Emergency shelter availability assessment method and system based on real-time population distribution

Abstract

Embodiments of the present application provide a real-time population distribution-based emergency shelter availability assessment method and system. The method comprises: obtaining real-time population spatial distribution data of a city; establishing an emergency shelter simulation space network based on city roads; calculating the real-time per capita emergency shelter area of each regular grid at a specific time without considering the disaster scenario; establishing a city emergency shelter simulation space network under different intensity disaster scenarios; recalculating the real-time per capita emergency shelter area of each regular grid under different intensity disaster scenarios; determining whether the real-time per capita emergency shelter area of each regular grid at a specific time under a specific disaster intensity reaches the planning set target, and performing spatial visualization expression. In this way, the real-time availability level of the emergency shelter can be accurately determined based on the population spatio-temporal distribution characteristics, the weak links and vulnerable areas can be quickly identified, and technical support can be provided for planning compilation and monitoring and early warning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban emergency management and planning analysis, and particularly relates to a method and system for evaluating the availability of emergency shelters based on real-time population distribution. BACKGROUND

[0002] Emergency shelters are an important part of urban public safety and emergency management, and play a crucial role in transferring risks, resettling people and ensuring life in extreme natural disasters and major emergencies. Reasonable layout of emergency shelters at all levels is an important prerequisite for effectively protecting the safety of residents' lives and property and improving the comprehensive effectiveness of the emergency management system. Currently, emergency shelter special planning has been included in the national spatial planning system.

[0003] Fully meeting the emergency shelter needs of urban and rural populations and achieving effective coverage of emergency shelters are the core principles and key objectives of emergency shelter construction. Therefore, during the planning and management stages of emergency shelters, it is necessary to accurately evaluate the availability of emergency shelters, identify the shortcomings in the current emergency shelter setup, and guide the scientific layout of emergency shelters and the rational allocation of emergency shelter resources.

[0004] Existing methods for evaluating the availability of emergency shelters are mostly based on urban road network data, emergency shelter distribution data and urban statistical population, combined with disaster scenarios, using quantitative methods such as buffer analysis, gravity model, two-step mobile search and network analysis to obtain emergency shelter accessibility distribution maps or service range distribution maps, for example:

[0005] (1) A method for urban public emergency shelter layout based on GIS technology (CN114357095A) draws a circular buffer zone from the departure source to determine the service range of the emergency shelter.

[0006] (2) A method for determining the accessibility of emergency shelters (CN117036136B) uses network analysis of road network structures under different earthquake disasters to determine the accessibility level of emergency shelters.

[0007] (3) A method and system for predicting the demand for emergency shelter population (CN110210650B) combines the statistical population of urban disaster prevention zones with building vulnerability to analyze the changing trend of emergency shelter demand.

[0008] Currently, the evaluation technology of emergency shelter availability has shifted from simply analyzing the accessibility of emergency shelters as a type of urban public service facility to focusing on the service efficiency of emergency shelters in actual disaster scenarios. However, these technologies mainly focus on the damage to the urban road network and buildings in disaster scenarios when designing disaster scenarios, often ignoring the real-time spatial distribution characteristics of urban population caused by the temporal and spatial behavior of residents. Using static statistical population data to analyze the evacuation scale of urban residents cannot effectively reflect the real evacuation demand of different regions in the city due to the time-varying characteristics of activity intensity at the actual time of disaster occurrence. At the same time, due to the lack of real-time spatial distribution data of urban population, it is also difficult to apply the evaluation technology of emergency shelter availability to real-time monitoring and management systems of emergency shelter system. SUMMARY

[0009] Embodiments of the present application provide a population real-time distribution based emergency shelter availability evaluation method and system.

[0010] In a first aspect, embodiments of the present application provide a population real-time distribution based emergency shelter availability evaluation method, which comprises:

[0011] S1, obtaining a mobile signaling data set corresponding to a target area, and based on the mobile signaling data set, extracting the declassified geographic location and time information of long-time series urban population residence behavior in the target area by dividing a regular grid; using a spatial correlation and summary algorithm to process the extracted data to obtain urban real-time population distribution data, i.e. real-time population data of each regular grid;

[0012] S2, collecting spatial vector data of urban roads, planar data and evacuation capacity data of emergency shelters in the target area; based on the geographic coordinate attributes of the collected data, establishing an emergency shelter simulation space network based on urban roads;

[0013] S3, according to the emergency shelter simulation space network of S2, using a network analysis method to obtain a distance cost matrix of each regular grid to each emergency shelter; according to the real-time population data of each regular grid, calculating the real-time per capita emergency shelter area of each regular grid at a specific time without considering the disaster scenario;

[0014] S4, taking the emergency shelter simulation space network of S2 as the initial scenario, obtaining the spatial distribution of the affected areas in the city under different intensity disaster scenarios through simulation or disaster monitoring, identifying damaged road networks and failed emergency shelters, and constructing an urban emergency shelter simulation space network under different intensity disaster scenarios;

[0015] S5, the urban emergency shelter simulation space network under different intensity disaster scenarios according to S4, calculating the real-time per capita emergency shelter area of each regular grid under different intensity disaster scenarios;

[0016] S6, determining whether the real-time per capita emergency shelter area of each regular grid under a specific time and a specific disaster intensity reaches the planning set target, and performing spatial visualization expression.

[0017] In some implementable manners of the first aspect, the extracted data is processed by using a spatial correlation and summary algorithm to obtain the urban real-time population distribution data in the target area, including:

[0018] A regular grid with spatial geographic coordinates is constructed, the area boundary of the regular grid is judged in spatial inclusion relationship with the mobile phone signaling location information, and the residence longitude and latitude position and time information of the mobile phone user are decrypted and imported into the regular grid;

[0019] Based on the time information and the grid number, the real-time residence number in each regular grid at each time section is summarized and statistically, and the spatial geographic coordinate attribute of each regular grid is associated to obtain the urban real-time population distribution data in the target area.

[0020] In some implementable manners of the first aspect, the urban emergency shelter simulation space network based on the geographic coordinate attribute of the collected data is established, including:

[0021] The spatial vector data of the urban road is topologically processed to construct a city road network G(N, E) with geographic position attribute;

[0022] The centroid of each emergency shelter is extracted, and the emergency shelter capacity is used as the weight to form an emergency shelter service node set F = {f1, f2, …, f i} with geographic position attribute;

[0023] The KD tree nearest neighbor search algorithm is used to determine the emergency shelter service node f i} in the emergency shelter service node set F = {f1, f2, …, f i} is the nearest neighbor road network node n i in the city road network G, the emergency shelter service node set F is mapped to the city road network G(N, E) to form an emergency shelter simulation space network G(N∪F, E) based on the city road.

[0024] In some implementable manners of the first aspect, the real-time per capita emergency shelter area of each regular grid at a specific time under a disaster scenario is not considered, including:

[0025] The centroid points of the regular grid are extracted, and the real-time resident population data at the specific time is taken as the weight to form a set of population real-time distribution points P = {p1, p2, …, p j} with spatial attributes.

[0026] The KD tree nearest neighbor search algorithm is used to determine the nearest neighbor road network node n j of each regular grid centroid point in the set P = {p1, p2, …, p j The set P is mapped to the simulation space network of emergency shelters based on urban roads to form a simulation space network G (N∪F∪P, E) of complex urban population real-time spatial distribution.

[0027] On the simulation space network G (N∪F∪P, E) of complex urban population real-time spatial distribution, the network shortest path algorithm is used to calculate the distance cost of each regular grid p j to the nearest neighbor emergency shelter f i to form a distance cost matrix D fp .

[0028] Based on the distance cost matrix D fp , the emergency shelter service level allocation model is used to calculate the real-time per capita emergency shelter area of each regular grid at a specific time without considering the disaster scenario.

[0029] In some implementable manners of the first aspect, the distance cost matrix D fp is used to calculate the real-time per capita emergency shelter area of each regular grid at a specific time without considering the disaster scenario based on the distance cost matrix D

[0030] According to the distance cost matrix D fp and the shelter capacity of the emergency shelter, the shelter capacity of the emergency shelter is allocated to the real-time population of each regular grid; the real-time per capita emergency shelter area A j of a certain regular grid p i at t is allocated from the corresponding emergency shelter node f is calculated according to the following formula:

[0031]

[0032] where A i represents the shelter capacity of the emergency shelter node i, represents the real-time population of the regular grid p j at t, the number of regular grids served by the emergency shelter f i , and D fp (i, j) is the distance cost matrix D fpThe value of the ith row and jth column, indicating the regular grid p j The distance cost to the emergency shelter node f i .

[0033] In some possible implementation manners of the first aspect, the constructing of the urban emergency shelter simulation space network under different intensity disaster scenarios comprises:

[0034] Determine the urban affected space range under the disaster scenario through urban disaster experiment simulation.

[0035] Overlay the urban affected space range under the disaster with the urban road network and the emergency shelter, and identify the roads that have failed to provide the traffic function and the emergency shelters that have failed to provide the shelter function under the disaster scenario.

[0036] In the emergency shelter simulation space network G(N U F, E) based on the urban road, remove the edges and points corresponding to the roads and intersections in the set S that have failed to provide the traffic function, and remove the points corresponding to the emergency shelters in the set F that have failed to provide the shelter function, to construct the urban emergency shelter simulation space network G'(N' U F', E') under the disaster scenario.

[0037] In some possible implementation manners of the first aspect, the calculating of the real-time per capita emergency shelter area of each regular grid under different intensity disaster scenarios comprises:

[0038] On the urban emergency shelter simulation space network G'(N' U F', E') under the disaster scenario, the shortest path algorithm is used to calculate the distance cost of each regular grid p j to the nearest neighbor emergency shelter f i , to form a distance cost matrix D' fp under the disaster scenario.

[0039] Based on the distance cost matrix D' fp , the emergency shelter service level allocation model is used to calculate the real-time per capita emergency shelter area of each regular grid under different intensity disaster scenarios.

[0040] In the second aspect, the embodiments of the present application provide an emergency shelter availability evaluation system based on real-time population distribution, which comprises:

[0041] The urban real-time population distribution data acquisition module is configured to acquire a mobile phone signaling data set corresponding to a target area, and based on the mobile phone signaling data set, extract the de-encryption geographic position and time information of long-time sequence urban population residence behaviors in the target area by dividing a regular grid; and utilize a spatial correlation and a summary calculation method to process the extracted data, so as to obtain urban real-time population distribution data, i.e., real-time population data of each regular grid.

[0042] The emergency shelter simulation space network construction module is configured to acquire spatial vector data of urban roads in the target area, planar data and shelter capacity data of emergency shelters; and based on the geographic coordinate attributes of the acquired data, establish an emergency shelter simulation space network with the urban roads as a base;

[0043] The emergency shelter availability measure calculation module is configured to acquire a distance cost matrix of each regular grid to each emergency shelter by using a network analysis method based on the emergency shelter simulation space network; and calculate real-time per capita emergency shelter area of each regular grid at a specific time without considering a disaster scenario based on the real-time population data of each regular grid.

[0044] The emergency shelter simulation space network construction module is further configured to: take the emergency shelter simulation space network as an initial scenario, obtain spatial distribution of an urban affected area under different intensity disaster scenarios by simulation simulation or disaster monitoring, identify damaged road networks and failed emergency shelters, and construct urban emergency shelter simulation space networks under different intensity disaster scenarios.

[0045] The emergency shelter availability measure calculation module is further configured to: calculate real-time per capita emergency shelter area of each regular grid under different intensity disaster scenarios based on the urban emergency shelter simulation space networks under different intensity disaster scenarios.

[0046] The emergency shelter availability monitoring and early warning module is configured to determine whether the real-time per capita emergency shelter area of each regular grid reaches a planning set target at a specific time under a specific disaster intensity, and perform spatial visual expression.

[0047] According to the embodiments of the present application, at least the following technical effects are achieved:

[0048] (1) The present application combines the real-time spatial distribution characteristics of urban population with the spatial damage under different disaster scenarios into the availability evaluation method of emergency shelter. Through this method, the emergency shelter system can be placed in a specific disturbance scenario with both time and disaster strength attributes, and in-depth availability level stress testing can be realized. Finally, the availability evaluation results of the emergency shelter can be accurately presented on the regular grid granularity of the target area, thereby realizing the accurate calculation of the availability level of the emergency shelter with high spatio-temporal accuracy.

[0049] (2) The traditional emergency shelter availability evaluation technology is mainly based on static population distribution characteristics, and can only reflect the shelter demand of residents in fixed residence, but it is difficult to reflect the real-time distribution and immediate demand of population when disaster or emergency occurs. In contrast, the present application innovatively integrates the population spatio-temporal distribution factor into the availability evaluation method of emergency shelter by introducing long time series mobile phone signaling data. Based on the real-time population spatial distribution characteristics of the city, the actual emergency shelter demand and real-time availability level of the shelter when disaster or emergency occurs can be accurately determined. This not only helps to effectively allocate emergency shelter resources, but also provides strong support for the conversion of urban planning and emergency, and realizes the scientific and fine of emergency management.

[0050] (3) The present application can calculate the real-time per capita emergency shelter area of each regular grid in the city at a specific time and scene based on the real-time distribution characteristics of population and actual disaster scene. The result can be directly compared with the planning target, which is convenient for quickly identifying the weak links and vulnerable areas of the urban emergency shelter system, and providing technical support for planning compilation and early warning. The scheme described in the application can be directly applied to the urban emergency shelter management platform or the city digital twin platform.

[0051] It should be understood that the content described in the summary section is not intended to limit the key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0052] The above and other features, advantages, and aspects of embodiments of the present application will become more apparent by describing in detail preferred embodiments thereof with reference to the attached drawings in which:

[0053] Figure 1 is a flowchart of an emergency shelter availability evaluation method based on real-time population distribution provided by an embodiment of the present application;

[0054] Figure 2is a real-time per capita emergency shelter area diagram of each regular grid at 15 o'clock in a non-disaster scenario provided by the embodiment of the present application;

[0055] Figure 3 is a real-time per capita emergency shelter area diagram of each regular grid at 15 o'clock in a disaster scenario provided by the embodiment of the present application;

[0056] Figure 4 is a structural diagram of an emergency shelter availability evaluation system based on real-time population distribution provided by the embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0058] In addition, the term "and / or" in this paper is only to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents an "or" relationship between the front and rear associated objects.

[0059] In view of the problems in the background art, the embodiments of the present application provide an emergency shelter availability evaluation method and system based on real-time population distribution. The purpose is to integrate the spatio-temporal behavior characteristics of residents and related analysis techniques into the emergency shelter facility availability evaluation method, to evaluate the real availability of emergency shelters at a specific time and under a certain disaster intensity based on the real-time spatial distribution of urban population, and to build an evaluation system that can directly support real-time monitoring and management of the emergency shelter system.

[0060] The embodiments of the present application provide an emergency shelter availability evaluation method and system based on real-time population distribution. The embodiments of the present application will be described in detail below with reference to the drawings.

[0061] Figure 1 The flowchart of the emergency shelter availability evaluation method based on real-time population distribution provided by the embodiment of the present application is shown in FIG. 1. Figure 1 As shown in FIG. 1, the emergency shelter availability evaluation method based on real-time population distribution 100 can include the following steps:

[0062] S1, obtain a mobile phone signaling data set corresponding to a target area, and based on the mobile phone signaling data set, extract the declassified geographic position and time information of long-time sequence urban population residence behaviors in the target area by dividing a regular grid; process the extracted data by using a spatial correlation and aggregation calculation method to obtain urban real-time population distribution data, i.e., real-time population data of each regular grid.

[0063] In some embodiments, the above steps can be unfolded as follows:

[0064] S1-1, construct a 250m*250m regular grid by using ArcGIS software, perform non-repeated ID numbering, and generate a WKT string to record the longitude and latitude coordinates in the WGS84 coordinate system of the grid area boundary.

[0065] S1-2, based on the boundary longitude and latitude coordinates of the regular grid, convert the original longitude and latitude coordinates of the mobile phone signaling collection base station and the longitude and latitude position information of the mobile phone user obtained by multi-base station weighting to the regular grid.

[0066] S1-3, process and analyze the residence of all users in the long-time sequence mobile phone signaling data set, associate the longitude and latitude position of each residence of the mobile phone user with the ID number of the regular grid, and record the time information.

[0067] S1-4, based on the time information and the grid number, aggregate and statistically analyze the real-time residence population in each regular grid at each time section; by using the ArcGIS software, use the grid number ID as an index to associate the spatial geographic coordinate attribute of each regular grid to obtain high spatial and temporal accuracy urban real-time population distribution data.

[0068] S2, collect spatial vector data of urban roads in the target area, planar data and refuge capacity data of emergency shelters; based on the geographic coordinate attributes of the collected data, establish an emergency shelter simulation space network based on urban roads.

[0069] In some embodiments, the above steps can be unfolded as follows:

[0070] S2-1, collect original spatial vector data of all urban road networks above the branch level on the ground in the open source map, including the rapid interchange system; record the basic information such as road name, traffic direction, road segment length, whether it is an elevated bridge, whether it is a tunnel, etc. in the attribute table of the corresponding road segment.

[0071] S2-2, using the OSMnx and NetworkX model library in Python language, the original spatial vector data of urban roads is topologically processed. The road section is regarded as the edge, the Euclidean distance length of the road section is the weight of the edge, the road intersection is regarded as the node, and the urban road network G(N, E) with WGS84 coordinate system latitude and longitude information is constructed.

[0072] S2-3, calling open source map API to collect emergency shelter area data; extracting the centroid points of the emergency shelter area data and the regular grid, and recording the WGS84 coordinate system latitude and longitude information in the attribute table of the corresponding centroid point.

[0073] S2-4, collecting the public emergency shelter capacity data of the city, standardizing it to a unified area unit, and recording the emergency shelter capacity information in the attribute table of the corresponding centroid point of the emergency shelter with the name as the index.

[0074] S2-5, using the GeoPandas model library in Python language, converting the emergency shelter service node set F into DataFrame data structure, and composing the emergency shelter service node set F={f1, f2,…, f i} with the weight of the emergency shelter capacity and the geographical location attribute.

[0075] S2-6, using the GeoPandas model library in Python language, converting the node set in the urban road network G(N, E) into DataFrame data structure.

[0076] S2-7, using the KD tree nearest neighbor search algorithm, based on geographical coordinates and Euclidean distance, determining the nearest neighbor road network node n i} in the emergency shelter service node set F={f1, f2,…, f i} in the city road network G. i , mapping the emergency shelter service node set F to the city road network G(N, E) to form the emergency shelter simulation space network G(N∪F, E) based on the city road.

[0077] S3, according to the emergency shelter simulation space network described in S2, using network analysis method, the distance cost matrix of each regular grid to each emergency shelter is obtained; according to the real-time population data of each regular grid, the real-time per capita emergency shelter area of each regular grid at a specific time without considering the disaster scenario is calculated.

[0078] In some embodiments, the above steps can be expanded as follows:

[0079] S3-1, based on the regular grid containing high spatial-temporal accuracy of urban real-time population distribution obtained in S1-4, the centroid point of each regular grid is extracted by ArcGIS software, and the real-time resident population data at a specific time is taken as the weight to form a population real-time distribution point set P = {p1, p2, …, p j}. For example, the real-time resident population data at 15 o'clock is taken as the weight to form a population real-time distribution point set P = {p1, p2, …, p j} at 15 o'clock.

[0080] S3-2, the node set in the urban road network G (N, E) is converted into a DataFrame data structure by using the GeoPandas model library in Python language.

[0081] S3-3, the nearest neighbor search algorithm of KD tree is used to determine the nearest neighbor road network node n j of each regular grid centroid point in the set P = {p1, p2, …, p j} based on geographic coordinates and Euclidean distance, and the set P is mapped to the simulation space network of emergency shelters based on urban roads to form a simulation space network G (N∪F∪P, E) of composite urban real-time spatial distribution of population.

[0082] S3-4, on the simulation space network G (N∪F∪P, E) of composite urban real-time spatial distribution of population, the Dijkstra shortest path algorithm provided by the NetworkX model package is used to calculate the distance cost of each regular grid p j to the nearest neighbor emergency shelter f i to form a distance cost matrix D fp .

[0083] S3-5, according to the distance cost matrix D fp and the emergency shelter capacity, the emergency shelter capacity is distributed to the real-time population of each regular grid; the real-time per capita emergency shelter area of a certain regular grid p j at t time can be obtained from the corresponding emergency shelter node f i , which is calculated as follows:

[0084]

[0085] Where A i represents the emergency shelter capacity of node i, represents the real-time population of regular grid p j at t time, the number of regular grids provided with emergency shelter service by emergency shelter f i , and Dfp (i,j) is the distance cost matrix D fp the value of the ith row, jth column, represents the regular grid p j to the emergency shelter node f i distance cost.

[0086] S3-6, according to the geographic coordinate attribute of the regular grid, the real-time per capita emergency shelter area of the city in the non-disaster scenario is visually expressed. Exemplarily, the visualization of the real-time per capita emergency shelter area of the city in the non-disaster scenario at 15 o'clock may be as shown in Figure 2 .

[0087] S4, taking the emergency shelter simulation space network as described in S2 as the initial scene, through simulation or disaster monitoring, the spatial distribution of the affected area of the city under different intensity disaster scenarios is obtained, the damaged road network and the failed emergency shelter are identified, and the emergency shelter simulation space network of the city under different intensity disaster scenarios is constructed.

[0088] In some embodiments, the above steps can be unfolded as follows:

[0089] S4-1, randomly select a certain proportion of road segments in the road network system of the city, and set them as impassable state, so as to simulate the real scenario of city road damage and function failure caused by disaster events.

[0090] S4-2, use the Remove function in Python language to randomly remove the selected road segments in the set E of the simulation space network G(N∪F∪P,E), and construct the city emergency shelter simulation space network G'(N'∪F',E') under the disaster simulation scenario.

[0091] S5, according to the city emergency shelter simulation space network under different intensity disaster scenarios described in S4, calculate the real-time per capita emergency shelter area of each regular grid under different intensity disaster scenarios.

[0092] In some embodiments, the above steps can be unfolded as follows:

[0093] S5-1, on the city emergency shelter simulation space network G'(N'∪F',E') under the disaster scenario, repeat the step S3-4, use the Dijkstra shortest path algorithm provided in the NetworkX model package to calculate the distance cost of each regular grid p j to the nearest neighbor emergency shelter f i under the simulation disaster scenario after removing a certain proportion of road network, and form the distance cost matrix D' fp under the disaster scenario.

[0094] S5-2, according to the distance cost matrix D' under the disaster scenario fp and the refuge capacity of the emergency shelter, the refuge capacity of the emergency shelter is distributed to the real-time population of each regular grid. The step S3-5 is repeated to calculate the real-time per capita emergency shelter area of each regular grid under the disaster scenario For example, the real-time per capita emergency shelter area of each regular grid under the disaster scenario at 15 o'clock

[0095] S5-3, by ArcGIS software, according to the geographic coordinate attribute of the regular grid, the real-time per capita emergency shelter area of the city under the disaster scenario is visually expressed. Exemplarily, the visualization of the real-time per capita emergency shelter area of the city under the disaster scenario at 15 o'clock may be as shown in FIG. 6. Figure 3

[0096] S6, determining whether the real-time per capita emergency shelter area of each regular grid under a specific time and a specific disaster intensity reaches the planning set target, and performing spatial visualization expression.

[0097] Exemplarily, the gap between the real-time per capita emergency shelter area of each regular grid under the disaster scenario at 15 o'clock and the per capita emergency shelter area target C0 set by the planning is compared, and the regular grid which is significantly lower than the set target C0 is prewarned and displayed.

[0098] According to the embodiments of the present application, the following technical effects are achieved at least:

[0099] (1) The present application combines the real-time spatial distribution characteristics of urban population with the spatial damage under different disaster scenarios, and integrates them into the availability evaluation method of emergency shelters. Through this method, the emergency shelter system can be placed in a specific disturbance scenario with time and disaster intensity attributes, and in-depth availability level stress testing can be realized. Finally, the availability evaluation result of the emergency shelter can be accurately presented on the regular grid granularity of the target area, thereby realizing the accurate calculation of the availability level of the emergency shelter with high spatio-temporal precision.

[0100] ​(2) The traditional emergency shelter availability assessment technology is mainly based on static population distribution characteristics, and can only reflect the shelter demand of residents at fixed residence, and is difficult to reflect the real-time distribution and immediate demand of population when disasters or emergencies occur. In contrast, the present application innovatively integrates the population spatiotemporal distribution factor into the emergency shelter availability assessment method by introducing long time series mobile phone signaling data. The real-time population spatial distribution characteristics of the city can be used to accurately determine the emergency shelter demand and real-time availability level of the shelter when the disaster or emergency actually occurs. This not only helps to effectively allocate emergency shelter resources, but also provides strong support for the conversion of urban planning and emergency, and realizes the scientization and refinement of emergency management.

[0101] (3) The present application can calculate the real-time per capita emergency shelter area of each regular grid in the city at a specific time and scene based on the real-time distribution characteristics of population and the actual disaster scene. The result can be directly compared with the planning target, which is convenient for quickly identifying the weak links and vulnerable areas of the urban emergency shelter system, and providing technical support for planning compilation and monitoring and early warning. The scheme described in the application can be directly applied to the urban emergency shelter management platform or the city digital twin platform.

[0102] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the action sequence described, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0103] The above is the introduction of the method embodiment, and the scheme described in the present application will be further described through the device embodiment.

[0104] Figure 4 is a structure diagram of an emergency shelter availability assessment system based on real-time population distribution provided by an embodiment of the present application, as shown in Figure 4 The emergency shelter availability assessment system based on real-time population distribution 400 can include:

[0105] The city real-time population distribution data acquisition module 410 is configured to acquire a mobile phone signaling data set corresponding to a target area, and based on the mobile phone signaling data set, extract the declassified geographic location and time information of the long time series city population residence behavior in the target area by dividing a regular grid; and process the extracted data by using a spatial correlation and aggregation algorithm to obtain city real-time population distribution data, i.e., real-time population data of each regular grid.

[0106] The emergency shelter simulation space network construction module 420 is configured to collect spatial vector data of urban roads in the target region, planar data and shelter capacity data of the emergency shelter, and establish an emergency shelter simulation space network based on geographical coordinate attributes of the collected data, with the urban roads as the base.

[0107] The emergency shelter availability measure calculation module 430 is configured to obtain a distance cost matrix of each regular grid to each emergency shelter by using a network analysis method based on the emergency shelter simulation space network, and calculate real-time per capita emergency shelter area of each regular grid at a specific time without considering a disaster scenario based on real-time population data of each regular grid.

[0108] The emergency shelter simulation space network construction module 420 is further configured to take the emergency shelter simulation space network as an initial scenario, obtain spatial distribution of a city affected area under different intensity disaster scenarios by simulation or disaster monitoring, identify damaged road networks and failed emergency shelters, and construct an emergency shelter simulation space network of the city under different intensity disaster scenarios.

[0109] The emergency shelter availability measure calculation module 430 is further configured to calculate real-time per capita emergency shelter area of each regular grid under different intensity disaster scenarios based on the emergency shelter simulation space network of the city under different intensity disaster scenarios.

[0110] The emergency shelter availability monitoring and early warning module 440 is configured to determine whether the real-time per capita emergency shelter area of each regular grid reaches a planning set target at a specific time under a specific disaster intensity, and perform spatial visualization expression.

[0111] It can be understood that, Figure 4 The various modules / units in the emergency shelter availability evaluation system 400 based on real-time population distribution shown above have the functions of implementing Figure 1 The functions of each step in the emergency shelter availability evaluation method 100 based on real-time population distribution shown above can achieve the corresponding technical effects, and for the sake of brevity, will not be repeated here.

[0112] It should be understood that the various forms of processes shown above can be reordered, added or deleted steps. For example, each step described in the present application can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions disclosed in the present application can be achieved, and the present application does not limit this.

[0113] The above detailed description does not limit the scope of the application. Various modifications, combinations, sub-combinations and alternatives can be made to the detailed description. Any modification, equivalent replacement and improvement etc. made within the spirit and principle of the application shall be included in the scope of the application.

Claims

1. A method for assessing the availability of emergency shelters based on real-time population distribution, characterized in that, The method includes: S1. Obtain the mobile signaling dataset corresponding to the target area, and based on the mobile signaling dataset, extract the declassified geographical location and time information of long-term urban population residence behavior in the target area by dividing it into regular grids; process the extracted data using spatial correlation and summary statistics algorithms to obtain real-time urban population distribution data, i.e., real-time population data of each regular grid. S2. Collect spatial vector data of urban roads, areal data of emergency shelters, and shelter capacity data within the target area; based on the geographic coordinate attributes of the collected data, establish a simulated spatial network of emergency shelters with urban roads as the base. S3. Based on the simulated spatial network of emergency shelters described in S2, network analysis methods are used to obtain the distance cost matrix from each regular grid to each emergency shelter; based on the real-time population data of each regular grid, the real-time per capita emergency shelter area of ​​each regular grid at a specific time is calculated without considering disaster scenarios. S4. Using the simulated spatial network of emergency shelters described in S2 as the initial scenario, through simulation or disaster monitoring, obtain the spatial distribution of urban affected areas under different intensity disaster scenarios, identify damaged road networks and failed emergency shelters, and construct a simulated spatial network of urban emergency shelters under different intensity disaster scenarios. S5. Based on the simulation spatial network of urban emergency shelters under different disaster intensities described in S4, calculate the real-time per capita area of ​​emergency shelters in each regular grid under different disaster intensities. S6. Determine whether the real-time per capita emergency shelter area of ​​each rule grid reaches the planned target under a specific time and specific disaster intensity, and express it in spatial visualization. Based on the geographic coordinate attributes of the collected data, a simulated spatial network of emergency shelters based on urban roads is established, including: Topological processing is performed on the spatial vector data of urban roads to construct an urban road network with geographic location attributes. ; Extract the centroid of each emergency shelter and use its shelter capacity as the weight to form a set of emergency shelter service nodes with geographical location attributes. ; The set of emergency shelter service nodes is determined using the KD-tree nearest neighbor search algorithm. Emergency shelter service nodes in China In urban road network Nearest Neighbor Nodes To aggregate emergency shelter service nodes Mapped to urban road network This will create a simulated space network of emergency shelters based on urban roads. ; The calculation does not consider the real-time per capita emergency shelter area of ​​each grid at a specific moment in a disaster scenario, including: The centroids of the regular grid are extracted, and their real-time resident population data at a specific moment is used as weights to form a set of real-time population distribution points with spatiotemporal attributes. ; Use the KD-tree nearest neighbor search algorithm to determine the set Centroids of various grid lines in urban road networks Nearest Neighbor Nodes , will set Mapped onto a simulated spatial network of emergency shelters based on urban roads, this forms a simulated spatial network depicting the real-time spatial distribution of the urban population. ; Simulated spatial network of real-time spatial distribution of population in complex cities Above, using the network shortest path algorithm, the calculation of each rule grid is performed. Go to the nearest emergency shelter The distance cost is used to form a distance cost matrix. ; Based on distance cost matrix Using the service level allocation model for emergency shelters, we calculate the real-time per capita area of ​​emergency shelters in each grid at a specific moment under disaster scenarios. The distance cost matrix Using an emergency shelter service level allocation model, the real-time per capita emergency shelter area for each grid at a specific time under disaster scenarios is calculated, including: Based on the distance cost matrix The refuge capacity of emergency shelters is allocated to the real-time population of each grid cell; a specific grid cell... At time t, from the corresponding emergency shelter node The allocated real-time per capita emergency shelter area Calculate using the following formula: ； in, Indicates emergency shelter nodes Refuge capacity, Representing a grid Real-time population at time t, emergency shelters The number of rule grids providing refuge services It is a distance cost matrix No. line, number The column values ​​represent the regular grid. Nodes to emergency shelters Distance cost.

2. The method according to claim 1, characterized in that, The process of using spatial correlation and summary statistical algorithms to process the extracted data yields real-time urban population distribution data within the target area, including: Construct a regular grid with spatial geographic coordinates, determine the spatial inclusion relationship between the area boundary of the regular grid and the mobile phone signaling location information, and decrypt and merge the mobile phone user's latitude and longitude location and time information into the regular grid; Based on time information and grid number, the real-time number of residents in each regular grid at each time segment is summarized and statistically analyzed. This data is then associated with the spatial geographic coordinate attributes of each regular grid to obtain the real-time urban population distribution data within the target area.

3. The method according to claim 1, characterized in that, The construction of a simulation space network for urban emergency shelters under different disaster intensities includes: Urban disaster experiments and simulations are used to determine the spatial extent of urban areas affected by disaster scenarios. By overlaying the affected urban space under disaster with the urban road network and emergency shelters, we can identify roads that can no longer provide passage under disaster scenarios, as well as emergency shelters that can no longer provide refuge. Simulated space network of emergency shelters based on urban roads In this process, edges and points corresponding to roads and intersections in set E that no longer provide passage are removed, and points corresponding to emergency shelters in set F that no longer provide refuge are removed. A simulation space network of urban emergency shelters for disaster scenarios is then constructed. .

4. The method according to claim 3, characterized in that, The calculation of the real-time per capita emergency shelter area for each regular grid under different disaster intensities includes: Simulation space network of urban emergency shelters in disaster scenarios Above, using the network shortest path algorithm, we calculate the grid of rules under disaster scenarios. Go to the nearest emergency shelter Distance costs are used to form a distance cost matrix in disaster scenarios. ; Based on distance cost matrix Using an emergency shelter service level allocation model, the real-time per capita emergency shelter area for each regular grid under different disaster intensities was calculated. .

5. An emergency shelter availability assessment system based on real-time population distribution, characterized in that, The system is used to implement the method according to any one of claims 1-4, comprising: The urban real-time population distribution data acquisition module is used to acquire the mobile signaling dataset corresponding to the target area, and based on the mobile signaling dataset, extract the declassified geographical location and time information of long-term urban population residence behavior in the target area by dividing it into regular grids; and process the extracted data using spatial correlation and summary statistical algorithms to obtain urban real-time population distribution data, i.e., real-time population data of each regular grid. The emergency shelter simulation spatial network construction module is used to collect spatial vector data of urban roads, areal data of emergency shelters, and shelter capacity data within the target area; based on the geographic coordinate attributes of the collected data, an emergency shelter simulation spatial network with urban roads as the base is established. The emergency shelter availability measurement and calculation module is used to obtain the distance cost matrix from each regular grid to each emergency shelter based on the simulated spatial network of the emergency shelter and using network analysis methods; and to calculate the real-time per capita emergency shelter area of ​​each regular grid at a specific time without considering disaster scenarios based on the real-time population data of each regular grid. The emergency shelter simulation space network construction module is also used to: take the emergency shelter simulation space network as the initial scene, and through simulation or disaster monitoring, obtain the spatial distribution of urban affected areas under different intensity disaster scenarios, identify damaged road networks and failed emergency shelters, and construct urban emergency shelter simulation space networks under different intensity disaster scenarios; The emergency shelter availability measurement and calculation module is also used to: calculate the real-time per capita emergency shelter area of ​​each regular grid under different disaster intensities based on the simulated spatial network of urban emergency shelters under different disaster intensities. The emergency shelter availability monitoring and early warning module is used to determine whether the real-time per capita emergency shelter area of ​​each rule grid reaches the planned target at a specific time and under a specific disaster intensity, and to express the spatial visualization.

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