Intelligent assessment method and system for urban health community space

Abstract

The invention discloses an urban healthy community space intelligent assessment method and system, and relates to the technical field of urban space intelligent assessment and healthy community planning, and the method introduces space grid division, street network modeling and graph theory analysis technologies, achieves the high-precision modeling and reachability simulation of an urban community space, and achieves the intelligent assessment of the urban community space. And the coverage capability and the service balance of the 15-minute walking life circle of residents can be accurately evaluated. According to the method, the reachable path network taking each grid unit as a starting point can be dynamically constructed, and the service accessibility level and the comprehensive service scoring index of each grid are quantitatively evaluated according to the quantity vector of the resident reachable service facilities. By setting dual thresholds of coverage rate and score, a'potential function blind area 'is intelligently identified, and a'cold space grid' is accurately discriminated by further combining a resident popularity activity sequence, so that scientific support and decision basis are provided for subsequent city updating, service facility layout optimization and community health space transformation.

Description

Technical Field

[0001] This invention relates to the field of urban spatial intelligent assessment and healthy community planning technology, specifically to an urban healthy community spatial intelligent assessment method and system. Background Technology

[0002] In the current development and spatial governance of urban communities, there is a widespread spatial mismatch and functional imbalance between facility allocation and the actual thermal distribution of residents' activities, especially in high-density residential areas. Traditional community planning mostly adopts a static approach based on the quantity of facilities and theoretical service radius, lacking in-depth perception and comprehensive analysis of actual accessible paths, walking behavior characteristics, and the dynamic needs of the population. This results in some areas becoming "over-utilized areas" with redundant services and low utilization rates, while other areas have long been in "functional blind spots" with insufficient facility coverage and a lack of basic living support.

[0003] Furthermore, existing methods for assessing the health of community spaces have significant limitations in terms of technical implementation. Most rely on Euclidean straight-line distances or static coverage models based on single-point facilities, failing to fully consider the constraints that the connectivity and accessibility of urban street networks place on residents' walking behavior. As a result, the assessment results deviate significantly from residents' actual experiences, making it difficult to provide accurate guidance for urban spatial renewal. At the same time, traditional methods often lack mechanisms for identifying "cold spaces" in communities—areas with consistently low resident activity and low environmental vitality. This leaves such low-activity areas in a persistent "blind spot" of spatial governance, hindering the optimal allocation of resources and the achievement of equitable public services.

[0004] In recent years, the "15-minute living circle" concept has been widely applied in planning practices such as healthy cities, resilient communities, and age-friendly spaces, becoming an important indicator for measuring the convenience and spatial accessibility of residents' daily lives. This time threshold reflects a people-centered scale logic and possesses good technical operability and institutional adaptability, gradually becoming an important reference framework for the refined governance of urban spaces. However, in practical applications, the lack of pedestrian accessibility simulation at the block scale and a grid-level precise assessment system for spatial services remains one of the key challenges hindering the effective implementation of the 15-minute living circle in community governance. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for intelligent assessment of urban healthy community spaces, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent assessment system for urban healthy community spaces, comprising: The grid generation module is used to divide the target community into several spatial grid cells, obtain the geographical coordinates, road connectivity structure and building outline information of each cell, and form a graph theory model that can be used to simulate walking paths; The facility mapping module is used to collect spatial distribution data of hospital, supermarket, school and park service facilities in the target community, label the functional type and service radius of each type of service facility in the spatial grid, and generate a spatial grid model of the target community based on a graph theory model that can be used to simulate walking paths. The walkability simulation module is used to construct a walkway network model based on the street network, starting from each spatial grid cell, and simulate the walking path within 15 minutes to build each spatial grid cell C. i Reachable raster set G i and the number of public service facilities ; The living circle coverage assessment module is used to assess the coverage of each spatial grid unit C. i Reachable raster set G i and the number of public service facilities Calculate the 15-minute walking reach coverage of the i-th grid cell. and comprehensive service rating index And preset a first coverage threshold A and a second scoring threshold B; when the conditions are met or If either of the two conditions is met, the corresponding grid is marked as a "potential functional blind spot", and the first candidate region group is obtained by summarizing them. The cold space discrimination module is used to collect resident activity data of the first candidate area group, obtain heat sequence data, mark the long-term sparse grid cells in the first candidate area group, determine whether they are "cold space grids" with low accessibility and low activity, and output the corresponding strategy.

[0007] Preferably, the grid division module includes a geospatial acquisition unit, a road connectivity structure identification unit, a building outline extraction unit, and a spatial raster coding unit; The geospatial acquisition unit is used to acquire elevation topographic maps, basic geomorphological maps, and street framework maps of the target community through remote sensing images, LiDAR, drone aerial photography, street view maps, and GIS data, and to construct a two-dimensional community geographic base map. The road connectivity structure identification unit is used to extract linear traffic elements such as streets, alleys, pedestrian passages, overpasses and underpasses from a two-dimensional community geographic base map based on a deep image recognition network, establish a road connectivity topology map, and form a graph theory model that can be used to simulate walking paths.

[0008] Preferably, the building outline extraction unit is used to extract the boundary outline of buildings within the community in a graph theory model that can be used to simulate walking paths, and project it into three-dimensional boundary data. Combined with the number of floors and height information, it generates grid obstacles in the graph theory model that can be used to simulate walking paths. The spatial raster coding unit is used to divide the target community into several spatial raster units according to a preset ratio in a graph theory model that can be used to simulate walking paths. Each raster is assigned a unique coding ID, and the center coordinates, boundary coordinates, area parameters, road connectivity status, obstacle coverage status, and functional attribute initialization label spatial attribute values ​​of each raster unit are obtained to generate a spatial raster model of the target community.

[0009] Preferably, the facility mapping module includes a service facility acquisition unit, a facility spatial positioning unit, and a function type labeling unit; The service facility acquisition unit is used to call GIS systems, street view image databases, urban public service information platforms or remote sensing images to obtain the original geographical location information of hospitals, supermarkets, schools and parks within the target community, including name, type, address, latitude and longitude coordinates and function labels; The facility spatial positioning unit is used to accurately map the service facility's latitude and longitude coordinates onto the spatial grid model of the target community, identify the specific grid unit ID to which the facility belongs, and establish a one-to-one correspondence between the facility point and the grid space. The function type labeling unit is used to label each type of facility with a function category label according to its service attributes. Function categories include medical services, food supply, basic education, and public green space. The corresponding function codes are assigned and marked in the spatial grid model of the target community.

[0010] The function type labeling unit is used to label each type of facility with a function category label according to its service attributes. Function categories include medical services, food supply, basic education, and public green space. The corresponding function codes are assigned and marked in the spatial grid model of the target community.

[0011] Preferably, the walkability simulation module includes a path network construction unit, an reachable path simulation unit, and a service facility hit unit; The path network construction unit is used to construct the road connectivity structure in the spatial grid model of the target community. The center point of each spatial grid unit is used as the node in the graph structure, and the passage relationship between streets, alleys, sidewalks, bridges and underpasses is represented as the edge in the graph structure. The reachable path simulation unit is used to assign walking time weights to each edge constructed in the graph structure, and the weights satisfy the following calculation expression; ; in, This represents the path length of the k-th edge, calculated using GIS to determine the Euclidean distance or actual path length between adjacent nodes. V(e k () represents the standard walking speed for the corresponding road type of the k-th edge; the standard walking speed for the corresponding road type includes: When the road type is a main urban street, V(e) k =80m / min; When the road type is a community secondary street, V(e) k =70m / min; When the road type is an alley or passage, V(e) k =60m / min; When the road type is an indoor passage, V(e) k ) = 50 m / min; When the road type is a hillside section, V(e) k ) = 40 m / min; When the road type is a downhill section, V(e) k ) = 45 m / min; This represents the adjustment factor for the k-th edge, taking into account obstacles, slope, and passage conditions. The service facility hit unit is used to traverse the shortest walking path range within a 15-minute time frame, starting from any spatial grid cell, based on the walking path network model. Within this path range, it identifies whether at least one type of service facility is hit. Service facilities include hospitals, schools, supermarkets, parks, and other facilities that meet the basic living needs of residents, and records the hit results. For each spatial grid cell C i Its center point is the starting node c in the graph network. i Then its reachable raster set G i Represented as: ; in, Indicates starting from node c i To target node c j The total time for the shortest path; Set to 15 minutes as the maximum walking time limit; reachable grid set G i Indicates starting from node C i Departure point: a grid collection point 15 minutes away; Each edge is assigned a weight based on the walking time. The walking time for the k-th edge is: ; Total path time for: ； in, Indicates starting from node c i To target node c j The set of edges on the shortest path; Finally, for each spatial grid cell C i Obtain the reachable raster set G i and the number of public service facilities .

[0012] Preferably, the living circle coverage assessment module includes a coverage calculation unit, a service hit vector assessment unit, and a functional blind spot identification unit; Coverage calculation unit, used for each spatial raster cell C i and the reachable raster set G i and the number of public service facilities Calculate the 15-minute walking reach coverage of the i-th grid cell. ; ; This represents the total number of grid cells in the community space; Service hit vector evaluation unit, used for evaluation based on each spatial raster cell C i The reachable raster set G i and the number of public service facilities The comprehensive service score index of the i-th grid is calculated using the following formula. : ； in, Let w be the weight value of the p-th type of service facility, used to reflect the importance of different facilities in residents' lives, and the sum of the weights satisfies ∑w p =1; The number of service facilities of type p that can be reached from the i-th grid; n represents the total number of service facility categories covered. A functional blind spot identification unit is used to set a first coverage threshold A and a second scoring threshold B; When the coverage of the 15-minute walking distance of the i-th grid is satisfied simultaneously ≥ First coverage threshold A, and when the comprehensive service score index of the i-th grid A score ≥ the second scoring threshold B indicates that the spatial grid living circle has qualified coverage and good walkability and service functions. When the 15-minute walking distance coverage of the i-th grid is... <First coverage threshold A, means: the spatial range that residents can reach within 15 minutes from this grid, indicating that residents' activities are restricted and accessibility is abnormal; When the comprehensive service score index of the i-th grid <Second scoring threshold B indicates that, taking this grid as the resident's starting point, the distribution of living service facilities within a 15-minute reach is uneven, and the service functions are unbalanced. When satisfied or If either of the two conditions is met, the corresponding raster is marked as a "potential functional blind spot", and the first candidate region group is obtained by summarizing them.

[0013] Preferably, the cold space discrimination module includes a population thermal data acquisition unit, a functional blind spot cross-screening unit, and a comprehensive strategy unit; The population thermal data acquisition unit is used to collect resident activity data of the first candidate area group and map it to the spatial grid model of the target community, mapping each population location information to the corresponding spatial grid cell C according to latitude and longitude. i Then, under the daily cumulative time dimension, extract the daily average activity heat value D(C) of the i-th grid. i ); and accumulate the number of people activities in each grid within a set period of 1 month on a daily basis to obtain heat sequence data; The functional blind zone cross-screening unit is used to perform population heat sequence analysis on the first candidate region group based on heat sequence data. A low-activity threshold Dmin is preset. If the daily average activity heat value D(C) of each grid cell within the first candidate region group is lower than the threshold value, the unit is considered to have a low activity threshold. i If the daily average activity intensity value D(C) ≤ Dmin, it indicates that the population intensity of the spatial grid is not up to standard and meets the low activity level; if the daily average activity intensity value D(C) of each grid in the first candidate region group is less than or equal to the minimum daily activity intensity value Dmin, it indicates that the population intensity of the spatial grid is not up to standard and meets the low activity level. i If Dmin > Dmin, it means that the population heat index of the spatial grid is qualified and will be continuously monitored. Define a continuous risk trigger counter Cr, when D(C i If D(C) ≤ Dmin, it indicates that the population heat level is not up to standard, and the counter Cr increments by 1; otherwise, the counter resets to 0. When it is identified that D(C) ≤ Dmin for 21 out of 30 days in a month... i When )≤Dmin, that is, when the counter Cr≥21, it is marked as "cold space grid".

[0014] Preferably, the integrated strategy unit, used to generate corresponding strategies for the "cold space grid", includes: When the proportion of "cold space grids" in the target community exceeds 20% but is less than 30%, a "first-level moderate functional optimization strategy" is generated, including: adding convenient facilities such as pocket parks, small book kiosks, and rest seats, the number of which should not be less than 5% of the total number of space grids in the target area; embedding new functional commercial stalls or shared sports corners into the previously idle and underutilized vacant land in the area through "micro-updates", the updated area should reach more than 30% of the original inefficient vacant land area; in the set of space grids identified as functional blind spots, at least 60% of the grids must be upgraded through the establishment of new functions. Community service stations are used to achieve functional coverage, thereby alleviating the problem of missing public services; within each identified "cold space grid," at least one previously unconnected street or alleyway path should be opened up to enhance the connectivity between the cold area and the surrounding space; new pedestrian paths should be added, including street greenways or pedestrian crossings, and the total length of the new pedestrian paths should not be less than 30% of the total length of the original paths; streetlights and security monitoring facilities should be installed within the cold space area and along its paths, with a coverage rate of 30%-50% or more, to improve safety and accessibility; If the proportion of "cold space grids" in the target community exceeds 40%, a "second key renovation strategy" will be generated. This involves systematically embedding convenient service nodes, including pocket parks, community bookstores, smart parcel lockers, rest seats, and retail kiosks, within densely populated areas of cold space grids. These will form small-scale, high-frequency resident activity hubs, and their number should be no less than 10% of the total number of space grids in the target area. Previously idle, abandoned, or underutilized land will be reorganized by embedding multi-functional scenarios, including commercial stalls, fitness corners, and public bookstores, achieving "multi-use of one piece of land" and improving the overall land efficiency and spatial vitality. Government convenience service centers, public welfare organization sites, and volunteer service points will be integrated into "community stations," achieving a service point coverage rate of ≥80% in functional blind spots, effectively reducing service supply gaps. Within each identified "cold space grid," at least two previously unconnected or non-exiting street paths should be opened up to enhance the connectivity between the cold space and the surrounding space; new pedestrian paths should be added, including street greenways or pedestrian crossings, and the total length of the new pedestrian paths should not be less than 40% of the total length of the original paths; streetlights and security monitoring facilities should be installed within the cold space area and along its paths, with a coverage rate of 60%-70% or more, to improve safety and accessibility.

[0015] A method for intelligent spatial assessment of urban healthy communities includes the following steps: Step 1: Divide the target community into several spatial grid cells, obtain the geographical coordinates, road connectivity, and building outline information of each cell, and form a graph theory model that can be used to simulate walking paths; Step 2: Collect spatial distribution data of hospital, supermarket, school and park service facilities in the target community, label the functional type and service radius of each type of service facility in the spatial grid, and generate a spatial grid model of the target community based on the graph theory model that can be used to simulate walking paths. Step 3: Starting with each spatial grid cell, construct a pedestrian path network model based on the street network, simulating the walking path within 15 minutes, to build each spatial grid cell C. i Reachable raster set G i and the number of public service facilities ; Step 4: Based on each spatial grid cell C i Reachable raster set G i and the number of public service facilities Calculate the 15-minute walking reach coverage of the i-th grid cell. and comprehensive service rating index And preset a first coverage threshold A and a second scoring threshold B; when the conditions are met or If either of the two conditions is met, the corresponding grid is marked as a "potential functional blind spot", and the first candidate region group is obtained by summarizing them. Step 5: Collect resident activity data for the first candidate area group, obtain heat sequence data, mark long-term sparse grid cells in the first candidate area group, determine whether they are "cold space grids" with low accessibility and low activity, and output the corresponding strategy.

[0016] This invention provides a method and system for intelligent spatial assessment of urban healthy communities. It has the following beneficial effects: (1) This method and system for intelligent spatial assessment of urban healthy communities, by introducing street network graph theory modeling and walking path simulation mechanism, breaks through the traditional static assessment mode based on the number of facilities and service radius, and can more realistically simulate residents' actual walking paths and service accessibility, significantly improving the accuracy and effectiveness of community function assessment. This invention divides the community into high-precision spatial grid units, and performs facility mapping, walking simulation and coverage assessment at the grid level, overcoming the "spatial average" distortion problem caused by coarse-grained assessment based on administrative units or street boundaries in traditional methods, and realizing refined identification and governance of urban microspaces.

[0017] (2) This method and system for intelligent spatial assessment of urban healthy communities constructs each spatial grid unit C i Reachable raster set G i and the number of public service facilities Calculate the 15-minute walking reach coverage of the i-th grid cell. and comprehensive service rating index The system employs multi-dimensional quantitative indicators to effectively reflect the spatial balance of different types of services, helping to identify "potential functional blind spots" and improving the targeting and fairness of resource allocation. Building upon spatial functional coverage assessment, the system further incorporates resident activity heatmap data to label and identify low-activity areas within candidate area clusters, forming a dynamic monitoring capability for "cold space grids" and providing support for community spatial regeneration and precise intervention. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the process of an intelligent assessment system for urban healthy community space according to the present invention; Figure 2 This is a schematic diagram illustrating the steps of an intelligent spatial assessment method for urban healthy communities according to the present invention. Detailed Implementation

[0019] 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 only some embodiments of the present invention, and 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.

[0020] Example 1 Please see Figure 1 This invention provides an intelligent assessment system for urban healthy community spaces, comprising: The grid generation module is used to divide the target community into several spatial grid cells, obtain the geographical coordinates, road connectivity structure and building outline information of each cell, and form a graph theory model that can be used to simulate walking paths; The facility mapping module is used to collect spatial distribution data of hospital, supermarket, school and park service facilities in the target community, label the functional type and service radius of each type of service facility in the spatial grid, and generate a spatial grid model of the target community based on a graph theory model that can be used to simulate walking paths. The walkability simulation module is used to construct a walkway network model based on the street network, starting from each spatial grid cell, and simulate the walking path within 15 minutes to build each spatial grid cell C. i Reachable raster set G i and the number of public service facilities ; The living circle coverage assessment module is used to assess the coverage of each spatial grid unit C. i Reachable raster set G i and the number of public service facilities Calculate the 15-minute walking reach coverage of the i-th grid cell. and comprehensive service rating index And preset a first coverage threshold A and a second scoring threshold B; when the conditions are met or If either of the two conditions is met, the corresponding grid is marked as a "potential functional blind spot", and the first candidate region group is obtained by summarizing them. The cold space discrimination module is used to collect resident activity data of the first candidate area group, obtain heat sequence data, mark the long-term sparse grid cells in the first candidate area group, determine whether they are "cold space grids" with low accessibility and low activity, and output the corresponding strategy.

[0021] In this embodiment, by introducing street network graph theory modeling and walking path simulation mechanisms, the system breaks through the traditional static evaluation model based on the number of facilities and service radius. It can more realistically simulate residents' actual walking paths and service accessibility, significantly improving the accuracy and effectiveness of community function assessment. This invention divides the community into high-precision spatial grid units and performs facility mapping, walking simulation, and coverage assessment at the grid level. It overcomes the "spatial averaging" distortion problem caused by coarse-grained assessments based on administrative units or street boundaries in traditional methods, achieving refined identification and governance of urban microspaces.

[0022] By constructing each spatial grid cell C i Reachable raster set G i and the number of public service facilities Calculate the 15-minute walking reach coverage of the i-th grid cell. and comprehensive service rating index The system employs multi-dimensional quantitative indicators to effectively reflect the spatial balance of different types of services, helping to identify "potential functional blind spots" and improving the targeting and fairness of resource allocation. Building upon spatial functional coverage assessment, the system further incorporates resident activity heatmap data to label and identify low-activity areas within candidate area clusters, forming a dynamic monitoring capability for "cold space grids" and providing support for community spatial regeneration and precise intervention.

[0023] Example 2 This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically, the grid division module includes geospatial acquisition units, road connectivity structure identification units, building outline extraction units, and spatial raster coding units; The geospatial acquisition unit is used to acquire elevation topographic maps, basic geomorphological maps, and street framework maps of the target community through remote sensing images, LiDAR, drone aerial photography, street view maps, and GIS data, and to construct a two-dimensional community geographic base map. The road connectivity structure identification unit is used to extract linear traffic elements such as streets, alleys, pedestrian passages, overpasses and underpasses from a two-dimensional community geographic base map based on a deep image recognition network, establish a road connectivity topology map, and form a graph theory model that can be used to simulate walking paths.

[0024] In this embodiment, a road network topology map is constructed using road connectivity structure identification units, forming a graph theory model with node-edge attributes. This provides a foundation of realistic reachable paths for subsequent walkability analysis and community simulation, significantly improving the realism and practicality of spatial behavior simulation. Fine-grained community segmentation using spatial grid coding units allows each spatial unit to be assigned attributes such as geographic coordinates, topological relationships, and building outlines. This facilitates spatial linkage with multi-source information such as service facilities and behavioral data, enabling a comprehensive assessment of spatial function and activity. High-precision road connectivity structure and building boundary data provide a reliable foundation for subsequent cold space identification, path congestion analysis, and service facility coverage optimization, enhancing the system's decision support capabilities in refined spatial governance.

[0025] Example 3 This embodiment is an explanation based on Embodiment 2. Please refer to it. Figure 1 Specifically, the building outline extraction unit is used to extract the boundary outlines of buildings within the community in a graph theory model that can be used to simulate walking paths, and project them into three-dimensional boundary data. Combined with the number of floors and height information, it generates grid obstacles in the graph theory model that can be used to simulate walking paths. The spatial raster coding unit is used to divide the target community into several spatial raster units according to a preset ratio in a graph theory model that can be used to simulate walking paths. Each raster is assigned a unique coding ID, and the center coordinates, boundary coordinates, area parameters, road connectivity status, obstacle coverage status, and functional attribute initialization label spatial attribute values ​​of each raster unit are obtained to generate a spatial raster model of the target community.

[0026] In this embodiment, the building outline extraction unit, based on the building boundary line extraction technology within the block, combined with the number of floors and building height information, realizes the projection modeling of three-dimensional obstacles. This processing method effectively enhances the ability to identify inaccessible areas of buildings during pedestrian path simulation, avoiding paths from mistakenly passing through buildings or invalid areas, thereby significantly improving the realistic constraint effect of accessibility analysis. The community is divided into grid units with unique coded IDs through spatial grid coding units, and assigned multi-dimensional spatial attributes including center coordinates, boundary information, area, road connectivity, obstacle coverage status, and functional labels. This helps to form a spatial grid system with complete geographic topology and semantic structure, providing a solid data foundation for subsequent functional blind spot identification, cold space identification, and service facility configuration optimization. The unique grid coding mechanism ensures the locationability and consistency of each spatial unit in analysis and application, making the generation, statistics, and traceability of spatial evaluation indicators more logically closed-loop and data consistent, facilitating multi-phase evolution analysis and dynamic monitoring.

[0027] Example 4 This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically, the facility mapping module includes a service facility acquisition unit, a facility spatial positioning unit, and a function type labeling unit; The service facility acquisition unit is used to call GIS systems, street view image databases, urban public service information platforms or remote sensing images to obtain the original geographical location information of hospitals, supermarkets, schools and parks within the target community, including name, type, address, latitude and longitude coordinates and function labels; The facility spatial positioning unit is used to accurately map the service facility's latitude and longitude coordinates onto the spatial grid model of the target community, identify the specific grid unit ID to which the facility belongs, and establish a one-to-one correspondence between the facility point and the grid space. The functional type labeling unit is used to label each type of facility with a functional category label according to its service attributes. The functional categories include medical services, food supply, basic education and public green space. The corresponding functional codes are assigned and marked in the spatial grid model of the target community.

[0028] The functional type labeling unit is used to label each type of facility with a functional category label according to its service attributes. Functional categories include medical services, food supply, basic education, and public green space, and assign corresponding functional codes (e.g., hospitals are C1, supermarkets are C2, schools are C3, and parks are C4), and mark them in the spatial grid model of the target community. Based on the typical service radius of each type of service facility (e.g., hospitals are 800 meters, supermarkets are 500 meters, schools are 600 meters, and parks are 700 meters), a service coverage buffer zone is constructed with the facility point as the center, and it is calculated whether each grid unit is within any service radius, and the service hit mark is recorded.

[0029] In this embodiment, based on the typical service radii of various facilities (such as 800 meters for hospitals, 500 meters for supermarkets, 600 meters for schools, and 700 meters for parks), a coverage buffer zone is automatically generated with the facility point as the center, which effectively reflects the spatial influence range of different service facilities. Combined with the spatial grid model, it accurately calculates whether each grid cell is within any service radius and records the service hit mark, thereby improving the spatial refinement analysis capability of community service coverage.

[0030] Example 5 This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically, the walkability simulation module includes a path network construction unit, an reachable path simulation unit, and a service facility hit unit; The path network construction unit is used to construct the road connectivity structure in the spatial grid model of the target community. The center point of each spatial grid unit is used as the node in the graph structure, and the passage relationship between streets, alleys, sidewalks, bridges and underpasses is represented as the edge in the graph structure. Reachable path simulation unit, used to assign walking time weights to each edge constructed in the graph structure. The weights satisfy the following calculation expression; ; in, This represents the path length of the k-th edge, calculated using GIS to determine the Euclidean distance or actual path length between adjacent nodes. V(e k () represents the standard walking speed for the corresponding road type of the k-th edge; the standard walking speed for the corresponding road type includes: When the road type is a main urban street, V(e) k =80m / min; When the road type is a community secondary street, V(e) k =70m / min; When the road type is an alley or passage, V(e) k =60m / min; When the road type is an indoor passage, V(e) k ) = 50 m / min; When the road type is a hillside section, V(e) k ) = 40 m / min; When the road type is a downhill section, V(e) k ) = 45 m / min; This represents the adjustment factor for the k-th edge, taking into account obstacles, slope, and passage conditions. The service facility hit unit is used to traverse the shortest walking path range within a 15-minute time frame, starting from any spatial grid cell, based on the walking path network model. Within this path range, it identifies whether at least one type of service facility is hit. Service facilities include hospitals, schools, supermarkets, parks, and other facilities that meet the basic living needs of residents, and records the hit results. For each spatial grid cell C i Its center point is the starting node c in the graph network. i Then its reachable raster set G i Represented as: ; in, Indicates starting from node c i To target node c j The total time for the shortest path; Set to 15 minutes as the maximum walking time limit; reachable grid set G i Indicates starting from node C i Departure point: a grid collection point 15 minutes away; Each edge is assigned a weight based on the walking time. The walking time for the k-th edge is: ; Total path time for: ； in, Indicates starting from node c i To target node c j The set of edges on the shortest path; Finally, for each spatial grid cell C i Obtain the reachable raster set G i and the number of public service facilities for:

[0031] in, This indicates the number of hospitals that can be reached within 15 minutes, including community health service centers and hospitals; This indicates the number of schools within a 15-minute reach, including kindergartens, primary schools, and secondary schools; This indicates the number of supermarkets within a 15-minute reach, including supermarkets, convenience stores, and farmers' markets; This indicates the number of accessible parks, including city parks, pocket parks, and street green spaces; This indicates the number of accessible cultural service facilities, including libraries and community cultural centers; This indicates the number of accessible fitness facilities, including outdoor fitness equipment, gymnasiums, and fitness centers; This indicates the number of accessible elderly care service facilities, including community elderly care centers and senior service stations; This indicates the number of accessible charging points for electric vehicles / shared vehicles, including electric vehicle charging stations and bicycle charging stations; Indicates the number of accessible public transportation stops, including bus stops and subway stations; This indicates the number of accessible facilities, including accessible ramps, elevators, and handrails; This indicates the number of accessible government / financial service outlets, including banks and government service points.

[0032] For examples of adjustment factors (considering obstacles, slope, and traffic lights), please refer to Table 1 below:

[0033] Example calculation: Assume an edge e i It is a 120-meter-long alleyway, with a standard walking speed V(e) i =60m / min; Due to construction in part of the alley, traffic is affected, therefore the adjustment factor F(e) is adjusted. i) =1.3F; The walking time weight for this edge segment is: Tw(e i = (120 / 60) × 1.3 = 2 × 1.3 = 2.6 minutes; Example from real life: Example 1: Suppose you live on the edge of a community (such as next to a railway, behind a mountain, or outside a closed factory wall). Although this place is also part of the urban community, you find that: there is only one small road leading to the main road; high walls, rivers, or highways block your way; there are no pedestrian bridges or underpasses; and the streets are dead ends or have a one-way flow structure.

[0034] At this point, even if you look close to the school or supermarket on the map, in reality you can only walk to a very small area within 15 minutes, and you can't even reach the first service facility, or there are very few places you can reach. This is a typical scenario of "insufficient coverage".

[0035] Example 2: Traffic disruption area; Location: On the other side of the main road, but without a pedestrian overpass; 15-minute walking range: Although the straight-line distance is not far, the main road cannot be crossed due to road detours; The area that should be accessible is "physically isolated", and one can only move towards the community behind; Consequences: The 15-minute walking range is very limited, resulting in very few accessible service facilities.

[0036] Example 3: Location: Residential building grid next to the factory wall; 15-minute walking range: only includes one street in front of the building, with no entrances or exits nearby; Insufficient coverage: only 2-3 grids can be reached within a 15-minute walk (very few); Consequence: Unable to access hospitals, schools, and parks; service hit rate is 0. Example 4: Steep mountainous area; Location: Residential area on a mountain slope on the edge of a city; Manifestation: Walking speed is greatly affected by the terrain. Although many areas are close in a straight line, walking takes a long time and is difficult to reach within 15 minutes; Consequence: It results in a small actual walking coverage area, forming a "functional island".

[0037] In summary, in Example 5, suppose a spatial grid cell C exists within the target community. i Located at node C in the graph structure i The surrounding area has different types of roads and service facilities, as detailed in Table 2 below:

[0038] Assume the shortest path from ci to c5 is: edge 1 → edge 2 → edge 3 → edge 4; the total path time is... =2.0 + 1.07 + 1.0 + 2.0 = 6.07 minutes < 15 minutes; An example vector of the number of service facilities within a 15-minute walk is shown in Table 3 below:

[0039] For each spatial grid cell C i Obtain the reachable raster set G i and the number of public service facilities For: Vs i =[1,2,3,1,0,2,0,1,2,1,1]; The corresponding categories are: [Hospitals, Schools, Supermarkets, Parks, Cultural Services, Fitness Facilities, Elderly Care Services, Electric Vehicle / Shared Vehicle Charging Points, Public Transportation Stations, Barrier-Free Facilities, Government / Financial Outlets].

[0040] In this embodiment, the path network construction unit, based on the spatial grid model of the target community, transforms linear traffic elements such as roads, alleys, sidewalks, bridges, and underpasses into a graph theory model of nodes and edges, realistically reflecting the complex road connectivity relationships within the community and providing a solid structural foundation for pedestrian path simulation. The reachability path simulation unit sets standard walking speeds according to different road types and introduces adjustment factors for obstacles, slopes, and traffic conditions, scientifically calculating the walking time weight of each path edge. This makes path time estimation closer to the actual walking experience, improving the accuracy and real-world adaptability of path simulation. The service facility hit unit traverses the pedestrian path network, simulating the shortest walking time from the starting point to surrounding grids, determining whether various service facilities, including medical, educational, shopping, public green spaces, and other services meeting residents' needs, are covered. This comprehensively reflects the accessibility of community services and supports in-depth analysis of the spatial distribution of service facilities. The coverage capacity vector generation unit, based on the hit detection results, counts the number of various service facilities reachable within a 15-minute walk from each starting grid unit, forming a coverage capacity vector. This vector not only reflects the spatial density of service facilities but also serves as an important quantitative indicator for spatial function evaluation, classification, and optimization planning. Through accurate path time calculation and multi-category service coverage analysis using the walkability accessibility simulation module, the spatial equity and deficiencies of service facilities within the community can be objectively revealed, helping governments and planning agencies to formulate more reasonable facility layout strategies and improve residents' convenience and the overall functional quality of the community.

[0041] Example 6 This embodiment is an explanation based on Embodiment 5. Please refer to it. Figure 1 Specifically, the living circle coverage assessment module includes a coverage calculation unit, a service hit vector assessment unit, and a functional blind spot identification unit; Coverage calculation unit, used for each spatial raster cell C i and the reachable raster set G i and the number of public service facilities Calculate the 15-minute walking reach coverage of the i-th grid cell. ; ; This represents the total number of grid cells in the community space; Service hit vector evaluation unit, used for evaluation based on each spatial raster cell C i The reachable raster set G i and the number of public service facilities The comprehensive service score index of the i-th grid is calculated using the following formula. : ； in, Let w be the weight value of the p-th type of service facility, used to reflect the importance of different facilities in residents' lives, and the sum of the weights satisfies ∑w p =1; The number of service facilities of type p that can be reached from the i-th grid; n represents the total number of service facility categories covered. A functional blind spot identification unit is used to set a first coverage threshold A and a second scoring threshold B; When the coverage of the 15-minute walking distance of the i-th grid is satisfied simultaneously ≥ First coverage threshold A, and when the comprehensive service score index of the i-th grid A score ≥ the second scoring threshold B indicates that the spatial grid living circle has qualified coverage and good walkability and service functions. When the 15-minute walking distance coverage of the i-th grid is... <First coverage threshold A, means: the spatial range that residents can reach within 15 minutes from this grid, indicating that residents' activities are restricted and accessibility is abnormal; When the comprehensive service score index of the i-th grid <Second scoring threshold B indicates that, taking this grid as the resident's starting point, the distribution of living service facilities within a 15-minute reach is uneven, and the service functions are unbalanced. When satisfied or If either of the two conditions is met, the corresponding raster is marked as a "potential functional blind spot", and the first candidate region group is obtained by summarizing them.

[0042] The first coverage threshold A is determined by experts in urban planning, public services, and data analysis, who set a reasonable range based on local population density and land use characteristics. For example, in densely populated residential areas, A ≥ 0.7. The second scoring threshold B is derived from statistical analysis of a large amount of urban community spatial service coverage data. Combined with the distribution of various service facilities and resident satisfaction evaluation data in typical community samples, the distribution characteristics of comprehensive service scores under different service levels are extracted. Furthermore, in conjunction with the guidelines for building healthy urban communities, the evaluation system for livable communities, and the standards for basic public service configuration, a reasonable critical value for service capacity assessment is determined. This threshold is used to accurately identify spatial units with insufficient comprehensive service supply within a 15-minute walk, ensuring the balance of urban community functions and the convenience of residents' lives.

[0043] In this embodiment, the coverage calculation unit combines each spatial grid unit with its 15-minute walking reach grid set to scientifically calculate the walking reach coverage of each grid, effectively reflecting the size of community space accessibility and residents' activity range, quantitatively revealing the impact of community road connectivity on residents' daily travel, and providing data support for evaluating the quality of the community walking environment.

[0044] Secondly, the service hit vector assessment unit is based on the quantity and weight of multiple categories of public service facilities, and reflects the quality of life service functions in different grids through a comprehensive service score index. This score comprehensively considers the importance of various service facilities such as medical care, education, shopping, and green spaces in residents' lives, scientifically evaluates the balance and completeness of the spatial distribution of community service facilities, and enhances the multi-dimensionality and detail of community function assessment.

[0045] The functional blind spot identification unit sets dual thresholds for coverage rate and comprehensive score, effectively distinguishing between areas with adequate coverage and potential functional blind spots in the community living circle. This module can not only identify areas where pedestrian activity is restricted due to insufficient road accessibility, but also accurately identify areas with unbalanced distribution of service facilities resulting in missing service functions, comprehensively revealing the functional bottlenecks of the community living circle.

[0046] Furthermore, based on the marking and aggregation of functional blind spots, the system can quickly locate "potential functional blind spots," providing urban planners and managers with precise suggestions and improvement directions for optimizing living areas, and promoting the rational layout and balanced development of community service resources. By combining spatial grid models with service facility distribution data, a scientific, detailed, and dynamic evaluation system for living area coverage capacity has been constructed, achieving a collaborative assessment of community walkability and service functions.

[0047] Example 7 This embodiment is an explanation based on Embodiment 6. Please refer to it. Figure 1 Specifically, the cold space discrimination module includes a population thermal data acquisition unit, a functional blind spot cross-screening unit, and a comprehensive strategy unit; The population thermal data acquisition unit is used to collect resident activity data of the first candidate area group and map it to the spatial raster model of the target community, mapping each population location information to the corresponding spatial raster cell C according to latitude and longitude. i Then, under the daily cumulative time dimension, extract the daily average activity heat value D(C) of the i-th grid. i ); and accumulate the number of people activities in each grid within a set period of 1 month on a daily basis to obtain heat sequence data; The functional blind zone cross-screening unit is used to perform population heat sequence analysis on the first candidate region group based on heat sequence data. A low-activity threshold Dmin is preset. If the daily average activity heat value D(C) of each grid cell within the first candidate region group is lower than the threshold value, the unit is considered to have a low activity threshold. i If the daily average activity intensity value D(C) ≤ Dmin, it indicates that the population intensity of the spatial grid is not up to standard and meets the low activity level; if the daily average activity intensity value D(C) of each grid in the first candidate region group is less than or equal to the minimum daily activity intensity value Dmin, it indicates that the population intensity of the spatial grid is not up to standard and meets the low activity level. i If Dmin > Dmin, it means that the population heat index of the spatial grid is qualified and will be continuously monitored. Define a continuous risk trigger counter Cr, when D(C i If D(C) ≤ Dmin, it indicates that the population heat level is not up to standard, and the counter Cr increments by 1; otherwise, the counter resets to 0. When it is identified that D(C) ≤ Dmin for 21 out of 30 days in a month... i When )≤Dmin, that is, when the counter Cr≥21, it is marked as "cold space grid".

[0048] The low activity threshold Dmin is derived from a combination of experts in urban planning, sociology, community management, and other fields, who set a reasonable range of activity thresholds based on the community's expected activity level and actual situation.

[0049] In this embodiment, the population thermal data acquisition unit collects residents' activity location information within the target community and maps it to a spatial grid model. Based on the daily average activity heat value within a set period, it scientifically reflects the activity level of residents in different spatial grid units, achieving dynamic monitoring of population heat with high spatiotemporal resolution.

[0050] Secondly, the functional blind zone cross-screening unit, combined with heat sequence data, effectively identifies "cold space grids" with long-term low traffic by setting low activity thresholds and continuous risk trigger counters. This method avoids misjudgments caused by short-term fluctuations and improves the stability and accuracy of cold space identification.

[0051] The cold space identification module can promptly identify areas within the community where residents' activities are insufficient, providing a scientific basis for urban planning and the optimal allocation of public resources, helping to enhance community vitality and balanced functional development, and improving residents' quality of life and community livability.

[0052] Example 8 This embodiment is an explanation based on Embodiment 7. Please refer to it. Figure 1 Specifically, the integrated strategy unit is used to generate corresponding strategies for "cold space grids," including: When the proportion of "cold space grids" in the target community exceeds 20% but is less than 30%, a "first-level moderate functional optimization strategy" is generated, including: adding convenient facilities such as pocket parks, small book kiosks, and rest seats, the number of which should not be less than 5% of the total number of space grids in the target area; embedding new functional commercial stalls or shared sports corners into the previously idle and underutilized vacant land in the area through "micro-updates", the updated area should reach more than 30% of the original inefficient vacant land area; in the set of space grids identified as functional blind spots, at least 60% of the grids must be upgraded through the establishment of new functions. Community service stations are used to achieve functional coverage, thereby alleviating the problem of missing public services; within each identified "cold space grid," at least one previously unconnected street or alleyway path should be opened up to enhance the connectivity between the cold area and the surrounding space; new pedestrian paths should be added, including street greenways or pedestrian crossings, and the total length of the new pedestrian paths should not be less than 30% of the total length of the original paths; streetlights and security monitoring facilities should be installed within the cold space area and along its paths, with a coverage rate of 30%-50% or more, to improve safety and accessibility; If the proportion of "cold space grids" in the target community exceeds 40%, a "second key renovation strategy" will be generated. This involves systematically embedding convenient service nodes, including pocket parks, community bookstores, smart parcel lockers, rest seats, and retail kiosks, within densely populated areas of cold space grids. These will form small-scale, high-frequency resident activity hubs, and their number should be no less than 10% of the total number of space grids in the target area. Previously idle, abandoned, or underutilized land will be reorganized by embedding multi-functional scenarios, including commercial stalls, fitness corners, and public bookstores, achieving "multi-use of one piece of land" and improving the overall land efficiency and spatial vitality. Government convenience service centers, public welfare organization sites, and volunteer service points will be integrated into "community stations," achieving a service point coverage rate of ≥80% in functional blind spots, effectively reducing service supply gaps. Within each identified "cold space grid," at least two previously unconnected or non-exiting street paths should be opened up to enhance the connectivity between the cold space and the surrounding space; new pedestrian paths should be added, including street greenways or pedestrian crossings, and the total length of the new pedestrian paths should not be less than 40% of the total length of the original paths; streetlights and security monitoring facilities should be installed within the cold space area and along its paths, with a coverage rate of 60%-70% or more, to improve safety and accessibility.

[0053] In this embodiment, for communities where the proportion of "cold space grids" is between 20% and 30%, a "first-level moderate functional optimization strategy" is implemented. This involves adding convenient facilities such as pocket parks, small book kiosks, and rest benches to effectively improve the coverage and diversity of public service facilities, meeting the diverse needs of residents. Long-term idle land is transformed through "micro-renewal" projects, embedding new functional commercial stalls and shared sports corners, which not only revitalizes the space but also promotes community commercial prosperity. The construction of community service stations covers more than 60% of the functional blind spots in the grids, addressing the lack of public services. Unblocking previously unaccessible streets and alleys within the cold spaces, adding pedestrian greenways and pathways, improves regional accessibility and safety, enhances residents' walking experience and spatial connectivity, and creates a safer and more livable living environment.

[0054] Secondly, when the proportion of "cold space grids" exceeds 40%, the "second key renovation strategy" is launched. This involves systematically deploying convenient service nodes in densely populated cold space areas, forming small-scale, high-frequency activity hubs, greatly enhancing resident activity concentration and community cohesion. Idle and abandoned plots are reconstructed into multi-functional scenarios, promoting the concept of "multi-use of one piece of land" and improving land use efficiency and spatial vitality. Through the high-coverage service layout of community stations, service blind spots are reduced, effectively improving the balance of public services. The opening up of multiple streets and alleys and the significant increase in pedestrian paths strengthen the spatial connectivity network; at the same time, the deployment of streetlights and security monitoring facilities is strengthened, greatly improving nighttime safety and residents' travel convenience, enhancing the overall community safety atmosphere and livability index.

[0055] Example 9 Please refer to Figure 2 A method for intelligent spatial assessment of urban healthy communities includes the following steps: Step 1: Divide the target community into several spatial grid cells, obtain the geographical coordinates, road connectivity, and building outline information of each cell, and form a graph theory model that can be used to simulate walking paths; Step 2: Collect spatial distribution data of hospital, supermarket, school and park service facilities in the target community, label the functional type and service radius of each type of service facility in the spatial grid, and generate a spatial grid model of the target community based on the graph theory model that can be used to simulate walking paths. Step 3: Starting with each spatial grid cell, construct a pedestrian path network model based on the street network, simulating the walking path within 15 minutes, to build each spatial grid cell C. i Reachable raster set G i and the number of public service facilities ; Step 4: Based on each spatial grid cell C i Reachable raster set G i and the number of public service facilities Calculate the 15-minute walking reach coverage of the i-th grid cell. and comprehensive service rating index And preset a first coverage threshold A and a second scoring threshold B; when the conditions are met or If either of the two conditions is met, the corresponding grid is marked as a "potential functional blind spot", and the first candidate region group is obtained by summarizing them. Step 5: Collect resident activity data for the first candidate area group, obtain heat sequence data, mark long-term sparse grid cells in the first candidate area group, determine whether they are "cold space grids" with low accessibility and low activity, and output the corresponding strategy.

[0056] This invention, by introducing spatial grid division, street network modeling, and graph theory analysis techniques, achieves high-precision modeling and accessibility simulation of urban community spaces, accurately assessing the coverage and service balance of residents' 15-minute walking radius. Compared to traditional methods relying on manual statistics or two-dimensional heat maps, this method dynamically constructs an accessibility path network starting from each grid unit and quantitatively evaluates the service accessibility level and comprehensive service index of each grid based on the number of accessible service facilities. By setting dual thresholds for coverage and scoring, it intelligently identifies "potential functional blind spots" and further combines them with residents' activity sequences to accurately identify "cold space grids," providing scientific support and decision-making basis for subsequent urban renewal, service facility layout optimization, and community health space transformation. It boasts advantages such as high intelligence, fine-grained assessment, and highly targeted strategies.

[0057] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0058] The above formulas are all derived from software simulation using a large amount of data and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A smart spatial assessment system for urban healthy communities, characterized in that, include: The grid generation module is used to divide the target community into several spatial grid cells, obtain the geographical coordinates, road connectivity structure and building outline information of each cell, and form a graph theory model that can be used to simulate walking paths; The facility mapping module is used to collect spatial distribution data of hospital, supermarket, school and park service facilities in the target community, label the functional type and service radius of each type of service facility in the spatial grid, and generate a spatial grid model of the target community based on a graph theory model that can be used to simulate walking paths. The walkability simulation module is used to construct a walkway network model based on the street network, starting from each spatial grid cell, and simulate the walking path within 15 minutes to build each spatial grid cell C. i Reachable raster set G i and the number of public service facilities ; The living circle coverage assessment module is used to assess the coverage of each spatial grid unit C. i Reachable raster set G i and the number of public service facilities Calculate the 15-minute walking reach coverage of the i-th grid cell. and comprehensive service rating index And preset a first coverage threshold A and a second scoring threshold B; when the conditions are met or If either of the two conditions is met, the corresponding grid is marked as a "potential functional blind spot", and the first candidate region group is obtained by summarizing them. The cold space discrimination module is used to collect resident activity data of the first candidate area group, obtain heat sequence data, and mark the long-term sparse grid cells in the first candidate area group to determine whether they are "cold space grids" with low accessibility and low activity, and output the corresponding strategy.

2. The urban healthy community spatial intelligent assessment system according to claim 1, characterized in that, The grid division module includes a geospatial acquisition unit, a road connectivity structure identification unit, a building outline extraction unit, and a spatial raster coding unit; The geospatial acquisition unit is used to acquire elevation topographic maps, basic geomorphological maps, and street framework maps of the target community through remote sensing images, LiDAR, drone aerial photography, street view maps, and GIS data, and to construct a two-dimensional community geographic base map. The road connectivity structure identification unit is used to extract linear traffic elements such as streets, alleys, pedestrian passages, overpasses and underpasses based on a deep image recognition network in the two-dimensional community geographic base map, establish a road connectivity topology map, and form a graph theory model that can be used to simulate walking paths.

3. The urban healthy community spatial intelligent assessment system according to claim 2, characterized in that, The building outline extraction unit is used to extract the boundary outlines of buildings within the community from the graph theory model that can be used to simulate walking paths, project them into three-dimensional boundary data, and generate grid obstacles in the graph theory model that can be used to simulate walking paths by combining the number of floors and height information. The spatial grid encoding unit is used to divide the target community into several spatial grid units according to a preset ratio in a graph theory model that can be used to simulate walking paths, and to assign a unique encoding ID to each grid unit, and to obtain the center coordinates, boundary coordinates, area parameters, road connectivity status, obstacle coverage status and functional attribute initialization label spatial attribute values ​​of each grid unit, so as to generate a spatial grid model of the target community.

4. The urban healthy community spatial intelligent assessment system according to claim 3, characterized in that, The facility mapping module includes a service facility acquisition unit, a facility spatial positioning unit, and a function type labeling unit; The service facility acquisition unit is used to call GIS systems, street view image databases, urban public service information platforms or remote sensing images to obtain the original geographical location information of hospitals, supermarkets, schools and parks within the target community, including name, type, address, latitude and longitude coordinates and function labels. The facility spatial positioning unit is used to accurately map the service facility to the spatial grid model of the target community based on the latitude and longitude coordinates of the service facility, identify the specific grid unit ID to which the facility belongs, and establish a one-to-one correspondence between the facility point and the grid space. The functional type labeling unit is used to label each type of facility with a functional category label according to its service attributes. The functional categories include medical services, food supply, basic education and public green space, assign corresponding functional codes, and mark them in the spatial grid model of the target community.

5. The urban healthy community spatial intelligent assessment system according to claim 4, characterized in that, The walkability simulation module includes a path network construction unit, an reachable path simulation unit, and a service facility hit unit; The path network construction unit is used to represent the road connectivity structure in the spatial grid model of the target community, with the center point of each spatial grid unit as a node in the graph structure, and the passage relationship between streets, alleys, sidewalks, bridges and underpasses as edges in the graph structure. The reachable path simulation unit is used to assign a walking time weight to each edge constructed in the graph structure, and the weight satisfies the following calculation expression; ； in, This represents the path length of the k-th edge, calculated using GIS to determine the Euclidean distance or actual path length between adjacent nodes. V(e k () represents the standard walking speed for the corresponding road type of the k-th edge; the standard walking speed for the corresponding road type includes: When the road type is a main urban street, V(e) k =80m / min; When the road type is a community secondary street, V(e) k =70m / min; When the road type is an alley or passage, V(e) k =60m / min; When the road type is an indoor passage, V(e) k ) = 50 m / min; When the road type is a hillside section, V(e) k ) = 40 m / min; When the road type is a downhill section, V(e) k ) = 45 m / min; This represents the adjustment factor for the k-th edge, taking into account obstacles, slope, and passage conditions. The service facility hit unit is used to traverse the shortest walking path range within a 15-minute time frame, starting from any spatial grid cell, based on the walking path network model. Within this path range, it identifies whether at least one type of service facility is hit. The service facilities include hospitals, schools, supermarkets, parks, and other facilities that meet the basic living needs of residents, and records the hit results. For each spatial grid cell C i Its center point is the starting node c in the graph network. i Then its reachable raster set G i Represented as: ； in, Indicates starting from node c i To target node c j The total time for the shortest path; Set to 15 minutes as the maximum walking time limit; reachable grid set G i Indicates starting from node c i Departure point: a grid collection point 15 minutes away; Each edge is assigned a weight based on the walking time. The walking time for the k-th edge is: ； Total path time for: ； in, Indicates starting from node c i To target node c j The set of edges on the shortest path; Finally, for each spatial grid cell C i Obtain the reachable raster set G i and the number of public service facilities .

6. The urban healthy community spatial intelligent assessment system according to claim 5, characterized in that, The living circle coverage assessment module includes a coverage calculation unit, a service hit vector assessment unit, and a functional blind spot identification unit. The coverage calculation unit is used for each spatial grid cell C i and the reachable raster set G i and the number of public service facilities Calculate the 15-minute walking reach coverage of the i-th grid cell. ; ; This represents the total number of grid cells in the community space; The service hit vector evaluation unit is used to evaluate each spatial grid cell C. i The reachable raster set G i and the number of public service facilities The comprehensive service score index of the i-th grid is calculated using the following formula. : ； in, Let w be the weight value of the p-th type of service facility, used to reflect the importance of different facilities in residents' lives, and the sum of the weights satisfies ∑w p =1; The number of service facilities of type p that can be reached from the i-th grid; n represents the total number of service facility categories covered. The functional blind spot identification unit is used to set a first coverage threshold A and a second scoring threshold B; When the coverage of the 15-minute walking distance of the i-th grid is satisfied simultaneously ≥ First coverage threshold A, and when the comprehensive service score index of the i-th grid A score ≥ the second scoring threshold B indicates that the spatial grid living circle has qualified coverage and good walkability and service functions. When the 15-minute walking distance coverage of the i-th grid is... <First coverage threshold A, means: the spatial range that residents can reach within 15 minutes from this grid, indicating that residents' activities are restricted and accessibility is abnormal; When the comprehensive service score index of the i-th grid <Second scoring threshold B indicates that, taking this grid as the resident's starting point, the distribution of living service facilities within a 15-minute reach is uneven, and the service functions are unbalanced. When satisfied or If either of the two conditions is met, the corresponding raster is marked as a "potential functional blind spot", and the first candidate region group is obtained by summarizing them.

7. The urban healthy community spatial intelligent assessment system according to claim 6, characterized in that, The cold space discrimination module includes a population thermal data acquisition unit, a functional blind spot cross-screening unit, and a comprehensive strategy unit; The population thermal data acquisition unit is used to collect resident activity data of the first candidate area group and map it to the spatial grid model of the target community, mapping each population location information to each corresponding spatial grid cell C according to latitude and longitude. i Then, under the daily cumulative time dimension, extract the daily average activity heat value D(C) of the i-th grid. i ); and accumulate the number of people activities in each grid within a set period of 1 month on a daily basis to obtain heat sequence data; The functional blind zone cross-screening unit is used to perform population heat sequence analysis on the first candidate region group based on heat sequence data. A preset low activity threshold Dmin is set. If the daily average activity heat value D(C) of each grid cell within the first candidate region group is lower than the threshold value, the unit is considered to perform a low activity threshold Dmin. i If the daily average activity intensity value D(C) ≤ Dmin, it indicates that the population intensity of the spatial grid is not up to standard and meets the low activity level; if the daily average activity intensity value D(C) of each grid in the first candidate region group is less than or equal to the minimum daily activity intensity value Dmin, it indicates that the population intensity of the spatial grid is not up to standard and meets the low activity level. i If Dmin > Dmin, it means that the population heat index of the spatial grid is qualified and will be continuously monitored. Define a continuous risk trigger counter Cr, when D(C i If D(C) ≤ Dmin, it indicates that the population heat level is not up to standard, and the counter Cr increments by 1; otherwise, the counter resets to 0. When it is identified that D(C) ≤ Dmin for 21 out of 30 days in a month... i When )≤Dmin, that is, when the counter Cr≥21, it is marked as "cold space grid".

8. The urban healthy community spatial intelligent assessment system according to claim 7, characterized in that, The integrated strategy unit is used to generate corresponding strategies for the "cold space grid", including: When the proportion of "cold space grids" in the target community exceeds 20% but is less than 30%, a "first-level moderate functional optimization strategy" is generated, including: adding convenient facilities such as pocket parks, small book kiosks, and rest seats, the number of which should not be less than 5% of the total number of space grids in the target area; embedding new functional commercial stalls or shared sports corners into the previously long-term idle and underutilized vacant land in the area through "micro-updates", the updated area should reach more than 30% of the original inefficient vacant land area; in the set of space grids identified as functional blind spots, at least 60% of the grids must be upgraded through the establishment of new functions. Community service stations are used to achieve functional coverage, thereby alleviating the problem of missing public services; within each identified "cold space grid," at least one previously unconnected street or alleyway path should be opened up to enhance the connectivity between the cold area and the surrounding space; new pedestrian paths should be added, including street greenways or pedestrian crossings, and the total length of the new pedestrian paths should not be less than 30% of the total length of the original paths; streetlights and security monitoring facilities should be installed within the cold space area and along its paths, with a coverage rate of 30%-50% or more, to improve safety and accessibility; If the proportion of "cold space grids" in the target community exceeds 40%, a "second key renovation strategy" will be generated. This involves systematically embedding convenient service nodes, including pocket parks, community bookstores, smart parcel lockers, rest seats, and retail kiosks, within densely populated areas of cold space grids. These will form small-scale, high-frequency resident activity hubs, and their number should be no less than 10% of the total number of space grids in the target area. Previously idle, abandoned, or underutilized plots will be reorganized by embedding multi-functional scenarios, including commercial stalls, fitness corners, and public bookstores, achieving "multi-use of one piece of land" and improving the overall land efficiency and spatial vitality. Government convenience service centers, public welfare organization sites, and volunteer service points will be integrated into "community stations," achieving a service point coverage rate of ≥80% in functional blind spots, effectively reducing service supply gaps. Within each identified "cold space grid," at least two previously unconnected street or alleyway paths should be opened up to enhance the connectivity between the cold space and the surrounding space; new pedestrian paths should be added, including street greenways or pedestrian crossings, and the total length of the new pedestrian paths should not be less than 40% of the total length of the original paths; streetlights and security monitoring facilities should be installed within the cold space area and along its paths, with a coverage rate of 60%-70% or more, to improve safety and accessibility.

9. A method for intelligent spatial assessment of urban healthy communities, applied to an intelligent spatial assessment system for urban healthy communities as described in any one of claims 1-8, characterized in that, Includes the following steps: Step 1: Divide the target community into several spatial grid cells, obtain the geographical coordinates, road connectivity, and building outline information of each cell, and form a graph theory model that can be used to simulate walking paths; Step 2: Collect spatial distribution data of hospital, supermarket, school and park service facilities in the target community, label the functional type and service radius of each type of service facility in the spatial grid, and generate a spatial grid model of the target community based on the graph theory model that can be used to simulate walking paths. Step 3: Starting with each spatial grid cell, construct a pedestrian path network model based on the street network, simulating the walking path within 15 minutes, to build each spatial grid cell C. i Reachable raster set G i and the number of public service facilities ; Step 4: Based on each spatial grid cell C i Reachable raster set G i and the number of public service facilities Calculate the 15-minute walking reach coverage of the i-th grid cell. and comprehensive service rating index And preset a first coverage threshold A and a second scoring threshold B; when the conditions are met or If either of the two conditions is met, the corresponding grid is marked as a "potential functional blind spot", and the first candidate region group is obtained by summarizing them. Step 5: Collect resident activity data for the first candidate area group, obtain heat sequence data, mark the long-term sparse grid cells in the first candidate area group, determine whether they are "cold space grids" with low accessibility and low activity, and output the corresponding strategy.

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