A method for constructing spatial graph networks that achieve cross-layer coupling between the ecological layer and the public space layer.

Abstract

This invention relates to a method for constructing a spatial graph network that realizes cross-layer coupling between the ecological layer and the public space layer, comprising: Step S1: obtaining the grid range covered by each ecological patch and public patch; Step S2: generating ecological nodes and public nodes, as well as the area and centroid position of all nodes; Step S3: using ecological nodes as nodes of the ecological layer graph network, generating inner edges of the ecological layer between ecological nodes whose centroid position distance is less than an ecological distance threshold, and generating the weights of each inner edge of the ecological layer graph network; Step S4: using public nodes as nodes of the public layer graph network, generating inner edges of the public layer between public nodes whose centroid position distance is less than a public distance threshold, and generating the weights of each inner edge of the public layer graph network; Step S5: constructing inter-layer edges between ecological nodes and public nodes whose centroid position distance is less than a public distance threshold, and generating the weights of the inter-layer edges based on the straight-line distance, traffic accessibility, ecological impact intensity, and human flow relationship between the corresponding ecological nodes and public nodes. Compared with existing technologies, this invention can deeply explore the mutual coupling relationship between the ecological layer and the public layer.

Description

Technical Field

[0001] This invention relates to the fields of urban spatial analysis, urban multi-system optimization, and spatial intelligent computing technology, and in particular to a method for constructing a spatial graph network that realizes cross-layer coupling between the ecological layer and the public space layer. Background Technology

[0002] With urbanization, the interaction between urban ecosystems and public space systems is constantly increasing. Urban green spaces not only serve functions such as ecological regulation, biodiversity maintenance, and climate mitigation, but also act as important public spaces for residents to conduct social activities. While providing recreational spaces, urban public spaces also offer connectivity optimization opportunities for ecosystems. However, existing urban spatial analysis methods typically study ecosystems and public space systems separately, resulting in a disconnect between the analytical units and evaluation criteria for the two systems, and a lack of a modeling framework that can comprehensively assess both systems.

[0003] Existing technologies mainly fall into two categories: ecological network analysis methods and public space accessibility analysis methods. Ecological network research typically focuses on ecological connectivity by identifying ecological source areas, constructing resistance surfaces, and calculating ecological corridors, but it is difficult to reflect the actual needs of human activities and the intensity of public space use.

[0004] Public space research is mostly based on analysis of road network accessibility, pedestrian flow data, or facility distribution, emphasizing service efficiency and spatial equity, but lacks ecological constraint mechanisms and is difficult to reflect the carrying capacity of the ecological environment.

[0005] The above method has the following problems: 1. The ecosystem and public space systems use different spatial units and lack a unified node system; 2. Spatial relationships are expressed in a simplistic way, making it difficult to describe complex urban interactions; 3. The lack of cross-layer connection mechanisms makes it impossible to characterize the coupling relationship between ecological impacts and public activities; 4. It is difficult to use multi-source spatiotemporal data for dynamic update analysis. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of the prior art by providing a method for constructing a spatial graph network that enables cross-layer coupling between the ecological layer and the public space layer.

[0007] The objective of this invention can be achieved through the following technical solutions: A method for constructing a spatial graph network that enables cross-layer coupling between the ecological layer and the public space layer includes: Step S1: Divide the target area into grids, obtain ecological patch information and common patch information, and match the ecological patches and common patches with the grid of the target area to obtain the grid range covered by each ecological patch and common patch; Step S2: Based on the grid range covered by each ecological patch and the common patch, generate ecological nodes and common nodes respectively, as well as the area and centroid location of all nodes; Step S3: Use ecological nodes as nodes of the ecological layer graph network, and generate ecological layer inner edges between ecological nodes whose centroid distance is less than the ecological distance threshold. Generate the weights of each ecological layer inner edge of the ecological layer graph network based on the straight-line distance between the corresponding two ecological nodes, functional characteristics, functional similarity, minimum cumulative resistance and traffic accessibility. Step S4: Use common nodes as nodes of the common layer graph network, and generate common layer inner edges between common nodes whose centroid distance is less than the common distance threshold. Generate the weights of each common layer inner edge of the common layer graph network based on the straight-line distance between the corresponding two common nodes, functional characteristics, functional similarity, pedestrian flow relationship and traffic accessibility. Step S5: Construct inter-layer edges between ecological nodes whose centroid locations are less than the common distance threshold and common nodes, and generate the weights of the inter-layer edges based on the straight-line distance, traffic accessibility, ecological impact intensity, and human flow relationship between the corresponding ecological nodes and common nodes.

[0008] The weights of the edges within each ecological layer of the ecological layer network are a weighted sum of straight-line distance weight, ecological function similarity weight, resistance weight, and traffic accessibility weight.

[0009] The process of obtaining the resistance weight includes: Get any two ecological nodes connected by the inner edges of the ecological layer; Calculate all grid paths connecting two ecological nodes; Calculate the overall ecological resistance of all grids in each grid path: Where: R represents the overall ecological resistance of the grid. The weight of the k-th type of environmental factor. Let n be the resistance value of the k-th type of environmental factor, and n be the number of environmental factors. The total ecological resistance of each grid within a single grid path is summed to obtain the cumulative resistance of each grid. The cumulative resistance of the grid path with the lowest cumulative resistance is selected as the minimum cumulative resistance between two nodes, and the resistance weight is further obtained. in: Let be the minimum cumulative resistance of nodes i and j.

[0010] The weights of the edges within each common layer of the common layer graph network are a weighted sum of straight-line distance weight, common function similarity weight, social mobility weight, and traffic accessibility weight.

[0011] The process of obtaining the intra-layer social mobility weights includes: The number of trips between two public nodes connected by the inner edge of the public layer within the statistical period is obtained based on mobile signaling data or OD data. The passenger flow intensity is obtained based on the number of trips between two common nodes connected by the inner edge of the common layer within the statistical period: in: Let i be the pedestrian flow intensity at node i and j. The number of trips between two public nodes connected by an edge within the public layer during the statistical period, where T is the market during the statistical period; Determine whether the pedestrian flow intensity of node i and node j exceeds the pre-configured pedestrian flow threshold. If yes, use the pedestrian flow intensity of node i and node j as the social mobility weight within the layer; otherwise, configure the social mobility weight within the layer to 0.

[0012] The weight of the inter-layer edge is a weighted sum of the straight-line distance weight, coupling strength weight, inter-layer social mobility weight, and traffic accessibility weight.

[0013] The process of obtaining the coupling strength weight includes: Ecological nodes and public nodes connected by inter-layer edges are obtained. Ecological function vectors of ecological nodes and public space activity intensity vectors of public nodes are obtained respectively. The ecological function vectors are determined based on biodiversity, habitat quality, carbon sequestration capacity and cooling effect. The public space activity intensity vectors are determined based on mobile phone signaling density, social media frequency, transportation convenience and comprehensive service facility scores. Calculate the ecological impact intensity of the ecological function vector of ecological nodes and the public space activity intensity vector of public nodes: in: Let i be the ecological function vector of node i. Let be the public space activity intensity vector of node j; Determine whether the ecological impact intensity of node i and node j exceeds the pre-configured intensity threshold. If yes, use the coupling strength of node i and node j as the coupling strength weight; otherwise, configure the coupling strength weight to 0.

[0014] The process of obtaining the inter-level social mobility weights includes: Statistical analysis of the frequency of visits between public space nodes and ecological nodes connected to them via inter-layer edges: in: The frequency of visits between node i and node j. Let be the number of visits between node i and node j, and be the total number of visits across all public and ecosystem nodes; Determine whether the access frequency between node i and node j exceeds the pre-configured frequency threshold. If yes, use the access frequency between node i and node j as the inter-layer social mobility weight; otherwise, configure the inter-layer social mobility weight to 0.

[0015] The process of obtaining the traffic accessibility weight includes: Calculate the shortest path between two nodes using urban road network data; After normalizing the shortest path, the traffic accessibility weights are obtained. For the weights of edges within the ecological layer and the common layer, the traffic accessibility weight is the reciprocal of the normalized shortest path. For the weights of edges between layers, the traffic accessibility weight is: in: The traffic accessibility weights are part of the weights for inter-layer edges. This is the shortest path after normalization.

[0016] A spatial graph network construction device for realizing cross-layer coupling between the ecological layer and the public space layer includes a memory, a processor, and a program stored in the memory, wherein the processor executes the program to implement the method described above.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. By introducing multi-source spatial data and spatial graph network analysis methods, this invention systematically models the relationship between urban ecological space and public space, realizing a shift from single spatial element analysis to multi-layered spatial network comprehensive analysis. In terms of data processing and result presentation, this invention expresses the complex spatial relationship calculation results in a vectorized form and visualizes them at a two-dimensional spatial level. Specifically, it quantifies different public space nodes and their relationships using concrete numerical values, and presents the analysis results intuitively in the form of surface regions or network structures, thereby improving the accuracy and readability of spatial identification and analysis results.

[0018] Furthermore, by constructing a cross-layer coupling network between the ecological layer and the public space layer, this invention can not only identify the spatial connections between different types of urban public spaces and ecological spaces, but also reveal their interactive relationships and influence paths in the urban spatial structure, providing more scientific, intuitive and operable technical support for urban public space planning, ecological space protection and urban spatial structure optimization.

[0019] 2. The assessment of ecological connectivity has been upgraded from a single distance or resistance model to a multi-factor integrated model. This makes the calculation of the connection strength between ecological nodes more comprehensive and quantitative, taking into account not only spatial proximity but also functional complementarity and the suitability of landscape processes, thereby significantly improving the accuracy and ecological significance of ecological network analysis.

[0020] 3. Integrate ecological resistance surface analysis in geographic information systems with graph network models. By calculating the comprehensive ecological resistance of all possible paths connecting two nodes and selecting the minimum value and its reciprocal as the weight, this method makes the identification of ecological corridors no longer limited to simple straight-line distances, but based on simulations of actual land use and environmental factors. This allows the constructed ecological layer network to better reflect the true ease or difficulty of species migration or ecological process flow.

[0021] 4. In the modeling of public space networks, quantitative data on pedestrian flow and interaction is innovatively introduced, so that the connection between public spaces is no longer determined solely by static facility attributes or distance, but dynamically reflects the actual activity patterns and spatial connections of residents, making the analysis results of public space networks closer to real-world usage and social interaction patterns.

[0022] 5. It achieves dynamic and accurate measurement of social connections between public spaces by utilizing big data resources; secondly, by setting thresholds for screening, it can filter out accidental or low-intensity invalid human flow connections, ensuring that the constructed public space network can prominently reflect stable and high-intensity population flow patterns, and improving the identification capability of the core structure of the network.

[0023] 6. For the first time within a graph network framework, a quantifiable connection channel was established for the interaction between the two major systems of ecology and public space. The inter-layer edges are the core of cross-layer coupling, and the multi-dimensional design of their weights enables the mutual influence between systems to be modeled and calculated, solving the key problem of the lack of cross-layer connection mechanisms.

[0024] 7. By adopting a multidimensional feature vector approach, the potential impact or carrying capacity of ecological space on public space activities was comprehensively assessed from both qualitative and quantitative perspectives. The abstract ecological impact was transformed into calculable and comparable numerical indicators, and effective connection screening was achieved through threshold judgment. This provides an innovative tool for scientifically assessing the recreational carrying capacity or ecological service efficiency of urban green spaces.

[0025] 8. It directly quantifies the intensity of residents' recreational activities from public living areas to ecological spaces. This weight reveals the direct path through which human social activities exert pressure on the ecosystem, enabling the model to assess which ecological nodes are subject to higher levels of human activity disturbance, or which ecological nodes can more effectively serve the surrounding community residents, thus achieving a precise mapping of the impact of "human flow" on "ecology".

[0026] 9. Not only does it consider the fundamental impact of the transportation network on inter-node connections, but it also distinguishes the different impact mechanisms of traffic attributes on connections between similar nodes and connections between heterogeneous systems through different mathematical processing methods. For example, the weight function designed for cross-layer edges may more robustly handle cases with extremely high or low accessibility, making the weight calculation more reasonable and enhancing the overall model's adaptability to complex urban traffic environments. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the main steps of the method of the present invention; Figure 2 This is a schematic diagram of the technical route of the present invention; Figure 3 To study the extent and distribution of plaques. Detailed Implementation

[0028] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0029] A method for constructing a spatial graph network that achieves cross-layer coupling between the ecological layer and the public space layer, such as... Figure 1 and Figure 2 As shown, it includes: Step S1: Divide the target area into grids, obtain ecological patch information and common patch information, and match the ecological patches and common patches with the grid of the target area to obtain the grid range covered by each ecological patch and common patch; The basic spatial units for ecological and public spaces within the study area are identified. Ecological spaces typically include urban green patches, woodlands, water bodies, and ecological protection land; public spaces include urban parks, squares, open street spaces, waterfront spaces, and corner green spaces. Because ecological and public spaces differ in spatial scale, functional attributes, and spatial boundaries, a unified spatial node system needs to be constructed first to achieve unified modeling of the two systems.

[0030] Multi-source spatial data was used to integrate and preprocess the study area, including remote sensing data, land use data, green patch data, and public space vector data. By performing coordinate system optimization, geometric cleaning, and spatial clipping on the above data, complete basic data of ecological space and public space were obtained.

[0031] Then, unified spatial node data is generated and exported as a point-like Shapefile file, which serves as input data for the subsequent construction of the ecosystem layer network and the public space layer network.

[0032] Step S2: Based on the grid range covered by each ecological patch and the common patch, generate ecological nodes and common nodes respectively, as well as the area and centroid location of all nodes; The node extraction method will differ when processing different types of spatial units. Taking the extraction of ecological green space patch nodes as an example, it is necessary to first calculate the centroid of the green space patch surface elements, take the centroid position as the spatial node, and save the node information as a new spatial data file.

[0033] The specific steps for extracting spatial nodes are as follows: Ensure that the CRS is a projected coordinate system and perform buffering operations in meters. If the original data is in a geographic coordinate system (such as WGS84), convert the data to a projected coordinate system suitable for distance calculation, such as UTM projection or EPSG:3395 (World Mercator projection). Perform geometric checks and repairs on ecological patch surface features to eliminate topological errors and duplicate features.

[0034] The area of ​​each ecological patch is calculated using the following formula: Where A is the total area of ​​the patch, a i This represents the area of ​​the grid cells that make up the patch.

[0035] Ecological patches are selected based on a preset area threshold, and patches that are too small or too fragmented are removed.

[0036] The geometric centroid of the screened ecological patches is calculated, and the centroid coordinates are calculated as follows: Where, x i With y i Here, represents the coordinates of the patch boundary points, and m represents the number of boundary points. The calculated centroids are used as ecological nodes. The geometric centroids of public space surface elements are also calculated and extracted as public space nodes. If there are multiple entrances / exits in the public space, the locations of these entrances / exits are used as child nodes for subsequent traffic accessibility analysis. All nodes are integrated into a unified node dataset, and a node attribute table is constructed, including ecological and public space attributes. A GeoDataFrame is created to store the node data, and a unified coordinate reference system is added. Finally, the node data is exported as a new node Shapefile, which serves as the foundational input data for subsequent spatial graph network construction. Step S3: Use ecological nodes as nodes of the ecological layer graph network, and generate ecological layer inner edges between ecological nodes whose centroid distance is less than the ecological distance threshold. Generate the weights of each ecological layer inner edge of the ecological layer graph network based on the straight-line distance between the corresponding two ecological nodes, functional characteristics, functional similarity, minimum cumulative resistance and traffic accessibility. The weights of the edges within each ecological layer of the ecological layer graph network are the weighted sum of straight-line distance weight, ecological function similarity weight, resistance weight, and traffic accessibility weight. All of these weights need to be normalized.

[0037] First, the straight-line distance weight is obtained based on the straight-line distance between the two nodes. Secondly, the ecological function similarity weight mainly reflects the synergistic relationship of ecological functions between ecological spaces. This application determines the weight based on biodiversity, habitat quality, carbon sequestration capacity, and cooling effect using an ecological function vector, specifically: in: Let i be the ecological function vector of node i. As an indicator of biodiversity; As an indicator of habitat quality; This is an indicator of carbon sequestration capacity. This is an indicator of cooling effect.

[0038] Then, the cosine similarity of the ecological functions of the two nodes is calculated. If the cosine similarity is greater than the pre-configured first similarity threshold, the cosine similarity of the ecological functions is used as the ecological function similarity weight; otherwise, the ecological function similarity weight is set to 0.

[0039] The subsequent process for obtaining the resistance weights includes: Get any two ecological nodes connected by the inner edges of the ecological layer; Calculate all grid paths connecting two ecological nodes; Calculate the overall ecological resistance of all grids in each grid path: Where: R represents the overall ecological resistance of the grid. The weight of the k-th type of environmental factor. Let n be the resistance value of the k-th type of environmental factor, and n be the number of environmental factors. The total ecological resistance of each grid within a single grid path is summed to obtain the cumulative resistance of each grid. The cumulative resistance of the grid path with the lowest cumulative resistance is selected as the minimum cumulative resistance between two nodes, and the resistance weight is further obtained. in: Let be the minimum cumulative resistance of nodes i and j.

[0040] Finally, the process of obtaining the traffic accessibility weights includes: Calculate the shortest path between two nodes using urban road network data; The shortest path is normalized to obtain the traffic accessibility weight, which is the reciprocal of the normalized shortest path.

[0041] Step S4: Use common nodes as nodes of the common layer graph network, and generate common layer inner edges between common nodes whose centroid distance is less than the common distance threshold. Generate the weights of each common layer inner edge of the common layer graph network based on the straight-line distance between the corresponding two common nodes, functional characteristics, functional similarity, pedestrian flow relationship and traffic accessibility. The weights of the edges within each common layer of the common layer graph network are the weighted sum of straight-line distance weight, common function similarity weight, social mobility weight, and transportation accessibility weight.

[0042] The process of obtaining the intra-layer social mobility weights includes: The number of trips between two public nodes connected by the inner edge of the public layer within the statistical period is obtained based on mobile signaling data or OD data. The passenger flow intensity is obtained based on the number of trips between two common nodes connected by the inner edge of the common layer within the statistical period: in: Let i be the pedestrian flow intensity at node i and j. The number of trips between two public nodes connected by an edge within the public layer during the statistical period, where T is the market during the statistical period; Determine whether the pedestrian flow intensity of node i and node j exceeds the pre-configured pedestrian flow threshold. If yes, use the pedestrian flow intensity of node i and node j as the social mobility weight within the layer; otherwise, configure the social mobility weight within the layer to 0.

[0043] Furthermore, the public function similarity weight is used to reflect the functional similarity and synergistic relationship of public spaces within the urban activity system. First, a public space activity intensity vector is constructed, determined based on a comprehensive score of mobile phone signaling density, social media engagement frequency, transportation convenience, and service facilities. in: Let i be the public space activity intensity vector. The overall score for mobile phone signaling. As a result of social media integration, The combined score for bus and subway stations The overall score for service facilities.

[0044] Then, the cosine similarity of the common space activity intensity vectors of the two nodes is calculated. If the cosine similarity is greater than the pre-configured second similarity threshold, the cosine similarity of the common space activity intensity vectors is used as the common function similarity weight; otherwise, the common function similarity weight is set to 0.

[0045] Step S5: Construct inter-layer edges between ecological nodes whose centroid locations are less than the common distance threshold and common nodes, and generate the weights of the inter-layer edges based on the straight-line distance, traffic accessibility, ecological impact intensity, and human flow relationship between the corresponding ecological nodes and common nodes.

[0046] The weights of the inter-layer edges are a weighted sum of the straight-line distance weight, coupling strength weight, inter-layer social mobility weight, and transportation accessibility weight.

[0047] The process of obtaining the coupling strength weights includes: Ecological nodes and public nodes connected by inter-layer edges are obtained. Ecological function vectors of ecological nodes and public space activity intensity vectors of public nodes are obtained respectively. Ecological function vectors are determined based on biodiversity, habitat quality, carbon sequestration capacity and cooling effect. Public space activity intensity vectors are determined based on mobile phone signaling density, social media frequency, transportation convenience and service facility comprehensive scores. Calculate the ecological impact intensity of the ecological function vector of ecological nodes and the public space activity intensity vector of public nodes: in: Let i be the ecological function vector of node i. Let be the public space activity intensity vector of node j; Determine whether the ecological impact intensity of node i and node j exceeds the pre-configured intensity threshold. If yes, use the coupling strength of node i and node j as the coupling strength weight; otherwise, configure the coupling strength weight to 0.

[0048] The process of obtaining the weights for inter-level social mobility includes: Statistical analysis of the frequency of visits between public space nodes and ecological nodes connected to them via inter-layer edges: in: The frequency of visits between node i and node j. Let be the number of visits between node i and node j, and be the total number of visits across all public and ecosystem nodes; Determine whether the access frequency between node i and node j exceeds the pre-configured frequency threshold. If yes, use the access frequency between node i and node j as the inter-layer social mobility weight; otherwise, configure the inter-layer social mobility weight to 0.

[0049] The process of obtaining traffic accessibility weights includes: Calculate the shortest path between two nodes using urban road network data; After normalizing the shortest path, the traffic accessibility weights are obtained. For the weights of edges within the ecological layer and the common layer, the traffic accessibility weight is the reciprocal of the normalized shortest path. For the weights of edges between layers, the traffic accessibility weight is: in: The traffic accessibility weights are part of the weights for inter-layer edges. This is the shortest path after normalization.

[0050] This application uses a main urban area of ​​a city in a certain province as an example to illustrate the specific implementation method.

[0051] Step A: Multi-source spatial data acquisition and preprocessing. First, acquire multi-source spatial data related to ecological and public spaces within the target area and construct a unified spatial node system. Specifically, this includes: extracting ecological green space patches, public space units, or street block spatial elements; calculating the centroid of ecological patches or the entrance location of public spaces as representative node points; performing coordinate system matching and spatial matching on data from different sources; mapping ecological and public spaces to a unified spatial unit system to form a unified node set, ensuring that each node simultaneously possesses basic expressions of both ecological and public space attributes, providing a unified spatial foundation for subsequent multi-layer network construction. The patch distribution results are as follows: Figure 3 As shown.

[0052] The attributes of each node are shown in Table 1. Table 1 Step B: Ecological Layer Network Construction. An ecological layer network is constructed based on a unified node system, and nodes are assigned ecological attributes, including ecological function indicators such as biodiversity level, habitat quality index, carbon sequestration capacity, and cooling regulation capacity. These indicators are derived from remote sensing inversion results or ecological model calculations. Connections between nodes are established based on different mechanisms within the ecosystem, including: establishing spatial adjacency connections based on spatial contact relationships or distance thresholds of ecological patches; constructing ecological resistance surfaces based on land use type, topographic conditions, and water system distribution, and calculating the minimum cumulative resistance path between ecological source areas as ecological corridor connections; establishing functional coupling connections based on the similarity between ecological function feature vectors; and establishing accessibility connections between ecological nodes in conjunction with transportation network conditions. A weighted ecological layer network structure is formed through the integration of multiple connection types to express the spatial connectivity and functional synergy within the ecosystem. The correlation strength of spatial adjacency connections in the ecological layer is shown in Table 2.

[0053] Table 2 Table 3 shows the connectivity strength of the ecological layer. Table 3 The functional coupling strength of the ecological layer is shown in Table 4. Table 4 Step C: Construction of the Public Space Layer Network. A public space layer network is constructed based on the same set of nodes, and each node is assigned public space attributes, including indicators such as the intensity of mobile phone signaling activity, social media activity, service levels of public transportation and subway stations, and comprehensive scores of public service facilities. Relevant data sources include mobile phone signaling data, social media data, POI data, and urban transportation network data. Connections between nodes are established based on public space activity mechanisms, including: establishing spatial critical connections based on spatial distance thresholds; forming accessibility connections based on the shortest travel time or distance between nodes calculated from the urban road network; establishing functional coupling connections based on the similarity of public space functional characteristics; and constructing social mobility connections reflecting population flow relationships using mobile phone signaling or trajectory OD data. A public space layer network is constructed through multi-dimensional connections to express the organizational structure and interactive relationships of the urban public activity system. The correlation strength of public layer spatial adjacency connections is shown in Table 5. Table 5 The functional coupling strength of the common layer is shown in Table 6.

[0054] Table 6 The connectivity strength of public level transportation is shown in Table 7.

[0055] Table 7 Step D: Construction of Cross-Layer Coupling Network. Based on the ecological layer network and the public space layer network, cross-layer connections are established to achieve coupled expression between the ecosystem and the public space system. Cross-layer connections include: establishing spatial proximity coupling connections based on spatial proximity relationships; forming cross-layer accessibility connections by calculating the shortest path cost from public space nodes to ecological nodes through transportation networks; establishing functional impact propagation connections based on the correlation between ecological function indicators and public activity characteristics to characterize the role of ecosystem services in the environmental quality of public spaces; and constructing social activity feedback connections by combining population activity data to reflect the potential impact of public space usage behavior on ecological spaces. Through comprehensive weight calculation of multiple types of cross-layer relationships, a multi-layer spatial graph network structure of cross-layer coupling between the ecological layer and the public space layer is formed, achieving a holistic and coordinated expression of the urban ecosystem and the public space system.

[0056] The cross-layer spatial adjacency connection strength is shown in Table 8.

[0057] Table 8 The coupling strength between ecological and public impacts is shown in Table 9.

[0058] Table 9 Table 10 shows the connectivity strength of cross-level transportation.

[0059] Table 10 If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for constructing a spatial graph network that achieves cross-layer coupling between the ecological layer and the public space layer, characterized in that, include: Step S1: Divide the target area into grids, obtain ecological patch information and common patch information, and match the ecological patches and common patches with the grid of the target area to obtain the grid range covered by each ecological patch and common patch; Step S2: Based on the grid range covered by each ecological patch and the common patch, generate ecological nodes and common nodes respectively, as well as the area and centroid location of all nodes; Step S3: Use ecological nodes as nodes of the ecological layer graph network, and generate ecological layer inner edges between ecological nodes whose centroid distance is less than the ecological distance threshold. Generate the weights of each ecological layer inner edge of the ecological layer graph network based on the straight-line distance between the corresponding two ecological nodes, functional characteristics, functional similarity, minimum cumulative resistance and traffic accessibility. Step S4: Use common nodes as nodes of the common layer graph network, and generate common layer inner edges between common nodes whose centroid distance is less than the common distance threshold. Generate the weights of each common layer inner edge of the common layer graph network based on the straight-line distance between the corresponding two common nodes, functional characteristics, functional similarity, pedestrian flow relationship and traffic accessibility. Step S5: Construct inter-layer edges between ecological nodes whose centroid locations are less than the common distance threshold and common nodes, and generate the weights of the inter-layer edges based on the straight-line distance, traffic accessibility, ecological impact intensity, and human flow relationship between the corresponding ecological nodes and common nodes.

2. The method for constructing a spatial graph network that realizes cross-layer coupling between the ecological layer and the public space layer according to claim 1, characterized in that, The weights of the edges within each ecological layer of the ecological layer network are a weighted sum of straight-line distance weight, ecological function similarity weight, resistance weight, and traffic accessibility weight.

3. The method for constructing a spatial graph network that realizes cross-layer coupling between the ecological layer and the public space layer according to claim 2, characterized in that, The process of obtaining the resistance weight includes: Get any two ecological nodes connected by the inner edges of the ecological layer; Calculate all grid paths connecting two ecological nodes; Calculate the overall ecological resistance of all grids in each grid path: Where: R represents the overall ecological resistance of the grid. The weight of the k-th type of environmental factor. Let n be the resistance value of the k-th type of environmental factor, and n be the number of environmental factors. The total ecological resistance of each grid within a single grid path is summed to obtain the cumulative resistance of each grid. The cumulative resistance of the grid path with the lowest cumulative resistance is selected as the minimum cumulative resistance between two nodes, and the resistance weight is further obtained. in: Let be the minimum cumulative resistance of nodes i and j.

4. The method for constructing a spatial graph network that realizes cross-layer coupling between the ecological layer and the public space layer according to claim 1, characterized in that, The weights of the edges within each common layer of the common layer graph network are a weighted sum of straight-line distance weight, common function similarity weight, social mobility weight, and traffic accessibility weight.

5. The method for constructing a spatial graph network that realizes cross-layer coupling between the ecological layer and the public space layer according to claim 5, characterized in that, The process of obtaining the intra-layer social mobility weights includes: The number of trips between two public nodes connected by the inner edge of the public layer within the statistical period is obtained based on mobile signaling data or OD data. The passenger flow intensity is obtained based on the number of trips between two common nodes connected by the inner edge of the common layer within the statistical period: in: Let i be the pedestrian flow intensity at node i and j. The number of trips between two public nodes connected by an edge within the public layer during the statistical period, where T is the market during the statistical period; Determine whether the pedestrian flow intensity of node i and node j exceeds the pre-configured pedestrian flow threshold. If yes, use the pedestrian flow intensity of node i and node j as the social mobility weight within the layer; otherwise, configure the social mobility weight within the layer to 0.

6. The method for constructing a spatial graph network that realizes cross-layer coupling between the ecological layer and the public space layer according to claim 1, characterized in that, The weight of the inter-layer edge is a weighted sum of the straight-line distance weight, coupling strength weight, inter-layer social mobility weight, and traffic accessibility weight.

7. The method for constructing a spatial graph network that realizes cross-layer coupling between the ecological layer and the public space layer according to claim 6, characterized in that, The process of obtaining the coupling strength weight includes: Ecological nodes and public nodes connected by inter-layer edges are obtained. Ecological function vectors of ecological nodes and public space activity intensity vectors of public nodes are obtained respectively. The ecological function vectors are determined based on biodiversity, habitat quality, carbon sequestration capacity and cooling effect. The public space activity intensity vectors are determined based on mobile phone signaling density, social media frequency, transportation convenience and comprehensive service facility scores. Calculate the ecological impact intensity of the ecological function vector of ecological nodes and the public space activity intensity vector of public nodes: in: Let i be the ecological function vector of node i. Let be the public space activity intensity vector of node j; Determine whether the ecological impact intensity of node i and node j exceeds the pre-configured intensity threshold. If yes, use the coupling strength of node i and node j as the coupling strength weight; otherwise, configure the coupling strength weight to 0.

8. The method for constructing a spatial graph network that realizes cross-layer coupling between the ecological layer and the public space layer according to claim 6, characterized in that, The process of obtaining the inter-level social mobility weights includes: Statistical analysis of the frequency of visits between public space nodes and ecological nodes connected to them via inter-layer edges: in: The frequency of visits between node i and node j. Let be the number of visits between node i and node j, and be the total number of visits across all public and ecosystem nodes. Determine whether the access frequency between node i and node j exceeds the pre-configured frequency threshold. If yes, use the access frequency between node i and node j as the inter-layer social mobility weight; otherwise, configure the inter-layer social mobility weight to 0.

9. A method for constructing a spatial graph network that realizes cross-layer coupling between the ecological layer and the public space layer according to any one of claims 2, 4, and 6, characterized in that, The process of obtaining the traffic accessibility weight includes: Calculate the shortest path between two nodes using urban road network data; After normalizing the shortest path, the traffic accessibility weights are obtained. For the weights of edges within the ecological layer and the common layer, the traffic accessibility weight is the reciprocal of the normalized shortest path. For the weights of edges between layers, the traffic accessibility weight is: in: The traffic accessibility weights are part of the weights for inter-layer edges. This is the shortest path after normalization.

10. A spatial graph network construction device for realizing cross-layer coupling between the ecological layer and the public space layer, comprising a memory, a processor, and a program stored in the memory, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-9.

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