrounding neighboring grids, The modified resilience index for the neighborhood grid; Safety Resilience Hotspot Index Essentially a metric grid i The degree to which the corrected resilience index deviates from the mean is the sum of the products of the degree of deviation of the index from the surrounding grid. S72: Based on the safety resilience hotspot index By analyzing the positive and negative values ​​of the index and the magnitude of the modified resilience index, high-security-resilience clusters and low-security-resilience clusters in the study community are selected, areas with weak security resilience are located, and a security resilience distribution map of the study community is generated to guide the precise allocation of disaster prevention resources.

[0041] when and At that time, the grid i If the security resilience hotspot index of a grid is higher than the global mean, and the security resilience hotspot indexes of its neighboring grids are also generally higher than the global mean, then the grid is considered to be... i Located within a high-safety, resilient cluster area; when and At that time, the grid i If the security resilience hotspot index of a grid is lower than the global mean, and the security resilience hotspot index of its neighboring grids is also generally lower than the global mean, then the grid is considered to be... i Located within a low-safety-resilience cluster area; Otherwise, determine the grid. i This refers to anomalies in the safety and resilience space. In the safety resilience distribution map, areas with high safety resilience are marked as safety resilience hotspots, and areas with low safety resilience are marked as safety resilience depressions. Furthermore, safety resilience hotspots are marked in dark green on the safety resilience distribution map, indicating that these areas are located in areas of high safety resilience where emergency resources can appropriately radiate to the surrounding areas. Safety resilience depressions are marked in dark red, indicating that these areas are contiguous areas with extremely poor safety resilience and require comprehensive regional management.

[0042] A safety resilience assessment system for urban communities, used to perform the aforementioned safety resilience assessment method for urban communities, includes: a computer and a processor; The processor is equipped with a data acquisition module and a data processing module. The data acquisition module is used to collect multi-source map data of the area where the research community is located. The data processing module is used to filter out high-security-resilience clusters and low-security-resilience clusters of the research community based on the multi-source map data, and to construct a security-resilience distribution map. The computer is equipped with a map display module, which is used to display the constructed security resilience distribution map and to view the high security resilience clusters and low security resilience clusters of the research community.

[0043] This invention enables fine-grained spatial assessment of safety resilience based on map grid division and multi-source map data. The evaluation results can be accurate down to each building cluster within the community. The autocorrelation hotspot detection of community space can automatically divide safety resilience early warning zones, providing an intuitive decision-making basis for precise disaster prevention planning in the community.

Claims

1. A method for evaluating the security resilience of an urban community, characterized in that, Includes the following steps: S1: Obtain multi-source map data of the area where the research community is located, divide it into uniform grids, and calculate the distance between the center of each grid and the surrounding hazard sources; S2: Introduce a state correction factor, use the hazard source index to calculate the comprehensive hazard coefficient of the hazard source, and combine the distance between the grid center and the surrounding hazard sources to calculate the spatial exposure index of each grid. S3: Calculate the building vulnerability index of each building based on its structural type, construction year, and number of floors within the grid, and take the average of the building vulnerability indices of each building to obtain the building vulnerability index of the grid. S4: Extract the population density of each grid from the population distribution raster and calculate the population vulnerability index for each grid; Furthermore, structural vulnerability weights are introduced, and the spatial vulnerability index of each grid is calculated using the building vulnerability index and population vulnerability index of each grid. S5: By taking all fire stations, medical points and refuge sites in the research community as emergency service points, a distance attenuation function for emergency service capacity is introduced to calculate the supply-demand ratio of emergency services in the grid, and then the accessibility of the grid to emergency services is calculated. S6: The initial resilience index of the grid is obtained by weighted summing of the grid's accessibility to emergency services, spatial vulnerability index, and spatial exposure index; A random disaster simulation is performed on the road network to calculate the resilience of the road network, and the initial resilience index is corrected to obtain the corrected resilience index of the grid. S7: Calculate the safety resilience hotspot index for each grid based on the average variance of the corrected resilience index of all grids. Based on the sign of the safety resilience hotspot index and the magnitude of the corrected resilience index, screen the high safety resilience clusters and low safety resilience clusters in the study community, locate areas with weak safety resilience, and generate a safety resilience distribution map of the study community to guide the precise allocation of disaster prevention resources.

2. The method for evaluating the safety resilience of urban communities according to claim 1, characterized in that, The calculation of the spatial exposure index for each grid The method is as follows: ； ； in, i For grid numbering, j Number the hazards around the grid. The distance between the grid and the hazard source. J The number of hazards around the grid. Hazardous source j The overall risk factor, Hazardous source j The maximum danger impact distance, This refers to the spatial diffusion bandwidth of the impact of this type of hazard source. As a sensitivity modulator, Hazardous source j Inherent risk index This serves as the baseline hazard index threshold for this type of hazard source. State correction factor The state correction factor Calculated based on the cumulative dimensions of obstacles between the grid and the hazard source; ； in, Hazardous source j With grid i The vertically accumulated thickness of the obstacles between them Hazardous source j With grid i The lateral width of the obstacles between them Hazardous source j With grid i Maximum height of obstacles between them Hazardous source j With grid i The cumulative height of obstacles between them These are the weighting coefficients for the obstacle's thickness, width, and height, respectively.

3. The method for evaluating the safety resilience of urban communities according to claim 2, characterized in that, The method for calculating the building vulnerability index of each building is as follows: Calculate the foundation vulnerability coefficient based on the structural type, construction year, and number of stories of each building within the grid. Degeneration coefficient Layer-height effect coefficient and reinforcement reduction factor Then, the building vulnerability index of each building is calculated. ; ； in, m Number the buildings within the grid.

4. The method for evaluating the safety resilience of urban communities according to claim 3, characterized in that, Step S4 includes: S41: Extract population density for each grid cell from the population distribution raster. Calculate the population vulnerability index for each grid. ; ； in, The minimum population density across all grid cells. This represents the maximum population density across all grid cells. S42: Introduce structural vulnerability weights and utilize the population vulnerability index. Building vulnerability index Calculate the spatial vulnerability index of the grid ; ； in, This represents the structural vulnerability weight.

5. The method for evaluating the safety resilience of urban communities according to claim 4, characterized in that, Step S5 includes: S51: All fire stations, medical points, and shelters within the research community area will be designated as emergency service points. The emergency service supply-demand ratio of the grid will be calculated based on the emergency service capacity of each emergency service point and the population within the grid. ; S52: Combining the supply and demand ratio of emergency services and distance decay function Accessibility of emergency services to computational grids ; 。 in, u This is the number of the emergency service point. The distance from the grid to the emergency service point is the road network distance. K The number of emergency service points.

6. The method for evaluating the safety resilience of urban communities according to claim 5, characterized in that, Step S6 includes: S61: Accessibility of emergency services to the grid Space Vulnerability Index and spatial exposure index The initial toughness index of the mesh is obtained by performing a weighted summation. ; S62: Abstract the road network of the area where the research community is located into a graph. , N For road intersections and The number of connection points between roads and grids L Calculate the global efficiency of the road network given the number of road segments. ; S63: Simulate a random disaster on a road network, cut off road segments from the network, remove the cut segments, and calculate the efficiency of the road network after removal. Improve road network efficiency With overall efficiency The proportion serves as the resilience of the regional road network. ; S64: Utilizing toughness Multiply by the initial toughness index The initial toughness index of the mesh After making corrections, the corrected toughness index is obtained. .

7. The method for evaluating the safety resilience of urban communities according to claim 6, characterized in that, Step S7 includes: S71: Modified toughness index based on all grids within the research community's area. variance and average Calculate the safety resilience hotspot index for each grid. ; S72: Based on the safety resilience hotspot index The positive and negative values ​​of the index and the magnitude of the modified resilience index are used to screen high-safety-resilience clusters and low-safety-resilience clusters in the study community, locate areas with weak safety resilience, and generate a safety resilience distribution map of the study community to guide the precise allocation of disaster prevention resources. when and When, then the grid i If the security resilience hotspot index of a grid is higher than the global mean, and the security resilience hotspot indexes of its neighboring grids are also generally higher than the global mean, then the grid is considered to be... i Located within a high-safety, resilient cluster area; when and When, then the grid i If the security resilience hotspot index of a grid is lower than the global mean, and the security resilience hotspot index of its neighboring grids is also generally lower than the global mean, then the grid is considered to be... i Located within a low-safety-resilience cluster area; Otherwise, determine the grid. i These are anomalies in the safety and resilience space.

8. A safety resilience assessment system for urban communities, used to execute the safety resilience assessment method for urban communities according to any one of claims 1-7, characterized in that, include: Computers and processors; The processor is equipped with a data acquisition module and a data processing module. The data acquisition module is used to collect multi-source map data of the area where the research community is located. The data processing module is used to filter out high security resilience clusters and low security resilience clusters of the research community based on the multi-source map data, and construct a security resilience distribution map. The computer is equipped with a map display module, which is used to display the constructed security resilience distribution map and to view the high security resilience clusters and low security resilience clusters of the research community.

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