Urban space resilience measurement method and system based on rain flood process

Abstract

The application discloses a kind of urban space resilience measurement method and system based on rain flood process, belong to urban and rural planning technical field.The application considers the dynamic performance change that different function services expression is supported in actual extreme rain flood process city space, and the ability set of multiple city service functions under the analysis of urban space resilience is supported multiple space elements.By coupling the process and result measure of resilience, the measurement framework of the multi-functional service "process-result" coupling of urban space resilience is established, the direct measurement of process resilience under multiple extreme rainfall scenarios and the indirect measurement of result resilience based on process resilience coupling are unified, the quantitative measurement of urban space resilience is limited in the target domain range of specific disaster influence, so that the pertinence and operability of resilience measurement are stronger, and the accurate measurement of urban space resilience under rain flood scene is realized.

Description

Technical Field

[0001] This invention belongs to the field of urban and rural planning technology, and more specifically, relates to a method and system for measuring urban spatial resilience based on rainwater processes. Background Technology

[0002] Climate change and urban development have significantly increased the likelihood of extreme rainfall events and their associated flood disasters. Floods, as a periodic disaster in specific climate zones, are now simultaneously facing the combined effects of short-duration, uncertain extreme weather events. Cities are increasingly exposed to floods, making traditional flood risk management and disaster prevention models increasingly challenging.

[0003] "Resilience" refers to a system's ability to prepare for, resist, recover from, and adapt to disturbances. As the importance of resilient city construction as a national strategy grows, urban resilience measurement is gradually shifting from comprehensive assessments to refined resilience assessments targeting specific disaster processes. Urban spatial resilience, an extension of the resilience concept into the urban spatial dimension, focuses more on the supporting role of urban physical spatial structure, land use patterns, and infrastructure networks in the overall resilience of the city. Accurately measuring urban spatial resilience and identifying how spatial land use elements and infrastructure elements play their functional service and protection roles during disasters is crucial for promoting the construction of urban spatial safety and resilience.

[0004] Existing methods for measuring urban spatial resilience mainly rely on indirect measurement and assessment. They focus on key indicators of urban spatial elements such as form and structure, and conduct indirect measurement of resilience indicators from a static perspective from the construction of indicator assessment systems. They rarely consider the dynamic performance changes of urban space in supporting different functional services during actual extreme rainstorms, and thus cannot achieve accurate measurement of urban spatial resilience in rainstorm scenarios. Summary of the Invention

[0005] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a method and system for measuring urban spatial resilience based on rainwater processes, so as to solve the technical problem that the existing technology cannot achieve accurate measurement of urban spatial resilience in rainwater scenarios.

[0006] To achieve the above objectives, in a first aspect, the present invention provides a method for measuring urban spatial resilience based on stormwater processes, comprising:

[0007] The urban spatial area to be measured is taken as the study area. Historical rainfall data of the study area are analyzed to determine the various extreme rainfall scenarios involved in the study area. A hydrological and hydrodynamic model is constructed, and then a preset time step is set for each type of extreme rainfall scenario. Generate a flood inundation depth map and construct a corresponding flood inundation depth map sequence;

[0008] The spatial element data and population data of the study area were analyzed to determine the key urban service function types of the study area; the study area was divided into multiple grid units according to the preset unit scale.

[0009] Calculate the urban spatial resilience of each raster cell C with a population distribution:

[0010] Regarding the first m For extreme rainfall scenarios, obtain the flood inundation depth map sequence. The first in t Current flood inundation depth map Location of areas exceeding the preset flooding threshold ; ; and Corresponding sequences The start and end times; if grid cell C is located in the region position At that time, the first m The first extreme rainfall scenario t At any given time, the accessibility levels of various key urban service functions in grid cell C are all set to 0; otherwise, the accessibility levels of the study area are set to 0. Calculate the number after removal m The first extreme rainfall scenario t At time C, the first n Accessibility level of key urban service functions ; Calculate the first m The first grid cell C under extreme rainfall scenarios n Process resilience level of key urban service functions ;in, For the grid cell C under the no-rainfall scenario, the first n Accessibility level of key urban service functions; ; M represents the number of extreme rainfall scenarios involved in the study area; N represents the number of key urban service functions in grid cell C.

[0011] Calculate the first grid cell C n Outcome resilience of key urban service functions ;in, For the first m Under extreme rainfall scenarios, the first n The weights corresponding to the process resilience levels of key urban service functions; For the first m The probability of occurrence of rainstorm disasters corresponding to extreme rainfall scenarios;

[0012] The urban spatial resilience of grid cell C is obtained by weighted summation of the resilience results of various key urban service functions.

[0013] More preferably, the above calculation of the first m The first extreme rainfall scenario t At time C, the first n Accessibility level of key urban service functions ,include:

[0014] Obtain the supply and demand matching network of the study area Among them, supply and demand matching network ; It is a set of all supply points in the study area; each supply point carries the centroid coordinate information of the supply point; the supply points include: POI facility data corresponding to various key urban service functions in the study area; It is a set consisting of all demand points in the study area; each demand point carries its coordinate information; demand points include: the center point of the grid cell in the study area where there is a population distribution; Let be the set of all edges in the road traffic network of the study area; This is the set of all intersections of edges in the road traffic network of the study area; the road traffic network is obtained by performing topological processing on the road traffic vector data of the study area; the edges in the road traffic network represent roads in the study area and carry the location coordinates of the road and the driving speed information on the road; the intersections in the road traffic network carry the location coordinates of the intersection.

[0015] Delete supply and demand matching network Centrally located in the region The point or edge, and multiply the driving speed information carried on the remaining edge by the road speed corresponding to that edge in the th position. m Sensitivity coefficient under extreme rainfall scenarios , obtained the m Supply and demand matching network under extreme rainfall scenarios Based on supply and demand matching network Calculate the first m The first extreme rainfall scenario t At time C, the first n Accessibility level of key urban service functions ; .

[0016] More preferably, the first m The first extreme rainfall scenario t At time C, the first n Accessibility level of key urban service functions For: Supply and demand matching network Demand points corresponding to the middle grid cell C The n The accessibility level of key urban service functions is specifically as follows:

[0017]

[0018] in, In order to match supply and demand networks Search engines use preset travel modes to start from demand points Departure time corresponding to this mode of transportation The set of all accessible supply points within the area; In order to match supply and demand networks From the demand point Departure point selection The probability of; supply point The ratio of facilities and services to population demand ; supply point Service capabilities, through supply points The capacity of facilities to provide services is characterized; In order to match supply and demand networks Search from supply point Depart at the preset time The set of demand points that are accessible within the area; In order to match supply and demand networks From the demand point Departure point selection The probability of; For demand points Population size; Based on Distance correction factor; To use pre-set travel methods from demand points Departure to the supply point Travel time;

[0019] For any demand point and supply points In supply and demand matching networks From the demand point Departure point selection probability for:

[0020]

[0021] based on The distance correction factor is:

[0022]

[0023] in, supply point Demand points The attraction; To use pre-set travel methods from demand points Departure to the supply point Travel time; In order to match supply and demand networks Search engines use preset travel modes to start from demand points Departure time corresponding to this mode of transportation The set of all accessible supply points within the area; To start from the demand point Departure to the supply point Walking time; To supply point The starting walking time threshold takes into account the distance decay effect.

[0024] More preferably, in the no-rainfall scenario, the first... n Accessibility level of key urban service functions Based on supply and demand matching network Calculated.

[0025] More preferably, in the no-rainfall scenario, the first... n Accessibility level of key urban service functions For: Supply and demand matching network Demand points corresponding to the middle grid cell C The n The accessibility level of key urban service functions is specifically as follows:

[0026]

[0027] in, In order to match supply and demand networks Search engines use preset travel modes to start from demand points Departure time corresponding to this mode of transportation The set of all accessible supply points within the area; In order to match supply and demand networks From the demand point Departure point selection The probability of; supply point The ratio of facilities and services to population demand; ; supply point Service capabilities, through supply points The capacity of facilities to provide services is characterized; In order to match supply and demand networks Search from supply point Depart at the preset time The set of demand points that are accessible within the area; 'For supply and demand matching networks From the demand point Departure point selection The probability of; For demand points Population size; Based on Distance correction factor; To use pre-set travel methods from demand points Departure to the supply point Travel time;

[0028] For any demand point and supply points In supply and demand matching networks From the demand point Departure point selection probability 'for:

[0029]

[0030] based on The distance correction factor is:

[0031]

[0032] in, supply point Demand points The attraction; To use pre-set travel methods from demand points Departure to the supply point Travel time; In order to match supply and demand networks Search engines use preset travel modes to start from demand points Departure time corresponding to this mode of transportation The set of all accessible supply points within the area; To start from the demand point Departure to the supply point Walking time; To supply point The starting walking time threshold takes into account the distance decay effect.

[0033] More preferably, the first m The probability of occurrence of flood disasters corresponding to extreme rainfall scenarios is:

[0034]

[0035] in, It is the first m Recurrence period of similar extreme rainfall scenarios; For the first m The conditional probability of extreme rainfall scenarios triggering rainstorm disasters; These are preset coefficients; For the future climate change m The percentage increase in risk for various rainstorm disaster scenarios.

[0036] More preferably, the key urban service functions include one or more of the following: residential service functions, medical service functions, green space leisure service functions, industrial production service functions, commercial office service functions, and cultural service functions.

[0037] More preferably, the ordered weighted average method is used to sum the weighted results of the resilience of various key urban service functions of grid cell C to obtain the urban spatial resilience of grid cell C.

[0038] More preferably, a preset time step For 5 minutes, half an hour, or 1 hour.

[0039] More preferably, the spatial element data includes: administrative division data at all levels of the study area, land use data, building outline data, various POI data, and street network data.

[0040] Secondly, the present invention provides an urban spatial resilience measurement system based on rainwater processes, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor executes the urban spatial resilience measurement method provided in the first aspect of the present invention when executing the computer program.

[0041] Fifthly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein the computer program, when executed by a processor, controls the device containing the storage medium to execute the urban spatial resilience measurement method provided in the first aspect of the present invention.

[0042] In a sixth aspect, the invention also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the urban spatial resilience measurement method provided in the first aspect of the invention.

[0043] In summary, the above-described technical solutions conceived in this invention can achieve the following beneficial effects:

[0044] 1. This invention provides a method for measuring urban spatial resilience based on stormwater processes. Considering the dynamic performance changes of urban space supporting different functional services during actual extreme stormwater events, urban spatial resilience is analyzed as a set of multi-city service functional capabilities supported by multiple spatial elements. By coupling the process and outcome measurements of resilience, a multi-functional service "process-outcome" coupled measurement framework for urban spatial resilience is established. By unifying the direct measurement of process resilience under specific extreme rainfall scenarios and the indirect measurement of outcome resilience based on process resilience coupling, the quantitative measurement of urban spatial resilience is limited to the target domain of specific disaster impacts. This makes the resilience measurement more targeted and operable, achieving accurate measurement of urban spatial resilience under stormwater scenarios.

[0045] 2. Furthermore, in the urban spatial resilience measurement method based on rainwater processes provided by this invention, the accessibility level is calculated based on the supply and demand matching network. It matches the spatial element supply services with residents' needs and calculates based on real road traffic vector data, which is conducive to directly reflecting the accessibility level of various urban service functions. This serves as a process resilience performance indicator for various key urban service functions, which is accurate and intuitive, thereby enabling rapid measurement of various process resilience.

[0046] 3. Furthermore, the urban spatial resilience measurement method based on rainwater processes provided by this invention considers the variability of climate risk in resilience measurement, particularly addressing the shortcoming that extreme rainwater disasters have a low probability of occurrence and are easily overlooked in general assessments. This invention incorporates the probability of rainwater disasters occurring under the influence of climate change, considering the recurrence period of extreme rainfall, the conditional probability of rainwater disasters, and the risk of future extreme climate change escalation, so as to dynamically adjust the sensitivity of decision-making regarding extreme rainwater risk according to the actual climate risk situation in the study area. Specifically, the method... m The probability of rainstorm disasters occurring under extreme rainfall scenarios is: ;in, It is the first m The recurrence period of similar extreme rainfall scenarios For the first m The conditional probability of extreme rainfall scenarios triggering rainstorm disasters; These are preset coefficients; For the future climate change m The percentage increase in risk for various rainstorm disaster scenarios; based on this, the present invention further improves the accuracy of measuring urban spatial resilience in response to rainstorm disasters under the background of future climate change.

[0047] 4. Furthermore, in the urban spatial resilience measurement method based on rainwater processes provided by this invention, an ordered weighted average method is used to sum the weighted resilience results of various key urban service functions of grid cell C to obtain the urban spatial resilience of grid cell C. This method not only considers the objectivity of various urban service function resilience indicators, but also incorporates the differences in decision-making risks of different urban service functions when facing extreme rainwater disasters. It can provide urban spatial resilience measurement and assessment results under multiple decision-making risk comparisons, which helps to flexibly extend the measurement and application of urban spatial resilience in the research area. Attached Figure Description

[0048] Figure 1 A flowchart of a method for measuring urban spatial resilience based on rainwater processes, provided as an embodiment of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0050] To address the shortcomings or improvement needs of existing technologies, this invention provides a method for measuring urban spatial resilience based on rainwater processes from the perspective of resident service functions. This solves the technical problems of existing technologies, such as ignoring changes in urban service functions and their resilience differences during extreme disasters, having an abundance of indirect measurement of resilience indicators but insufficient direct measurement of core performance indicators, and having diverse indicators but insufficient technical model transformation and engineering practice application.

[0051] To achieve the above objectives, firstly, such as Figure 1 As shown, this invention provides a method for measuring urban spatial resilience based on rainwater processes, including:

[0052] S1. Taking the urban spatial area to be measured as the study area, analyze the historical rainfall data of the study area to determine the various extreme rainfall scenarios involved in the study area, and construct a hydrological and hydrodynamic model. Then, for each type of extreme rainfall scenario, set a preset time step. Generate a flood inundation depth map and construct a corresponding flood inundation depth map sequence;

[0053] The spatial element data and population data of the study area were analyzed to determine the key urban service function types of the study area; the study area was divided into multiple grid units according to the preset unit scale.

[0054] In one optional implementation, the types of extreme rainfall scenarios include a 5-year return period extreme rainfall scenario, a 10-year return period extreme rainfall scenario, a 20-year return period extreme rainfall scenario, a 50-year return period extreme rainfall scenario, and a 100-year return period extreme rainfall scenario; preset time step. It can be in minute, half-hour or hour increments, preferably 5 minutes, half an hour or 1 hour increments.

[0055] In one alternative implementation, the spatial element data includes: administrative division data at all levels of the study area, land use data, building outline data, various POI data, and street network data; key urban service functions include one or more of the following: residential service functions, medical service functions, green space service functions, industrial production service functions, commercial office service functions, and cultural and educational service functions.

[0056] In one optional implementation, the grid unit scale of the study area is determined by analyzing the functions of land parcels, the distribution of facilities, the block scale, and the composition of the street network. Preferably, the grid unit scale of the old city area in the study area is 100-200m, and the grid unit scale of the new city area in the study area is 300-500m.

[0057] S2. Calculate the urban spatial resilience of each raster cell C with a population distribution:

[0058] Regarding the first m For extreme rainfall scenarios, obtain the flood inundation depth map sequence. The first in t Current flood inundation depth map Location of areas exceeding the preset flooding threshold ; ; and Corresponding sequences The start and end times; if grid cell C is located in the region position At that time, the first m The first extreme rainfall scenario t At any given time, the accessibility levels of various key urban service functions in grid cell C are all set to 0; otherwise, the accessibility levels of the study area are set to 0. After removal, calculate the first... m The first extreme rainfall scenario t At time C, the first n Accessibility level of key urban service functions ; Calculate the first m The first grid cell C under extreme rainfall scenarios n Process resilience level of key urban service functions ;in, For the grid cell C under the no-rainfall scenario, the first n Accessibility level of key urban service functions; ; M represents the number of extreme rainfall scenarios involved in the study area; N represents the number of key urban service functions in grid cell C.

[0059] Calculate the first grid cell C n Outcome resilience of key urban service functions ;in, For the first m Under extreme rainfall scenarios, the first n The weights corresponding to the process resilience levels of key urban service functions; For the first m The probability of occurrence of rainstorm disasters corresponding to extreme rainfall scenarios; preferably, The weights can be determined using methods primarily based on objective assignment, such as entropy weighting and CRITIC weighting, which are not limited here.

[0060] The urban spatial resilience of grid cell C is obtained by weighted summation of the resilience results of various key urban service functions.

[0061] It should be noted that the urban spatial resilience mentioned above takes into account stormwater disasters. It should also be noted that the aforementioned preset inundation threshold... The threshold for the submerged water depth is determined based on the specific conditions of the study area, with a preferred value of 30-50 cm.

[0062] It should be noted that the above-mentioned... m The probability of rainstorm disasters occurring under extreme rainfall scenarios can be represented as the first... m The historical probability of occurrence of rainstorm disasters under extreme rainfall scenarios can also be used to consider the future climate change. m The probability of rainstorm disasters occurring under extreme rainfall scenarios; the second method is preferred. Specifically, in one optional implementation, the first... m The probability of occurrence of flood disasters corresponding to extreme rainfall scenarios is:

[0063]

[0064] in, It is the first m Recurrence period of similar extreme rainfall scenarios; For the first mThe conditional probability of extreme rainfall scenarios triggering rainstorm disasters is influenced by factors such as the characteristics of the underlying surface in the study area (e.g., topography, vegetation, infrastructure), social vulnerability, and historical risk probability. Indicators such as rainstorm disaster intensity (e.g., historical inundation depth, inundation area) and direct economic losses can be selected. The preset coefficient can be determined based on empirical values. It is preferably a sensitivity index of decision-makers to future disaster risk trends, used to amplify or reduce the impact of future climate change on the probability of rain and flood disasters. For the future climate change m The percentage increase in risk for various rainstorm disaster scenarios can be output based on various climate prediction models.

[0065] It should be noted that the above-mentioned method for calculating accessibility level can be any existing method for calculating accessibility level, such as the cost grid method or the cumulative opportunity method, and there is no limitation here.

[0066] Preferably, in an optional implementation, the above calculation of the first... m The first extreme rainfall scenario t At time C, the first n Accessibility level of key urban service functions ,include:

[0067] Obtain the supply and demand matching network of the study area Among them, supply and demand matching network ; It is a set of all supply points in the study area; each supply point carries the centroid coordinate information of the supply point; the supply points include: POI facility data corresponding to various key urban service functions in the study area; It is a set consisting of all demand points in the study area; each demand point carries its coordinate information; demand points include: the center point of the grid cell in the study area where there is a population distribution; Let be the set of all edges in the road traffic network of the study area; This is the set of all intersections of edges in the road traffic network of the study area; the road traffic network is obtained by performing topological processing on the road traffic vector data of the study area; the edges in the road traffic network represent roads in the study area and carry the location coordinates of the road and the driving speed information on the road; the intersections in the road traffic network carry the location coordinates of the intersection.

[0068] Delete supply and demand matching network Centrally located in the region The point or edge, and multiply the driving speed information carried on the remaining edge by the road speed corresponding to that edge in the th position. m Sensitivity coefficient under extreme rainfall scenarios , obtained the m Supply and demand matching network under extreme rainfall scenarios Based on supply and demand matching network Calculate the first m The first extreme rainfall scenario t At time C, the first n Accessibility level of key urban service functions ; .

[0069] It should be noted that the vehicle speed information on the road can be the average vehicle speed on that road, or it can be determined according to the road grade. In one optional implementation, the first... m The road grade under extreme rainfall scenarios is No. j Speed ​​limits for vehicles on roads of different grades Among them, the roads in the study area will be in the first... m Under extreme rainfall scenarios, roads in the study area were classified into different speed levels, with each level corresponding to a specific driving speed. Similarly, roads in the study area were classified under no-rainfall scenarios, with each level corresponding to a specific driving speed. The road classification is No. 1 under no rainfall scenario. j The driving speed at the specified level; The sensitivity coefficient of driving speed to extreme rainfall and flooding is determined based on the severity of the rainfall and flooding scenario. ∈[0,1].

[0070] Correspondingly, in one optional implementation, the first grid cell C in the no-rainfall scenario... n Accessibility level of key urban service functions Based on supply and demand matching network Calculated.

[0071] It should be noted that there are various methods for calculating the reachability level based on the supply and demand matching network, such as the two-step move search method and the improved two-step move search method, etc., which are not limited here.

[0072] Preferably, in one optional implementation, the first m The first extreme rainfall scenario t At time C, the first n Accessibility level of key urban service functions For: Supply and demand matching network The demand point located in grid cell C The n The accessibility level of key urban service functions is specifically as follows:

[0073]

[0074] in, In order to match supply and demand networks Search engines use preset travel modes to start from demand points Departure time corresponding to this mode of transportation The set of all accessible supply points within the area; In order to match supply and demand networks From the demand point Departure point selection The probability of; supply point The ratio of facility services to population demand (i.e., facility service capacity per unit of population). ; supply point Service capabilities, through supply points The capacity of facilities to provide services can be characterized (by indicators such as the service scale of the supply points; for example, for medical service facility supply points, ...). The number of hospital beds can be used to characterize its service capacity. In order to match supply and demand networks Search from supply point Depart at the preset time The set of demand points that are accessible within the area; In order to match supply and demand networks From the demand point Departure point selection The probability of; For demand points Population size; Based on Distance correction factor; To use pre-set travel methods from demand points Departure to the supply point Travel time;

[0075] For any demand point and supply points In supply and demand matching networks From the demand point Departure point selection probability for:

[0076]

[0077] based on The distance correction factor is:

[0078]

[0079] in, supply point Demand points The attractiveness; it should be noted that the attractiveness can be characterized by the service quality and level of the supply facilities. For example, optionally, for park and green space facilities, the higher the level, the larger the M value; for medical service facilities, tertiary hospitals (5 points) > secondary hospitals (4 points) > primary hospitals (3 points) > community hospitals (2 points). To use pre-set travel methods from demand points Departure to the supply point Travel time; In order to match supply and demand networks Search engines use preset travel modes to start from demand points Departure time corresponding to this mode of transportation The set of all accessible supply points within the area; To start from the demand point Departure to the supply point Walking time; To supply point The starting point considers the walking time threshold due to distance decay; in one alternative implementation, it considers the functional service needs of residents within a 5-15 minute community living circle. The optional value is 5 to 15 minutes.

[0080] In one alternative implementation, under a no-rainfall scenario, the first... n Accessibility level of key urban service functions For: Supply and demand matching network Demand points corresponding to the middle grid cell C The n The accessibility level of key urban service functions is specifically as follows:

[0081]

[0082] in, In order to match supply and demand networks Search engines use preset travel modes to start from demand points Departure time corresponding to this mode of transportation The set of all accessible supply points within the area; In order to match supply and demand networks From the demand point Departure point selection The probability of; supply point The ratio of facilities and services to population demand; ; supply point Service capabilities, through supply points The capacity of facilities to provide services is characterized; In order to match supply and demand networks Search from supply point Depart at the preset time The set of demand points that are accessible within the area; 'For supply and demand matching networks From the demand point Departure point selection The probability of; For demand points Population size; Based on Distance correction factor; To use pre-set travel methods from demand points Departure to the supply point Travel time;

[0083] For any demand point and supply points In supply and demand matching networks From the demand point Departure point selection probability 'for:

[0084]

[0085] based on The distance correction factor is:

[0086]

[0087] in, supply point Demand points The attraction; To use pre-set travel methods from demand points Departure to the supply point Travel time; In order to match supply and demand networks Search engines use preset travel modes to start from demand points Departure time corresponding to this mode of transportation The set of all accessible supply points within the area; To start from the demand point Departure to the supply point Walking time; To supply point The starting walking time threshold takes into account the distance decay effect.

[0088] It should be noted that the above preset time The time is determined based on a preset travel mode, and can be determined by empirical values ​​under the preset travel mode or by the average shortest travel time between demand and supply points of a certain type of facility in the study area under the preset travel mode. In one optional implementation, the aforementioned preset time... The value is 20 minutes.

[0089] It should be noted that the preset travel modes are such as cycling, walking, and driving, which are mainly based on the main modes of transportation for residents to travel under the road traffic network.

[0090] The above method matches the supply of spatial elements with residents' needs, and based on the shortest travel time under a real road traffic network, the calculation process considers both the facility carrying capacity of the supply points and the population size of the demand points, which is conducive to reflecting the accessibility level of various urban service functions. This serves as a precise and intuitive indicator of the process resilience of various key urban service functions, thereby enabling rapid measurement of the resilience of various processes.

[0091] It should be noted that there are multiple methods for obtaining the weights in the weighted summation of the resilience of various key urban service functions of grid cell C to obtain the urban spatial resilience of grid cell C. For example, the relevant weights can be determined in advance based on experience and then weighted summed. Alternatively, methods such as ordered weighted average can be used. No specific method is specified here.

[0092] Preferably, in one optional implementation, the specific process of the ordered weighted average method is as follows:

[0093] The resilience results of various urban service functions in the normalized raster cell C are sorted in descending order, with criterion weights. The city is also ranked accordingly, and based on the weighted criteria after ranking, a risk coefficient is given for the trade-off decision regarding different types of urban service functions. Calculate the order weights of various urban service function resilience indicators under different decision-making risks. The formula is as follows:

[0094]

[0095]

[0096] In the formula, The importance level of the resilience of various resident service functions in grid cell C is determined based on the magnitude of the indicator value; ( () represents the risk coefficient for functional trade-off decisions; To assign importance levels based on the resilience index values ​​of various urban service functions in grid cell C, the maximum value is 1, and the values ​​decrease sequentially to the minimum. .

[0097] The urban spatial resilience of grid cell C is calculated accordingly. .

[0098] Based on a comprehensive evaluation of both subjective and objective factors, a method for measuring urban spatial resilience based on an ordered weighted average approach is proposed. This method not only considers the objectivity of various urban service function resilience indicators, but also incorporates the differences in decision-making risks of different urban service functions when facing extreme rain and flood disasters. It can provide urban spatial resilience measurement and assessment results under multiple decision-making risk comparisons, which is conducive to the flexible extension of urban spatial resilience measurement and application in the study area.

[0099] In one alternative implementation, the urban spatial resilience of different grid units in the study area is classified using the natural discontinuity method, and the results are visualized using ArcGIS spatial technology to obtain an urban spatial resilience classification map based on the rainwater process.

[0100] In summary, the urban spatial resilience measurement method based on rainwater processes provided by this invention has the following beneficial effects:

[0101] First, from the perspective of resident service functions, this study considers the people-centered concept and demands of resilient city construction, analyzing urban spatial resilience as a collection of multifunctional service capabilities supported by multiple spatial elements. Based on this, by coupling the process and outcome measurements of resilience, a measurement framework for the multifunctional service "process-outcome" coupling of urban spatial resilience is established. This framework unifies the direct measurement of process resilience under specific rainstorm disaster scenarios with the indirect measurement of outcome resilience based on the coupling of process resilience. This limits the quantitative measurement of urban spatial resilience to the target domain of specific disaster impacts, thereby making the resilience measurement more targeted and operable, and contributing to the optimization of resilience measures for the spatial element regulation of single disaster processes and the comprehensive regulation of multiple indicators.

[0102] Second, the resilience measurement considers the variability of climate risk, particularly addressing the weakness of extreme disasters with low probability, which is easily overlooked in general assessments. The technical method of this invention incorporates the probability of stormwater disaster risk under the influence of climate change, considering the recurrence period of extreme rainfall, the conditional probability of stormwater disasters, and the risk of future extreme climate change escalation. This allows for dynamic adjustment of the decision-making sensitivity to extreme stormwater risk based on the actual climate risk situation in the study area. The results, based on this method, are largely consistent with the actual situation of increasingly frequent and escalating extreme stormwater risk in cities, meeting the requirements of practicality.

[0103] Secondly, the present invention provides an urban spatial resilience measurement system based on rainwater processes, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor executes the urban spatial resilience measurement method provided in the first aspect of the present invention when executing the computer program.

[0104] The relevant technical solutions are the same as the urban spatial resilience measurement method provided in the first aspect of this invention, and will not be described in detail here.

[0105] Fifthly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein the computer program, when executed by a processor, controls the device containing the storage medium to execute the urban spatial resilience measurement method provided in the first aspect of the present invention.

[0106] The relevant technical solutions are the same as the urban spatial resilience measurement method provided in the first aspect of this invention, and will not be described in detail here.

[0107] In a sixth aspect, the invention also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the urban spatial resilience measurement method provided in the first aspect of the invention.

[0108] The relevant technical solutions are the same as the urban spatial resilience measurement method provided in the first aspect of this invention, and will not be described in detail here.

[0109] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for measuring urban space resilience based on rain flood process, characterized in that, The method comprises the following steps: Taking a city space region to be tested as a research area, historical rainfall data of the research area are analyzed, each type of extreme rainfall scenario involved in the research area is determined, and the number of categories thereof is recorded as M; a hydrological and hydrodynamic model is constructed, and then each type of extreme rainfall scenario is divided into a plurality of preset time steps A flood submergence depth map is generated to form a corresponding flood submergence depth map sequence. analyzing spatial element data and population data of a research area, determining key urban service function types of the research area, and recording the number of the categories as N; dividing the research area into a plurality of grid units according to a preset unit scale; and calculating urban spatial resilience of each grid unit C with population distribution: computing the first m extreme rainfall scenarios the first n process resilience level of the key urban service function of the grid cell C ; ; ; ; and are the start time and the end time of the sequence of flood inundation depth maps corresponding to the scenario ; is the accessibility level of the first key urban service function of the grid cell C under the no rainfall scenario; n is the accessibility level of the first key urban service function of the grid cell C at the time instant of the scenario t ; n is the accessibility level of the first key urban service function of the grid cell C when the grid cell C is located at the regional location ; otherwise, By studying the area Calculated after removal; For sequence The t Current flood inundation depth map Locations of areas exceeding the preset flooding threshold; the first n Result resilience of the class of key urban service functions ; wherein, is the scenario the second n corresponding to the process resilience level of the class of key urban service functions; is the scenario the occurrence probability of the corresponding rain flood disaster; performing weighted summation on the result resilience of each key urban service function of the grid unit C to obtain the urban spatial resilience of the grid unit C; When the grid cell C is not located in the region position , is calculated by Obtaining a supply-demand matching network of a study area ; wherein the supply-demand matching network ; is a set of all supply points in the study area; each supply point carries information of the centroid coordinate of the supply point; the supply points include POI facility data corresponding to various types of key urban service functions in the study area; is a set of all demand points in the study area; each demand point carries information of the coordinate of the demand point; the demand points include the center points of grid cells with population distribution in the study area; is a set of all edges in the road traffic network of the study area; is a set of all intersection points of all edges in the road traffic network of the study area; the road traffic network is obtained by topological processing of road traffic vector data of the study area; each edge in the road traffic network represents a road in the study area and carries information of the location coordinate of the road and the driving speed on the road; each intersection point in the road traffic network carries information of the location coordinate of the intersection point; Deleting supply-demand matching network Located in the regional location The point or edge, and multiply the speed limit information carried on the remaining edge by the sensitivity coefficient of the road corresponding to the edge under the first m Class extreme rainfall scenario , get the supply-demand matching network under the first m Class extreme rainfall scenario ; based on the supply-demand matching network , the first m Class extreme rainfall scenario is calculated to obtain the accessibility level of the first t Class key urban service function of grid unit C at the first n Time ; .

2. The urban space resilience measurement method according to claim 1, characterized in that, : supply-demand matching network : demand point corresponding to the middle grid cell C : the first n : accessibility level of the key urban service function of the first class, specifically in, In order to match supply and demand networks Search engines use preset travel modes to start from demand points Departure time corresponding to this mode of transportation The set of all accessible supply points within the area; In order to match supply and demand networks From the perspective of demand Departure point selection The probability of; supply point The ratio of facilities and services to population demand ; supply point Service capabilities, through supply points The capacity of facilities and services to be carried out is characterized; In order to match supply and demand networks Search from supply point Depart at the preset time The set of demand points that are accessible within the area; In order to match supply and demand networks From the perspective of demand Departure point selection The probability of; For demand points Population size; For based on Distance correction factor; To use pre-set travel methods from demand points Departure to the supply point Travel time; For any demand point and supply point , the probability of choosing the supply point from the demand point in the supply-demand matching network is: Based on the distance correction coefficient is: wherein, is a supply point is an attraction of a demand point ; is a travel time from a demand point to a supply point in a preset travel mode; is a set of all reachable supply points from a demand point in a preset time in a preset travel mode in a supply-demand matching network ; is a walking time from a demand point to a supply point ; is a walking time threshold considering distance decay effect from a supply point .

3. The urban space resilience measurement method of claim 1, wherein, The first n The level of accessibility of the key urban service functions Based on supply-demand matching network Is calculated.

4. The urban space resilience measurement method according to claim 3, characterized in that, : supply-demand matching network : demand point corresponding to the middle grid cell C : the first n : accessibility level of the key urban service function of the first class, specifically: in, In order to match supply and demand networks Search engines use preset travel modes to start from demand points Departure time corresponding to this mode of transportation The set of all accessible supply points within the area; In order to match supply and demand networks From the perspective of demand Departure point selection The probability of; supply point The ratio of facilities and services to population demand; ; supply point Service capabilities, through supply points The capacity of facilities and services to be carried out is characterized; In order to match supply and demand networks Search from supply point Depart at the preset time The set of demand points that are accessible within the area; 'For supply and demand matching networks From the perspective of demand Departure point selection The probability of; For demand points Population size; For based on Distance correction factor; To use pre-set travel methods from demand points Departure to the supply point Travel time; For any demand point and supply point , the probability of choosing the supply point from the demand point in the supply-demand matching network is: Based on the distance correction coefficient is: in, supply point Demand points The attraction; To use pre-set travel methods from demand points Departure to the supply point Travel time; In order to match supply and demand networks Search engines use preset travel modes to start from demand points Departure time corresponding to this mode of transportation The set of all accessible supply points within the area; To start from the demand point Departure to the supply point Walking time; To supply point The starting walking time threshold takes into account the distance decay effect.

5. The urban space resilience measurement method according to any one of claims 1-4, characterized in that, Scenario The corresponding probability of occurrence of rain flood disaster is: in, It is the first m Recurrence period of similar extreme rainfall scenarios; For the first m The conditional probability of extreme rainfall scenarios triggering rainstorm disasters; These are preset coefficients; For the future climate change m The percentage increase in risk for various rainstorm disaster scenarios.

6. The urban space resilience measurement method according to any one of claims 1 to 4, characterized in that, the key urban service functions include one or more of residential service function, medical service function, green leisure service function, industrial production service function, commercial office service function and cultural service function.

7. The urban space resilience measurement method according to any one of claims 1 to 4, characterized in that, The weighted summation on the result resilience of each key urban service function of the grid unit C is performed by using an ordered weighted average method to obtain the urban spatial resilience of the grid unit C.

8. A system for measuring urban space resilience based on rain flood process, characterized in that, The method comprises the following steps: a memory and a processor, the memory stores a computer program, and the processor executes the computer program to execute the urban spatial resilience measurement method in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored computer program, wherein the computer program is run by a processor to control the device where the storage medium is located to execute the urban spatial resilience measurement method in any one of claims 1-7.

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