Urbanization space correlation network analysis system based on territorial space planning
By constructing an analysis system for spatial connections in urbanization, the problems of quantitative characterization of spatial connections between towns and identification of collaborative mechanisms have been solved, enabling scientific basis and dynamic adjustment of cross-city resource allocation and improving the scientificity and operability of territorial spatial planning.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- JIANGXI PROVINCIAL LAND & SPACE SURVEY & PLANNING RES INST
- Filing Date
- 2026-01-12
- Publication Date
- 2026-06-02
Smart Images
Figure CN122133948A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of network analysis, specifically to an urban spatial correlation network analysis system based on territorial spatial planning. Background Technology
[0002] As territorial spatial planning continues to deepen, urbanization is gradually shifting from individual expansion to regional collaboration and coordinated resource allocation. Significant differences exist among towns in areas such as scientific and educational innovation, urban-rural development, ecological livability, and livelihood security. The spatial relationships and collaborative mechanisms formed between towns have a crucial impact on overall regional development. However, existing technologies still have shortcomings in analyzing spatial relationships in urbanization and supporting resource allocation: on the one hand, existing methods for measuring urbanization levels often focus on static evaluations of the development status of individual towns, making it difficult to depict the relationships formed between towns in terms of functional complementarity, resource flow, and spatial interaction; on the other hand, existing spatial relationship analysis methods often employ fixed parameters. Existing technologies, such as single-time-section modeling, fail to identify collaborative constraints in spatial interactions by considering differences in urban functional states. They also lack a systematic analysis of the structural characteristics and evolution of spatial linkage networks. Consequently, cross-city resource allocation still relies primarily on empirical judgments or macro-level indicators, making it difficult to dynamically adjust in accordance with the stages of urbanization. Thus, it is evident that current technologies cannot provide a unified, quantitative, and evolvable analytical model of spatial relationships and collaborative constraints between cities, resulting in a lack of effective data support and decision-making basis for cross-city resource allocation and collaborative implementation. Therefore, it is essential to design an urbanization spatial linkage network analysis system based on territorial spatial planning that improves scientific rigor and operability. Summary of the Invention
[0003] To address the shortcomings of existing technologies, this invention provides an urbanization spatial correlation network analysis system based on territorial spatial planning, which has the advantages of improved scientific rigor and operability, and solves the problems mentioned in the background technology.
[0004] To achieve the aforementioned improvements in scientific rigor and operability, this invention provides the following technical solution: an urbanization spatial correlation network analysis system based on territorial spatial planning, comprising: Indicator processing module: Standardizes multi-dimensional indicators reflecting scientific and educational innovation, urban and rural development, ecological livability and livelihood security, and determines functional weights based on indicator correlation to form an urbanization functional state vector for each town; Spatial Network Module: Based on the functional state vector of urbanization, it introduces population size, economic scale and spatial distance constraint factors, calculates the spatial interaction intensity between towns by modifying the gravity model, constructs the urbanization spatial interaction matrix and generates a spatial association network and records the network structure parameters; Constraint identification module: Performs structural feature analysis on spatially associated networks, calculates the overall network density, hierarchical features and node centrality parameters, identifies collaborative constraint factors in combination with differences in urbanization functional status, and analyzes the impact on network structure based on the changing characteristics under different time series data, generating a set of collaborative constraint factor influence parameters; Stage-based regulation module: Based on the urbanization functional state vector, spatial correlation network structure parameters, and set of parameters influencing the collaborative constraint factors, it identifies the development stage of the urbanization spatial correlation network, and dynamically adjusts the collaborative weights and resource allocation priorities of urban nodes under constraints at different stages, generating a set of stage-based collaborative regulation strategies. Resource simulation module: Based on a set of phased collaborative regulation strategies, it simulates and analyzes the resource allocation process between urban nodes, and outputs a feasible range for cross-city resource allocation that meets the requirements of network stability and collaborative efficiency.
[0005] Preferably, the process of forming the urbanization functional state vector of each town is as follows: Collect raw indicator data for each town within a preset time period, corresponding to scientific and educational innovation, urban and rural development, ecological livability, and livelihood security. Dimensionless processing is performed on various indicator data to eliminate dimensional differences and obtain a standardized set of indicators. Based on a standardized set of indicators, the correlation coefficient matrix between the indicators is calculated, and the functional weight of each indicator is determined according to the magnitude of the correlation. The standardized values of each indicator are weighted and combined with their corresponding functional weights to form an urbanization functional state vector that represents the comprehensive functional characteristics of each town.
[0006] Preferably, the process of introducing population size, economic scale, and spatial distance constraints is as follows: Obtain population size data, economic size data, and spatial distance data between towns for each town; Population size and economic size data are processed to a uniform scale to form a size constraint factor; Construct distance constraint factors based on the spatial distance between towns; By associating the scale constraint factor and distance constraint factor with the urbanization function state vector of the corresponding town, a set of spatial action constraint parameters is formed.
[0007] Preferably, the process of constructing the urbanization spatial interaction matrix, generating a spatial correlation network, and recording the network structure parameters is as follows: Using the urbanization functional state vector as the functional strength term of the gravity model, population size, economic scale and spatial distance constraint factors are introduced, and combined with the set of spatial action constraint parameters, the gravity model is parameterized. Based on the modified gravity model, the intensity of spatial interaction between any two towns is calculated. Arrange the spatial interaction intensity between each pair of towns to form an urbanization spatial interaction matrix; Extract effective spatial relationships, generate an urbanization spatial relationship network, and record network structure parameters, including node set, edge set and weight, node centrality, overall network density and hierarchical features.
[0008] Preferably, the process of calculating the overall network density, hierarchical characteristics, and node centrality parameters is as follows: Based on the node and edge structure of the urban spatial association network, the ratio of the actual number of associated edges to the theoretical maximum number of edges in the network is calculated to obtain the overall network density parameter. Based on the connection direction and hierarchical relationship between nodes, extract the hierarchical structure features of the network; Calculate the degree centrality, betweenness centrality, and proximity centrality parameters of each town node in the spatial association network to form a set of node centrality parameters.
[0009] Preferably, the process of identifying collaborative constraint factors is as follows: For town nodes with related relationships in a spatial network, calculate the degree of difference between urbanization functional state vectors; The functional state difference is analyzed in conjunction with the centrality parameter set and connection relationship of the corresponding node. Based on the combined relationships of functional state differences, node centrality parameter sets, and network structure characteristics, we identify the influencing factors that constrain urban collaborative behavior and use them as collaborative constraint factors.
[0010] Preferably, the process of generating the set of parameters affecting the collaborative constraint factors is as follows: Repeatedly constructing spatial connection networks of urbanization at different time scales; Track the changes in the values of the collaborative constraint factors at various time scales; Analyze the correspondence between changes in collaborative constraint factors and changes in overall network density, hierarchical structure, and node centrality parameters; The corresponding relationships are expressed in a parameterized manner to form a set of parameters affecting the collaborative constraint factors.
[0011] Preferably, the process of identifying the development stage of the spatial network of urbanization is as follows: The current timescale urbanization functional state vector, spatial correlation network structure parameters, and set of influence parameters of collaborative constraint factors are used as inputs. The collaborative characteristics, functional state distribution, and constraint factors of network nodes are analyzed. Based on the analysis results, the network is divided into different development stages.
[0012] Preferably, the process of generating a set of phased collaborative control strategies is as follows: Based on the network development stage, key nodes are selected and urbanization functional state vectors and collaborative constraint factors are extracted; The staged collaborative weights are calculated by combining the differences in node functional status and constraint factors. Based on the collaborative weights and network topology, combined with resource constraints, the node priorities are calculated and sorted to generate an allocation sequence; Develop a node collaboration weight adjustment plan, resource allocation strategy, and constraint triggering conditions; By integrating the collaborative weights, resource priorities, and control rules of each stage node, a set of staged collaborative control strategies is formed.
[0013] Preferably, the process of outputting the feasible range of cross-city resource allocation that meets the requirements of network stability and collaborative efficiency is as follows: A simulation model for cross-city resource allocation is constructed using a set of phased coordinated regulation strategies as constraints. The simulation model simulates the allocation process of different resource allocation schemes in a spatially interconnected network. Statistical analysis was performed on the network structure stability parameters and cooperative efficiency parameters corresponding to each simulation result; Based on the statistical results, the range of cross-city resource allocation schemes that meet the preset constraints is determined, and the feasible range of cross-city resource allocation is output.
[0014] Compared with existing technologies, the present invention provides an urbanization spatial correlation network analysis system based on land spatial planning, which has the following beneficial effects: This invention constructs an urbanization functional state vector reflecting the differences in urban functional states by uniformly and quantitatively modeling multidimensional urbanization indicators. Based on this, it introduces constraint factors such as population, economy, and spatial distance to form a calculable urban spatial interaction network, thus achieving a quantitative characterization of spatial relationships between cities. Furthermore, by analyzing the structural characteristics and temporal evolution of the spatial relationship network, it identifies the synergistic constraint factors formed between cities in the process of resource carrying capacity and functional coordination, and explicitly introduces these constraint factors into the subsequent regulation process. This avoids the shortcomings of existing technologies that rely solely on static indicators or single correlations for analysis, which fail to reflect urban synergy. This addresses the issue of shared constraints; simultaneously, by identifying the development stages of the spatial network of urbanization, it dynamically adjusts the collaborative weights and resource allocation priorities of urban nodes at different stages under constraint-driven conditions. Combined with resource allocation simulation analysis, it outputs feasible intervals for cross-city resource allocation that meet the requirements of network stability and collaborative efficiency. This achieves a complete technical closed loop from state quantification, network modeling, constraint identification to dynamic control and simulation verification. It can provide interpretable and operable cross-city collaborative analysis and allocation basis in the context of complex urbanization, significantly improving the scientific and refined level of cross-city resource allocation and collaborative implementation in territorial spatial planning. Attached Figure Description
[0015] Figure 1 This is a schematic diagram of the structure of the present invention. Detailed Implementation
[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.
[0017] Example 1: Please refer to Figure 1 As shown in the embodiment of the present invention, the urbanization spatial correlation network analysis system based on territorial spatial planning includes: Indicator processing module: Standardizes multi-dimensional indicators reflecting scientific and educational innovation, urban and rural development, ecological livability and livelihood security, and determines functional weights based on indicator correlation to form an urbanization functional state vector for each town.
[0018] The process of generating the urbanization function state vector for each town in the indicator processing module is as follows: Collect raw indicator data for each town within a preset time period, corresponding to scientific and educational innovation, urban and rural development, ecological livability, and livelihood security. Within a preset time period, multidimensional raw indicator data reflecting the comprehensive development level of each town will be collected. The raw indicator data will include at least four categories of indicators: science and education innovation, urban and rural development, ecological livability, and livelihood security. Among them, the science and education innovation indicator is used to characterize the town's science and technology input, innovation output, and talent agglomeration level; the urban and rural development indicator is used to characterize the town's economic scale, industrial structure, and urban-rural coordination; the ecological livability indicator is used to characterize the ecological environment quality and spatial carrying capacity; and the livelihood security indicator is used to characterize the supply of public services and residents' living conditions. By collecting the above multiple types of indicators simultaneously, a raw indicator dataset covering the multidimensional characteristics of town functions will be constructed to obtain basic data that comprehensively reflects the comprehensive functional characteristics of towns.
[0019] Dimensionless processing is performed on various indicator data to eliminate dimensional differences and obtain a standardized set of indicators. For the various raw indicator data collected, the corresponding dimensionless method is selected according to the indicator attributes. For positive indicators, extreme value normalization or standardization is applied, and for negative indicators, direction consistency transformation is performed before normalization. Through dimensionless processing, indicators with different dimensions and numerical ranges are uniformly mapped to comparable intervals to obtain a standardized indicator set. This standardized indicator set maintains the relative differences between indicators and eliminates the interference of differences in dimensions and orders of magnitude on subsequent calculations, thereby eliminating the differences in indicator dimensions and making different functional indicators comparable.
[0020] Based on a standardized set of indicators, the correlation coefficient matrix between the indicators is calculated, and the functional weight of each indicator is determined according to the magnitude of the correlation. Based on a standardized set of indicators, the correlation coefficients between the indicators are calculated, and an indicator correlation coefficient matrix is constructed to reflect the degree of association between different indicators in the representation of urban functions. According to the correlation coefficient matrix, the information redundancy and complementarity between indicators are analyzed, and the weight coefficient of each indicator in the corresponding functional dimension is determined according to the magnitude of the correlation. In this way, the determination of functional weights reflects the intrinsic connection between indicators, avoids the bias caused by subjective weighting, and reasonably allocates the degree of influence of each functional indicator in the comprehensive functional representation.
[0021] The standardized values of each indicator are weighted and combined with the corresponding functional weights to form an urbanization functional state vector that represents the comprehensive functional characteristics of each town. The standardized values of each indicator are weighted and combined with their corresponding functional weights. The weighted results are calculated in the dimensions of scientific and educational innovation, urban and rural development, ecological livability, and livelihood security. The weighted results of each functional dimension are vectorized and encoded in a unified order to form an urbanization functional state vector that represents the comprehensive functional characteristics of a single town. This urbanization functional state vector serves as the basic input feature for subsequent spatial correlation analysis, collaborative constraint identification, and resource allocation decision-making, integrating multi-dimensional functional indicators into a calculable and comparable expression of urban functional state.
[0022] Spatial Network Module: Based on the urbanization functional state vector, it introduces population size, economic scale and spatial distance constraint factors, calculates the spatial interaction intensity between towns by modifying the gravity model, constructs the urbanization spatial interaction matrix, generates a spatial association network and records the network structure parameters.
[0023] The process of introducing population size, economic scale, and spatial distance constraint factors into the spatial network module is as follows: Obtain population size data, economic size data, and spatial distance data between towns for each town; For each town within the study area, corresponding population size data, economic size data, and spatial distance data between towns are obtained. Among them, population size data is used to reflect the population carrying capacity and social activity scale of towns, economic size data is used to reflect the intensity of economic activities and resource agglomeration level of towns, and spatial distance data between towns is calculated based on the geographical coordinates of towns. This distance can be a straight-line distance, road network distance, or effective accessibility distance determined based on territorial spatial planning. All the above data are uniformly organized according to town identification and matched with the same spatial indexing system as the urbanization functional state vector, providing basic parameters for spatial constraint modeling, so that spatial collaborative analysis takes into account the functional characteristics, scale conditions, and spatial distance factors of towns.
[0024] Population size and economic size data are processed to a uniform scale to form a size constraint factor; After obtaining population size and economic scale data, the population size data and economic scale data are standardized or normalized respectively, and then merged according to the preset combination rules to form a scale constraint factor to characterize the comprehensive scale level of the town. This combination rule is used to balance the relative influence of population size and economic scale in spatial action, so that the scale constraint factor can simultaneously reflect the population carrying capacity and economic activity capacity, and quantify the population size and economic scale into spatial constraint parameters in a unified manner to improve the rationality of spatial action intensity calculation.
[0025] Construct distance constraint factors based on the spatial distance between towns; After obtaining the spatial distance data between towns, the distance between towns is functionalized based on the attenuation relationship between distance and spatial interaction intensity. This results in a larger distance constraint value for towns that are spatially closer and a smaller distance constraint value for towns that are spatially farther apart. This processing method is used to reflect the limiting effect of spatial proximity on town synergy, transforming the spatial distance between towns into a quantifiable constraint parameter, so that spatial correlation analysis can reflect the impact of distance factors on town synergy.
[0026] The scale constraint factor and distance constraint factor are associated with the urbanization function state vector of the corresponding town to form a set of spatial action constraint parameters; After obtaining the scale constraint factor and distance constraint factor, the urbanization function state vector is used as the functional representation basis. The scale constraint factor is used to correct the external influence capacity of urban functions, and the distance constraint factor is used to correct the accessibility of functional influence between urban areas. This forms a set of spatial influence constraint parameters that comprehensively reflect the functional characteristics, scale conditions and spatial restriction relationships of urban areas. The functional characteristics of urban areas are modeled in a unified manner with scale factors and spatial distance, providing a parameter basis with realistic constraints for the calculation of spatial influence intensity and correlation analysis.
[0027] The process of constructing the urbanization spatial interaction matrix, generating the spatial association network, and recording the network structure parameters in the spatial network module is as follows: Using the urbanization functional state vector as the functional strength term of the gravity model, population size, economic scale and spatial distance constraint factors are introduced, and combined with the set of spatial action constraint parameters, the gravity model is parameterized. Using the urbanization function state vector of each town as the function intensity term in the spatial gravity model, population size constraint factors and economic size constraint factors are introduced to correct the scale conditions of the spatial effects of town functions. At the same time, spatial distance constraint factors are introduced to characterize the attenuation effect of spatial distribution between towns on the intensity of the effect. Furthermore, the set of spatial effect constraint parameters is embedded into the parameter structure of the gravity model as a whole, and the traditional gravity model that only relies on scale and distance is modified by function-scale-distance coordination to obtain the modified urban spatial effect calculation model, so that the intensity of spatial effect can truly reflect the comprehensive influence capacity of towns in the national spatial system.
[0028] Based on the modified gravity model, the intensity of spatial interaction between any two towns is calculated. Based on the modified gravity model, for any pair of towns, the functional state vector, scale constraint factor, and distance constraint factor of the corresponding towns are substituted into the modified gravity model to calculate the spatial interaction strength between the pair of towns. This spatial interaction strength is used to characterize the degree of mutual influence between towns in terms of functional connection, resource flow, and coordinated development. It provides the basic calculation results for matrix representation and network structure analysis, making the relationship between towns comparable and analyzable.
[0029] Arrange the spatial interaction intensity between each pair of towns to form an urbanization spatial interaction matrix; After obtaining the spatial interaction strength between all town pairs, the spatial interaction strength is arranged according to the town number order to construct an urbanization spatial interaction matrix. In this matrix, the rows and columns correspond to different towns, and the matrix elements represent the spatial interaction strength between the corresponding town pairs, forming a complete two-dimensional matrix structure that reflects the interaction relationship between towns in the region. This provides a standardized data carrier for spatial correlation extraction and network modeling, facilitating subsequent unified processing and analysis.
[0030] Extract effective spatial relationships, generate an urbanization spatial relationship network, and record network structure parameters, including node set, edge set and weight, node centrality, overall network density and hierarchical features; Based on the constructed urbanization spatial interaction matrix, towns are used as network nodes, and pairs of towns that meet the association conditions are used as network edges. The corresponding spatial interaction strength is used as the edge weight to generate an urbanization spatial association network. At the same time, the generated spatial association network is structurally analyzed to record network structural parameters, including the set of nodes, the set of edges and their weights, the node centrality index, the overall network density, and the network hierarchy or graded characteristics. This provides a structured basis for subsequent development stage identification, coordinated weight adjustment, and resource allocation optimization.
[0031] Constraint Identification Module: Performs structural feature analysis on spatially related networks, calculates the overall network density, hierarchical features, and node centrality parameters, identifies collaborative constraint factors based on differences in urbanization functional status, and analyzes their impact on network structure based on the changing characteristics under different time series data, generating a set of collaborative constraint factor influence parameters.
[0032] The process of calculating the overall network density, hierarchical features, and node centrality parameters in the constraint identification module is as follows: Based on the node and edge structure of the urban spatial association network, the ratio of the actual number of associated edges to the theoretical maximum number of edges in the network is calculated to obtain the overall network density parameter. Based on the completed urban spatial connection network, the node set and edge set information in the network are read, the actual number of spatial connection edges in the network is counted, and the theoretical maximum number of connectable edges is calculated according to the total number of network nodes. The ratio of the actual number of connection edges to the theoretical maximum number of edges is calculated to obtain the overall network density parameter that characterizes the overall connectivity of the network, providing a basic indicator for judging the network's level of collaboration and development stage.
[0033] Based on the connection direction and hierarchical relationship between nodes, extract the hierarchical structure features of the network; After obtaining the node and edge structure of the urban spatial association network, the nodes are divided into different levels according to the dominance relationship of the nodes in the network, the length of the connection path and the degree of association concentration. The hierarchical structure features of the network are extracted to characterize the hierarchical distribution of towns in the spatial association system, and to provide a structural basis for identifying core nodes and hierarchical collaborative relationships.
[0034] Calculate the degree centrality, betweenness centrality, and proximity centrality parameters of each town node in the spatial association network to form a set of node centrality parameters; Based on the analysis of the overall network density and hierarchical structure, the degree centrality, betweenness centrality, and proximity centrality of each node are calculated according to the network topology. All centrality indicators are recorded in a unified manner to form a set of node centrality parameters, which provides a quantitative basis for subsequent collaborative weight allocation and resource allocation.
[0035] The process of identifying collaborative constraint factors in the constraint identification module is as follows: For town nodes with related relationships in a spatial network, calculate the degree of difference between urbanization functional state vectors; Based on the established urban spatial association network, for any pair of town nodes with associated edges, the urbanization functional state vectors are compared dimension by dimension according to a preset vector distance calculation method. The functional state difference degree is calculated to characterize the difference level of towns in functional dimensions such as scientific and educational innovation, urban and rural development, ecological livability, and livelihood security. The difference degree results are associated with the corresponding town node pairs and recorded to provide basic data for analyzing the constraint and impact of functional differences on collaborative behavior.
[0036] The functional state difference is analyzed in conjunction with the centrality parameter set and connection relationship of the corresponding node. After obtaining the functional state difference degree of the town node pairs, the functional state difference degree is jointly analyzed with the degree centrality, betweenness centrality and proximity centrality parameters of the nodes. At the same time, the influence characteristics of functional differences on collaborative behavior under different network structure positions are analyzed by combining the connection strength and connection path position between town nodes, and the difference-structure joint analysis results are formed to characterize the constraint performance of different towns in collaborative relationships.
[0037] Based on the combined relationship of functional state differences, node centrality parameter set and network structure characteristics, the influencing factors that constrain urban collaborative behavior are identified as collaborative constraint factors. After completing the comprehensive analysis of the difference degree and network structure parameters, factors that repeatedly appear in multiple pairs of town nodes and have a stable impact on the changes in spatial association strength and cooperative relationship are identified as cooperative constraint factors. Each cooperative constraint factor is identified and parameterized to form a set of cooperative constraint factors that can be used for subsequent time series analysis and stage control, providing input parameters for cooperative weight adjustment and stage identification.
[0038] The process of generating the set of influence parameters for collaborative constraint factors in the constraint identification module is as follows: Repeatedly constructing spatial connection networks of urbanization at different time scales; Multiple continuous or discrete time scales are selected, and the urbanization functional state vector, population size, economic scale and spatial distance data within each time scale are processed respectively. The urbanization spatial role matrix and spatial association network under the corresponding time scale are constructed according to the aforementioned method. Different time scales can correspond to years, planning stages or statistical periods. The network under each time scale independently records its node set, edge weight structure and network structure parameters, providing a comparable network structure basis for analyzing the temporal evolution characteristics of collaborative constraint factors.
[0039] Track the changes in the values of the collaborative constraint factors at various time scales; After completing the construction of the multi-timescale spatial association network, the cooperative constraint factors identified at each time scale are uniformly numbered and stored, and their corresponding values or state parameters are recorded in chronological order. By aligning the continuous values of the same cooperative constraint factor at different time scales, a temporal change sequence of the cooperative constraint factors is formed, transforming the statically identified cooperative constraint factors into dynamic constraint descriptions that can change with time.
[0040] Analyze the correspondence between changes in collaborative constraint factors and changes in overall network density, hierarchical structure, and node centrality parameters; Based on the time-series changes of the collaborative constraint factors, this study analyzes the correlation trends between changes in the values of the collaborative constraint factors and changes in network connection strength, hierarchical differentiation, and key node centrality. It identifies the corresponding relationships between the impact of collaborative constraint factors on network structure evolution and clarifies the direction and extent of the impact of changes in collaborative constraint factors on the evolution of urban spatial network structure.
[0041] The correspondence is parameterized to form a set of parameters affecting the collaborative constraint factors; After completing the correspondence analysis, the influence relationship between the collaborative constraint factors and network structure parameters is parametrically modeled. By establishing mapping parameters between the values of the collaborative constraint factors and the overall network density, hierarchical characteristics, and node centrality parameters, this influence relationship is expressed in the form of a set of parameters, forming a set of collaborative constraint factor influence parameters. This set serves as the input condition for identification and collaborative regulation in subsequent development stages, providing a calculable and callable basis for constraint influence parameters for collaborative regulation and resource allocation.
[0042] Stage-based regulation module: Based on the urbanization functional state vector, spatial correlation network structure parameters, and the set of parameters influencing the collaborative constraint factors, it identifies the development stage of the urbanization spatial correlation network, and dynamically adjusts the collaborative weights and resource allocation priorities of urban nodes under constraints at different stages, generating a set of stage-based collaborative regulation strategies.
[0043] The process of identifying the development stage of the urbanization spatial correlation network in the stage regulation module is as follows: The current timescale urbanization functional state vector, spatial correlation network structure parameters, and set of influence parameters of collaborative constraint factors are used as inputs. For the current time scale, the urbanization functional state vector, spatial correlation network structure parameters, and collaborative constraint factor influence parameter set of each town at the corresponding time scale are used as unified input data. Among them, the urbanization functional state vector is used to characterize the development level of each town in different functional dimensions, the spatial correlation network structure parameters are used to characterize the connection strength and hierarchical relationship between towns, and the collaborative constraint factor influence parameter set is used to reflect the degree of influence of constraint factors on network structure evolution. The three types of data are consistent in time scale and town number, providing a unified input basis for functional state, structural characteristics and constraint mechanism for development stage identification.
[0044] The collaborative characteristics, functional state distribution, and constraint factors of network nodes are analyzed. After completing the input construction, the collaborative characteristics of each town node in the spatial association network are analyzed. These collaborative characteristics include the changing trend of node centrality, the hierarchical position of the node in the network, and the distribution of collaborative weights among nodes. At the same time, the spatial distribution of the urbanization function state vector in the network is statistically analyzed to identify functional agglomeration, functional differentiation, or functional imbalance characteristics. Combined with the set of parameters affecting collaborative constraint factors, the influence of constraint factors on collaborative behavior and structural stability at the current time scale is analyzed to characterize the overall operating status of the current network at the level of functional distribution and collaborative relationships.
[0045] Based on the analysis results, the network is divided into different development stages; After obtaining the network's collaborative characteristics, functional state distribution characteristics, and collaborative constraint influence characteristics, the urbanization spatial network is divided into stages according to the preset development stage determination rules. These rules can divide the network into different development stages based on the overall network density level, hierarchical differentiation degree, key node centrality concentration degree, and collaborative constraint strength combination, so as to reflect the evolution of urban spatial connections from initial connection to stable collaboration, clarify the evolution stage of the urbanization spatial network, and provide a basis for subsequent staged collaborative regulation.
[0046] The process of generating a set of phased collaborative control strategies in the phased control module is as follows: Based on the network development stage, key nodes are selected and urbanization functional state vectors and collaborative constraint factors are extracted; Based on the identified development stages of the spatial network of urbanization, and according to the differences in network characteristics corresponding to different development stages, node selection rules are determined. These rules include at least the connectivity, betweenness centrality, functional radiation range, and impact on the overall network stability of nodes in the spatial network. According to the selection rules, key nodes that play a leading or constraining role in the current development stage are selected from the network nodes. For each key node, its corresponding urbanization functional state vector is extracted, including quantitative indicators of functional dimensions such as scientific and educational innovation, urban and rural development, ecological livability, and livelihood security. At the same time, collaborative constraint factors related to key nodes are extracted. These collaborative constraint factors include at least population size constraints, resource carrying capacity constraints, spatial distance constraints, and policy constraints, which are used to characterize the external constraints of nodes in collaborative regulation and clarify the nodes that play a key role in collaborative regulation and their constraint basis at different development stages.
[0047] The staged collaborative weights are calculated by combining the differences in node functional status and constraint factors. A difference analysis is performed on the urbanization function state vectors of the key nodes selected from the screening. The relative differences and complementarity between nodes in each functional dimension are calculated. Based on the importance of different functional dimensions in the current development stage, corresponding functional dimension weights are set to reflect the influence intensity of different urbanization functions on the phased coordinated development. The node functional state difference results are jointly modeled with the coordination constraint factors. A penalty coefficient is introduced for nodes with a high degree of constraint, and an incentive coefficient is introduced for nodes with coordination potential. On this basis, a coordination weight matrix between nodes reflecting the characteristics of the current development stage is generated through a weighted calculation method. This coordination weight is used to quantify the priority and intensity of coordination regulation between nodes, and to quantify the relative importance and feasibility of different nodes carrying out coordination regulation in the current stage.
[0048] Based on the collaborative weights and network topology, combined with resource constraints, the node priorities are calculated and sorted to generate an allocation sequence; Based on the topology of spatially interconnected networks, we analyze the hierarchical relationships, transmission paths, and collaborative influence ranges among nodes. The collaborative weight matrix is mapped onto the network topology, and the comprehensive collaborative contribution value of each node in the network is calculated. The comprehensive collaborative contribution value is used to reflect the actual role of the node in resource allocation. Further resource constraints are introduced, including fiscal resources, land resources, public service resources and ecological capacity constraints, to correct the comprehensive collaborative contribution value of the nodes. Based on the corrected results, the stage priority index of each node is calculated, and the nodes are sorted from high to low according to the priority index to generate a node sequence for resource allocation and regulation execution, forming a node regulation sequence that conforms to the network structure and resource constraints.
[0049] Develop a node collaboration weight adjustment plan, resource allocation strategy, and constraint triggering conditions; Differentiated collaborative weight adjustment schemes are formulated for nodes with different priorities. The collaborative weight of high-priority nodes is increased, while the weight adjustment strategy for low-priority nodes is set to be stable or suppressive. Based on the node priority ranking results, corresponding resource allocation strategies are formulated, clarifying the resource input ratio, input rhythm and collaborative support method of each node in different functional dimensions. At the same time, according to the changes in collaborative constraint factors, constraint triggering conditions are set. When population, resource or spatial constraints exceed preset thresholds, the collaborative weight recalculation or resource allocation strategy adjustment is automatically triggered. This adjustment scheme and the triggering conditions together constitute a dynamic control mechanism within the stage, which is used to ensure the stability and sustainability of the collaborative control process, and realize the dynamic adjustability and risk controllability of the collaborative control process.
[0050] Integrate the collaborative weights, resource priorities, and control rules of each stage node to form a set of staged collaborative control strategies; The key node sets, collaborative weight matrices, node priority ranking results, and resource allocation strategies corresponding to each development stage are uniformly integrated. The control rules for different stages are structurally encapsulated to form staged collaborative control strategy units that can be invoked according to the development stage. The collaborative control strategy units of each stage are combined to form a staged collaborative control strategy set. This strategy set can be switched or iteratively updated as the network development stage changes. Through this staged collaborative control strategy set, differentiated and refined collaborative control of the urbanization spatial network under different development stages can be achieved, forming a complete staged collaborative control strategy set that can be directly used for implementation and dynamic switching.
[0051] Resource simulation module: Based on a set of phased collaborative regulation strategies, it simulates and analyzes the resource allocation process between urban nodes, and outputs a feasible range for cross-city resource allocation that meets the requirements of network stability and collaborative efficiency.
[0052] The process by which the resource simulation module outputs the feasible range for cross-city resource allocation that meets the requirements of network stability and collaborative efficiency is as follows: A simulation model for cross-city resource allocation is constructed using a set of phased coordinated regulation strategies as constraints. The established set of phased collaborative regulation strategies is input into the cross-city resource allocation simulation module. The set of phased collaborative regulation strategies serves as the global constraint condition of the simulation model. Based on the spatial network structure, a cross-city resource allocation simulation model with town nodes as the basic unit is established. The model clearly defines the resource demand of nodes, the types of configurable resources, and the resource flow paths between nodes. The collaborative weights, node priorities, and constraint triggering rules corresponding to each stage are embedded in the simulation model to limit the allocation ratio, allocation order, and maximum allocation amount of resources among nodes. This ensures that the simulation model always follows the phased collaborative regulation strategy during the resource allocation process, avoiding configuration results that violate the characteristics of the network development stage. This provides a simulation constraint environment that meets the requirements of phased collaborative regulation for cross-city resource allocation.
[0053] The simulation model simulates the allocation process of different resource allocation schemes in a spatially interconnected network. In the cross-city resource allocation simulation model, multiple sets of different resource allocation schemes are generated according to the preset total resource amount and configuration step size. The resource allocation schemes include the resource allocation ratio and delivery rhythm between different nodes. For each resource allocation scheme, based on the spatial network topology, the transmission, allocation and feedback process of resources between nodes is simulated, and the node resource status and network connection strength are updated in real time. During the simulation, the triggering conditions of collaborative constraints are dynamically detected. When the configuration scheme causes the constraint to exceed the limit, the corresponding scheme is marked or terminated. Through multiple rounds of simulation, a set of resource allocation simulation results covering different configuration strengths and allocation structures is formed, which comprehensively describes the impact of different resource allocation schemes on the operation status of the spatial network.
[0054] Statistical analysis was performed on the network structure stability parameters and cooperative efficiency parameters corresponding to each simulation result; For each simulation result, the corresponding network structure stability parameters are calculated. These stability parameters include at least the network connectivity change rate, node load balancing degree, and critical node load fluctuation amplitude. Simultaneously, collaboration efficiency parameters reflecting cross-city collaboration effectiveness are calculated. These collaboration efficiency parameters include at least resource utilization rate, functional collaboration improvement degree, and cross-node response time. The aforementioned stability and collaboration efficiency parameters are then subjected to unified statistical and normalization processing to form a set of simulation evaluation indicators for comparative analysis. Through statistical analysis, the differences in network stability and collaboration efficiency performance of different resource configuration schemes are clarified, and the impact of different resource configuration schemes on network stability and collaboration efficiency is quantitatively evaluated.
[0055] Based on the statistical results, the range of cross-city resource allocation schemes that meet the preset constraints is determined, and the feasible range of cross-city resource allocation is output. The statistically obtained network structure stability parameters and collaborative efficiency parameters are compared with preset threshold conditions to select resource allocation schemes that simultaneously meet the stability and collaborative efficiency requirements. The selected resource allocation schemes are then processed into intervals to determine the minimum, maximum, and recommended configuration ranges for each resource type across different nodes. The configuration ranges are mapped to feasible intervals for cross-city resource allocation under the spatial association network. The feasible intervals are used to characterize the resource allocation boundaries that can improve collaborative efficiency without compromising network stability. Finally, the feasible intervals for cross-city resource allocation are output as a reference for subsequent cross-city resource scheduling and policy formulation, and a safe range for cross-city resource allocation that balances network stability and collaborative efficiency is output.
[0056] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.
[0057] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. An urbanization spatial correlation network analysis system based on territorial spatial planning, characterized in that, include: Indicator processing module: Standardizes multi-dimensional indicators reflecting scientific and educational innovation, urban and rural development, ecological livability and livelihood security, and determines functional weights based on indicator correlation to form an urbanization functional state vector for each town; Spatial Network Module: Based on the functional state vector of urbanization, it introduces population size, economic scale and spatial distance constraint factors, calculates the spatial interaction intensity between towns by modifying the gravity model, constructs the urbanization spatial interaction matrix and generates a spatial association network and records the network structure parameters; Constraint identification module: Performs structural feature analysis on spatially associated networks, calculates the overall network density, hierarchical features and node centrality parameters, identifies collaborative constraint factors in combination with differences in urbanization functional status, and analyzes the impact on network structure based on the changing characteristics under different time series data, generating a set of collaborative constraint factor influence parameters; Stage-based regulation module: Based on the urbanization functional state vector, spatial correlation network structure parameters, and set of parameters influencing the collaborative constraint factors, it identifies the development stage of the urbanization spatial correlation network, and dynamically adjusts the collaborative weights and resource allocation priorities of urban nodes under constraints at different stages, generating a set of stage-based collaborative regulation strategies. Resource simulation module: Based on a set of phased collaborative regulation strategies, it simulates and analyzes the resource allocation process between urban nodes, and outputs a feasible range for cross-city resource allocation that meets the requirements of network stability and collaborative efficiency.
2. The urbanization spatial correlation network analysis system based on territorial spatial planning according to claim 1, characterized in that, The process of forming the urbanization functional state vector of each town is as follows: Collect raw indicator data for each town within a preset time period, corresponding to scientific and educational innovation, urban and rural development, ecological livability, and livelihood security. Dimensionless processing is performed on various indicator data to eliminate dimensional differences and obtain a standardized set of indicators. Based on a standardized set of indicators, the correlation coefficient matrix between the indicators is calculated, and the functional weight of each indicator is determined according to the magnitude of the correlation. The standardized values of each indicator are weighted and combined with their corresponding functional weights to form an urbanization functional state vector that represents the comprehensive functional characteristics of each town.
3. The urbanization spatial correlation network analysis system based on territorial spatial planning according to claim 2, characterized in that, The process of introducing population size, economic scale, and spatial distance constraints is as follows: Obtain population size data, economic size data, and spatial distance data between towns for each town; Population size and economic size data are processed to a uniform scale to form a size constraint factor; Construct distance constraint factors based on the spatial distance between towns; By associating the scale constraint factor and distance constraint factor with the urbanization function state vector of the corresponding town, a set of spatial action constraint parameters is formed.
4. The urbanization spatial correlation network analysis system based on territorial spatial planning according to claim 3, characterized in that, The process of constructing the urbanization spatial effect matrix, generating a spatial correlation network, and recording the network structure parameters is as follows: Using the urbanization functional state vector as the functional strength term of the gravity model, population size, economic scale and spatial distance constraint factors are introduced, and combined with the set of spatial action constraint parameters, the gravity model is parameterized. Based on the modified gravity model, the intensity of spatial interaction between any two towns is calculated. Arrange the spatial interaction intensity between each pair of towns to form an urbanization spatial interaction matrix; Extract effective spatial relationships, generate an urbanization spatial relationship network, and record network structure parameters, including node set, edge set and weight, node centrality, overall network density and hierarchical features.
5. The urbanization spatial correlation network analysis system based on territorial spatial planning according to claim 4, characterized in that, The process of calculating the overall network density, hierarchical features, and node centrality parameters is as follows: Based on the node and edge structure of the urban spatial association network, the ratio of the actual number of associated edges to the theoretical maximum number of edges in the network is calculated to obtain the overall network density parameter. Based on the connection direction and hierarchical relationship between nodes, extract the hierarchical structure features of the network; Calculate the degree centrality, betweenness centrality, and proximity centrality parameters of each town node in the spatial association network to form a set of node centrality parameters.
6. The urbanization spatial correlation network analysis system based on territorial spatial planning according to claim 5, characterized in that, The process of identifying collaborative constraint factors is as follows: For town nodes with related relationships in a spatial network, calculate the degree of difference between urbanization functional state vectors; The functional state difference is analyzed in conjunction with the centrality parameter set and connection relationship of the corresponding node. Based on the combined relationships of functional state differences, node centrality parameter sets, and network structure characteristics, we identify the influencing factors that constrain urban collaborative behavior and use them as collaborative constraint factors.
7. The urbanization spatial correlation network analysis system based on territorial spatial planning according to claim 6, characterized in that, The process of generating the set of parameters affecting the collaborative constraint factors is as follows: Repeatedly constructing spatial connection networks of urbanization at different time scales; Track the changes in the values of the collaborative constraint factors at various time scales; Analyze the correspondence between changes in collaborative constraint factors and changes in overall network density, hierarchical structure, and node centrality parameters; The corresponding relationships are expressed in a parameterized manner to form a set of parameters affecting the collaborative constraint factors.
8. The urbanization spatial correlation network analysis system based on territorial spatial planning according to claim 7, characterized in that, The process of identifying the development stage of the spatial network of urbanization is as follows: The current timescale urbanization functional state vector, spatial correlation network structure parameters, and set of influence parameters of collaborative constraint factors are used as inputs. The collaborative characteristics, functional state distribution, and constraint factors of network nodes are analyzed. Based on the analysis results, the network is divided into different development stages.
9. The urbanization spatial correlation network analysis system based on territorial spatial planning according to claim 8, characterized in that, The process of generating a set of phased coordinated control strategies is as follows: Based on the network development stage, key nodes are selected and urbanization functional state vectors and collaborative constraint factors are extracted; The staged collaborative weights are calculated by combining the differences in node functional status and constraint factors. Based on the collaborative weights and network topology, combined with resource constraints, the node priorities are calculated and sorted to generate an allocation sequence; Develop a node collaboration weight adjustment plan, resource allocation strategy, and constraint triggering conditions; By integrating the collaborative weights, resource priorities, and control rules of each stage node, a set of staged collaborative control strategies is formed.
10. The urbanization spatial correlation network analysis system based on territorial spatial planning according to claim 9, characterized in that, The process of outputting the feasible range of cross-city resource allocation that meets the requirements of network stability and collaborative efficiency is as follows: A simulation model for cross-city resource allocation is constructed using a set of phased coordinated regulation strategies as constraints. The simulation model simulates the allocation process of different resource allocation schemes in a spatially interconnected network. Statistical analysis was performed on the network structure stability parameters and cooperative efficiency parameters corresponding to each simulation result; Based on the statistical results, the range of cross-city resource allocation schemes that meet the preset constraints is determined, and the feasible range of cross-city resource allocation is output.