Urban space structure collaboration measurement method and system and storage medium

Abstract

The invention relates to the technical field of data processing, and discloses an urban space structure collaboration measurement method and system, and a storage medium. The method comprises the following steps: performing gridding processing on urban space elements through multi-temporal data acquisition to obtain a three-dimensional data matrix; calculating a space-time dynamic coupling coefficient by adopting an improved gravity model according to the three-dimensional data matrix; constructing a space-time bidirectional weighting network through a bidirectional weighting algorithm; a dynamic collaboration degree index DSCI is calculated based on network self-adaption; and determining a collaboration degree threshold value through multi-level nesting identification, and obtaining a measurement report. According to the method, through constructing the space-time bidirectional weighted network and the dynamic coordination degree index DSCI adaptive calculation algorithm, the space-time dynamic coupling relation quantification precision of the urban space structure coordination measurement is improved. Meanwhile, a self-adaptive collaboration degree threshold combination is established through multi-level nesting recognition, and the dynamic adaptability of the collaboration evaluation standard to the urban development stage and policy environment change is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, system and storage medium for measuring the synergy of urban spatial structure. Background Art

[0002] Existing technologies primarily rely on traditional static analysis methods to assess urban spatial structure coordination, such as gravity models and spatial autocorrelation analysis, using single models to evaluate urban spatial elements. These methods typically use single-temporal data for analysis, evaluating the degree of coordination of urban spatial structures by calculating distance decay and correlation between spatial elements, and quantifying coordination based on fixed statistical indicators.

[0003] However, existing technologies have obvious shortcomings: first, traditional gravity models ignore the dynamic changes in the time dimension and cannot capture the evolution laws and interaction relationships of urban spatial elements in time series; second, existing methods lack effective characterization of the complex coupling relationships between multidimensional spatial elements, making it difficult to accurately reflect the dynamic characteristics of spatial element flows; third, the synergy measurement indicator system is not perfect and lacks adaptability and multi-level nested recognition capabilities.

[0004] The problem with existing technologies is that the static analysis framework cannot adapt to the dynamic evolution characteristics of urban spatial structures. It is a problem of effective organization and processing of multi-temporal data, which requires the construction of a three-dimensional data structure that can simultaneously characterize spatial, temporal and attribute dimensions; secondly, the problem of accurate calculation of dynamic coupling relationships, which requires the introduction of time decay and mobility coefficients on the basis of traditional gravity models to improve the model structure; thirdly, the problem of constructing complex network relationships, which requires the conversion of spatiotemporal dynamic coupling coefficients into network topology structures through a bidirectional weighted algorithm; finally, the problem of adaptive synergy evaluation, which requires the establishment of a dynamic synergy index that can be adaptively adjusted according to network characteristics, and the adaptive determination of synergy thresholds through multi-level nested recognition. Summary of the Invention

[0005] This application provides a method, system, and storage medium for measuring the synergy of urban spatial structures. By constructing a spatiotemporal bidirectional weighted network and an adaptive calculation algorithm for the Dynamic Synergy Index (DSCI), this method improves the quantification accuracy of the spatiotemporal dynamic coupling relationship in measuring the synergy of urban spatial structures. Furthermore, through multi-level nested recognition and the establishment of adaptive synergy threshold combinations, the synergy evaluation criteria are enhanced in their ability to dynamically adapt to changes in urban development stages and policy environments.

[0006] In the first aspect, the present application provides a method for measuring the synergy of urban spatial structure, which includes: gridding urban spatial elements through multi-temporal data collection to obtain a three-dimensional data matrix; coupling calculation of spatial element flow vectors using an improved gravity model based on the three-dimensional data matrix to obtain a spatiotemporal dynamic coupling coefficient; network construction processing of the spatiotemporal dynamic coupling coefficient through a bidirectional weighted algorithm to obtain a spatiotemporal bidirectional weighted network; adaptive calculation of the dynamic synergy index DSCI based on the spatiotemporal bidirectional weighted network to obtain a synergy quantification result; adaptive determination of the synergy threshold through multi-level nested recognition based on the synergy quantification result to obtain an urban spatial structure synergy measurement report.

[0007] In a second aspect, the present application provides a system for measuring the synergy of urban spatial structure, the system comprising: A processing module is used to perform grid processing on urban spatial elements through multi-temporal data collection to obtain a three-dimensional data matrix; A coupling module, configured to perform coupling calculation on the spatial element flow vectors using an improved gravity model according to the three-dimensional data matrix to obtain a spatiotemporal dynamic coupling coefficient; A construction module is used to perform network construction processing on the spatiotemporal dynamic coupling coefficient through a bidirectional weighted algorithm to obtain a spatiotemporal bidirectional weighted network; A calculation module, configured to perform adaptive calculation of the dynamic synergy index DSCI based on the spatiotemporal bidirectional weighted network to obtain a synergy quantitative result; The identification module is used to adaptively determine the synergy threshold through multi-level nested identification according to the synergy quantification result to obtain an urban spatial structure synergy measurement report.

[0008] In a third aspect, a device for measuring the synergy of urban spatial structure is provided, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the device for measuring the synergy of urban spatial structure executes the above-mentioned method for measuring the synergy of urban spatial structure.

[0009] In a fourth aspect, a computer-readable storage medium is provided, wherein instructions are stored in the computer-readable storage medium, which, when executed on a computer, enables the computer to execute the above-mentioned method for measuring the synergy of urban spatial structure.

[0010] The technical solution provided in this application uses multi-temporal data collection to grid urban spatial elements to obtain a three-dimensional data matrix, effectively solving the problem that traditional static data analysis cannot capture the dynamic changes in urban spatial structure. The three-dimensional data matrix simultaneously contains spatial coordinates, element types, and time dimensions, laying the data foundation for subsequent dynamic analysis. By adding a time decay term and a mobility coefficient, the improved gravity model accurately depicts the flow characteristics and time delay effects of different spatial elements on the basis of the traditional gravity model. This makes the coupled calculation of spatial element flow vectors more consistent with the actual laws of urban spatial element flow, and significantly improves the calculation accuracy of the spatiotemporal dynamic coupling coefficient. The bidirectional weighted algorithm comprehensively considers spatial adjacency and temporal correlation strength to construct a spatiotemporal bidirectional weighted network that can simultaneously reflect the proximity effect of the spatial dimension and the correlation effect of the temporal dimension. The network structure more realistically reflects the complex correlations between urban spatial elements. The adaptive calculation of the dynamic synergy index (DSCI) is based on the comprehensive weighting of network centrality, element synergy importance, and spatial position correction. It not only considers the importance of the grid in the network, but also takes into account the differences in the contribution of different elements to synergy and the influence of spatial location. The synergy quantification results are more scientific and accurate. Multi-level nested identification uses a synergy threshold system at the macro, meso and micro levels, combined with a dynamic correction mechanism of economic growth rate and policy intensity index, so that the synergy threshold can be adaptively adjusted according to the city's development stage and changes in the policy environment, avoiding the applicability problem of fixed thresholds in different periods, and significantly enhancing the practicality and guidance of the measurement report.

[0011] The improved time decay term and mobility coefficient design of the gravity model enable the algorithm to accurately distinguish the flow characteristics of different factors such as population, economy, and land use, solving the limitation of traditional gravity models that cannot handle factor heterogeneity; the linear weighted balance parameter of the bidirectional weighted algorithm controls the relative importance of the two dimensions of time and space, allowing the network construction process to flexibly adjust the time and space weight ratio according to specific application scenarios; the adaptive calculation algorithm of the DSCI index introduces a comprehensive evaluation of network centrality, so that the synergy measurement no longer relies on subjective weight allocation, but automatically adjusts the weight based on the objective characteristics of the network topology structure; the three-level threshold system design of the multi-level nested recognition algorithm ensures the consistency of multi-scale synergy evaluation from the city as a whole to functional areas and then to grid units, and the introduction of a dynamic correction mechanism gives the algorithm the ability to adapt to time variations. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 This is a schematic diagram of an embodiment of a method for measuring the synergy of urban spatial structure in an embodiment of the present application; Figure 2 This is a schematic diagram of an embodiment of a system for measuring the synergy of urban spatial structure in an embodiment of the present application; Figure 3 It is a schematic block diagram of the structure of a device for measuring the coordination of urban spatial structure in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The embodiments of the present application provide a method, system and storage medium for measuring the synergy of urban spatial structure. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices.

[0015] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of the method for measuring the synergy of urban spatial structure includes: Step S101: gridding urban spatial elements through multi-temporal data collection to obtain a three-dimensional data matrix; Step S102: performing coupling calculation on the spatial element flow vectors using an improved gravity model according to the three-dimensional data matrix to obtain a spatiotemporal dynamic coupling coefficient; Step S103: The spatiotemporal dynamic coupling coefficient is subjected to network construction processing by a bidirectional weighted algorithm to obtain a spatiotemporal bidirectional weighted network; Step S104: Adaptively calculate the dynamic synergy index DSCI based on the spatiotemporal bidirectional weighted network to obtain a synergy quantification result; Step S105: Adaptively determine the synergy threshold through multi-level nested identification based on the synergy quantification result to obtain an urban spatial structure synergy measurement report.

[0016] It is understandable that the execution subject of this application can be a measurement system of urban spatial structure coordination, or a terminal or a server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0017] Specifically, remote sensing imagery and point of interest (POI) data were collected to obtain time-series data on population density, economic density, and land use intensity. A standardization algorithm was used to eliminate dimensional differences between different indicators. The urban area was then divided into regular grid cells according to preset grid specifications, with each grid cell serving as the basic spatial analysis unit. The spatiotemporal kriging interpolation algorithm completes missing data in regular grid cells. This interpolation method calculates the influence of surrounding known data points on missing points by assigning weights based on spatial distance and time interval, with closer and more recent data points receiving greater weights. The complete spatiotemporal dataset was organized into a three-dimensional matrix structure, forming a three-dimensional data matrix containing spatial coordinates, feature types, and time dimensions.

[0018] Based on the spatial coordinate information in the three-dimensional data matrix, the Euclidean distance between grids is calculated, and a spatial distance matrix is obtained through square root operations. The improved gravity model adds a time decay term and a mobility coefficient to the traditional gravity model. The mobility coefficient reflects the differences in flow characteristics of different spatial elements, and the time decay term considers the impact of time interval on element flow. The element density values in the three-dimensional data matrix are input into the improved gravity model. The product of the element masses between two grids is first calculated as the basis for attraction. The distance decay factor is then exponentially raised using the distance decay parameter. The time decay factor is also calculated based on the preset time interval and time decay parameter. The element flow intensity is calculated by dividing the product of the element attraction by the product of the distance decay factor and the time decay factor. Subsequently, vector decomposition is performed along the eight-neighborhood direction to form a spatial element flow vector containing both direction and intensity information. Based on the spatial element flow vector, the quotient of the vector dot product divided by the vector modulus product is used to quantify the synergistic relationship between elements, resulting in the initial coupling coefficient. This is then indexed with a preset time lag parameter for time offset correction. Correlation statistical analysis using a three-temporal sliding window is used to obtain the spatiotemporal dynamic coupling coefficient.

[0019] Using the spatiotemporal dynamic coupling coefficient as the baseline data, the spatial adjacency relationships between network nodes are weighted by an exponential function. The spatial adjacency weight is calculated by the negative exponential operation of the natural logarithm, where the exponential term consists of the ratio of spatial distance to a decay parameter. The temporal correlation weight is calculated based on the correlation between the spatiotemporal dynamic coupling coefficients at different time phases. An exponential decay operation is used to adjust the temporal correlation strength, with higher weights assigned to phases with higher correlations. The spatial adjacency weight and the temporal correlation weight are weighted and summed using a linear weighted balance parameter to form a comprehensive weight matrix. This balance parameter controls the relative importance of the spatial and temporal dimensions. The comprehensive weight matrix is sparsified using a preset connection threshold. Connections below the threshold are removed to reduce computational complexity and highlight key connections. Once the network topology is determined, topological characteristic parameters such as the node degree distribution and clustering coefficient are calculated. The node degree reflects the number of connections per node, and the clustering coefficient measures the density of connections between node neighbors. A spatiotemporal bidirectional weighted network is constructed.

[0020] Adaptive weights are calculated based on node centrality in a spatiotemporal bidirectional weighted network. Grid adaptive weights are obtained by taking the arithmetic mean of betweenness centrality, closeness centrality, and eigenvector centrality. Betweenness centrality reflects the intermediary role of a node in the network, closeness centrality measures the average distance between a node and other nodes, and eigenvector centrality considers the importance of a node's neighbors. Principal component analysis calculates the contribution of spatial element combinations to the synergistic importance. Eigenvalue decomposition is used to determine the variance contribution of each principal component, resulting in the element synergistic importance coefficient. A spatial location correction factor is calculated as an exponential function of the distance to the city center, with grids closer to the city center receiving higher correction factors. The raw value of the Dynamic Synergy Index (DSCI) is obtained by the weighted summation of the grid adaptive weights, element synergistic importance coefficients, and the spatial location correction factor. This index comprehensively reflects the combined influence of network structure, element importance, and spatial location. Normalization mapping converts the raw DSCI value to a standard range of zero to one. Minimum and maximum value scaling is used to normalize the range, resulting in a quantitative synergy result.

[0021] Macro-level thresholds are calculated based on the time series of synergy quantification results. The overall city synergy threshold is determined by adding the mean to an adjustment coefficient times the standard deviation. The adjustment coefficient controls the strictness of the threshold. An improved clustering algorithm is used to classify the synergy quantification results into three categories: high, medium, and low. This algorithm iteratively optimizes cluster center locations, categorizing synergy values into high, medium, and low levels. A specific multiple of the inter-cluster distance is used as the meso-level synergy threshold. Local spatial autocorrelation analysis calculates local statistics for each grid cell, and a confidence level statistical test determines the significance threshold as the micro-level synergy threshold. This analysis identifies the significance of spatial clustering and dispersion patterns. An adaptive synergy threshold combination dynamically adjusts the three-level thresholds using economic growth rate and policy intensity index. The correction formula considers the influence of the city's development stage and policy orientation. Hierarchical color coding maps synergy values to different color levels based on the adaptive synergy threshold combination. Trend forecasting predicts future synergy trends based on time series analysis of historical data. Spatial distribution maps and evolution curves are visualized to produce intuitive measurement reports.

[0022] In a specific embodiment, the process of executing step S101 may specifically include the following steps: Through remote sensing images and POI data collection, the time series data of population density, economic density and land use intensity are acquired to obtain the original spatial element dataset; Based on the standardization algorithm, the original spatial feature dataset is dimensionally eliminated to obtain a standardized dataset; Divide the standardized data set into spatial grids according to the preset grid specifications to obtain regular grid cells; The missing data in the regular grid cells are supplemented using the spatiotemporal Kriging interpolation algorithm to obtain a spatiotemporal dataset; Based on the spatiotemporal data set, data is organized according to a three-dimensional matrix structure to obtain a three-dimensional data matrix.

[0023] Specifically, spectral imagery data of urban areas is acquired via satellite or aerial platforms. Remote sensing images analyze pixel spectral values to identify different land use types. Residential, commercial, and industrial land use types have distinct spectral characteristics. A supervised classification algorithm is used to classify pixels into specific land use types, and the area percentage of each type is calculated as an indicator of land use intensity. Point of Interest (POI) data, or point of interest (POI) data, contains geographic coordinates and attribute information. The density of different POI types within a unit area reflects the intensity of economic activity. The density of commercial POIs, such as those for restaurants, shopping, and finance, directly reflects economic density, while the density of life-service POIs, such as residential communities, schools, and hospitals, indirectly reflects population concentration. Population density data is estimated through a combination of building density, building height, and residential POIs. Building density is calculated by extracting building outlines from remote sensing images, while building height is obtained by taking the elevation difference between the digital surface model and the digital terrain model. Time series data is acquired by regularly collecting remote sensing images and POI data for the same area at different points in time, forming a dynamic data series with a time dimension. The original spatial feature dataset contains population density, economic density, and land use intensity values for each grid cell at different points in time. The standardization algorithm uses the Z-score standardization method to eliminate the dimension of the original spatial feature dataset. This method eliminates the dimensional differences between different indicators by calculating the value of each indicator minus the mean and then dividing it by the standard deviation. In the specific calculation process, the global mean and standard deviation of the three indicators of population density, economic density and land use intensity are calculated for all grids and time points. Then, each value is standardized. The mean of the transformed value is zero and the standard deviation is one. Different indicators have the same numerical range and distribution characteristics. The three indicator values of each grid cell in the standardized dataset are converted into dimensionless standard scores, eliminating the dimensional differences in the original data, such as population density in people per square kilometer, economic density in ten thousand yuan per square kilometer, and land use intensity in percentage.

[0024] Spatial gridding discretizes standardized datasets according to a preset grid size, typically a 500-by-500-meter square grid. This scale maintains sufficient spatial resolution while balancing computational efficiency. The gridding process divides the urban area into a regularly arranged grid matrix using a geographic coordinate system. Each grid cell has a unique row and column index. Spatial aggregation operations are used to calculate the representative value of all spatial feature data within the grid. Aggregation operations include area-weighted averaging, quantity accumulation, and density calculations. Population density is calculated by dividing the total population within a grid by the grid area, economic density is calculated by dividing the total economic output within a grid by the grid area, and land use intensity is calculated by the weighted average of the area proportions of each land use type within the grid. Once regular grid cells are formed, the previously irregularly distributed spatial feature data is transformed into a regular gridded data structure.

[0025] The spatiotemporal kriging interpolation algorithm completes missing data in regularly spaced grid cells. Based on the principle of spatial autocorrelation, the algorithm assumes that data values at adjacent locations are more similar, while also accounting for correlation in the temporal dimension. The interpolation process identifies known data points surrounding the missing grid cells and calculates weight coefficients based on their spatial distance and time interval. Data points closer in distance and time are given greater weights. Weight calculations use an exponential decay function, with spatial weights decreasing exponentially with distance and temporal weights decreasing exponentially with time interval. The weight is the product of the spatial and temporal weights. Missing values are calculated using a weighted average: the sum of the products of the values of all known data points and their corresponding weights divided by the total weights. Spatiotemporal datasets contain complete data for all grid cells at every point in time, eliminating gaps in the original data caused by cloud cover, sensor failures, or missing data.

[0026] The three-dimensional matrix structure data organization converts spatiotemporal datasets into a matrix format that is easy to compute and process. The three dimensions of the matrix correspond to spatial row coordinates, spatial column coordinates, and time series, respectively. Specifically, the first dimension of the matrix represents the grid row index, the second dimension represents the grid column index, and the third dimension represents the time point sequence. Each matrix element stores the spatial element value of the corresponding grid at a specific point in time. The three-dimensional data matrix actually contains three sub-matrices, storing population density, economic density, and land use intensity data respectively. Each sub-matrix has the same dimensional structure but stores different types of spatial element values. The matrix indexing method directly accesses the data value of any grid at any point in time through the triplet coordinates of row, column, and time, supporting data query and computation operations.

[0027] In a specific embodiment, the process of executing step S102 may specifically include the following steps: Based on the spatial coordinate information in the three-dimensional data matrix, the Euclidean distance between grids is measured one by one to obtain a spatial distance matrix; The three-dimensional data matrix and the spatial distance matrix are input into the improved gravity model to perform the product and division operations of the element flow intensity to obtain the spatial element flow vector; According to the spatial element flow vector, the synergy relationship between elements is quantified by dividing the vector dot product by the vector modulus product to obtain the initial coupling coefficient. Performing time offset index correction processing on the initial coupling coefficient based on a preset time lag parameter to obtain a corrected coupling coefficient; The modified coupling coefficient is subjected to correlation statistical analysis through a three-phase sliding window to obtain the spatiotemporal dynamic coupling coefficient.

[0028] Specifically, the Euclidean distance is measured and processed one by one based on the spatial coordinate information of each grid cell in the three-dimensional data matrix, and the spatial distance matrix is obtained by calculating the straight-line distance between the center points of any two grid cells. In the specific calculation process, each grid cell has row coordinates and column coordinates. The row coordinates are multiplied by the grid size to obtain the longitudinal geographic coordinates, and the column coordinates are multiplied by the grid size to obtain the transverse geographic coordinates. The Euclidean distance between the two grids is calculated by taking the square root of the square of the difference between the transverse coordinates plus the square of the difference between the longitudinal coordinates. The spatial distance matrix is a symmetric matrix. The rows and columns of the matrix correspond to different grid cells respectively. The matrix element values represent the spatial distance between the corresponding grids. The diagonal elements are zero, which represents the distance between the grids themselves. The non-diagonal elements are positive values, which represent the actual distance between different grids.

[0029] The improved gravity model's factor flow intensity multiplication and division operations use the factor density values in the three-dimensional data matrix as quality parameters. A time decay term, added to the traditional gravity model, is calculated by multiplying the difference between the current time and the baseline time by the time decay parameter. The mobility coefficient is predefined based on the flow characteristics of different spatial elements. The factor attraction product is calculated by multiplying the factor density of the starting grid by the factor density of the target grid. This product reflects the basic strength of the mutual attraction between the two grids. The distance decay factor is calculated using the negative exponential form of the natural logarithm. The exponential term is the spatial distance divided by the distance decay parameter. The greater the distance, the smaller the decay factor, indicating the impact of distance on factor flow. The time decay factor is also calculated using an exponential form. The longer the time interval, the smaller the decay factor, indicating the impact of time on the continuity of factor flow. The factor flow intensity is calculated by dividing the factor attraction product by the quotient of the product of the distance decay factor and the time decay factor. The flow intensity is then decomposed into eight directional vector components based on the eight neighborhood directions. Each directional component is proportionally distributed based on the relative position and flow intensity of the neighboring grid in that direction, forming a spatial factor flow vector containing both directional and intensity information.

[0030] The quotient of the vector dot product divided by the vector modulus product quantifies the synergistic relationship between elements. This calculation method measures the cosine of the angle between two vectors based on the principles of vector geometry. Spatial element flow vectors contain flow vectors of multiple element types. The synergistic relationship between different element types is calculated by the dot product of the corresponding flow vectors. The dot product value is equal to the sum of the products of the corresponding components of the two vectors. The vector modulus is calculated as the square root of the sum of the squares of the vector components and represents the length or strength of the vector. The product of the two vector moduli is used as the denominator, and the dot product value is used as the numerator. The quotient ranges from negative one to positive one. Positive values indicate synergistic relationships between elements, while negative values indicate competitive relationships between elements. The absolute value indicates the strength of synergy or competition. The initial coupling coefficient is calculated by traversing all combinations of element types to form a coefficient matrix reflecting the strength of the synergistic relationship between different elements.

[0031] The time-shift index correction process adjusts the initial coupling coefficients in the temporal dimension based on a preset lag parameter. The lag parameter reflects the time-delayed nature of the response relationships between different spatial elements. The correction process accesses the coupling coefficient values at different time points by changing the time index. Specifically, the lag parameter is subtracted from the current time point to obtain the corrected time index. The initial coupling coefficient at the corresponding time point is then accessed through the corrected time index as the corrected coupling coefficient. The lag parameter is set based on the actual response characteristics of the feature type combination. The response lag of economic elements to demographic elements is typically short, while the response lag of demographic elements to land use elements is relatively long. The corrected coupling coefficient matrix reflects the lag effect of the response relationships between elements in the temporal dimension, more accurately reflecting the dynamic synergistic relationships between elements.

[0032] The three-phase sliding window correlation statistical analysis calculates the dynamic correlation of the modified coupling coefficient in the time series. The sliding window length is set to three time points, and the window is gradually moved across the time series to cover different time periods. The correlation calculation uses the Pearson correlation coefficient method. The covariance and standard deviation are calculated using the modified coupling coefficients of the three time points within the window. The correlation coefficient is obtained by dividing the covariance by the product of two standard deviations. During the sliding window analysis, the window starts at the beginning of the time series and moves forward one time point at a time until the end of the series. A correlation coefficient value is calculated for each window position. The spatiotemporal dynamic coupling coefficient is calculated by taking a weighted average of the correlation coefficients of all window positions. The weight is set according to the distance from the current time point. The closer the window is to the current time point, the greater the weight. The resulting coupling coefficient comprehensively reflects the spatiotemporal dynamic synergy between elements.

[0033] In a specific embodiment, the process of executing step S103 may specifically include the following steps: Based on the traditional gravity model, the time decay term and mobility coefficient are added to improve the model structure and obtain the improved gravity model. Input the element density values in the three-dimensional data matrix into the improved gravity model to perform element mass attraction product operation to obtain the element attraction product; Based on the spatial distance matrix and the distance decay parameter, the distance decay term is subjected to exponential power operation to obtain the distance decay factor; Performing incremental accumulation operations on the time decay term according to the preset time interval and the time decay parameter to obtain a time decay factor; The product of the element attraction is divided by the product of the distance attenuation factor and the time attenuation factor, and the quotient is calculated and vector decomposition is performed according to the eight neighborhood directions to obtain the spatial element flow vector.

[0034] Specifically, the traditional gravity model was improved by adding two new parameters, a time decay term and a mobility coefficient, to the original model. The time decay term quantifies the weakening effect of time intervals on the flow intensity of elements, while the mobility coefficient distinguishes the differences in the inherent flow characteristics of different spatial element types. The structural adjustments to the improved gravity model include adding a product operation for the time decay term in the denominator and a product operation for the mobility coefficient in the numerator. The time decay term uses an exponential function to express the negative impact of time on flow intensity, while the mobility coefficient uses a linear coefficient to express the differences in the mobility capacity of different elements. During the model improvement process, the mobility coefficient of the population element was set to the highest value to reflect the high flexibility of population mobility, the mobility coefficient of the economic element was set to a medium value to reflect the relative restrictions on capital flow, and the mobility coefficient of the land use element was set to the lowest value to reflect the rigid constraints on land change. Through parameter adjustments, the improved gravity model more accurately simulates the flow patterns of urban spatial elements in both time and space. The element quality attraction product operation uses the element density values in the three-dimensional data matrix as quality parameters to input into the improved gravity model. The element density values of the starting grid and the target grid are multiplied to obtain the basic attraction value. During the product calculation, grids with higher population density and higher economic density have larger product values, reflecting strong mutual attraction potential between the two grids. However, grids with lower population density and lower economic density have smaller product values, indicating a lack of effective interaction between the two grids. The factor attraction product calculation requires traversing all grid pairs, calculating the product value for each grid pair at each time point, to form a complete attraction dataset across time and space. The mobility coefficient serves as a magnification or reduction factor in the product calculation. By multiplying the product result with the product, it adjusts the attraction strength of different factor types, resulting in a factor attraction product that accounts for differences in factor characteristics.

[0035] The exponential power operation of the distance attenuation term is based on the distance values in the spatial distance matrix and the preset distance attenuation parameter. The negative exponential operation of the natural logarithm base is used to calculate the obstruction effect of distance on the flow of elements. In the exponential power operation, the distance value is divided by the distance attenuation parameter to obtain the exponential term. The distance attenuation parameter controls the strength of the distance effect. The smaller the parameter value, the stronger the distance effect, and the larger the parameter value, the weaker the distance effect. The result of the negative exponential operation of the natural logarithm base ranges from zero to one. When the distance is zero, the attenuation factor is one, indicating no attenuation. When the distance is infinite, the attenuation factor approaches zero, indicating complete attenuation. The distance attenuation factor calculation requires an operation for each grid pair. The attenuation factor value is inversely proportional to the distance between the grids. The attenuation factor of adjacent grids is close to one, while the attenuation factor of distant grids is close to zero, forming a distance attenuation factor matrix that reflects the spatial resistance effect.

[0036] The time decay term's incremental accumulation operation calculates the effect of time on the flow continuity of a feature based on a preset time interval and a time decay parameter. The time interval represents the time difference between the current time point and a baseline time point, and the time decay parameter controls the rate at which the time effect decays. The incremental accumulation operation multiplies the time interval by the time decay parameter to produce an accumulated term. This accumulated term increases linearly with increasing time intervals, reflecting the gradual weakening effect of time on the flow intensity of a feature. The time decay factor is calculated using the negative exponential operation of the natural logarithm. The exponential term represents the accumulated term value, and the result also ranges from zero to one. A decay factor of one at the baseline time point indicates no time decay, while longer time intervals result in smaller decay factors, indicating a stronger time decay effect. The time decay factor is calculated once for each time point, forming a factor sequence that reflects the time decay pattern. The quotient operation and eight-neighborhood directional vector decomposition use the product of the feature attraction as the numerator and the product of the distance decay factor and the time decay factor as the denominator to obtain the feature flow intensity value through division. The quotient calculation results represent the net element flow intensity under the dual influence of distance resistance and time attenuation. Larger values indicate more active element flow, while smaller values indicate weaker element flow. Eight-neighborhood directional vector decomposition distributes flow intensity values according to the target grid's eight neighborhood directions: four orthogonal directions (up, down, left, and right) and four diagonal directions. The allocation ratio for each direction is calculated based on the element density and distance weight of the neighborhood grid in that direction. During the vector decomposition process, neighborhood directions with higher element density receive a greater proportion of flow intensity, while neighborhood directions with closer distances receive higher weight coefficients. This results in a spatial element flow vector consisting of eight directional components, each representing the element flow intensity in that direction.

[0037] In a specific embodiment, the process of executing step S104 may specifically include the following steps: Based on the spatiotemporal dynamic coupling coefficient, the spatial adjacency relationship between network nodes is weighted by exponential function to obtain the spatial adjacency weight; According to the correlation between the spatiotemporal dynamic coupling coefficients at different time phases, the time correlation strength is processed by exponential decay operation to obtain the time correlation weight; The spatial adjacency weight and the temporal correlation weight are weighted and summed using a linear weighted balance parameter to obtain a comprehensive weight matrix. Based on the preset connection threshold, the comprehensive weight matrix is subjected to sparse screening processing to obtain the network topology connection relationship; According to the network topology connection relationship, the network node degree distribution and clustering coefficient are statistically analyzed and processed to obtain a spatiotemporal bidirectional weighted network.

[0038] Specifically, the spatial adjacency exponential function weighting process quantifies the spatial proximity between network nodes based on the spatiotemporal dynamic coupling coefficient. The exponential function uses a negative exponential form based on the natural logarithm, and the exponential term consists of the ratio of spatial distance to a spatial attenuation parameter. During the weighting process, the spatiotemporal dynamic coupling coefficient serves as a basic weight multiplier, reflecting the strength of the synergistic relationship between node elements. A larger coupling coefficient indicates stronger internode synergy and a correspondingly larger spatial adjacency weight. The exponential function calculates the exponential term by dividing the internode distance by a preset spatial attenuation parameter. The spatial attenuation parameter controls the rate at which distance attenuates the weight: a smaller parameter indicates a stronger distance effect, while a larger parameter indicates a weaker distance effect. The negative exponential operation based on the natural logarithm ensures that the weight values range between zero and one. When the distance is zero, the weight approaches the maximum value of the coupling coefficient, and the weight decreases exponentially as the distance increases. The spatial adjacency weight matrix is a symmetric matrix, whose elements represent the strength of the spatial connection between corresponding node pairs. A diagonal element of zero indicates that the node itself has no connection weight. The exponential decay calculation for temporal correlation strength calculates the connection weight in the temporal dimension based on the correlation between the spatiotemporal dynamic coupling coefficients at different time phases. The Pearson correlation coefficient method is used to calculate the degree of linear correlation between the coupling coefficient sequences at different time points. The exponential decay calculation uses the time interval as the decay factor. The time interval is calculated as the absolute time difference between the two time phases. A larger time interval indicates a greater distance between the time phases and a smaller corresponding temporal correlation weight. The correlation coefficient, as the basic weight, reflects the synchronization of changes in the coupling coefficients between the time phases. A positive correlation indicates changes in the same direction, while a negative correlation indicates changes in opposite directions. A larger absolute value of the correlation coefficient indicates a stronger correlation. The exponential decay factor is calculated by the negative exponential operation of the natural logarithm. The exponential term is the quotient of the time interval divided by the time decay parameter, which controls the influence of temporal distance on the weight. The temporal correlation weight is calculated by multiplying the correlation coefficient by the exponential decay factor. This factor accounts for both the degree of correlation between the time phases and the decay effect of temporal distance, resulting in a weight value that reflects the strength of the temporal connection.

[0039] The linearly weighted balance parameter weighted summation operation combines spatial adjacency weights and temporal correlation weights in a preset ratio. The balance parameter controls the relative importance of the spatial and temporal dimensions in the overall weight. This weighted summation uses a linear combination method: the balance parameter multiplied by the spatial adjacency weight yields the spatial weight contribution, and the temporal weight contribution is derived by subtracting the balance parameter value multiplied by the temporal correlation weight. The two contributions are summed to yield the overall weight. The balance parameter ranges from zero to one. Values closer to one emphasize spatial connections, values closer to zero emphasize temporal connections, and a value of 0.5 emphasizes both spatial and temporal connections equally. The overall weight matrix contains the combined connection strengths for all node pairs. The matrix element values reflect the degree of connection between nodes in both spatial and temporal dimensions. Larger values indicate closer connections between nodes, while smaller values indicate looser connections.

[0040] The sparsification screening process binarizes the comprehensive weight matrix based on a preset connection threshold. The connection threshold is used as a criterion to distinguish valid connections from invalid connections. The screening process compares the size of each matrix element with the connection threshold. Elements greater than or equal to the threshold are retained as valid connections and assigned a value of one, while elements less than the threshold are deleted as invalid connections and assigned a value of zero. Threshold setting requires a balance between network connection density and computational efficiency. A threshold that is too low results in an overly dense network, increasing computational complexity, while a threshold that is too high results in an overly sparse network, which loses important connection information. The matrix after sparsification is converted into a binary matrix, where the matrix elements contain only two values: zero and one. One indicates a connection relationship between nodes, and zero indicates no connection relationship between nodes. The network topology connection relationship is determined by the position of the non-zero elements in the sparsification matrix. Each non-zero element corresponds to a network edge, connecting the corresponding two nodes to form the basic structural unit of the network.

[0041] Statistical analysis of node degree distribution and clustering coefficients calculates basic topological parameters of a network based on topological connectivity. Node degree represents the number of edges connecting each node and is calculated by counting the number of nonzero elements in the corresponding row or column of the topological matrix. Node degree distribution, by counting the number of nodes with different degrees, forms a frequency distribution of degree values, reflecting the degree of concentration and dispersion of network connectivity. A high number of high-degree nodes indicates relatively even network connectivity, while a high number of low-degree nodes indicates relatively concentrated network connectivity. The clustering coefficient measures the density of connections between node neighbors. The calculation process determines the set of neighboring nodes for each node, then counts the actual number of connections between neighboring nodes, and finally divides the value by the maximum number of connections between neighboring nodes to obtain the clustering coefficient. The global clustering coefficient is calculated by taking the arithmetic average of the clustering coefficients of all nodes and reflects the local density of connectivity across the entire network. A higher clustering coefficient indicates a tighter local network structure, while a lower clustering coefficient indicates a looser local network structure. A spatiotemporal bidirectional weighted network is constructed by integrating topological parameters such as node degree distribution and clustering coefficient. The network structure reflects the dual constraints of spatial adjacency and temporal correlation.

[0042] In a specific embodiment, the process of executing step S105 may specifically include the following steps: Based on the node centrality in the spatiotemporal bidirectional weighted network, the adaptive weight is processed by arithmetic mean operation to obtain the grid adaptive weight; The contribution of the synergistic importance of the spatial element combination is calculated based on principal component analysis to obtain the element synergistic importance coefficient; The distance from the city center is input into the exponential function to perform correction operation on the degree of influence of the spatial position, and the spatial position correction factor is obtained; Based on the grid adaptive weight, element synergy importance coefficient and spatial position correction factor, a weighted summation and accumulation operation is performed to obtain the original value of the dynamic synergy index DSCI; The original value of the dynamic synergy index (DSCI) was normalized by normalization mapping to obtain the quantitative result of synergy.

[0043] Specifically, the arithmetic mean calculation of node centrality calculates grid adaptive weights based on three different centrality metrics in a spatiotemporal bidirectional weighted network: betweenness centrality, closeness centrality, and eigenvector centrality. Betweenness centrality is calculated by calculating the ratio of the number of shortest paths passing through a node to the total number of shortest paths in the network, reflecting the strength of the node's intermediary role in the network. Nodes with high betweenness centrality play an important role as bridges in information transmission and factor flow. Closeness centrality is calculated by calculating the reciprocal mean of the shortest path lengths from a node to all other nodes in the network, reflecting the node's reachability within the network. Nodes with high closeness centrality have shorter average distances to other nodes and better connectivity. Eigenvector centrality is calculated by calculating the principal eigenvector of the network adjacency matrix. Each component of the eigenvector represents the importance score of the corresponding node. Nodes with high eigenvector centrality not only have a large number of connections themselves, but also have equally important neighboring nodes. The arithmetic mean calculation calculates a composite centrality score by adding the three centrality values and dividing by three. The grid adaptive weight directly uses the composite centrality score to reflect the overall importance of each grid in the entire network. The principal component analysis (PCA) synergistic importance contribution calculation process performs eigenvalue decomposition on the covariance matrix of the spatial element combination to extract the principal components that reflect the synergistic relationships between the elements. The covariance matrix is calculated by statistically analyzing the covariance relationships of population density, economic density, and land use intensity across all grids and time points. The matrix elements represent the strength of the linear association between different pairs of elements. Eigenvalue decomposition decomposes the covariance matrix into the product of eigenvalues and eigenvectors. The eigenvalues represent the variance explained by each principal component, and the eigenvectors represent the weight coefficient of each element within the principal component. The principal component contribution is calculated by dividing each eigenvalue by the sum of all eigenvalues. It reflects the proportion of the total variance explained by that principal component. A larger contribution indicates a more important principal component. The factor synergistic importance coefficient is calculated by multiplying the weight coefficient of each factor within the principal component by the corresponding principal component contribution, and then summing the results. It comprehensively reflects the importance of different factors to the overall synergy. A larger coefficient indicates a more important role for the factor in the synergistic relationship.

[0044] The spatial location influence correction calculation uses the distance to the city center as the input variable and feeds an exponential function to calculate the intensity of the spatial location's influence on synergy. The city center distance is calculated by calculating the Euclidean distance from each grid center point to the city's geometric center point. The exponential function uses a negative exponential form based on the natural logarithm. The exponential term contains the ratio of distance to the influence range parameter. The influence range parameter controls the rate of decay of the distance effect. A smaller parameter indicates a faster decay of the distance effect, while a larger parameter indicates a slower decay of the distance effect. During the correction calculation, the correction factor corresponding to a grid with a distance of zero is the maximum. As the distance increases, the correction factor decreases exponentially, approaching zero at infinite distance. The spatial location correction factor is calculated by adding one to the result of the exponential function to ensure that the correction factor is greater than one. Grids closer to the city center receive a larger correction factor, while grids farther from the city center receive a smaller correction factor, reflecting the positive impact of the city center's location advantage on spatial synergy.

[0045] The weighted summation operation combines grid adaptive weights, element synergy importance coefficients, and spatial location correction factors according to a specific calculation logic to obtain the raw value of the Dynamic Synergy Index (DSCI). The operation multiplies the grid adaptive weights with the element synergy importance coefficients. This product reflects the combined effect of the grid's importance in the network and the element synergy contribution. This product is then multiplied a second time with the spatial location correction factor to account for the influence of spatial location. The accumulation operation is obtained by summing these product results for all combinations of element types. Each element combination corresponds to a product value, and the sum of all product values constitutes the raw value of the Dynamic Synergy Index for the grid at a specific point in time. The raw value comprehensively reflects the strength of the combined effects of network importance, element synergy, and spatial location. Larger values indicate higher spatial structural synergy for the grid, while smaller values indicate lower synergy.

[0046] Normalization and Mapping: The normalization process converts the raw values of the Dynamic Synergy Index (DSCI) from an arbitrary range to a standard interval between zero and one. This mapping method utilizes a maximum-minimum scaling algorithm. The normalization process counts the raw DSCI values for all grids at all time points, finding the maximum and minimum values in the dataset through traversal calculations. The scaling algorithm calculates the normalized values by subtracting the minimum value from each raw value and dividing the result by the difference between the maximum and minimum values, ensuring that all normalized results fall between zero and one. A normalized value of zero corresponds to the minimum value, indicating the lowest level of synergy, while a normalized value of one corresponds to the maximum value, indicating the highest level of synergy. Other values are proportionally mapped to corresponding positions within the zero-to-one interval. The normalization process eliminates the dimensionality of the raw values, making the synergy levels at different time points and grids directly comparable, facilitating temporal and spatial comparative analysis and grading.

[0047] In a specific embodiment, the process of executing step S106 may specifically include the following steps: Based on the time series of the synergy quantification results, the macro-level threshold is weighted by the mean and standard deviation to obtain the overall synergy threshold of the city; The synergy quantification results were input into the improved clustering algorithm to classify the synergy level of functional areas into three categories, and the meso-level synergy threshold was obtained. The confidence level statistical test of the synergy quantification results is carried out based on the local spatial autocorrelation analysis to obtain the micro-level synergy threshold. Based on the economic growth rate and policy intensity index, dynamic correction calculations are performed on the city's overall synergy threshold, the meso-level synergy threshold, and the micro-level synergy threshold to obtain an adaptive synergy threshold combination. The adaptive synergy threshold combination is used to visualize the spatial distribution map and evolution curve through hierarchical color coding and trend prediction to obtain a city spatial structure synergy measurement report.

[0048] Specifically, a weighted calculation of the mean and standard deviation of macro-level thresholds is performed based on the statistical characteristics of the time series of synergy quantification results. The time series contains the synergy quantification values of all grid cells in the city at consecutive time points. The mean is calculated by adding all values in the time series and dividing it by the total number of data points, reflecting the average level of synergy quantification results. The standard deviation is calculated by taking the square root of the sum of the squared differences between each value and the mean, divided by the number of data points, and reflecting the dispersion of synergy values. The weighted calculation uses the mean plus an adjustment coefficient multiplied by the standard deviation to calculate the overall synergy threshold of the city. The adjustment coefficient controls the strictness and coverage of the threshold. A larger coefficient increases the threshold and covers fewer grid cells, while a smaller coefficient decreases the threshold and covers more grid cells. The overall synergy threshold of the city serves as a macro-level criterion. Grid cells above this threshold are classified as high-synergy areas, while grid cells below this threshold are classified as low-synergy areas. The threshold directly affects the identification range and number distribution of high-synergy areas. The improved clustering algorithm uses the synergy quantification results as input to automatically classify the synergy levels of functional areas. Based on the traditional K-means algorithm, the improved clustering algorithm adds optimization of initial cluster centers and convergence criterion adjustment. The algorithm determines the locations of three initial cluster centers by statistically analyzing the quantiles of the synergy values, corresponding to representative values of high, medium, and low synergy levels, avoiding local optima caused by random initialization. During the iterative calculation process, the distance between each grid's synergy value and the three cluster centers is calculated using the Euclidean distance formula, and the grid is assigned to the category of the cluster center with the closest distance. Cluster centers are updated by calculating the arithmetic mean of the synergy values of all grids within each category. This iterative process continues until the change in cluster center position is less than a preset convergence threshold. In the three-category classification results, high synergy levels correspond to clusters with the highest synergy values, medium synergy levels correspond to clusters with intermediate values, and low synergy levels correspond to clusters with the lowest values. The inter-cluster boundary value serves as the meso-level synergy threshold, dividing the continuous synergy values into discrete categories.

[0049] The confidence level statistical test for local spatial autocorrelation analysis identifies the significance of spatial clustering patterns by calculating the local Moran's I statistic for each grid. The local Moran's I statistic measures the degree of spatial correlation between the synergy values of a single grid and its neighboring grids. This statistic is calculated by multiplying the standardized product of the target grid's synergy value with the neighboring grid's synergy value. A positive product indicates a clustering of high or low values, while a negative product indicates a mixed distribution of high and low values. A spatial weight matrix defines neighborhood relationships, typically using an adjacency or distance criterion. An adjacency weight of one indicates proximity, while a weight of zero indicates non-adjacency. The confidence level statistical test uses Monte Carlo simulation to generate a random distribution of Moran's I statistics. The calculated statistic is then compared with the random distribution to determine the significance level. The 95th percentile threshold for micro-level synergy is used. Grids with an absolute value of the statistic greater than the critical value indicate a significant spatial clustering pattern, while grids with an absolute value less than the critical value indicate an insignificant clustering pattern. The threshold determines the strictness of significance judgment.

[0050] A dynamic correction algorithm adjusts the synergy thresholds at the three levels over time based on the economic growth rate and the policy intensity index. The economic growth rate is calculated as the annual percentage growth of the urban area's gross domestic product, while the policy intensity index is calculated by weighting the number and importance of relevant policy documents related to urban spatial planning. The correction algorithm utilizes a multiplicative adjustment model, multiplying the baseline threshold by a correction factor consisting of the logarithm of the economic growth rate and the policy intensity index to produce the adjusted threshold. Higher economic growth rates and larger correction factors indicate higher synergy requirements, while greater policy intensity indicates higher synergy standards driven by policy. The adaptive synergy threshold combination includes dynamically corrected threshold values at the macro, meso, and micro levels. This threshold combination automatically adjusts as economic development levels and the policy environment change, avoiding the applicability of fixed thresholds at different stages of development. This corrected threshold combination maintains the scientific nature of its statistical basis while reflecting the dynamic nature of economic and social development. The relative magnitude of the three thresholds remains logically consistent.

[0051] Hierarchical color coding and trend prediction visualization processes transform adaptive synergy threshold combinations into intuitive graphical reports. Hierarchical color coding maps synergy values to different color levels based on the threshold combination. The color coding uses a five-level classification scheme: areas above the macrothreshold are colored dark green to indicate extremely high synergy; areas between the mesothreshold and the macrothreshold are colored light green to indicate high synergy; areas between the microthreshold and the mesothreshold are colored yellow to indicate moderate synergy; and areas below the microthreshold are colored orange and red to indicate low and extremely low synergy, respectively. A spatial distribution map is generated using a geographic information system platform, with each grid rendered according to the color coding results, creating a thematic map reflecting the distribution characteristics of urban spatial structure synergy. Trend prediction, based on time series analysis of historical synergy data, uses moving average and trend extrapolation methods to predict synergy trends at future points. The prediction results are presented as a graph showing the temporal evolution of synergy. The urban spatial structure synergy measurement report integrates distribution maps, evolution curves, statistical tables, and textual analysis to form a comprehensive report document reflecting the current status, changing trends, and spatial pattern of urban synergy.

[0052] The above describes the measurement method of the urban spatial structure synergy in the embodiment of the present application. The following describes the measurement system of the urban spatial structure synergy in the embodiment of the present application. Figure 2 In one embodiment of the present application, a system for measuring the synergy of urban spatial structure includes: A processing module is used to perform grid processing on urban spatial elements through multi-temporal data collection to obtain a three-dimensional data matrix; A coupling module, configured to perform coupling calculation on the spatial element flow vectors using an improved gravity model according to the three-dimensional data matrix to obtain a spatiotemporal dynamic coupling coefficient; A construction module is used to perform network construction processing on the spatiotemporal dynamic coupling coefficient through a bidirectional weighted algorithm to obtain a spatiotemporal bidirectional weighted network; A calculation module, configured to perform adaptive calculation of the dynamic synergy index DSCI based on the spatiotemporal bidirectional weighted network to obtain a synergy quantitative result; The identification module is used to adaptively determine the synergy threshold through multi-level nested identification according to the synergy quantification result to obtain an urban spatial structure synergy measurement report.

[0053] above Figure 2 The system for measuring the synergy of urban spatial structure in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The device for measuring the synergy of urban spatial structure in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0054] Reference Figure 3In an embodiment of the present invention, a device for measuring the synergy of urban spatial structure is also provided. The device for measuring the synergy of urban spatial structure can be a server, and its internal structure can be as follows: Figure 3 As shown. The device for measuring the synergy of urban spatial structure includes a processor, a memory, a display screen, an input device, a network interface and a database connected via a system bus. The computer-designed processor is used to provide computing and control capabilities. The memory of the device for measuring the synergy of urban spatial structure includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the device for measuring the synergy of urban spatial structure is used to store the corresponding data in this embodiment. The network interface of the device for measuring the synergy of urban spatial structure is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the above method is implemented.

[0055] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the device for measuring the synergy of urban spatial structures to which the solution of the present invention is applied.

[0056] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the steps of the method for measuring the synergy of urban spatial structure.

[0057] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0058] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a device for measuring the synergy of urban spatial structure (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0059] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for measuring the synergy of urban spatial structure, characterized by: The method comprises: Through multi-temporal data collection, urban spatial elements are gridded to obtain a three-dimensional data matrix; According to the three-dimensional data matrix, an improved gravity model is used to couple the spatial element flow vectors to obtain a spatiotemporal dynamic coupling coefficient; The spatiotemporal dynamic coupling coefficient is subjected to network construction processing by a bidirectional weighted algorithm to obtain a spatiotemporal bidirectional weighted network; Adaptively calculating the dynamic synergy index DSCI based on the spatiotemporal bidirectional weighted network to obtain a synergy quantitative result; According to the synergy quantification result, the synergy threshold is adaptively determined through multi-level nested identification to obtain an urban spatial structure synergy measurement report.

2. The method for measuring the synergy of urban spatial structure according to claim 1 is characterized in that: The gridding of urban spatial elements through multi-temporal data collection to obtain a three-dimensional data matrix includes: Through remote sensing images and POI data collection, the time series data of population density, economic density and land use intensity are acquired to obtain the original spatial element dataset; Performing dimension elimination processing on the original spatial element dataset based on a standardization algorithm to obtain a standardized dataset; Dividing the standardized data set into spatial grids according to preset grid specifications to obtain regular grid cells; Completing the missing data in the regular grid cells according to the spatiotemporal Kriging interpolation algorithm to obtain a spatiotemporal dataset; The spatiotemporal data set is organized according to a three-dimensional matrix structure to obtain a three-dimensional data matrix.

3. The method for measuring the synergy of urban spatial structure according to claim 1, characterized in that: The coupling calculation of the spatial element flow vector using the improved gravity model according to the three-dimensional data matrix to obtain the spatiotemporal dynamic coupling coefficient includes: Measuring and processing the Euclidean distances between grids one by one based on the spatial coordinate information in the three-dimensional data matrix to obtain a spatial distance matrix; Inputting the three-dimensional data matrix and the spatial distance matrix into the improved gravity model to perform element flow intensity multiplication and division operations to obtain a spatial element flow vector; According to the spatial element flow vector, the synergistic relationship between the elements is quantified by dividing the vector dot product by the vector modulus product to obtain an initial coupling coefficient; Performing time offset index correction processing on the initial coupling coefficient based on a preset time lag parameter to obtain a corrected coupling coefficient; The modified coupling coefficient is subjected to correlation statistical analysis through a three-phase sliding window to obtain a spatiotemporal dynamic coupling coefficient.

4. The method for measuring the synergy of urban spatial structure according to claim 3 is characterized in that: The step of inputting the three-dimensional data matrix and the spatial distance matrix into the improved gravity model to perform a multiplication and division operation of element flow intensity to obtain a spatial element flow vector includes: Based on the traditional gravity model, the time decay term and mobility coefficient are added to improve the model structure and obtain the improved gravity model. Inputting the element density values in the three-dimensional data matrix into the improved gravity model to perform element mass attraction product operation to obtain the element attraction product; Performing exponential power operation on the distance decay term based on the spatial distance matrix and the distance decay parameter to obtain a distance decay factor; Performing incremental accumulation operations on the time decay term according to the preset time interval and the time decay parameter to obtain a time decay factor; The product of the element attraction is divided by the product of the distance attenuation factor and the time attenuation factor, and the quotient operation is performed and vector decomposition is performed according to the eight neighborhood directions to obtain the spatial element flow vector.

5. The method for measuring the synergy of urban spatial structure according to claim 1 is characterized in that: The process of constructing a network by using a bidirectional weighted algorithm on the spatiotemporal dynamic coupling coefficient to obtain a spatiotemporal bidirectional weighted network comprises: Performing exponential function weighting processing on the spatial adjacency relationship between network nodes based on the spatiotemporal dynamic coupling coefficient to obtain a spatial adjacency weight; Performing exponential decay processing on the time correlation strength according to the correlation of the spatiotemporal dynamic coupling coefficient between different time phases to obtain a time correlation weight; Performing a weighted sum operation on the spatial adjacency weight and the temporal association weight using a linear weighted balance parameter to obtain a comprehensive weight matrix; Performing a sparse screening process on the comprehensive weight matrix based on a preset connection threshold to obtain a network topology connection relationship; According to the network topology connection relationship, the network node degree distribution and clustering coefficient are statistically analyzed and processed to obtain a spatiotemporal bidirectional weighted network.

6. The method for measuring the synergy of urban spatial structure according to claim 1, characterized in that: The adaptive calculation of the dynamic synergy index DSCI based on the spatiotemporal bidirectional weighted network to obtain a synergy quantification result includes: Performing arithmetic averaging on adaptive weights based on node centralities in the spatiotemporal bidirectional weighted network to obtain grid adaptive weights; The contribution of the synergistic importance of the spatial element combination is calculated based on principal component analysis to obtain the element synergistic importance coefficient; The city center distance is input into the exponential function to perform correction operation on the spatial position influence degree to obtain the spatial position correction factor; A weighted summation and accumulation operation is performed based on the grid adaptive weight, the element coordination importance coefficient and the spatial position correction factor to obtain the original value of the dynamic coordination index DSCI; The original value of the dynamic synergy index DSCI is normalized by normalization mapping to obtain a synergy quantitative result.

7. The method for measuring the synergy of urban spatial structure according to claim 1 is characterized in that: The synergy threshold is adaptively determined through multi-level nested identification based on the synergy quantification result to obtain an urban spatial structure synergy measurement report, including: Based on the time series of the synergy quantification results, a weighted operation of the mean and standard deviation of the macro-level threshold is performed to obtain the overall synergy threshold of the city; The synergy quantification result is input into the improved clustering algorithm to perform three-category classification of the functional area synergy level to obtain the meso-level synergy threshold; Performing a confidence level statistical test on the synergy quantification results based on local spatial autocorrelation analysis to obtain a micro-level synergy threshold; Based on the economic growth rate and policy intensity index, dynamic correction calculation is performed on the overall city synergy threshold, the meso-level synergy threshold, and the micro-level synergy threshold to obtain an adaptive synergy threshold combination; The adaptive synergy threshold combination is used to visualize the spatial distribution map and evolution curve through hierarchical color coding and trend prediction to obtain an urban spatial structure synergy measurement report.

8. A system for measuring the coordination of urban spatial structure, characterized by: A method for measuring the synergy of urban spatial structure according to any one of claims 1 to 7, wherein the system for measuring the synergy of urban spatial structure comprises: A processing module is used to perform grid processing on urban spatial elements through multi-temporal data collection to obtain a three-dimensional data matrix; A coupling module, configured to perform coupling calculation on the spatial element flow vectors using an improved gravity model according to the three-dimensional data matrix to obtain a spatiotemporal dynamic coupling coefficient; A construction module is used to perform network construction processing on the spatiotemporal dynamic coupling coefficient through a bidirectional weighted algorithm to obtain a spatiotemporal bidirectional weighted network; A calculation module, configured to perform adaptive calculation of the dynamic synergy index DSCI based on the spatiotemporal bidirectional weighted network to obtain a synergy quantitative result; The identification module is used to adaptively determine the synergy threshold through multi-level nested identification according to the synergy quantification result to obtain an urban spatial structure synergy measurement report.

9. A device for measuring the coordination of urban spatial structure, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the method for measuring the synergy of urban spatial structure according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the processor is caused to execute the method for measuring the urban spatial structure coordination according to any one of claims 1 to 7.

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