A simulation calculation method, device, equipment, medium and computer program product for urban stormwater resilience

Abstract

The present application relates to the field of urban flood control simulation assessment, and in particular to a simulation calculation method, device, equipment, medium and computer program product for urban stormwater resilience, which collects urban data of a study area, constructs an urban stormwater resilience spatial model to calculate the robustness index and rapidity index of the study area, and constructs an urban emergency evacuation spatial model to calculate the redundancy index and availability index of the study area. Finally, the robustness index, the rapidity index, the redundancy index and the availability index are added according to preset weights to obtain a quantitative result of urban resilience. A multi-dimensional urban stormwater resilience simulation calculation framework can be established from the four resilience characteristic attributes of robustness, redundancy, availability and rapidity, and the stormwater resilience characteristic dimensions taken into consideration are more comprehensive, thereby making the quantitative result of urban resilience more accurate.

Description

Technical Field

[0001] The present application relates to the field of urban flood control simulation assessment, and in particular to a simulation calculation method, device, equipment, medium and computer program product for urban stormwater resilience. Background Art

[0002] The United Nations International Strategy for Disaster Reduction (UNISDR) emphasizes that contemporary cities should have the ability to resist, absorb, adapt to and recover from external changes in a timely and effective manner, that is, to have "resilience". In 2003, Professor Michel Bruneau of the University at Buffalo, State University of New York, proposed the "4R" resilience characteristics, namely robustness, redundancy, resourcefulness, and rapidity. The more significant the 4R characteristics of an urban system, the stronger its urban resilience. In terms of urban stormwater resilience assessment, the 4R characteristics are mainly manifested as follows: (1) Robustness: the ability of urban buildings to resist floods; (2) Redundancy: the ability of urban transportation networks to evacuate people and provide emergency shelters; (3) Resourcefulness: the service capabilities of various emergency supplies and rescue forces in the city;

[0003] (4) Rapidity: The ability of the urban subsurface to dissipate floods. Currently, the main urban stormwater simulation models at home and abroad include the Stormwater Management Model (SWMM), the Digital Inundation Grid Model (DIGM), and the GPU-accelerated Surface Water Flow and Transport Model (GAST). These models are mainly used in well-known foreign cities such as New York, Tokyo, London, and Rotterdam, as well as first-tier cities in China such as Beijing, Shanghai, Guangzhou, and Shenzhen.

[0004] While existing technologies have attempted to apply visual simulation methods to resilience assessments, none fully consider the 4R resilience dimensions. In particular, they lack the integration of redundancy and availability, which are crucial for evacuating people and resupplying supplies during flood disasters. Furthermore, there is currently no simulation method that characterizes the 4R urban flood resilience using a unified time efficiency metric.

[0005] Therefore, how to comprehensively consider multiple 4R resilience dimensions when using visual simulation methods to simulate urban flood resilience and make the quantitative results of urban resilience more accurate is an urgent problem that needs to be solved. Summary of the Invention

[0006] This application provides a simulation and calculation method for urban stormwater resilience, which can establish a multi-dimensional urban stormwater resilience simulation and calculation framework based on the four resilience characteristic attributes of robustness, redundancy, availability and rapidity. The stormwater resilience characteristic attributes taken into consideration are more comprehensive, thereby making the quantitative results of urban resilience more accurate.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present invention provides a method for simulating urban flood resilience, the method comprising:

[0009] Collect urban data in the study area;

[0010] Importing the urban data into stormwater simulation software to obtain a spatial model of urban stormwater resilience;

[0011] Based on the urban stormwater resilience spatial model, dynamic demonstration data of water depth changes in the study area were calculated. Based on this data, the average time required for the outdoor water depth of each building block to reach the ground level of the first floor of the building and the average time required for the outdoor underlying surface water level of each block to return to zero were calculated. The robustness index and rapidity index of the study area were then obtained based on these two average values.

[0012] Importing the city data into evacuation simulation software to obtain a city emergency evacuation space model;

[0013] Calculating first dynamic demonstration data and second dynamic demonstration data based on the urban emergency evacuation spatial model, calculating the average time required for all residents of each residential area to evacuate on foot to surrounding temporary resettlement sites based on the first dynamic demonstration data, and obtaining a redundancy index based on this average value; then calculating the average time required for emergency rescue services to reach each temporary resettlement site based on the second dynamic demonstration data, and obtaining an availability index based on this average value;

[0014] The robustness index, the rapidity index, the redundancy index and the availability index are added according to preset weights to obtain a quantitative result of urban resilience.

[0015] In a preferred example of the present application, it can be further configured that the importing of the city data into the rain and flood simulation software includes:

[0016] The topographic data, urban underlying surface data, urban building height data, drainage network data and block boundary data in the urban data are imported into the model drawing platform of the stormwater simulation software.

[0017] In a preferred example of the present application, it can be further configured that the importing of the city data into the evacuation simulation software includes:

[0018] The municipal traffic data and functional business data in the city data are imported into the model drawing platform of the evacuation simulation software.

[0019] In a preferred example of the present application, it can be further configured to calculate the average time required for the outdoor water depth of each building in each block to reach the ground level of the first floor of the building based on the dynamic demonstration data of water depth changes, and obtain the robustness index of the study area based on this average value, including:

[0020] Assume that the real time after the rainstorm starts to enter the urban flood resilience spatial model is t, the total time is T, the block number of the study area is i (i = 1, 2...n), and the outdoor water depth of the building in the i-th block is h i , the ground level height of the building's first floor is H i , t i represents the duration from the beginning of the rainstorm to the input of the urban flood resilience spatial model. If t=t i (0≤t i ≤T) first meets h i ≥H i ,but:

[0021] a i =t i ;

[0022] Among them, a i It represents the time required for the outdoor water depth of the building in the i-th block to reach the ground level of the first floor of the building starting from t = 0;

[0023] If t = t i (0≤t i ≤T) always satisfies h i <H i ,but:

[0024] a i =T;

[0025] Then, the robustness index is calculated:

[0026]

[0027] Among them, t1 represents the average time required for the outdoor water depth of buildings in each block to reach the ground level of the first floor of the building, and P1 represents the robustness index of the study area after standardized processing.

[0028] In a preferred example of the present application, it can be further configured to calculate the average time required for the outdoor underlying surface water level of each block to return to zero based on the dynamic demonstration data of water depth change, and obtain a rapidity index based on this average value, including:

[0029] Assume that in n blocks, the time required for rainwater to seep into the outdoor underlying surface of each block and return to zero is b i (i=1,2…n), starting from t=T, if t=ti (t i ≥0) first meets h i =0, then:

[0030] b i =t i -T;

[0031] Then, the rapidity index is calculated:

[0032]

[0033] Among them, t2 represents the average time required for the outdoor underlying surface water level of each block to return to zero, and P2 represents the rapidity index of the study area after standardized processing.

[0034] In a preferred example of the present application, it can be further configured that the city data is imported into the evacuation simulation software to obtain the city emergency evacuation space model, and further includes:

[0035] A multidimensional coupling tool is used to connect the POI data of residential areas, convenience stores, hospitals, fire stations, hotels, public buildings, and public venues with the urban vehicle road system and the urban pedestrian network system, respectively, to form two complete transportation networks and destination systems, and obtain an urban emergency evacuation space model that includes municipal roads, slow-moving systems, residential areas, temporary resettlement points, and emergency rescue facilities.

[0036] In a second aspect, the present application provides a simulation and calculation device for urban flood resilience, the device comprising:

[0037] Data collection module, used to collect urban data in the study area;

[0038] A model building module is used to import the city data into the stormwater simulation software to obtain the city stormwater resilience spatial model; import the city data into the evacuation simulation software to obtain the city emergency evacuation spatial model;

[0039] An index quantification module is used to calculate dynamic demonstration data of water depth changes in the study area based on the urban stormwater resilience spatial model, calculate the average time required for the outdoor water depth of buildings in each block to reach the ground level of the first floor indoor of the building and the average time required for the outdoor underlying surface water level of each block to return to zero based on this data, and then obtain the robustness index and rapidity index of the study area based on these two average values; calculate first dynamic demonstration data and second dynamic demonstration data based on the urban emergency evacuation spatial model, calculate the average time required for all residents of each residential area to evacuate on foot to surrounding temporary resettlement points based on the first dynamic demonstration data, obtain the redundancy index based on this average value, and then calculate the average time required for emergency rescue services to reach each temporary resettlement point based on the second dynamic demonstration data, and obtain the availability index based on this average value;

[0040] The output module is used to add the robustness index, the rapidity index, the redundancy index and the availability index according to preset weights to obtain a quantitative result of urban resilience.

[0041] In a third aspect, the present application provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the simulation calculation method for urban stormwater resilience as described in any one of the above items are implemented.

[0042] In a fourth aspect, the present application provides a computer-readable storage medium having a program stored thereon, wherein when the program is executed by a processor, the simulation calculation method of urban stormwater resilience as described in any one of the above items is implemented.

[0043] In a fifth aspect, the present application provides a computer program product comprising computer instructions, which, when executed by a processor, implement the steps of the simulation method for urban stormwater resilience as described in any one of the above items.

[0044] In summary, compared with the prior art, the technical solutions provided by the embodiments of the present application have at least the following beneficial effects:

[0045] This application provides a simulation calculation method for urban stormwater resilience, which collects urban data of the study area, constructs an urban stormwater resilience spatial model to calculate the robustness index and rapidity index of the study area, and constructs an urban emergency evacuation spatial model to calculate the redundancy index and availability index of the study area. Finally, the robustness index, the rapidity index, the redundancy index and the availability index are added according to the preset weights to obtain the quantitative results of urban resilience. It can establish a multi-dimensional urban stormwater resilience simulation calculation framework based on the four resilience characteristic attributes of robustness, redundancy, availability and rapidity, and take into account the stormwater resilience characteristic dimensions more comprehensively, so that the quantitative results of urban resilience are more accurate. At the same time, this method also proposes the simulation ideas and calculation methods of urban stormwater resilience based on the time efficiency index, unifies the evaluation standards of different resilience dimensions, and makes the quantitative results more realistic. Furthermore, during the simulation process to obtain the final quantitative results of urban resilience, this method can also obtain analytical conclusions other than time efficiency. For example, by giving different initial rainstorm conditions and change curves, the stormwater simulation software can be used to analyze and evaluate the differences in the stormwater dissipation capacity of each block unit in the sample area, including identifying potential water accumulation points, major water collection and infiltration units, and high-efficiency drainage corridors. In addition, evacuation simulation software can be used to obtain the main evacuation routes of the crowd, determine whether there is an intersection with the drainage corridor, and determine whether there are safety hazards during personnel evacuation. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A flowchart of a method for simulating urban stormwater resilience provided in one embodiment of the present application.

[0047] Figure 2 A module diagram of a simulation and calculation device for urban stormwater resilience provided in one embodiment of the present application. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0049] In one embodiment of the present application, a simulation method for urban flood resilience is provided. Figure 1 As shown, the method includes:

[0050] S100: Collect urban data in the study area;

[0051] Specifically, the urban area of ​​the sample city to be evaluated is delineated and used as the study area. The urban spatial form and function data within the study area, i.e., the urban data, are summarized and organized and recorded in the database. The specific urban data collection method is as follows:

[0052] (1) Topographic data: The DEM elevation data within the research area was downloaded using the geospatial data cloud platform with an accuracy of 30 m. After the data was drawn, the vector file was imported into the ArcGIS platform for storage;

[0053] (2) Urban land use data: Use AutoCAD software to draw two-dimensional closed polygons to represent the block space and road space between blocks that make up the urban area. After the drawing is completed, the vector file is imported into the ArcGIS platform for storage;

[0054] (3) Urban building outline data: Use AutoCAD software to draw a two-dimensional closed polygon to represent the building's outer contour line. After the drawing is completed, the vector file is imported into the ArcGIS platform for storage;

[0055] (4) Urban building height data: Based on topographic data and urban building outline data, combined with the actual conditions of each building, the indoor ground level height data and overall building height data of each building are entered into the ArcGIS platform through the attribute table;

[0056] (5) Urban underlying surface data: Based on urban land use data and urban building outline data, urban road land and the areas inside each plot and outside the building outline are included in the underlying surface considerations, and AutoCAD software is used to classify plots with different underlying surface properties (including eight categories: asphalt, concrete, granite, wood, bare soil, lawn, woodland, and water surface); for plots with multiple underlying surface properties, two-dimensional closed polygons are drawn to distinguish them; after the drawing is completed, the vector file is imported into the ArcGIS platform and stored, and the underlying surface properties of each plot of land and the corresponding roughness and permeability coefficient are entered through the attribute table;

[0057] (6) Drainage network data: AutoCAD software was used to draw continuous one-dimensional line segments to represent the distribution of rainwater pipe corridors, and points were drawn to represent the distribution of water collection wells. After the drawing was completed, the vector file was imported into the ArcGIS platform and stored in the database. The flow, slope, and roughness data of each pipe were entered through the attribute table, and the flow data of each water collection well were recorded;

[0058] (7) Municipal traffic data: Use AutoCAD software to draw continuous one-dimensional line segments along the center line of the road to represent the urban traffic roads, and create two vector data files for the urban vehicle road system and the urban pedestrian network system. After the drawing is completed, the vector files are imported into the ArcGIS platform for storage;

[0059] (8) Functional business data: Use Baidu Satellite Map Platform (for domestic cities) and Google Satellite Map Platform (for foreign cities) to download POI data, including residential areas, convenience stores, hospitals, fire stations, hotels, public buildings, public venues, etc., and import them into the ArcGIS platform for storage;

[0060] (9) Block boundary data: Based on municipal traffic data, closed polygons are drawn without overlap along the one-dimensional line segment set of the urban road system and recorded as block boundaries. After the drawing is completed, the vector file is imported into the ArcGIS platform for storage.

[0061] S200: Importing the urban data into stormwater simulation software to obtain a spatial model of urban stormwater resilience;

[0062] Specifically, topographic data, urban underlying surface data, urban building height data, and drainage network data from the database were imported into the modeling platform of the stormwater simulation software. A multidimensional coupling tool was then used to connect the water collection wells to the urban underlying surface, forming a complete drainage system. This resulted in a spatial model of urban stormwater resilience that incorporated comprehensive spatial information, including topography, building form, underlying surface properties, and drainage network.

[0063] S300: Calculating dynamic demonstration data of water depth changes in the study area based on the urban stormwater resilience spatial model, calculating the average time required for the outdoor water depth of each building in each block to reach the ground level of the first floor of the building and the average time required for the outdoor underlying surface water level of each block to return to zero, and then calculating the robustness index and rapidity index of the study area based on these two average values;

[0064] Specifically, first, dynamic demonstration data on water depth changes in the study area were calculated based on the urban stormwater resilience spatial model. Initial simulation data was set in the model, including rainfall duration T, rainfall variation curves, and wind speed and direction variation curves. The simulation program was then run to obtain dynamic demonstration data on outdoor water depth changes in the study area from t = 0 to t = E, where t represents the real-time time after the onset of heavy rain and E represents the time when the outdoor water level in all blocks returns to zero.

[0065] Next, based on this data, the average time required for the outdoor water depth of each block to reach the ground level of the first floor of the building and the average time required for the outdoor underlying surface water level of each block to return to zero are calculated. Both average values ​​are then standardized to obtain the robustness index and rapidity index of the study area.

[0066] S400: Importing the city data into evacuation simulation software to obtain a city emergency evacuation space model;

[0067] Specifically, municipal traffic data and functional business data from the database were imported into the modeling platform of the evacuation simulation software. Then, using a multidimensional coupling tool, the point-of-interest data for residential areas, convenience stores, hospitals, fire stations, hotels, public buildings, and public venues were connected to the urban road system and the pedestrian network, respectively, forming two complete transportation networks and destination systems. This resulted in the creation of a spatial model for urban emergency evacuation, encompassing complete spatial information including municipal roads, slow-moving traffic systems, residential areas, temporary resettlement sites, and emergency rescue facilities.

[0068] S500: Calculating first dynamic demonstration data and second dynamic demonstration data based on the urban emergency evacuation spatial model, calculating the average time required for all residents of each residential area to evacuate on foot to surrounding temporary resettlement sites based on the first dynamic demonstration data, and obtaining a redundancy index based on the average time, then calculating the average time required for emergency rescue services to reach each temporary resettlement site based on the second dynamic demonstration data, and obtaining an availability index based on the average time;

[0069] Specifically, first, the first dynamic demonstration data and the second dynamic demonstration data are calculated based on the urban emergency evacuation space model. The initial simulation data are set in the model, including: for the urban vehicle road system, the road grade of each vehicle road is set (main road, secondary road or branch road), and the speed limit requirements of the three roads are specified; for the urban pedestrian network system, the average walking speed of pedestrians is set, and the location information of zebra crossings is supplemented; for each residential area, the number of residents to be evacuated is input according to the actual situation within the study range. Then, the simulation program is run to obtain the dynamic demonstration data of personnel evacuation based on pedestrian transportation (the first dynamic demonstration data) and the dynamic demonstration data of emergency rescue based on vehicle transportation (the second dynamic demonstration data) in the study area.

[0070] Next, the average time required for all residents of each settlement to evacuate to the surrounding temporary resettlement sites on foot is calculated based on the first dynamic demonstration data, and the redundancy index is obtained based on this average value. Assume that the real time after the rainstorm starts to enter the model is t, the total time of rainstorm input is T, the settlement number is j (j = 1, 2...m), m is the number of settlements, and the duration of walking from the first person to the last person in the jth settlement to the surrounding temporary resettlement site is c j , the average time t3 required for all residents of each settlement to evacuate to the surrounding temporary resettlement sites on foot is calculated by the following formula:

[0071]

[0072] The redundancy index P3 of the study area after standardization is calculated using the following formula:

[0073]

[0074] Finally, the average time required for emergency rescue services to reach each temporary resettlement point is calculated based on the second dynamic demonstration data, and the availability index is obtained based on this average. Assume that the temporary resettlement point is numbered as l (l = 1, 2...k), and for the lth temporary resettlement point, the duration from the various emergency rescue facilities to the resettlement point by vehicle transportation is d l , the average time t4 required for emergency rescue services to reach each temporary resettlement point is calculated by the following formula:

[0075]

[0076] The standardized accessibility index P4 of the study area is then calculated using the following formula:

[0077]

[0078] S600: Add the robustness index, the rapidity index, the redundancy index and the availability index according to preset weights to obtain a quantitative result of urban resilience.

[0079] Specifically, the score of each resilience indicator is obtained through weighted calculation, and the formula is as follows:

[0080]

[0081] Among them, y represents the number of the four indexes, ω y Indicates the preset weight values ​​of the four indexes, P y represents the resilience index after normalization of each dimension, x represents the total number of characteristic attributes, and S represents the comprehensive resilience score of the study area (0<S<1), that is, the quantitative result of urban resilience.

[0082] The value of the preset weight can be determined by the hierarchical analysis method after experts compare the relative importance of the four dimensions in pairs and obtain relative importance data.

[0083] In this embodiment, a multidimensional urban flood resilience simulation and calculation framework can be established based on the four resilience attributes of robustness, redundancy, availability, and rapidity. This takes into account a more comprehensive range of flood resilience characteristics, thereby making the resulting quantitative results of urban resilience more accurate. Furthermore, this method also proposes a simulation approach and calculation method for urban flood resilience based on the time efficiency metric, unifying the evaluation criteria for different resilience dimensions and making the quantitative results more realistic. Furthermore, during the simulation process leading to the final quantitative results of urban resilience, this method can also derive analytical conclusions beyond time efficiency. For example, by giving different initial rainstorm conditions and change curves, the flood simulation software can analyze and evaluate the differences in the flood dissipation capacity of each block in the sample area, including identifying potential waterlogging points, major water collection and infiltration units, and high-efficiency drainage corridors. Furthermore, through evacuation simulation software, the main evacuation routes can be determined, determining whether they intersect with drainage corridors and whether there are safety hazards during evacuation.

[0084] In some embodiments, the importing of the city data into the stormwater simulation software includes:

[0085] The topographic data, urban underlying surface data, urban building height data, drainage network data and block boundary data in the urban data are imported into the model drawing platform of the stormwater simulation software.

[0086] In this embodiment, by making full use of the satellite map data platform, the geospatial data platform and the commonly used engineering drawing software to construct the calculation model, the operation process is made more intuitive and concise, and suitable for wider application and promotion.

[0087] In some embodiments, importing the city data into evacuation simulation software includes:

[0088] The municipal traffic data and functional business data in the city data are imported into the model drawing platform of the evacuation simulation software.

[0089] In this embodiment, by making full use of the satellite map data platform, the geospatial data platform and the commonly used engineering drawing software to construct the calculation model, the operation process is made more intuitive and concise, and suitable for wider application and promotion.

[0090] In some embodiments, the average time required for the outdoor water depth of each building in each block to reach the ground level of the first floor of the building is calculated based on the dynamic demonstration data of water depth changes, and the robustness index of the study area is obtained based on this average value, including:

[0091] Assume that the real time after the rainstorm starts to enter the urban flood resilience spatial model is t, the total time is T, the block number of the study area is i (i = 1, 2...n), and the outdoor water depth of the building in the i-th block is hi , the ground level height of the building's first floor is H i , t i represents the duration from the beginning of the rainstorm to the input of the urban flood resilience spatial model. If t=t i (0≤t i ≤T) first meets h i ≥H i ,but:

[0092] a i =t i ;

[0093] Among them, a i It represents the time required for the outdoor water depth of the building in the i-th block to reach the ground level of the first floor of the building starting from t = 0;

[0094] If t = t i (0≤t i ≤T) always satisfies h i <H i ,but:

[0095] a i =T;

[0096] Then, the robustness index is calculated:

[0097]

[0098] Among them, t1 represents the average time required for the outdoor water depth of buildings in each block to reach the ground level of the first floor of the building, and P1 represents the robustness index of the study area after standardized processing.

[0099] In this embodiment, the accuracy of calculating the robustness index is improved.

[0100] In some embodiments, the average time required for the outdoor underlying surface water level of each block to return to zero is calculated based on the water depth change dynamic demonstration data, and the rapidity index is obtained based on the average value, including:

[0101] Assume that in n blocks, the time required for rainwater to seep into the outdoor underlying surface of each block and return to zero is b i (i=1,2…n), starting from t=T, if t=t i (t i ≥0) first meets h i =0, then:

[0102] b i =t i -T;

[0103] Then, the rapidity index is calculated:

[0104]

[0105] Among them, t2 represents the average time required for the outdoor underlying surface water level of each block to return to zero, and P2 represents the rapidity index of the study area after standardized processing.

[0106] In this embodiment, the accuracy of calculating the rapidity index is improved.

[0107] In some embodiments, the city data is imported into evacuation simulation software to obtain a city emergency evacuation space model, further comprising:

[0108] A multidimensional coupling tool is used to connect the POI data of residential areas, convenience stores, hospitals, fire stations, hotels, public buildings, and public venues with the urban vehicle road system and the urban pedestrian network system, respectively, to form two complete transportation networks and destination systems, and obtain an urban emergency evacuation space model that includes municipal roads, slow-moving systems, residential areas, temporary resettlement points, and emergency rescue facilities.

[0109] In this embodiment, the accuracy of the quantitative results of the urban resilience is improved.

[0110] This application also provides a simulation calculation device for urban flood resilience, please refer to Figure 2 As shown, the device includes:

[0111] Data collection module 100, used to collect urban data of the study area;

[0112] The model building module 200 is used to import the city data into the stormwater simulation software to obtain the city stormwater resilience spatial model; import the city data into the evacuation simulation software to obtain the city emergency evacuation spatial model;

[0113] Index quantification module 300 is used to calculate dynamic demonstration data of water depth changes in the study area based on the urban stormwater resilience spatial model, calculate the average time required for the outdoor water depth of each block to reach the ground level of the first floor of the building and the average time required for the outdoor underlying surface water level of each block to return to zero based on this data, and then obtain the robustness index and rapidity index of the study area based on these two average values; calculate first dynamic demonstration data and second dynamic demonstration data based on the urban emergency evacuation spatial model, calculate the average time required for all residents of each residential area to evacuate on foot to surrounding temporary resettlement points based on the first dynamic demonstration data, obtain the redundancy index based on this average value, and then calculate the average time required for emergency rescue services to reach each temporary resettlement point based on the second dynamic demonstration data, and obtain the availability index based on this average value;

[0114] The output module 400 is used to add the robustness index, the rapidity index, the redundancy index and the availability index according to preset weights to obtain a quantitative result of urban resilience.

[0115] The functional implementation of each module in the above-mentioned simulation calculation device for urban flood resilience corresponds to the steps in the above-mentioned simulation calculation method embodiment for urban flood resilience, and its functions and implementation processes will not be repeated here one by one.

[0116] The present application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the simulation calculation method for urban stormwater resilience as described in any of the above embodiments are implemented.

[0117] This application also provides a computer-readable storage medium having a program stored thereon. The computer-readable storage medium refers to a data storage medium and may include, but is not limited to, a floppy disk, an optical disk, a hard disk, a flash memory, a USB flash drive, and / or a memory stick. The computer may be a general-purpose computer, a dedicated computer, a computer network, or other programmable device. The operating process, operating details, and technical effects of the computer-readable storage medium provided in this embodiment can be found in the above-mentioned embodiment of a simulation method for urban flood resilience, and will not be further elaborated here.

[0118] The application also provides a computer program product comprising computer instructions, which, when executed by a processor, implement the steps of the method for simulating urban flood resilience as described in any of the above embodiments.

[0119] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0120] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification. The above-mentioned embodiments only express several implementation methods of the present application. The description is relatively specific and detailed, but it cannot be understood as a limitation on the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present application, several variations and improvements can be made, which all fall within the scope of protection of the present application. Therefore, the scope of protection of the patent of this application shall be based on the attached claims.

Claims

1. A simulation method for urban flood resilience, characterized by: include: Collect urban data of the study area, which at least includes urban underlying surface data, urban building height data, municipal transportation data, and functional business data; Importing the urban data into a model drawing platform of a stormwater simulation software to obtain a spatial model of urban stormwater resilience; Based on the urban stormwater resilience spatial model, dynamic demonstration data of water depth changes in the study area are calculated; Based on the dynamic demonstration data of water depth changes, the average time required for the outdoor water depth of each building block to reach the ground level of the first floor of the building and the average time required for the outdoor underlying surface water level of each block to return to zero were calculated. Then, based on these two average values, the robustness index and rapidity index of the study area were respectively obtained; The calculation process of the robustness index is: The real time after the rainstorm starts to enter the urban flood resilience spatial model is set to , the total time is , the block number of the study area is =1,2… , the outdoor water depth of the i-th block building is , the ground level height of the building's first floor is , It represents the duration from the beginning of the rainstorm to the input of the urban flood resilience spatial model. = and 0≤ ≤ First time satisfaction ≥ ,but: ; in, Indicates from =0Starting from The time required for the depth of outdoor water accumulation in a building block to reach the ground level of the first floor of the building; Ruodang = and 0≤ Always satisfied when ≤T < ,but: ； Then, the robustness index is calculated: ; ; in, It represents the average time required for the outdoor water depth of each building in each block to reach the ground level of the first floor of the building. represents the robustness index of the study area after normalization; The calculation process of the rapidity index is: Suppose that in n blocks, the time required for rainwater infiltration on the outdoor underlying surface of each block to subside and the water level to return to zero is =1,2… ,from = Start, if = ≥0 when the first satisfaction =0, then: ； Then, the rapidity index is calculated: ； ； in, It represents the average time required for the outdoor underlying surface water level of each block to return to zero. represents the rapidity index of the study area after normalization; Importing the municipal traffic data and functional business data in the urban data into the evacuation simulation software, and using a multidimensional coupling tool, connecting the POI data of residential areas, convenience stores, hospitals, fire stations, hotels, public buildings, and public venues with the urban pedestrian network system to obtain a first traffic network and destination system, and connecting the POI data of residential areas, convenience stores, hospitals, fire stations, hotels, public buildings, and public venues with the urban vehicle road system to form a second traffic network and destination system, thereby forming an urban emergency evacuation space model that includes municipal roads, slow-moving systems, residential areas, temporary resettlement points, and emergency rescue facilities; Based on the urban emergency evacuation spatial model, combined with the first transportation network and the destination system, first dynamic demonstration data is calculated, where the first dynamic demonstration data is dynamic demonstration data of personnel evacuation based on walking transportation in the study area; Based on the urban emergency evacuation spatial model and in combination with the second traffic network and the destination system, the second dynamic demonstration data is the emergency rescue dynamic demonstration data based on the vehicle traffic mode in the study area; Calculate the average time required for all residents of each residential area to evacuate on foot to surrounding temporary resettlement sites based on the first dynamic demonstration data, and obtain a redundancy index based on the average time; Calculating an average of the time required for emergency rescue services to reach each temporary resettlement site based on the second dynamic demonstration data, and obtaining an availability index based on the average; The robustness index, the rapidity index, the redundancy index and the availability index are added according to preset weights to obtain a quantitative result of urban resilience.

2. The simulation method for urban flood resilience according to claim 1 is characterized in that: The city data also includes topographic data, drainage network data and block boundary data.

3. The simulation method for urban flood resilience according to claim 2 is characterized in that: The average value of the time required for emergency rescue services to reach each temporary resettlement point is calculated based on the second dynamic demonstration data, and the availability index is obtained based on the average value, including: Set the temporary placement point number to =1,2… , for the The duration of driving from various emergency rescue facilities to the temporary resettlement point is , the average time required for emergency rescue services to reach each temporary resettlement point is calculated using the following formula : ； The standardized accessibility index of the study area is then calculated using the following formula: : 。 4. A simulation and calculation device for urban flood resilience, characterized in that: include: A data collection module is used to collect urban data of the study area, wherein the urban data at least includes urban underlying surface data, urban building height data, municipal transportation data, and functional business data; A spatial model building module is used to import the urban data into the model drawing platform of the stormwater simulation software to obtain a spatial model of urban stormwater resilience; A spatial quantification module is used to calculate the dynamic demonstration data of water depth changes in the study area based on the urban stormwater resilience spatial model; based on the dynamic demonstration data of water depth changes, calculate the average time required for the outdoor water depth of each block to reach the ground level of the first floor of the building and the average time required for the outdoor underlying surface water level of each block to return to zero, and then calculate the robustness index and rapidity index of the study area based on these two average values; The calculation process of the robustness index is: The real time after the rainstorm starts to enter the urban flood resilience spatial model is set to , the total time is , the block number of the study area is =1,2… , the outdoor water depth of the i-th block building is , the ground level height of the building's first floor is , It represents the duration from the beginning of the rainstorm to the input of the urban flood resilience spatial model. = and 0≤ ≤ When the first satisfaction ≥ ,but: ; in, Indicates from =0Starting from The time required for the depth of outdoor water accumulation in a building block to reach the ground level of the first floor of the building; Ruodang = and 0≤ Always satisfied when ≤T < ,but: ； Then, the robustness index is calculated: ; ; in, It represents the average time required for the outdoor water depth of each building in each block to reach the ground level of the first floor of the building. represents the robustness index of the study area after normalization; The calculation process of the rapidity index is: Suppose that in n blocks, the time required for rainwater infiltration on the outdoor underlying surface of each block to subside and the water level to return to zero is =1,2… ,from = Start, if = ≥0 when the first satisfaction =0, then: ； Then, the rapidity index is calculated: ； ； in, It represents the average time required for the outdoor underlying surface water level of each block to return to zero. represents the rapidity index of the study area after normalization; An evacuation model construction module is used to import the municipal traffic data and functional business data in the urban data into the evacuation simulation software, and use a multidimensional coupling tool to connect the POI data of residential areas, convenience stores, hospitals, fire stations, hotels, public buildings, and public venues with the urban pedestrian network system to obtain a first traffic network and destination system, and to connect the POI data of residential areas, convenience stores, hospitals, fire stations, hotels, public buildings, and public venues with the urban vehicle road system to form a second traffic network and destination system, thereby forming an urban emergency evacuation space model that includes municipal roads, slow-moving systems, residential areas, temporary resettlement points, and emergency rescue facilities; An evacuation quantification module is configured to calculate, based on the urban emergency evacuation spatial model, in combination with the first transportation network and the destination system, first dynamic demonstration data, which are dynamic demonstration data on personnel evacuation in the study area based on walking transportation; based on the urban emergency evacuation spatial model, in combination with the second transportation network and the destination system, second dynamic demonstration data, which are dynamic demonstration data on emergency rescue in the study area based on vehicle transportation; calculate, based on the first dynamic demonstration data, the average time required for all residents of each residential area to be evacuated on foot to surrounding temporary resettlement points, and obtain a redundancy index based on this average; calculate, based on the second dynamic demonstration data, the average time required for emergency rescue services to reach each temporary resettlement point, and obtain an availability index based on this average; A weighted output module is used to add the robustness index, the rapidity index, the redundancy index and the availability index according to preset weights to obtain a quantitative result of urban resilience.

5. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the simulation method for urban flood resilience according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, wherein when the program is executed by a processor, the simulation calculation method for urban stormwater resilience according to any one of claims 1 to 3 is implemented.

7. A computer program product comprising computer instructions, characterized in that When executed by a processor, the computer instructions implement the steps of the simulation method for urban flood resilience according to any one of claims 1 to 3.

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