Community life material supply resilience evaluation method and system based on network invulnerability

Abstract

The application discloses a community life material supply resilience evaluation method and system based on network invulnerability, and the method comprises the following steps: acquiring data of a target area and performing pretreatment; performing grid division on the target area, constructing a community resilience evaluation index system, performing preliminary evaluation on the community resilience evaluation index system, and obtaining community resilience ranking; establishing the connection between supply points, and constructing a life material supply and demand network; according to the community resilience ranking, finding all the corresponding supply points of the communities one by one and formulating an attack strategy; according to the attack strategy, performing deliberate attack on the supply points, detecting the global network efficiency, and thus measuring the influence of the community resilience evaluation index on the invulnerability of the overall supply network; giving weights to the community resilience indexes, combining the community resilience index values, comprehensively evaluating the community resilience, and obtaining the community resilience evaluation result. The application can realize the evaluation of the community resilience and identify low-resilience communities facing the risk of life material supply shortage.

Description

Technical Field

[0001] This invention relates to a method, system, computer equipment, and storage medium for assessing the resilience of community life supplies based on network resilience, belonging to the field of big data application in the quantitative measurement of community resilience. Background Technology

[0002] Community resilience is the ability of a community to fully mobilize available resources, quickly respond to and adapt to the impact of risks and disasters, and restore the stability of residents' lives within a complex internal and external environmental system. When cities experience sudden public health emergencies, heavy rains, typhoons, or other risks, communities, as the basic units and nerve endings of the city, are extremely sensitive to changes in demand for supplies. This often leads to a rapid increase in demand for supplies in certain areas of the city, exposing low-resilience communities to a significant risk of insufficient supply of essential goods. Therefore, constructing urban community resilience assessment models to address the risk of shortages in essential goods and identifying low-resilience communities is of great significance for improving modern governance capabilities and ensuring residents' well-being. From a systems and methodological perspective, existing research has limited studies that place communities within complex urban networks to assess community resilience. Furthermore, almost no representative community resilience models in existing research explore the correlation between community resilience and urban systems from the perspective of complex network science. Summary of the Invention

[0003] In view of this, the present invention provides a method, system, computer equipment and storage medium for assessing the resilience of community life supply based on network survivability. It constructs a community resilience evaluation index system and an urban life supply network using multi-source data such as mobile phone signaling, formulates network attack strategies based on the analysis of community resilience indicators by combining network survivability algorithms, and finally assigns weights to obtain the community resilience assessment results, thereby realizing the assessment of community resilience and identifying low-resilience communities facing the risk of life supply shortages.

[0004] The first objective of this invention is to provide a method for assessing the resilience of community life-sustaining material supply based on network survivability.

[0005] The second objective of this invention is to provide a resilience assessment system for community living supplies based on network survivability.

[0006] A third objective of this invention is to provide a computer device.

[0007] A fourth objective of this invention is to provide a storage medium.

[0008] The first objective of this invention can be achieved by adopting the following technical solution:

[0009] A method for assessing the resilience of community essential goods supply based on network survivability, the method comprising:

[0010] Data from the target area is acquired and preprocessed, including mobile signaling data, POI data, urban road data, and basic administrative data.

[0011] Based on the preprocessed data, the target area is divided into grids, a community resilience evaluation index system is constructed, and a preliminary evaluation of the community resilience evaluation index system is conducted to obtain the community resilience ranking under each index.

[0012] Establish connections between supply points and use mobile signaling data to build a supply and demand network for daily necessities;

[0013] Based on the community resilience ranking, we identified the corresponding supply points for each community in the supply and demand network of daily necessities and formulated attack strategies accordingly.

[0014] Based on the attack strategy, the supply points of the supply and demand network of daily necessities are deliberately attacked, and the efficiency of the entire network is tested, thereby measuring the impact of the community resilience evaluation index on the overall supply network's survivability.

[0015] Based on the global network efficiency after a deliberate attack, weights are assigned to the resilience indicators of each community. The community resilience is then comprehensively evaluated by combining the values ​​of each community resilience indicator, resulting in a community resilience assessment.

[0016] Furthermore, based on the preprocessed data, the target area is divided into grids to construct a community resilience evaluation index system. A preliminary evaluation of the community resilience evaluation index system is then performed to obtain a ranking of community resilience under each index. Specifically, this includes:

[0017] Based on basic administrative data, the target area is divided into grids and the grids are numbered.

[0018] Based on the community's self-organization capacity, population composition, and infrastructure, a community resilience evaluation index system is constructed. The indicators corresponding to the community's self-organization capacity include the rate of change in non-commuter traffic. The indicators corresponding to the community's population composition include population size, the proportion of elderly people, and per capita consumption capacity. The indicators corresponding to the community's infrastructure include the number of supermarkets and shopping malls.

[0019] The community resilience evaluation index system was initially evaluated by obtaining non-commuter flow change rate, population size, elderly ratio and per capita consumption capacity from mobile signaling data, and supermarket quantity from POI data, and the corresponding data of the five indicators were linked to the corresponding grid in space.

[0020] The values ​​of the grid corresponding to the five indicators are normalized and sorted to obtain the community resilience ranking under each indicator.

[0021] Furthermore, the establishment of connections between supply points, using mobile signaling data to construct a supply and demand network for daily necessities, specifically includes:

[0022] Based on the gravity model, the service connectivity matrix between any two supply points is calculated to establish the connections between supply points.

[0023] Based on the connections between supply points, a supply and demand network for daily necessities is constructed using human footprint data from mobile phone signaling data. This network is used to calculate the normal service population of the supply points.

[0024] Furthermore, the calculation process for the normal service population of the supply point is as follows:

[0025] The number of market service personnel purchasing vegetables is summed based on the grid ID of the supply point. The number of service personnel is then expanded using the average household size index, and further calibrated based on yearbook data to obtain the normal service population, as shown in the following formula:

[0026] Z serve =σ·α·θ·Z b

[0027] Among them, Z serve The supply point serves a regular population, σ is a correction coefficient, α is the average household size, and θ is the proportion of people who purchase daily necessities while traveling to the supply point.

[0028] Furthermore, the method of deliberately attacking supply points in the supply and demand network of daily necessities according to the attack strategy, and detecting the overall network efficiency to measure the impact of community resilience evaluation indicators on the overall supply network's survivability, specifically includes:

[0029] The attack strategy involves deliberately targeting the supply points of the supply and demand network for essential goods. The maximum load capacity of each supply point is L. imax When the load on supply point i exceeds a certain multiple of its capacity, supply point i becomes overloaded and fails, resulting in a loss of service quantity S. i As shown in the following formula:

[0030] L imax =L i ·α

[0031] S i =L r -L imax

[0032] Among them, L imax L represents the maximum load that supply point i can bear; α is the overload factor; L i S represents the normal node service volume of supply point i. i L represents the missing service quantity at supply point i; r The actual load at supply point i under a sudden event;

[0033] When provider i is attacked and fails, the lost service load will be redistributed to surrounding provider points according to certain redistribution rules, as follows:

[0034]

[0035] Among them, L ij R represents the load allocated from supply point i to supply point j; j R represents the remaining capacity at supply point j; k Let I represent the remaining capacity of node k; let I represent the set of supply points associated with supply point i.

[0036] When supply point i is overloaded and all surrounding supply points are overloaded, the excess load on supply point i cannot be allocated and is defined as missing service. At the same time, the connection between supply point i and surrounding supply points is lost. When no more missing service is added in the network, the network cascading effect ends. The global network efficiency is then tested to measure the impact of the community resilience index on the overall supply network's resilience, as shown in the following formula:

[0037]

[0038] Where E represents the global network efficiency; d ij is the shortest distance from supply point i to supply point j; n is the total number of supply points in the initial supply network.

[0039] Furthermore, based on the global network efficiency, weights are assigned to the resilience indicators of each community, and the resilience indicators of each community are combined to comprehensively evaluate community resilience and obtain the community resilience assessment result. Specifically, this includes:

[0040] Based on the global network efficiency after a deliberate attack, the community resilience index is assigned weights using the entropy weight method.

[0041] Based on the weights and values ​​of each community resilience index, a comprehensive evaluation of community resilience is conducted to obtain the community resilience assessment results.

[0042] Furthermore, the method of assigning weights to the community resilience index based on the entropy weight method according to the global network efficiency after a deliberate attack specifically includes:

[0043] The slope of the global network efficiency curve after a deliberate attack is used as an indicator for calculation. The weight of the community resilience indicator is calculated through simulation. The formula for calculating the slope of the global network efficiency curve is as follows:

[0044]

[0045] Where K is the slope, y is the global network efficiency, and there are n supply points;

[0046] The slope data is standardized using the following formula:

[0047]

[0048] Among them, P ij The data represents the standardized slope, where K is the slope.

[0049] The slope data is normalized using the following formula:

[0050]

[0051] The formula for calculating information entropy is:

[0052]

[0053] Among them, E i For calculating the information entropy, T ij The slope data is normalized;

[0054] The weight of the community resilience index is calculated using information entropy, and the calculation formula is as follows:

[0055]

[0056] Among them, W i E is the weight of the community resilience index. i The information entropy is calculated.

[0057] The second objective of this invention can be achieved by adopting the following technical solution:

[0058] A community-based resilience assessment system for essential goods supply based on network survivability, the system comprising:

[0059] The acquisition module is used to acquire data from the target area and preprocess it. The data includes mobile phone signaling data, POI data, urban road data, and basic administrative data.

[0060] The preliminary evaluation module is used to divide the target area into grids based on the preprocessed data, construct a community resilience evaluation index system, conduct a preliminary evaluation of the community resilience evaluation index system, and obtain the community resilience ranking under each index.

[0061] The network construction module is used to establish connections between supply points and uses mobile signaling data to build a supply and demand network for daily necessities.

[0062] The strategy formulation module is used to sort communities by resilience, find the corresponding supply points for each community in the supply and demand network of living materials, and formulate attack strategies.

[0063] The measurement module is used to launch deliberate attacks on the supply points of the supply and demand network of daily necessities according to the attack strategy, and to detect the global network efficiency, thereby measuring the impact of the community resilience evaluation index on the overall supply network's resilience.

[0064] The comprehensive evaluation module is used to assign weights to the resilience indicators of each community based on the global network efficiency after a deliberate attack, and combine the resilience indicator values ​​of each community to comprehensively evaluate the community resilience and obtain the community resilience assessment result.

[0065] The third objective of this invention can be achieved by adopting the following technical solution:

[0066] A computer device includes a processor and a memory for storing a processor-executable program, characterized in that, when the processor executes the program stored in the memory, it implements the above-described method for assessing the resilience of community living supplies.

[0067] The fourth objective of this invention can be achieved by adopting the following technical solution:

[0068] A storage medium storing a program that, when executed by a processor, implements the aforementioned method for assessing the resilience of community living supplies.

[0069] The present invention has the following advantages over the prior art:

[0070] This invention, from the perspective of complex networks, constructs a community resilience evaluation index system and an urban living supplies supply network. It combines network resilience algorithms to formulate attack strategies and finally uses the entropy weight method to assign weights for calculation, identifying low-resilience communities with potential supply shortages. Identifying the risk of living supplies shortages through community resilience assessment methods will help build network connections between community resilience and other subsystems within the complex urban system. This provides a research foundation and methodological experiments for planning and constructing community resilience enhancement, and is of great significance for improving modern governance capabilities and ensuring residents' well-being. Attached Figure Description

[0071] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0072] Figure 1 This is a flowchart of the community life material supply resilience assessment method based on network resilience in Embodiment 1 of the present invention.

[0073] Figure 2This refers to the six administrative districts of Guangzhou in Embodiment 1 of the present invention.

[0074] Figure 3 This is the evaluation result of the resilience of the community non-commuter flow rate in Embodiment 1 of the present invention.

[0075] Figure 4 This is the result of the community population resilience assessment in Embodiment 1 of the present invention.

[0076] Figure 5 This is the result of the community elderly population resilience assessment in Embodiment 1 of the present invention.

[0077] Figure 6 This is the community per capita consumption capacity resilience evaluation result of Embodiment 1 of the present invention.

[0078] Figure 7 This is the evaluation result of the number of community supermarkets in Embodiment 1 of the present invention.

[0079] Figure 8 This is a screenshot of the "Population Size-Resilience Sorting.csv" list page from Embodiment 1 of the present invention.

[0080] Figure 9 This is a diagram showing the connections between supply points in Embodiment 1 of the present invention.

[0081] Figure 10 This is a supply and demand network diagram of Embodiment 1 of the present invention.

[0082] Figure 11 This is a screenshot of the "supply and demand network.csv" list page from Embodiment 1 of the present invention.

[0083] Figure 12 This is a diagram illustrating the network cascading failure simulation steps of Embodiment 1 of the present invention.

[0084] Figures 13a-13e This is a global network efficiency diagram for the five indicators of Embodiment 1 of the present invention.

[0085] Figure 14 This is a global network efficiency diagram based on community resilience design in Embodiment 1 of the present invention.

[0086] Figure 15 This is the community resilience assessment result from the perspective of complex networks in Embodiment 1 of the present invention.

[0087] Figure 16 This is a structural block diagram of the community living supplies resilience assessment system based on network resilience, as described in Embodiment 2 of the present invention.

[0088] Figure 17 This is a structural block diagram of the computer device according to Embodiment 3 of the present invention. Detailed Implementation

[0089] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0090] Example 1:

[0091] This embodiment takes the six central districts of Guangzhou—Haizhu District, Panyu District, Liwan District, Yuexiu District, Baiyun District, and Tianhe District—as examples to provide a method for assessing the resilience of community living supplies based on network survivability.

[0092] like Figure 1 As shown, the community living supplies resilience assessment method in this embodiment includes the following steps:

[0093] S101. Obtain data from the target area and perform preprocessing.

[0094] In this embodiment, the data for the target area can be mobile signaling data and POI (Point of Interest) data. The mobile signaling data includes mobile signaling OD (Origin-Destination) data, and the POI data includes community location points and data on supply points for daily necessities. In addition, it may also include urban road data and basic administrative data. All kinds of data are obtained through a multi-source data open platform, as detailed below:

[0095] S1011. Acquire and preprocess mobile phone signaling data.

[0096] The mobile signaling data used in this implementation comes from China Unicom's Smart Footprint DaaS platform (including user travel, residence, and user attributes). This example selects the OD data of the urban space of the six central districts of Guangzhou (Tianhe District, Liwan District, Yuexiu District, Huangpu District, and Baiyun District) in June and September 2021, as follows:

[0097] Grid matching: The six districts of Guangzhou were divided into 500m*500m grids using the ArcGIS platform and imported into the Daas platform. Data statistics were performed using the grid as the smallest unit.

[0098] Data Acquisition and Filtering: OD data is acquired by writing SQL code, including data columns such as departure grid number (o_id), arrival grid number (d_id), travel purpose (ptype), and number of travelers (usum). To avoid interference from bidirectional OD and commuter OD, one-way non-commuter OD filtering is performed on the platform using SQL: O represents a market, ptype is 0 (visited), and D represents a community, ptype is 1 (residing).

[0099] The sample size is expanded based on the operator's market share to obtain the expanded resident population. Specifically, the expanded resident population data equals the mobile resident population multiplied by the expansion coefficient; the expansion coefficient is calculated using the following formula:

[0100]

[0101] Where S is the expansion coefficient, Mi is the operator market share in city M, and Mj is its mobile phone penetration rate.

[0102] Data raster scale differentiation processing: Using Python, the community D point data of OD data is scaled down to a 1000m raster scale, the population of each raster unit is counted, and the community location is taken as the center point of the raster, which helps to simplify the establishment and visualization of supply and demand networks.

[0103] Expanding the supply point service volume data: Since the purchase of daily necessities by people in a household serves the entire family, the OD traffic volume of each market grid is expanded using the average household size index in Guangzhou to obtain a more accurate population service volume for each market.

[0104] S1012. Obtain and preprocess POI data.

[0105] This embodiment utilizes the Baidu API and Python web crawler to retrieve 2022 supply and demand point of interest (POI) data. The selected area covers the urban space of the six central districts of Guangzhou. The supply point data mainly consists of POIs such as meat and vegetable markets, supermarkets, and convenience stores, totaling 20,945. The demand point data includes POIs in residential areas and dormitories, totaling 31,263.

[0106] Clean the supply and demand data that are repeated in the space, and delete invalid data that has format errors, duplicates, or empty content.

[0107] By connecting the Point of Interest (POI) data of the supply points to a 500m raster in the GIS based on spatial location, raster data containing the market is obtained, which simplifies the data volume and facilitates subsequent data processing.

[0108] S1013. Acquire and preprocess urban road data.

[0109] Road network data for 2022 within the scope of this embodiment was obtained using OpenStreetMap, including provincial highways, county roads, township roads, pedestrian roads, and other roads. Single-line processing was performed using Arcpy, and the road network .shp file was imported into ArcGIS software. After road network segmentation and topology checks, the "Network Analysis" extension module was used to complete the road network construction for the six districts of Guangzhou.

[0110] S1014. Obtain and preprocess basic administrative data.

[0111] The data was obtained from the National Geographic Information Public Service Platform (http: / / www.ngcc.cn / ngcc / ), primarily including the administrative boundaries of Guangzhou's city area and districts in 2022. The six districts of Guangzhou were selected as the research scope in ArcGIS. Figure 2 As shown.

[0112] S102. Based on the preprocessed data, the target area is divided into grids, a community resilience evaluation index system is constructed, and a preliminary evaluation of the community resilience evaluation index system is conducted to obtain the community resilience ranking under each index.

[0113] Furthermore, step S102 specifically includes:

[0114] S1021. Based on basic administrative data, the target area is divided into grids and the grids are numbered.

[0115] In this embodiment, the target area is gridded according to the administrative boundaries of the six districts of Guangzhou in S1014, and the grids are numbered. Specifically, the administrative boundaries of Guangzhou are extracted and imported into ArcGIS software, 500m*500m grid surface features are created to cover the entire administrative boundary, and the features are clipped according to the boundary and numbered.

[0116] S1022. Based on the community's self-organizing capacity, population composition, and infrastructure, construct a community resilience evaluation index system.

[0117] This embodiment measures community resilience from three dimensions: community self-organization capacity, community population composition, and community infrastructure. It constructs a community resilience evaluation index system. The indicators corresponding to community self-organization capacity include the rate of change in non-commuter traffic. The indicators corresponding to community population composition include population size, the proportion of elderly people, and per capita consumption capacity. The indicators corresponding to community infrastructure include the number of supermarkets and shopping malls, as shown in Table 1 below.

[0118] Table 1 Community Resilience Indicators

[0119]

[0120]

[0121] S1023. Obtain the non-commuter flow change rate, population size, elderly ratio and per capita consumption capacity from mobile signaling data, and obtain the number of supermarkets from POI data to conduct a preliminary evaluation of the community resilience evaluation index system, and link the corresponding data of the five indicators with the corresponding grid in space.

[0122] Based on the concept and characteristics of community resilience and related academic research, a community resilience evaluation index system is constructed from three dimensions: community self-organization capacity, community population composition, and community infrastructure, which respectively reflect the characteristics of self-organization, redundancy and robustness, and adaptability.

[0123] Community self-organization capacity reflects the community's self-governance capabilities and residents' initiative in resisting risks. It emphasizes the system's ability to reorganize itself through improving social networks, promoting effective leadership, building local trust, and strengthening cooperation among all parties. In this embodiment, the change in non-commuter traffic is selected as the indicator for measurement. Compared to commuter traffic, non-commuter traffic is more flexible in both time and space distribution, and its demand changes are less affected. Furthermore, its flexible working hours contribute to better community self-organization capacity. The number of non-commuter traffic is derived from mobile signaling data showing that people are primarily active within the community.

[0124] In this embodiment, the community population composition includes three indicators: population density, proportion of elderly people, and per capita consumption capacity. Typical old communities are vulnerable to disasters due to their large floating population and prominent aging population. Per capita consumption capacity, to a certain extent, represents the community's social capital strength. Specifically, population density is the quotient of the population number data from mobile phone signaling data and the land area; the proportion of elderly people is the percentage of the total population of the grid where elderly people aged 60 and above reside; and per capita consumption capacity is the per capita mobile phone bill data from the grid.

[0125] In this embodiment, the indicator for community infrastructure is the density of shopping mall POIs, which reflects the rationality and completeness of the facility layout, emphasizes a certain degree of redundancy to adapt to external disturbances and changes, ensures that the entire system will not be completely paralyzed in the event of a sudden disaster, and has alternative supply points, etc.

[0126] In this embodiment, a preliminary evaluation of the community resilience evaluation index system is conducted, and the corresponding data of the five indicators are linked to the corresponding spatial grid, specifically including:

[0127] 1) Using ArcGIS's spatial linking function, link the rate of change in non-commuter pedestrian traffic into the grid obtained in S1021 and visualize it, such as... Figure 3 As shown.

[0128] 2) Using ArcGIS's spatial linking function, link the data on the proportion of elderly people over 60 years old into the grid obtained in S1021, and then visualize it, such as... Figure 4 As shown.

[0129] 3) Use ArcGIS's spatial linking function to link the population data into the grid obtained in S1021 and visualize it, such as... Figure 5 As shown.

[0130] 4) Using ArcGIS's spatial linking function, link the average phone bill data into the grid obtained in S1021 and visualize it, such as... Figure 6 As shown.

[0131] 5) Use ArcGIS's spatial linking function to link the supermarket POI data into the grid obtained in S1021, and count the number of POIs in the grid, such as... Figure 7 As shown.

[0132] S1024. Normalize and sort the values ​​of the grid corresponding to the five indicators to obtain the community resilience ranking under each indicator.

[0133] Specifically, the values ​​of the five indicators corresponding to the grid are normalized and sorted in Excel. The normalization formula is as follows:

[0134]

[0135] Where x represents a certain value, x min Let x represent the minimum value in the dataset. max This represents the maximum value in the dataset, mapping the value of x to the range of 0-1.

[0136] Output the community resilience ranking results for each indicator, resulting in five files: "Non-commuter traffic change rate - resilience ranking.csv", "Population size - resilience ranking.csv", "Proportion of elderly people - resilience ranking.csv", "Per capita consumption capacity - resilience ranking.csv", and "Number of supermarkets - resilience ranking.csv". Taking "Population size - resilience ranking.csv" as an example, its table page is as follows: Figure 8 As shown.

[0137] S103. Establish connections between supply points and use mobile signaling data to build a supply and demand network for daily necessities.

[0138] In this embodiment, the supply and demand network for daily necessities is a network between supply points and demand points, and the supply network is a network between supply points. It is used to calculate the normal service population of each supply point and the degree of connection between supply points. Step S103 specifically includes:

[0139] S1031: Based on the gravity model, calculate the service connection degree matrix between any two supply points, and establish connections between supply points.

[0140] In this embodiment, based on the gravity model, Excel is used to calculate the service connection degree matrix between any supply point x and supply point y, so as to eliminate connections between nodes with weak service connection degree. The specific formula is:

[0141]

[0142] Wherein, Wxy is the connection degree between any supply point x and supply point y, Cx is the service volume of supply point x, Cy is the service volume of supply point y, dxy is the road network distance between supply point x and supply point y, which is obtained through O-D cost analysis in data preprocessing.

[0143] Set the threshold value W0=1. When Wxy<W0, it is considered that there is no material scheduling connection between supply point x and supply point y, and the value between the corresponding supply point x and supply point y in the OD cost matrix is set to 0 (no connection), and other values remain unchanged, so as to obtain the shortest distance matrix.

[0144] The connections between supply points are visualized through ArcGIS, and the results are shown in Figure 9 .

[0145] S1032: According to the connections between supply points, the human trajectory data in mobile phone signaling data is used to construct a living material supply and demand network.

[0146] Community resilience can be more accurately focused on in the urban living material supply and demand network, and the human trajectory data in mobile phone signaling data is used to construct the real supply and demand relationship in the city, which is specifically as follows:

[0147] First, spatially link the supply and demand point data with the 500m precision grid through ArcGIS, so that the data is provided with grid ID information. Then, further screen out the OD between supply and demand grids from the one-way non-commuting OD of mobile phone signaling data, which are the travel trajectories of citizens going to supply points such as markets to purchase materials. Finally, process the data into an OD matrix between supply and demand grids, and construct a living material supply and demand network based on real population trajectories in GIS, which is a spatial model.

[0148] In this embodiment, the calculation process of the normal service population of the supply point is as follows:

[0149] Sum the number of people purchasing vegetables at markets according to the grid ID of the supply points, expand the sample of the service population through the average family size indicator, and further calibrate according to the yearbook data to obtain the normal service population, which is shown in the following formula:

[0150] Zserve =σ·α·θ·Z b

[0151] Among them, Z serve The supply point serves a regular population, σ is a correction coefficient, α is the average household size, and θ is the proportion of people who purchase daily necessities while traveling to the supply point.

[0152] Visualizing the connections between supply and demand networks using ArcGIS, the results are as follows: Figure 10 As shown, the attribute table is exported and converted into a tabular format, resulting in a file named "supply and demand network.csv". Figure 11 As shown.

[0153] S104. Based on the community resilience ranking, locate the corresponding supply points for each community in the supply and demand network of daily necessities and formulate an attack strategy.

[0154] Based on the community resilience ranking obtained in step S102, this embodiment identifies the supply points corresponding to all communities in the supply and demand network of daily necessities and formulates an attack strategy, as follows:

[0155] S1041. Use Python to read five files: “Non-commuter traffic change rate - resilience ranking.csv”, “Population size - resilience ranking.csv”, “Proportion of elderly people - resilience ranking.csv”, “Per capita consumption capacity - resilience ranking.csv”, and “Number of supermarkets - resilience ranking.csv”.

[0156] S1042. Read "supply and demand network.csv" using Python.

[0157] S1043. Identify the main supply points corresponding to communities with resilience ranging from low to high under this evaluation index by analyzing supply and demand relationships.

[0158] Taking "Population Quantity - Resilience Ranking.csv" as an example, we use Python and the pandas library, employing the `merge` method to connect "Supply and Demand Network.csv" to "Population Quantity - Resilience Ranking.csv" based on the "id" attribute. We then sequentially filter the primary supply points corresponding to each low-resilience community. For the first low-resilience community, with an "id" of "3716", its primary supply point is the one with the largest "usum" value. Therefore, this supply point is identified as the first point to be attacked under the population quantity indicator. Similarly, we determine the primary supply points for the 2nd, 3rd, 4th...nth low-resilience communities by ranking them. If the primary supply point for a later-ranked low-resilience community has already been identified as the primary supply point for an earlier-ranked low-resilience community, we select the supply point with the second largest "usum" value. Likewise, if this point has already been identified, we determine the supply points based on the "usum" value, ultimately generating the supply point attack order under the "Population Quantity" indicator, i.e., generating "Population Quantity - Supply Point Attack Order.csv".

[0159] The resilience ranking files for the other four indicators are executed according to step S1043, ultimately generating "Non-commuter traffic change rate - supply point attack order.csv", "Elderly population ratio - supply point attack order.csv", "Per capita consumption capacity - supply point attack order.csv", and "Number of supermarkets - supply point attack order.csv". Supplying according to this order determines the attack strategy.

[0160] S105. Based on the attack strategy, deliberately attack the supply points of the supply and demand network of daily necessities, detect the overall network efficiency, and thus measure the impact of the community resilience evaluation index on the overall supply network's resilience.

[0161] Supply network simulation was conducted using Matlab, and process data was processed using Python and Excel. The load redistribution was primarily based on a cascading failure algorithm. Finally, the global network efficiency E of the low-resilience community after the main supply points were attacked was calculated, as follows:

[0162] S1051. In Matlab, read the following respectively: "Non-commuter flow change rate - supply point attack order.csv", "Population size - supply point attack order.csv", "Proportion of elderly people - supply point attack order.csv", "Per capita consumption capacity - supply point attack order.csv", and "Number of supermarkets - supply point attack order.csv".

[0163] S1052. Read "supply and demand network.csv" in Matlab.

[0164] S1053, Node failure due to intentional attack.

[0165] The attack strategy sequentially targets the supply points, with each supply point capable of handling a maximum load of L. imax When the load on supply point i exceeds a certain multiple of its capacity, supply point i becomes overloaded and fails, resulting in a loss of service quantity S. i As shown in the following formula:

[0166] L imax =L i ·α

[0167] S i =L r -L imax

[0168] Among them, L imax L represents the maximum load that supply point i can bear; α is the overload factor; L i S represents the normal node service volume of supply point i. i L represents the missing service quantity at supply point i; r This represents the actual load at supply point i under a sudden event.

[0169] S1054, Load redistribution.

[0170] When provider i fails due to an attack, the lost service load is redistributed to surrounding provider i according to a certain redistribution rule. The load of the failed provider i is distributed to adjacent associated provider j based on the ratio of the remaining capacity of provider j to the sum of the remaining capacities of all associated provider j surrounding the overloaded node. The redistribution load formula is as follows:

[0171]

[0172] Among them, L ij R represents the load allocated from supply point i to supply point j; j R represents the remaining capacity at supply point j; k I represents the remaining capacity of node k; I represents the set of supply points associated with supply point i.

[0173] S1055, the cascading effect ends, evaluate the global network efficiency.

[0174] When supply point i is overloaded and all surrounding nodes are overloaded, the excess load on supply point i cannot be allocated and is classified as missing service load. Simultaneously, its connection with surrounding nodes fails. When no more missing service load is added to the network, the network cascading effect ends, and the program attacks the next supply point in sequence. This embodiment uses global network efficiency to evaluate the resilience change of the supply network after a deliberate attack. It is typically used to reflect the ease of connection between nodes in the entire network, as shown in the following formula:

[0175]

[0176] Where E represents the global network efficiency; d ij is the shortest distance from supply point i to supply point j; n is the total number of supply points in the initial supply network.

[0177] S1056. Simulate the resilience of the supply network using Matlab, attacking the network point by point according to the attack strategy. The simulation steps for network cascading failure are as follows: Figure 12 As shown.

[0178] This embodiment uses "Population - Supply Point Attack Sequence.csv" as an example. For instance, the first attacked supply point has an "id" of 3716. When this point is attacked, its supply load is 0. The original supply load of this supply point will be transferred to several surrounding supply points according to the original load ratio. After the load transfer, the supply status of each supply point is judged. If a supply point with "id" 3916 takes over the supply load, and the supply load of this point does not exceed its own maximum load, it is marked as overloaded but not failed. If the supply point has exceeded its own maximum load, it is a failed node. Similarly, the supply load of the failed node is transferred to other surrounding supply points again until there are no new overloaded supply points in the network. The global network efficiency E′ after the supply point with "id" 3716 is attacked is recorded.

[0179] S1057. Following the next attack sequence, attack the second supply point and repeat the operation in S1056 above. Record the global network efficiency E″ after the supply point is attacked. Continue in this manner, supplying the third supply point and obtaining the global network efficiency E″′. Repeat this process until all supply points in the network have been attacked.

[0180] S1058. The resilience ranking files for the other four indicators are executed according to steps S1056 and S1057. Finally, the following are generated: "Population Size - Global Network Efficiency.csv", "Non-commuter Flow Change Rate - Global Network Efficiency.csv", "Proportion of Elderly People - Global Network Efficiency.csv", "Per Capita Consumption Capacity - Global Network Efficiency.csv", and "Number of Supermarkets - Global Network Efficiency.csv".

[0181] S1059. The global network efficiency after the attack is visualized using line graphs via Python's matplotlib library. Finally, a global network efficiency graph with five indicators and a global network efficiency graph based on community resilience design are obtained, such as... Figures 13a-13e and Figure 14 As shown.

[0182] S106. Based on the global network efficiency after the deliberate attack, assign weights to the resilience indicators of each community, combine the resilience indicator values ​​of each community to comprehensively evaluate the community resilience, and obtain the community resilience assessment results.

[0183] Step S106 specifically includes:

[0184] S1061. Based on the global network efficiency after a deliberate attack, assign weights to the community resilience index using the entropy weight method.

[0185] This embodiment uses the slope of the global network efficiency curve after a deliberate attack as an indicator for calculation. The community resilience index weight is calculated through simulation. The formula for calculating the slope of the global network efficiency curve is as follows:

[0186]

[0187] Where K is the slope, y is the global network efficiency, and there are n supply points.

[0188] The slope data is standardized using the following formula:

[0189]

[0190] Among them, P ij The data represents the standardized slope, where K is the slope.

[0191] The slope data is normalized using the following formula:

[0192]

[0193] The formula for calculating information entropy is:

[0194]

[0195] Among them, E i For calculating the information entropy, T ij The slope data is normalized;

[0196] The weight of the community resilience index is calculated using information entropy, and the calculation formula is as follows:

[0197]

[0198] Among them, W i E is the weight of the community resilience index. i The information entropy is calculated.

[0199] The weights of the community resilience index under the supply guarantee perspective in this embodiment are shown in Table 2 below.

[0200] Table 2 Weights of Community Resilience Indicators from the Perspective of Supply Security

[0201]

[0202] S1062. Based on the weights and values ​​of each community resilience index, a comprehensive evaluation of community resilience is conducted to obtain the community resilience assessment results.

[0203] Based on the resilience index values ​​f of each community obtained from S102 i The community resilience index weight w obtained from S1061 i The system comprehensively evaluates community resilience and generates a "Low Resilience Community Evaluation.csv" file. The calculation formula is as follows:

[0204]

[0205] In the formula, F represents the community resilience evaluation value; w i Indicates the indicator weight; f i Indicates the index value, f i The corresponding + / - is taken based on the positive or negative impact of the indicator on resilience.

[0206] In ArcGIS, import the "Low-Resilience Community Assessment.csv" file. Right-click the fishing net layer, select "Connect and Associate," then select "Connect." In the "Connect Data" dialog box, select "id" as the field the connection will be based on, and select "Low-Resilience Community Assessment.csv" as the table to be connected to. This connects the community resilience assessment values ​​F from that table to the fishing net layer. Visualize the results in ArcGIS to obtain the community resilience assessment map, as shown below. Figure 15 As shown. Low-resilience communities refer to communities whose resilience evaluation values ​​are lower than a set value. After obtaining the community resilience evaluation value F through comprehensive calculation, the community resilience comprehensive score is divided into 5 categories in ArcGIS using the natural discontinuity grading method. Communities with a resilience evaluation value < 0.0101953 are defined as low-resilience communities. This completes the community resilience assessment and low-resilience community identification for the six districts of Guangzhou.

[0207] It should be noted that although the method operations of the above embodiments are described in a specific order, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. On the contrary, the described steps may be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0208] Example 2:

[0209] like Figure 16As shown, this embodiment provides a community life supplies supply resilience assessment system based on network resilience. The system includes an acquisition module 1601, a preliminary evaluation module 1602, a network construction module 1603, a strategy formulation module 1604, a measurement module 1605, and a comprehensive evaluation module 1606. The specific functions of each module are as follows:

[0210] The acquisition module 1601 is used to acquire data from the target area and preprocess it. The data includes mobile phone signaling data, POI data, urban road data, and basic administrative data.

[0211] The preliminary evaluation module 1602 is used to divide the target area into grids based on the preprocessed data, construct a community resilience evaluation index system, conduct a preliminary evaluation of the community resilience evaluation index system, and obtain the community resilience ranking under each index.

[0212] Network construction module 1603 is used to establish connections between supply points and uses mobile signaling data to build a supply and demand network for daily necessities.

[0213] The strategy formulation module 1604 is used to sort communities by resilience, find the supply points corresponding to all communities with resilience from low to high in the supply and demand network of living materials, and formulate attack strategies.

[0214] The measurement module 1605 is used to deliberately attack the supply points of the supply and demand network of daily necessities according to the attack strategy, detect the global network efficiency, and thus measure the impact of the community resilience evaluation index on the overall supply network's resilience.

[0215] The comprehensive evaluation module 1606 is used to assign weights to the resilience indicators of each community based on the global network efficiency after a deliberate attack, and combine the resilience indicator values ​​of each community to comprehensively evaluate the community resilience and obtain the community resilience assessment results.

[0216] It should be noted that the system provided in this embodiment is only an example of the above-described division of functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure can be divided into different functional modules to complete all or part of the functions described above.

[0217] Example 3:

[0218] This embodiment provides a computer device, such as... Figure 17As shown, it includes a processor 1702, a memory, an input device 1703, a display 1704, and a network interface 1705 connected via a system bus 1701. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium 1706 and internal memory 1707. The non-volatile storage medium 1706 stores an operating system, computer programs, and a database. The internal memory 1707 provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. When the processor 1702 executes the computer programs stored in the memory, it implements the community living supplies resilience assessment method of Embodiment 1 described above, as follows:

[0219] Data from the target area is acquired and preprocessed, including mobile signaling data, POI data, urban road data, and basic administrative data.

[0220] Based on the preprocessed data, the target area is divided into grids, a community resilience evaluation index system is constructed, and a preliminary evaluation of the community resilience evaluation index system is conducted to obtain the community resilience ranking under each index.

[0221] Establish connections between supply points and use mobile signaling data to build a supply and demand network for daily necessities;

[0222] Based on the community resilience ranking, we identified the corresponding supply points for each community in the supply and demand network of daily necessities and formulated attack strategies accordingly.

[0223] Based on the attack strategy, the supply points of the supply and demand network of daily necessities are deliberately attacked, and the efficiency of the entire network is tested, thereby measuring the impact of the community resilience evaluation index on the overall supply network's survivability.

[0224] Based on the global network efficiency after a deliberate attack, weights are assigned to the resilience indicators of each community. The community resilience is then comprehensively evaluated by combining the values ​​of each community resilience indicator, resulting in a community resilience assessment.

[0225] Example 4:

[0226] This embodiment provides a storage medium, which is a computer-readable storage medium, storing a computer program. When the computer program is executed by a processor, it implements the community living supplies resilience assessment method of Embodiment 1 above, as follows:

[0227] Data from the target area is acquired and preprocessed, including mobile signaling data, POI data, urban road data, and basic administrative data.

[0228] Based on the preprocessed data, the target area is divided into grids, a community resilience evaluation index system is constructed, and a preliminary evaluation of the community resilience evaluation index system is conducted to obtain the community resilience ranking under each index.

[0229] Establish connections between supply points and use mobile signaling data to build a supply and demand network for daily necessities;

[0230] Based on the community resilience ranking, the supply points corresponding to all communities with resilience from low to high are identified one by one in the supply and demand network of daily necessities, and attack strategies are formulated accordingly.

[0231] Based on the attack strategy, the supply points of the supply and demand network of daily necessities are deliberately attacked, and the efficiency of the entire network is tested, thereby measuring the impact of the community resilience evaluation index on the overall supply network's survivability.

[0232] Based on the global network efficiency after a deliberate attack, weights are assigned to the resilience indicators of each community. The community resilience is then comprehensively evaluated by combining the values ​​of each community resilience indicator, resulting in a community resilience assessment.

[0233] It should be noted that the computer-readable storage medium in this embodiment can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0234] In this embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this embodiment, the computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0235] The computer-readable storage medium described above can be used to write computer programs for executing this embodiment in one or more programming languages ​​or combinations thereof. These programming languages ​​include object-oriented programming languages—such as Java, Python, and C++—and conventional procedural programming languages—such as C or similar programming languages. The program can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0236] In summary, this invention, from the perspective of complex networks, constructs a community resilience evaluation index system and an urban living supplies supply network. It combines network resilience algorithms to formulate attack strategies and finally uses the entropy weight method to assign weights for calculation, identifying low-resilience communities with potential supply shortages. Identifying the risk of living supplies shortages through community resilience assessment methods will help build network connections between community resilience and other subsystems within complex urban systems. This provides a research foundation and methodological experiments for planning and constructing community resilience enhancement, and is of great significance for improving modern governance capabilities and ensuring residents' well-being.

[0237] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, shall fall within the scope of protection of the present invention.

Claims

1. A method for assessing the resilience of community living supplies supply based on network survivability, characterized in that, The method includes: Data from the target area is acquired and preprocessed, including mobile signaling data, POI data, urban road data, and basic administrative data. Based on the preprocessed data, the target area is divided into grids, a community resilience evaluation index system is constructed, and a preliminary evaluation of the community resilience evaluation index system is conducted to obtain the community resilience ranking under each index. Establish connections between supply points and use mobile signaling data to build a supply and demand network for daily necessities; Based on the community resilience ranking, we identified the corresponding supply points for each community in the supply and demand network of daily necessities and formulated attack strategies accordingly. Based on the attack strategy, the supply points of the supply and demand network of daily necessities are deliberately attacked, and the efficiency of the entire network is tested, thereby measuring the impact of the community resilience evaluation index on the overall supply network's survivability. Based on the global network efficiency after a deliberate attack, weights are assigned to the resilience indicators of each community. The community resilience is then comprehensively evaluated by combining the values ​​of each community resilience indicator, resulting in a community resilience assessment. The process involves dividing the target area into grids based on the preprocessed data, constructing a community resilience evaluation index system, conducting a preliminary evaluation of the index system, and obtaining a ranking of community resilience under each index. Specifically, this includes: Based on basic administrative data, the target area is divided into grids and the grids are numbered. Based on the community's self-organization capacity, population composition, and infrastructure, a community resilience evaluation index system is constructed. The indicators corresponding to the community's self-organization capacity include the rate of change in non-commuter traffic. The indicators corresponding to the community's population composition include population size, the proportion of elderly people, and per capita consumption capacity. The indicators corresponding to the community's infrastructure include the number of supermarkets and shopping malls. The community resilience evaluation index system was initially evaluated by obtaining non-commuter flow change rate, population size, elderly ratio and per capita consumption capacity from mobile signaling data, and supermarket quantity from POI data, and the corresponding data of the five indicators were linked to the corresponding grid in space. The values ​​of the grid corresponding to the five indicators are normalized and sorted to obtain the community resilience ranking under each indicator. The establishment of connections between supply points, using mobile signaling data, and the construction of a supply and demand network for daily necessities, specifically includes: Based on the gravity model, the service connectivity matrix between any two supply points is calculated to establish the connections between supply points. Based on the connections between supply points, a supply and demand network for daily necessities is constructed using human footprint data from mobile phone signaling data. This supply and demand network is used to calculate the normal service population of the supply points. The process involves assigning weights to resilience indicators for each community based on global network efficiency, and combining these weights with the values ​​of each community's resilience indicators to comprehensively evaluate community resilience and obtain a community resilience assessment result. This process specifically includes: Based on the global network efficiency after a deliberate attack, the community resilience index is assigned weights using the entropy weight method. Based on the weights and values ​​of each community resilience index, a comprehensive evaluation of community resilience is conducted to obtain the community resilience assessment results.

2. The method for assessing the resilience of community living supplies according to claim 1, characterized in that, The calculation process for the normal service population of the supply points is as follows: The number of market service personnel purchasing vegetables is summed based on the grid ID of the supply point. The number of service personnel is then expanded using the average household size index, and further calibrated based on yearbook data to obtain the normal service population, as shown in the following formula: ； in, To meet the population size that the supply points regularly serve, For correction factors, This is an indicator of average household size. The percentage of daily necessities purchased while traveling to supply points.

3. The method for assessing the resilience of community living supplies according to claim 1, characterized in that, The method involves deliberately attacking supply points in the supply and demand network of daily necessities according to an attack strategy, detecting the overall network efficiency, and thus measuring the impact of community resilience evaluation indicators on the overall supply network's survivability. Specifically, this includes: The attack strategy involves deliberately targeting supply points in the supply and demand network for essential goods. The maximum load capacity of each supply point is... When the load on supply point i exceeds a certain multiple of its capacity, supply point i becomes overloaded and fails, resulting in a loss of service volume. As shown in the following formula: ； ； in, The maximum load that supply point i can bear; Overload factor; For the normal node service volume of supply point i, This represents the missing service quantity at supply point i; The actual load at supply point i under a sudden event; When provider i is attacked and fails, the lost service load will be redistributed to surrounding provider points according to certain redistribution rules, as follows: ； in, This represents the load allocated from supply point i to supply point j; This represents the remaining capacity at supply point j; This represents the remaining capacity of node k; Represents the set of supply points associated with supply point i; When supply point i is overloaded and all surrounding supply points are overloaded, the excess load on supply point i cannot be allocated and is defined as missing service. At the same time, the connection between supply point i and surrounding supply points is lost. When no more missing service is added in the network, the network cascading effect ends. The global network efficiency is then tested to measure the impact of the community resilience index on the overall supply network's resilience, as shown in the following formula: ； in, For overall network efficiency; The shortest distance from supply point i to supply point j; This represents the total number of supply points in the initial supply network.

4. The method for assessing the resilience of community living supplies according to claim 1, characterized in that, The process of assigning weights to the community resilience index based on the global network efficiency after a deliberate attack, using the entropy weight method, specifically includes: The slope of the global network efficiency curve after a deliberate attack is used as an indicator for calculation. The weight of the community resilience indicator is calculated through simulation. The formula for calculating the slope of the global network efficiency curve is as follows: ； in, The slope For overall network efficiency, there are n supply points; The slope data is standardized using the following formula: ； in, The data represents the standardized slope, where K is the slope. The slope data is normalized using the following formula: ； The formula for calculating information entropy is: ； in, For calculating information entropy, The slope data is normalized; The weight of the community resilience index is calculated using information entropy, and the calculation formula is as follows: ； in, As a weighting factor for community resilience indicators, The information entropy is calculated.

5. A community living supplies resilience assessment system based on network survivability, characterized in that, The system includes: The acquisition module is used to acquire data from the target area and preprocess it. The data includes mobile phone signaling data, POI data, urban road data, and basic administrative data. The preliminary evaluation module is used to divide the target area into grids based on the preprocessed data, construct a community resilience evaluation index system, conduct a preliminary evaluation of the community resilience evaluation index system, and obtain the community resilience ranking under each index. The network construction module is used to establish connections between supply points and uses mobile signaling data to build a supply and demand network for daily necessities. The strategy formulation module is used to sort communities by resilience, find the corresponding supply points for each community in the supply and demand network of living materials, and formulate attack strategies. The measurement module is used to launch deliberate attacks on the supply points of the supply and demand network of daily necessities according to the attack strategy, and to detect the global network efficiency, thereby measuring the impact of the community resilience evaluation index on the overall supply network's resilience. The comprehensive evaluation module is used to assign weights to the resilience indicators of each community based on the global network efficiency after a deliberate attack, and combine the resilience indicator values ​​of each community to comprehensively evaluate the community resilience and obtain the community resilience assessment results. The process involves dividing the target area into grids based on the preprocessed data, constructing a community resilience evaluation index system, conducting a preliminary evaluation of the index system, and obtaining a ranking of community resilience under each index. Specifically, this includes: Based on basic administrative data, the target area is divided into grids and the grids are numbered. Based on the community's self-organization capacity, population composition, and infrastructure, a community resilience evaluation index system is constructed. The indicators corresponding to the community's self-organization capacity include the rate of change in non-commuter traffic. The indicators corresponding to the community's population composition include population size, the proportion of elderly people, and per capita consumption capacity. The indicators corresponding to the community's infrastructure include the number of supermarkets and shopping malls. The community resilience evaluation index system was initially evaluated by obtaining non-commuter flow change rate, population size, elderly ratio and per capita consumption capacity from mobile signaling data, and supermarket quantity from POI data, and the corresponding data of the five indicators were linked to the corresponding grid in space. The values ​​of the grid corresponding to the five indicators are normalized and sorted to obtain the community resilience ranking under each indicator. The establishment of connections between supply points, using mobile signaling data, and the construction of a supply and demand network for daily necessities, specifically includes: Based on the gravity model, the service connectivity matrix between any two supply points is calculated to establish the connections between supply points. Based on the connections between supply points, a supply and demand network for daily necessities is constructed using human footprint data from mobile phone signaling data. This supply and demand network is used to calculate the normal service population of the supply points. The process involves assigning weights to resilience indicators for each community based on global network efficiency, and combining these weights with the values ​​of each community's resilience indicators to comprehensively evaluate community resilience and obtain a community resilience assessment result. This process specifically includes: Based on the global network efficiency after a deliberate attack, the community resilience index is assigned weights using the entropy weight method. Based on the weights and values ​​of each community resilience index, a comprehensive evaluation of community resilience is conducted to obtain the community resilience assessment results.

6. A computer device comprising a processor and a memory for storing a processor-executable program, characterized in that, When the processor executes the program stored in the memory, it implements the community living supplies resilience assessment method according to any one of claims 1-4.

7. A storage medium storing a program, characterized in that, When the program is executed by the processor, it implements the community living supplies resilience assessment method according to any one of claims 1-4.

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