Method and system for dynamic evaluation of urban green space ecological service based on multi-source data fusion

Abstract

This invention provides a method and system for dynamic assessment of urban green space ecosystem services based on multi-source data fusion, relating to the fields of urban ecology and geographic information systems. The method sequentially integrates green space patch landscape data and multi-source socioeconomic proxy data, relying on local spatial clustering to screen typical green spaces with high and low activity intensities; it conducts multi-period ecosystem service value accounting, and analyzes the spatial response patterns of landscape patterns and ecosystem value using concentric buffer zones. A symbiotic dynamic coupled differential equation is introduced, and parameters are fitted to obtain the cross-influence coefficient of the two systems to quantify the symbiotic relationship; multiple coverage scenarios are set by combining environmental Gini and green contribution coefficients to determine the green space coverage balance threshold under the synergistic achievement of multi-dimensional indicators, and the rate of change and evolution trend of ecosystem service value are calculated over time. This invention relies on multi-source data to compensate for the shortcomings of single data sources, quantitatively characterizes the symbiotic feedback relationship between green space and socioeconomic factors, and provides quantitative support for urban green space planning and management.

Description

Technical Field

[0001] This invention relates to the fields of urban ecology and geographic information system technology, and in particular to a method and system for dynamic assessment of urban green space ecological services based on multi-source data fusion. Background Technology

[0002] As a core component of the urban ecosystem, urban green space carries multiple ecological service functions, such as regulating microclimate, sequestering carbon and releasing oxygen, maintaining biodiversity, and providing recreational space for residents. The quantitative assessment of its pattern characteristics and service efficiency is an important scientific basis for urban sustainable planning and ecological civilization construction.

[0003] However, existing methods for assessing the ecosystem services of urban green spaces generally have the following shortcomings: The assessment system is mainly based on static analysis and lacks a dynamic evolution perspective. Existing studies are mostly based on remote sensing image data at a single time section, and conduct isolated assessments of indicators such as vegetation coverage and patch area of ​​green spaces. They cannot reveal the dynamic evolution trajectory of the ecological service value of green spaces over time, nor can they identify the internal driving mechanisms that drive this evolution, making it difficult to provide a basis for the long-term dynamic management of urban green spaces.

[0004] The data sources are limited and lack effective representation of socioeconomic activities. The actual effectiveness of urban green space use largely depends on its spatial coupling with surrounding socioeconomic activities, i.e., which green spaces are subject to high-intensity social use and which are underutilized. Existing methods generally rely on remote sensing data or questionnaires. The former cannot directly reflect population behavior, while the latter has limited coverage and is costly, resulting in a serious lack of characterization of the spatial distribution of green space social use intensity.

[0005] The feedback relationship between the ecological function of green spaces and urban socio-economic activities lacks quantitative expression. Although existing research has recognized the attractiveness of high-quality green spaces to commercial agglomeration and resident activities from a qualitative perspective, it still lacks an analytical framework that can quantify the mutually promoting or mutually restrictive relationship between the two from the perspective of system dynamics. It is even more difficult to determine the quantitative boundary of their coordinated coexistence, and thus cannot provide a threshold reference for the scientific decision-making on the scale of urban green space construction.

[0006] In view of this, there is an urgent need for a dynamic assessment method and system for urban green space ecosystem services based on multi-source data fusion, in order to at least address the above-mentioned shortcomings. Summary of the Invention

[0007] One of the objectives of this invention is to provide a method and system for dynamic evaluation of urban green space ecological services based on multi-source data fusion, so as to solve the problems mentioned in the background art.

[0008] The method for dynamic assessment of urban green space ecosystem services based on multi-source data fusion provided in this invention includes: Step S1: Obtain patch data of urban green space landscape composition types and construct a dataset of urban green space landscape pattern characteristic parameters; Step S2: Obtain at least two types of spatiotemporal distribution proxy data of socioeconomic activity intensity, and construct a spatiotemporal feature dataset of socioeconomic activity intensity; Step S3: Based on the spatiotemporal characteristic dataset of socioeconomic activity intensity, the spatial clustering test of urban green spaces across the entire region is conducted using the local spatial correlation statistical test method to identify typical green spaces with high activity intensity and typical green spaces with low activity intensity. Step S4: Conduct multi-period ecosystem service value assessments on typical green spaces and construct an ecosystem service value assessment dataset; Step S5: Delineate multiple buffer analysis units with different radii based on typical green spaces, analyze the spatial response relationship between landscape pattern characteristic parameters and ecosystem service value within each buffer zone, and output spatial response relationship characteristic data; Step S6: Define the intensity of urban green space ecological services and the intensity of socio-economic activities as the first symbiotic unit and the second symbiotic unit, respectively. Construct a symbiotic analysis model based on a set of coupled differential equations containing cross-influence coefficients. Use the values ​​of each period of the ecological service value assessment dataset and the spatiotemporal characteristic dataset of socio-economic activity intensity as the state variables of the two symbiotic units, respectively. Fit the parameters of the set of coupled differential equations to obtain the cross-influence coefficient as a symbiotic quantitative indicator. Step S7: Based on the spatiotemporal distribution proxy data of socioeconomic activity intensity and the ecological service value assessment dataset, calculate the environmental Gini coefficient and green contribution coefficient respectively, set at least five urban green space coverage gradient scenarios, simulate the response trajectory of environmental Gini coefficient, green contribution coefficient and symbiotic quantitative indicators under each scenario, and determine the green space coverage interval in which environmental Gini coefficient, green contribution coefficient and symbiotic quantitative indicators simultaneously meet the conditions of coordinated symbiosis as the coordinated balance threshold interval. Step S8: Arrange the multi-period ecosystem service value assessment datasets according to time series, combine them with the multi-period landscape pattern characteristic parameter datasets, calculate the periodic change rate and direction of ecosystem service value of each typical green space during the assessment period, and output the dynamic change trend analysis results.

[0009] Preferably, in step S2, at least two types of spatiotemporal distribution proxy data of socioeconomic activity intensity are selected from the following data types: Geographic point of interest data, including geographic coordinates, type labels, and recording time information; Location service signaling data, including anonymized user location records and timestamps; Online map travel data includes origin and destination coordinates and travel time information.

[0010] Preferably, the local spatial correlation statistical test method in step S3 is performed according to the following sub-steps: Using a regular grid with a predetermined spatial resolution or administrative units as the basic analysis units, the density values ​​of the spatiotemporal distribution proxy data of socioeconomic activity intensity within each unit are statistically analyzed to generate a spatial density distribution matrix of socioeconomic activity intensity. The spatial weight matrix between basic analysis units is constructed based on the inverse distance weighting method or the fixed distance band method. The Getis-Ord local spatial correlation statistic is used to conduct a local spatial autocorrelation test on the spatial density distribution matrix of socio-economic activity intensity, and the standardized statistic Z value and its corresponding significance probability p value of each unit are obtained. Spatial cells with Z-values ​​greater than 1.96 and p-values ​​less than 0.05 were identified as high-activity hotspot cells, and the corresponding spatial cells passed the positive spatial clustering significance test; spatial cells with Z-values ​​less than -1.96 and p-values ​​less than 0.05 were identified as low-activity coldspot cells, and the corresponding spatial cells passed the negative spatial clustering significance test. Urban green spaces whose geometric boundaries intersect with high-activity-intensity hotspot units and whose intersecting area accounts for more than 50% of the total area of ​​the green space are selected as typical high-activity-intensity green spaces. Urban green spaces whose geometric boundaries intersect with low-activity-intensity coldspot units and whose intersecting area accounts for more than 50% of the total area of ​​the green space are selected as typical low-activity-intensity green spaces. An equal number of samples are selected from both typical high-activity-intensity and typical low-activity-intensity green spaces as typical green spaces.

[0011] Preferably, in step S1, the landscape pattern characteristic parameters are calculated using the following indicators across the three dimensions of spatial composition, morphological structure, and spatial configuration: Spatial composition dimensions include: patch area ratio index and maximum patch area index; Morphological and structural dimensions include: landscape shape index and patch fractal dimension; Spatial configuration dimensions include: clustering index, spread index, and Shannon diversity index.

[0012] Preferably, the method for assessing the value of ecosystem services in step S4 includes: Using land use data, meteorological data, and soil data as inputs, the system simulates ecological processes such as water production, carbon sequestration, habitat quality, and soil conservation, and outputs the service value for each period. And / or, The total value for each period was calculated after localizing the equivalent service value per unit area of ​​various types of green spaces based on the biomass correction coefficient of the study area. And / or, The questionnaire survey quantifies residents' willingness to pay for specific ecological services in monetary form.

[0013] Preferably, in step S5, multiple buffer analysis units with different radii are generated using buffer technology based on typical green space boundaries, including a first-level buffer, a second-level buffer, and a third-level buffer. The radii of the three-level buffers are determined based on the walkability standard or public transportation service radius standard of the study area, so that the three-level buffers correspond to the accessibility range of three travel modes: walking, non-motorized vehicles, and public transportation, respectively. The comparative indicators involved in spatial response relationship analysis include: green area, landscape shape index, vegetation species composition and vertical structure, patch connectivity, aggregation degree and species diversity index. The values ​​of the above indicators for typical green spaces with high activity intensity and typical green spaces with low activity intensity in each level of buffer zone were calculated respectively. Nonparametric statistical test methods were used to compare the differences of the two types of typical green spaces in each indicator, and key landscape pattern indicators that have a differentiating effect on the intensity of socio-economic activities were identified.

[0014] Preferably, the specific form of the coupled differential equation system in step S6 is as follows: dN1(t) / dt=r1*N1(t)*(1-N1(t) / K1)+α 12 *N1(t)*N2(t); dN2(t) / dt=r2*N2(t)*(1-N2(t) / K2)+α 21 *N1(t)*N2(t); Where N1(t) is the state variable value of the first symbiotic unit at time t; N2(t) is the state variable value of the second symbiotic unit at time t; r1 is the natural growth rate parameter of the first symbiotic unit; r2 is the natural growth rate parameter of the second symbiotic unit; K1 is the environmental carrying capacity limit parameter when the first symbiotic unit grows independently; K2 is the environmental carrying capacity limit parameter when the second symbiotic unit grows independently; α 12 α is the influence coefficient of the second symbiotic unit on the growth rate of the first symbiotic unit. 21 α is the influence coefficient of the growth rate of the first symbiotic unit on the second symbiotic unit. 12 and α 21 These are two cross-influence coefficients, which together constitute a symbiotic quantitative indicator.

[0015] Preferably, the environmental Gini coefficient in step S7 is calculated using the following method: The spatial units within the study area are sorted by population from smallest to largest. The Lorenz curve is plotted with the proportion of the cumulative population of each spatial unit to the total population as the horizontal axis and the proportion of the cumulative green space ecological service value of the corresponding spatial unit to the total value as the vertical axis. The ratio of the enclosed area between the Lorenz curve and the line of absolute fairness to the area of ​​the triangle below the line of absolute fairness is used as the environmental Gini coefficient, with a value range of 0 to 1. The green contribution coefficient is calculated as follows: the ratio of the green area of ​​each spatial unit to the total green area of ​​the study area is used as the numerator, and the ratio of the construction land area of ​​the spatial unit to the total construction land area of ​​the study area is used as the denominator. The ratio of the two is the green contribution coefficient.

[0016] Preferably, the criteria for determining whether the environmental Gini coefficient, green contribution coefficient, and symbiotic quantitative indicators simultaneously meet the conditions for coordinated symbiosis in step S7 are as follows: Condition 1: The environmental Gini coefficient is less than the minimum environmental Gini coefficient for each period within the assessment period; Condition 2: The green contribution coefficient is greater than 1; Condition 3: Both cross-influence coefficients in the symbiotic quantitative indicators are positive.

[0017] The urban green space ecological service dynamic assessment system based on multi-source data fusion provided in this embodiment of the invention includes: The first data acquisition module is used to acquire patch data of urban green space landscape composition types and construct a dataset of urban green space landscape pattern characteristic parameters. The second data acquisition module is used to acquire at least two types of spatiotemporal distribution proxy data of socioeconomic activity intensity, and to construct a spatiotemporal feature dataset of socioeconomic activity intensity. The typical green space identification module is used to conduct spatial clustering tests on urban green spaces across the entire region based on a dataset of spatiotemporal characteristics of socioeconomic activity intensity, and to identify typical green spaces with high activity intensity and typical green spaces with low activity intensity. The assessment module is used to conduct multi-period ecosystem service value assessments of typical green spaces and to construct an ecosystem service value assessment dataset. The spatial response relationship analysis module is used to delineate multiple buffer analysis units with different radii based on typical green spaces, analyze the spatial response relationship between landscape pattern characteristic parameters and ecosystem service value within each buffer, and output spatial response relationship characteristic data. The symbiotic quantitative index acquisition module is used to define the intensity of urban green space ecological service and the intensity of socio-economic activities as the first symbiotic unit and the second symbiotic unit, respectively. It constructs a symbiotic analysis model based on a set of coupled differential equations containing cross-influence coefficients. The values ​​of each period of the ecological service value assessment dataset and the spatiotemporal characteristic dataset of socio-economic activity intensity are used as the state variables of the two symbiotic units, respectively. The parameters of the coupled differential equations are fitted to obtain the cross-influence coefficients as symbiotic quantitative indicators. The coordination and balance threshold interval determination module is used to calculate the environmental Gini coefficient and green contribution coefficient based on the spatiotemporal distribution proxy data of socioeconomic activity intensity and the ecological service value assessment dataset, respectively. It sets at least five urban green space coverage gradient scenarios, simulates the response trajectory of environmental Gini coefficient, green contribution coefficient and symbiotic quantitative indicators under each scenario, and determines the green space coverage interval in which environmental Gini coefficient, green contribution coefficient and symbiotic quantitative indicators simultaneously meet the coordination and symbiotic conditions as the coordination and balance threshold interval. The dynamic trend analysis output module is used to arrange the multi-period ecosystem service value assessment datasets in time series, combine them with multi-period landscape pattern characteristic parameter datasets, calculate the periodic change rate and direction of the ecosystem service value of each typical green space during the assessment period, and output the dynamic trend analysis results.

[0018] The beneficial effects of this invention are as follows: This invention overcomes the shortcomings of previous assessment methods that relied on a single data source and could not effectively depict the spatial distribution of green space social use intensity by integrating remote sensing interpretation data with geographic point of interest data, location service signaling data, and other types of socio-economic activity proxy data, and effectively expands the information dimensions of urban green space assessment.

[0019] This invention combines ecosystem service theory with population symbiotic dynamics equations, and for the first time incorporates the intensity of urban green space ecosystem services and the intensity of urban socio-economic activities into the same coupled differential equation framework. By fitting parameters to measured data of both at different periods, it quantifies and outputs cross-influence coefficients describing the direction and intensity of the interaction between the two system units, thus achieving a quantitative characterization of the mutually reinforcing symbiotic relationship between green space ecosystem services and urban economic development. This method overcomes the limitations of previous studies that could only provide qualitative descriptions, and provides a quantifiable basis for precise decision-making in urban ecological governance.

[0020] This invention constructs a multi-dimensional system for determining the conditions for coordinated symbiosis by introducing three types of indicators: the environmental Gini coefficient, the green contribution coefficient, and symbiotic quantitative indicators. Through multi-scenario simulation, it determines the threshold range for green space coverage to achieve a coordinated balance when all three types of indicators simultaneously meet the conditions for coordinated symbiosis. This threshold range simultaneously considers the spatial equity of green space ecological services, the rationality of the economic benefits of green space construction, and the mutually beneficial symbiotic state between the green space ecosystem and the urban economic system, providing a comprehensive and operable quantitative reference boundary for the scientific decision-making on the scale of urban green space construction.

[0021] This invention combines multi-period ecosystem service value assessment data with multi-period landscape pattern characteristic parameters to output the periodic change rate and direction of ecosystem service value of each typical green space during the assessment period. This realizes the transformation from static cross-sectional assessment to dynamic trend assessment, enabling the assessment results to reflect the historical evolution trajectory of urban green space ecosystem services and providing time-series data support for dynamic management and early warning of green spaces.

[0022] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0023] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0024] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the dynamic assessment method for urban green space ecological services based on multi-source data fusion in an embodiment of the present invention; Figure 2 This is a schematic diagram of a dynamic evaluation system for urban green space ecological services based on multi-source data fusion, as described in an embodiment of the present invention. Detailed Implementation

[0025] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0026] This invention provides a method for dynamic assessment of urban green space ecosystem services based on multi-source data fusion. The method is implemented using a geographic information processing platform, spatial statistical analysis tools, and mathematical modeling software, with the urban administrative area or specific functional zone as the research area.

[0027] In existing urban green space assessment practices, green spaces are typically treated as isolated physical entities, with static measurements of their area, vegetation cover, and other physical attributes performed using single-period remote sensing imagery. The resulting conclusions fail to reflect the actual functional status of green spaces within the urban socio-economic activity system and their dynamic characteristics over time. The core innovation of the assessment framework proposed in this invention lies in treating urban green spaces and their embedded socio-economic environment as an interacting coupled system. It uses multi-source data fusion to identify the differentiated patterns of green space social use, employs symbiotic dynamics modeling to quantify the feedback relationship between the two systems, and uses multi-indicator scenario simulation to determine the quantitative boundaries of coordinated development, thereby achieving a methodological upgrade from physical assessment to dynamic system assessment.

[0028] This invention provides a method for dynamic assessment of urban green space ecosystem services based on multi-source data fusion, such as... Figure 1 As shown, it includes: Step S1: Obtain patch data of urban green space landscape composition types and construct a dataset of urban green space landscape pattern characteristic parameters.

[0029] Step S1 is the spatial baseline data construction step of this invention. Its execution relies on multi-period remote sensing image data covering the study area, preferably using multispectral satellite imagery with a spatial resolution better than 10 meters, to ensure effective identification of the spatial boundaries of various green patches within the study area.

[0030] Remote sensing image interpretation employs an object-oriented classification method, taking into account the spectral, textural, and morphological features of the images as a comprehensive basis. The land cover types in the study area are divided into arbor forests, shrublands, urban park green spaces, road protection green spaces, and other construction land types. Landscape composition patch data of various urban green spaces are extracted, including the spatial geometric range, vegetation type label, and interpretation period information of each patch.

[0031] Based on the obtained patch data, the entire study area was used as the landscape analysis unit, and professional landscape pattern analysis software was employed to calculate landscape pattern characteristic parameters. These parameters were quantified using seven indicators across three dimensions: spatial composition, morphological structure, and spatial configuration. Spatial composition dimension: The patch area ratio index reflects the percentage of the area of ​​various green space patches in the total landscape area; the largest patch area index reflects the relative area of ​​the largest patch in the landscape, reflecting the degree of control of the dominant patch over the landscape pattern.

[0032] Morphological and structural dimensions: The landscape shape index reflects the irregularity of patch boundaries by calculating the standardized form of the ratio of the perimeter of various patches to the circumference of a circle of equal area; the patch fractal dimension is calculated based on the double logarithmic relationship between patch area and perimeter, with a value ranging from 1 to 2. The closer the value is to 2, the more complex the patch boundary.

[0033] Spatial configuration dimension: The aggregation index is calculated based on the type relationship matrix of adjacent grids, reflecting the degree of spatial aggregation among similar patches; the spread index comprehensively considers the mixed distribution characteristics of multiple types of patches, reflecting the degree of spread or fragmentation of the landscape; the Shannon diversity index is calculated based on the area ratio of various types of patches, reflecting the degree of diversity of landscape composition types.

[0034] Step S1 performs the above calculations on remote sensing images for multiple assessment periods to form a dataset of urban green space landscape pattern characteristic parameters containing the values ​​of landscape pattern characteristic parameters for each period.

[0035] Step S2: Obtain at least two types of spatiotemporal distribution proxy data of socioeconomic activity intensity, and construct a spatiotemporal feature dataset of socioeconomic activity intensity.

[0036] The intensity of socioeconomic activity is an abstract concept that is difficult to measure directly, but it can be indirectly characterized through various types of behavioral digital trace data. Single data types, due to their inherent collection biases and coverage limitations, often only reflect one aspect of socioeconomic activity in a one-sided way; while fusing multiple types of proxy data can more comprehensively and robustly depict the true spatiotemporal distribution of urban socioeconomic activity intensity.

[0037] At least two types of proxies for the spatiotemporal distribution of socioeconomic activity intensity are selected from the following three categories: The first category is geographic points of interest (POI) data. POI data originates from the public application programming interfaces (APIs) of map service platforms. Each record includes geographic coordinates, type labels (such as dining, shopping, cultural and sports, office, etc.), and the most recent update time. The spatial density of POI data reflects the degree of agglomeration of commercial service facilities in various urban areas, thus indirectly representing the scale of socio-economic activities carried by that area.

[0038] The second category is location service signaling data. This type of data originates from mobile communication network operators or mobile application service providers, recording user location information and corresponding timestamps in an anonymized manner. It can directly reflect the dynamic aggregation of people in urban space and is a powerful data source for characterizing the intensity of actual visitor flow in various urban areas. To address the issues of missed signaling samples, signal distortion, or timestamp breaks and offsets caused by extreme conditions such as strong electromagnetic interference in specific areas or short-term offline status due to base station maintenance and switching during the collection process of location service signaling data, this invention establishes a signaling gap fault-tolerant reconstruction mechanism based on spatiotemporal tensor decomposition. Specifically, the mechanism is implemented as follows: First, a threshold for identifying temporal abrupt changes in base station signaling density and a rule for checking the continuous period of timestamps are set. Drift anomalies are identified and removed from the read-in original signaling records, generating an initial sparse spatiotemporal signaling tensor with a gap value mask feature. Secondly, a regularization term for tensor kernel norm optimization is introduced, and a low-rank tensor completion model is constructed by combining the spatial smoothness of adjacent entity grids and the prior constraints of the temporal periodicity of weekdays / holidays. Finally, the alternating direction multiplier algorithm is used to extract the multidimensional implicit dynamic correlation patterns of signaling data under normal base station conditions, and iterative spatiotemporal interpolation is performed to complete the distorted or missing fracture dimensions in the initial sparse spatiotemporal signaling tensor, outputting the spatiotemporally continuous location signaling density matrix after fault-tolerant reconstruction, thereby avoiding the cliff-like distortion phenomenon in the calculation of fusion index due to signal interruption.

[0039] The third category is travel data from online maps. This type of data is based on user navigation behavior data and includes the origin and destination coordinates of the trip as well as travel time information. It can reveal the strength of functional connections and the patterns of population flow between different areas of the city from the perspective of travel flow.

[0040] For each type of selected proxy data, a regular grid with a predetermined spatial resolution is first used as the basic statistical unit. The density value of that type of data within each grid cell is calculated to form a spatial density raster layer for that type of data. Since the dimensions of different types of data are fundamentally different and cannot be directly superimposed, a min-max normalization method is used to normalize the spatial density raster layers of each type of data, uniformly mapping the value range of each type of data to the interval between 0 and 1, thus eliminating the difference in dimensions.

[0041] After normalization, the various data raster layers are weighted, summed, and superimposed with equal weights. When researchers have prior knowledge of the reliability of various data, the analytic hierarchy process (AHP) or expert scoring can be used to determine the differential weights. The resulting raster layer forms an activity intensity fusion index that comprehensively reflects the spatiotemporal distribution of urban socioeconomic activity intensity. For cases with data from multiple periods, the above processing procedure is performed separately for each period, ultimately forming a spatiotemporal feature dataset of socioeconomic activity intensity containing multi-period activity intensity fusion indices.

[0042] Step S3: Based on the spatiotemporal characteristic dataset of socioeconomic activity intensity, the spatial clustering test of urban green spaces across the entire region is conducted using the local spatial correlation statistical test method to identify typical green spaces with high activity intensity and typical green spaces with low activity intensity.

[0043] In order to identify two typical green spaces that are representative of the degree of socio-economic activity agglomeration in the city's green spaces as the research objects for subsequent refined analysis, firstly, using a regular grid with a predetermined spatial resolution or administrative units as the basic analysis units, the density values ​​of the spatiotemporal distribution proxy data of socio-economic activity intensity within each unit are statistically analyzed to generate a spatial density distribution matrix of socio-economic activity intensity.

[0044] Next, a spatial weight matrix is ​​constructed among the basic analytical units. This step provides two alternatives: the inverse distance weighting method and the fixed distance band method. The inverse distance weighting method uses the inverse of the distance between units as the weight, reflecting the geographical principle that closer distances have greater influence. The fixed distance band method assigns equal weights to all units within a predetermined distance from the center of the target unit. After the spatial weight matrix is ​​constructed, row standardization is performed to ensure that the sum of the neighborhood weights of each unit is 1.

[0045] Then, the Getis-Ord local spatial correlation statistic (i.e., the Gi* statistic) is used to perform a local spatial autocorrelation test on the spatial density distribution matrix of socioeconomic activity intensity. The calculation principle of the Gi* statistic is as follows: for each basic analysis unit, the weighted average of the activity intensity density values ​​of the unit itself and its neighboring units is compared with the global mean. If the density values ​​of a unit and its neighbors are all higher than the global mean, the unit is identified as a hot spot with high value clustering; if they are all lower than the global mean, it is identified as a cold spot with low value clustering. The Gi* statistic is transformed into the standardized statistic Z-value through the normal distribution assumption, and the corresponding significance probability p-value is calculated.

[0046] Subsequently, significance was determined based on the Z-score and p-score: spatial cells with a Z-score greater than 1.96 and a p-score less than 0.05 were classified as high-activity hotspot cells with a 95% confidence level; spatial cells with a Z-score less than -1.96 and a p-score less than 0.05 were classified as low-activity coldspot cells with the same 95% confidence level. These thresholds correspond to the 95% confidence interval boundaries of a two-tailed normal test and are widely accepted significance criteria in statistics.

[0047] Finally, the spatial extents of hotspot and coldspot units were spatially overlaid with urban green space patch data. Urban green spaces whose geometric extents intersect with high-activity-intensity hotspot units, and whose intersecting area accounts for more than 50% of the total area of ​​the green space, were extracted as typical high-activity-intensity green spaces. Similarly, urban green spaces whose geometric extents intersect with low-activity-intensity coldspot units, and whose intersecting area accounts for more than 50% of the total area of ​​the green space, were extracted as typical low-activity-intensity green spaces. The 50% area overlap threshold ensured that the main body of the extracted typical green spaces indeed fell within the corresponding significant clustering areas. Finally, an equal number of samples were selected from each type of typical green space to ensure sample balance for subsequent comparative analysis.

[0048] Step S4: Conduct multi-period ecosystem service value assessments on typical green spaces and construct an ecosystem service value assessment dataset.

[0049] Step S4 uses the typical green spaces with high activity intensity and low activity intensity selected in Step S3 as evaluation objects to systematically quantify the ecological service value of each typical green space in multiple historical periods.

[0050] At least one of the following three methods can be used to assess the value of ecosystem services: The first type is an assessment method based on ecological function process simulation. It takes land use data, meteorological data and soil data of each period as input, and simulates four core ecological processes: water production, carbon sequestration, habitat quality and soil conservation. It outputs the value of ecosystem services in each period in the form of monetization or physical quantity.

[0051] The second method is an assessment based on the equivalent value of ecosystem services per unit area. Using the biomass correction coefficient of the study area as an adjustment parameter, the published benchmark values ​​of equivalent value of ecosystem services per unit area for various land cover types are localized and corrected. Combined with the area and vegetation composition of each typical green space, the total value of ecosystem services for each period is calculated.

[0052] The third method is a conditional value assessment method based on residents' willingness to pay. This method involves designing a structured questionnaire to survey residents around typical green spaces about their willingness to pay for specific ecological services provided by the green space. The statistical summary of the willingness to pay is used to represent the residents' perceived value of the ecological services provided by the green space.

[0053] Step S4 executes the above assessment process for multiple assessment periods, summarizes and organizes the assessment results of the ecological service value of each typical green space in each period, and constructs an ecological service value assessment dataset with time-series attributes.

[0054] Step S5: Delineate multiple buffer analysis units with different radii based on typical green spaces, analyze the spatial response relationship between landscape pattern characteristic parameters and ecosystem service value within each buffer zone, and output spatial response relationship characteristic data.

[0055] Step S5 reveals the spatial response patterns between green space landscape pattern characteristics and green space ecosystem service value within different spatial ranges through multi-scale buffer analysis.

[0056] Multiple buffer analysis units with different radii are generated using buffer technology based on typical green space boundaries, including a first-level buffer, a second-level buffer, and a third-level buffer. The radii of the three-level buffers are determined based on the current pedestrian accessibility standards or public transportation service radius standards of the study area, so that the three-level buffers correspond to the accessibility range of three travel modes: walking, non-motorized vehicles, and public transportation, respectively.

[0057] Within each buffer zone, the values ​​of six indicators—green area, landscape shape index, vegetation species composition and vertical structure, patch connectivity, aggregation, and species diversity index—are calculated in the landscape pattern characteristic parameter dataset. The values ​​of these indicators are then paired and compared between typical green spaces with high and low activity intensities within the same buffer zone. Since the data for each indicator may not necessarily satisfy the normal distribution assumption, step S5 uses the Mann-Whitney U test (i.e., the Wilcoxon rank-sum test) to assess the statistical significance of the differences between the two independent samples on each indicator. Landscape pattern indicators with p-values ​​less than 0.05 are identified as key indicators that differentiate the intensity of socioeconomic activities, and spatial response relationship characteristic data are output.

[0058] Step S6: Define the intensity of urban green space ecological services and the intensity of socio-economic activities as the first symbiotic unit and the second symbiotic unit, respectively. Construct a symbiotic analysis model based on a set of coupled differential equations containing cross-influence coefficients. Use the values ​​of each period of the ecological service value assessment dataset and the spatiotemporal characteristic dataset of socio-economic activity intensity as the state variables of the two symbiotic units, respectively. Fit the parameters of the coupled differential equations to obtain the cross-influence coefficient as a symbiotic quantitative indicator.

[0059] The specific form of the coupled differential equations is as follows. Let N1(t) be the state variable value of the first symbiotic unit (urban green space ecological service intensity) at time t, and N2(t) be the state variable value of the second symbiotic unit (urban socio-economic activity intensity) at time t. Then the dynamic evolution of the two symbiotic units is described by the following set of equations: dN1(t) / dt=r1*N1(t)*(1-N1(t) / K1)+α 12 *N1(t)*N2(t); dN2(t) / dt=r2*N2(t)*(1-N2(t) / K2)+α 21 *N1(t)*N2(t); Where r1 and r2 are the natural growth rate parameters of the first and second symbiotic units when they are not affected by the other unit, respectively; K1 and K2 are the environmental carrying capacity parameters (i.e., carrying capacity parameters) of the first and second symbiotic units when they grow independently, respectively; α 12 This is the influence coefficient of the second symbiotic unit on the growth rate of the first symbiotic unit. A positive value indicates that the second symbiotic unit promotes the growth of the first symbiotic unit, while a negative value indicates that it inhibits it. α 21 This is the influence coefficient of the first symbiotic unit on the growth rate of the second symbiotic unit, with the same meaning as above. α 12 and α 21 These are two cross-influence coefficients, which together constitute a symbiotic quantitative indicator.

[0060] The first term in the system of equations (r1*N1(t)*(1-N1(t) / K1) is a Logistic growth term, describing the self-regulating growth pattern of each symbiotic unit under its own resource constraints when it is not affected by another unit; the second term (α) 12 *N1t)*N2(t)) is a coupling term that describes the interaction between two symbiotic units. The sign of the cross-influence coefficient determines the direction of the interaction, and the magnitude of the absolute value determines the strength of the interaction.

[0061] The parameter fitting uses the period values ​​of the ecosystem service value assessment dataset from step S4 as the time series observation data for N1(t), and the period values ​​of the spatiotemporal characteristics dataset of socioeconomic activity intensity from step S2 as the time series observation data for N2(t). After performing minimum-maximum normalization on the period values ​​to map them to the interval of 0 to 1, a nonlinear least squares fitting method is used, with r1, r2, K1, K2, α... 12 α 21 For the parameters to be fitted, the theoretical predicted values ​​of the equation system at each time period are solved using numerical integration. The sum of squared residuals between the theoretical predicted values ​​and the measured time-series data is minimized to obtain the optimal parameter estimates. After the fitting converges, α is extracted. 12 and α 21 The estimated value is output as a symbiotic quantitative indicator.

[0062] After fitting, the symbiotic relationship is determined based on the sign combination of the two cross-influence coefficients: when α 12 and α 21 When both are positive, it is determined that there is a mutually beneficial symbiotic relationship between the intensity of urban green space ecosystem services and the intensity of socio-economic activities, that is, the two promote each other; when α 12 and α 21When the product is negative, it indicates the existence of a symbiotic or antagonistic relationship, meaning one side promotes the other while the other inhibits the former; when α 12 and α 21 When both values ​​are negative, it is determined that there is a competitive relationship between them, that is, the two inhibit each other.

[0063] Step S7: Based on the spatiotemporal distribution proxy data of socioeconomic activity intensity and the ecological service value assessment dataset, calculate the environmental Gini coefficient and green contribution coefficient respectively, set at least five urban green space coverage gradient scenarios, simulate the response trajectory of environmental Gini coefficient, green contribution coefficient and symbiotic quantitative indicators under each scenario, and determine the green space coverage interval in which environmental Gini coefficient, green contribution coefficient and symbiotic quantitative indicators simultaneously meet the conditions of coordinated symbiosis as the coordinated balance threshold interval.

[0064] Step S7, based on the symbiotic quantitative analysis in step S6, further introduces two supplementary indicators that reflect ecological equity and the benefits of green space construction. The coordinated balance threshold range is determined through joint scenario simulation of the three types of indicators.

[0065] The Gini coefficient for the environment is calculated as follows: Using spatial units (such as streets or grid units) within the study area as the basic statistical objects, the number of permanent residents in each spatial unit and the total value of green space ecosystem services obtained from step S4 are statistically analyzed. The spatial units are then sorted by population size from smallest to largest, and the proportion of each spatial unit's population to the total population of the study area, as well as the proportion of each spatial unit's green space ecosystem service value to the total green space ecosystem service value of the study area, are calculated sequentially. The former is plotted on the x-axis, and the latter on the y-axis, to create a Lorenz curve. The ratio of the area enclosed between the Lorenz curve and the line of absolute equity to the area of ​​the triangle below the line of absolute equity is the Gini coefficient for the environment, ranging from 0 to 1. A value closer to 0 indicates a more equitable distribution of green space ecosystem services among spatial units of different population sizes.

[0066] The green contribution coefficient is calculated as follows: The numerator is the ratio of the green area of ​​each spatial unit to the total green area of ​​the study area, and the denominator is the ratio of the construction land area of ​​that spatial unit to the total construction land area of ​​the study area. The ratio of these two values ​​is the green contribution coefficient. A green contribution coefficient greater than 1 indicates that the green space supply contribution ratio of the spatial unit is higher than its development intensity ratio, meaning that the unit's contribution to ensuring urban green space supply exceeds the ecological pressure brought about by its development and construction. A coefficient less than 1 indicates that there is a gap between the green space supply and the development intensity of the unit.

[0067] The determination of the coordinated balance threshold range is performed according to the following logic: At least five gradient scenarios are set for urban green space coverage from low to high. The scenarios are set based on the current green space coverage rate of the study area, and are set at equal intervals or according to key nodes within a reasonable range of variation. Under each scenario, the prediction methods for the three types of indicators are as follows: For scenario prediction of the environmental Gini coefficient: Under a given green coverage scenario c, it is assumed that the increase or decrease in green area relative to the current coverage is spatially allocated according to the proportion of the current construction land area of ​​each spatial unit (i.e., the spatial unit with a larger area gains or loses more green space). Based on the green area of ​​each spatial unit after redistribution and the corresponding equivalent ecological service value, the ecological service value of each spatial unit is recalculated, and then the predicted value of the environmental Gini coefficient under this scenario is calculated according to the Lorenz curve method mentioned above.

[0068] For scenario prediction of green contribution coefficient: Under the given green coverage rate scenario c, based on the same spatial distribution assumption of green space increase and decrease as above, calculate the ratio of green space area to construction land area in each spatial unit, and take the area-weighted average of the green contribution coefficient of each spatial unit as the predicted value of the overall green contribution coefficient of the study area under this scenario.

[0069] For scenario prediction of co-occurrence quantification indicators: using coupled differential equations and their parameters (r1, r2, K1, K2, α) 12 α 21 Under a given green coverage scenario c, K1 (the carrying capacity parameter of the first symbiotic unit) is adjusted proportionally according to the change in green area. The adjusted equations are used to numerically solve the long-term equilibrium state of the two symbiotic units (i.e., the stable fixed point when dN1(t) / dt=0 and dN2(t) / dt=0). The sign combination of the two cross-influence coefficients at the equilibrium solution is determined to characterize the expected state of the symbiotic relationship under this scenario.

[0070] The green coverage rate range that simultaneously meets the following three conditions for all three types of indicators is defined as the coordination and balance threshold range: Condition 1, the environmental Gini coefficient is less than the minimum environmental Gini coefficient for each period within the assessment period; Condition 2, the green contribution coefficient is greater than 1; Condition 3, the two cross-influence coefficients α in the symbiotic quantitative indicators are... 12 and α 21 All values ​​are positive. The lower bound of the coordination balance threshold range represents the minimum level of green space coverage required to maintain coordinated coexistence, while the upper bound represents the upper limit of green space coverage that can achieve coordinated coexistence under existing constraints.

[0071] Step S8: Arrange the multi-period ecosystem service value assessment datasets according to time series, combine them with the multi-period landscape pattern characteristic parameter datasets, calculate the periodic change rate and direction of ecosystem service value of each typical green space during the assessment period, and output the dynamic change trend analysis results.

[0072] Specifically, taking each typical green space as the analysis object, the change in its ecosystem service value between adjacent periods within the assessment period is divided by the time interval to calculate the periodic ecosystem service value change rate. Simultaneously, based on the comparison of ecosystem service values ​​between adjacent periods, the direction of change (increase, decrease, or near-constancy) is determined. Combining the changes in landscape pattern characteristic parameters during the same period, Spearman rank correlation analysis is used to calculate the correlation coefficient between the ecosystem service value change rate sequence of each typical green space and the change sequence of each landscape pattern characteristic parameter, identifying key landscape pattern change characteristics associated with the dynamic trend of ecosystem service value change.

[0073] The output of step S8 is the dynamic change trend analysis result. This result comprehensively includes the time series of ecological service value, the periodic change rate sequence, the change direction label, and the correlation analysis results of landscape pattern characteristic parameters and ecological service value change rate of each typical green space during the assessment period. It can be directly used for decision support for the dynamic management of urban green spaces.

[0074] This invention sequentially integrates green space patch landscape data and multi-source socioeconomic agent data, using local spatial clustering to screen typical green spaces with high and low activity intensities. It conducts multi-period ecosystem service value accounting, analyzing the spatial response patterns of landscape patterns and ecosystem value through concentric buffer zones. A symbiotic dynamics coupled differential equation is introduced, and parameters are fitted to obtain the cross-influence coefficient of the two systems to quantify the symbiotic relationship. Multiple coverage scenarios are set up by combining environmental Gini coefficient and green contribution coefficient to determine the green space coverage balance threshold under the synergistic achievement of multi-dimensional indicators, and the rate of change and evolution trend of ecosystem service value are calculated over time. This invention leverages multi-source data to compensate for the shortcomings of single data sources, quantitatively characterizes the symbiotic feedback relationship between green spaces and socioeconomic factors, dynamically assesses the evolution patterns of green spaces, and provides quantitative support for urban green space planning and management.

[0075] This invention provides a dynamic assessment system for urban green space ecosystem services based on multi-source data fusion, such as... Figure 2 As shown, it includes: The first data acquisition module 1 is used to acquire patch data of urban green space landscape composition types and construct a dataset of urban green space landscape pattern characteristic parameters. The second data acquisition module 2 is used to acquire at least two types of spatiotemporal distribution proxy data of socioeconomic activity intensity, and to construct a spatiotemporal feature dataset of socioeconomic activity intensity. Typical green space determination module 3 is used to perform spatial clustering tests on urban green spaces across the entire region based on the spatiotemporal characteristic dataset of socioeconomic activity intensity and the local spatial correlation statistical test method, thereby determining typical green spaces with high activity intensity and typical green spaces with low activity intensity. Assessment Module 4 is used to conduct multi-period ecosystem service value assessments of typical green spaces and construct an ecosystem service value assessment dataset. The spatial response relationship analysis module 5 is used to delineate multiple buffer analysis units with different radii based on typical green spaces, analyze the spatial response relationship between landscape pattern characteristic parameters and ecosystem service value within each buffer, and output spatial response relationship characteristic data. The symbiotic quantitative index acquisition module 6 is used to define the intensity of urban green space ecological service and the intensity of socio-economic activities as the first symbiotic unit and the second symbiotic unit, respectively. It constructs a symbiotic analysis model based on a set of coupled differential equations containing cross-influence coefficients. It uses the values ​​of each period of the ecological service value assessment dataset and the spatiotemporal characteristic dataset of socio-economic activity intensity as the state variables of the two symbiotic units, respectively, and performs parameter fitting on the set of coupled differential equations to obtain the cross-influence coefficient as the symbiotic quantitative index. The coordination and balance threshold interval determination module 7 is used to calculate the environmental Gini coefficient and green contribution coefficient based on the spatiotemporal distribution proxy data of socioeconomic activity intensity and the ecological service value assessment dataset, respectively. It sets at least five urban green space coverage gradient scenarios, simulates the response trajectory of environmental Gini coefficient, green contribution coefficient and symbiotic quantitative indicators under each scenario, and determines the green space coverage interval that simultaneously meets the coordination and symbiotic conditions as the coordination and balance threshold interval. The dynamic trend analysis output module 8 is used to arrange the multi-period ecosystem service value assessment datasets in time series, combine them with multi-period landscape pattern characteristic parameter datasets, calculate the periodic change rate and direction of ecosystem service value of each typical green space during the assessment period, and output the dynamic trend analysis results.

[0076] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A dynamic assessment method for urban green space ecosystem services based on multi-source data fusion, characterized in that, include: Step S1: Obtain patch data of urban green space landscape composition types and construct a dataset of urban green space landscape pattern characteristic parameters; Step S2: Obtain at least two types of spatiotemporal distribution proxy data of socioeconomic activity intensity, and construct a spatiotemporal feature dataset of socioeconomic activity intensity; Step S3: Based on the spatiotemporal characteristic dataset of socioeconomic activity intensity, the spatial clustering test of urban green spaces across the entire region is conducted using the local spatial correlation statistical test method to identify typical green spaces with high activity intensity and typical green spaces with low activity intensity. Step S4: Conduct multi-period ecosystem service value assessments on typical green spaces and construct an ecosystem service value assessment dataset; Step S5: Delineate multiple buffer analysis units with different radii based on typical green spaces, analyze the spatial response relationship between landscape pattern characteristic parameters and ecosystem service value within each buffer zone, and output spatial response relationship characteristic data; Step S6: Define the intensity of urban green space ecological services and the intensity of socio-economic activities as the first symbiotic unit and the second symbiotic unit, respectively. Construct a symbiotic analysis model based on a set of coupled differential equations containing cross-influence coefficients. Use the values ​​of each period of the ecological service value assessment dataset and the spatiotemporal characteristic dataset of socio-economic activity intensity as the state variables of the two symbiotic units, respectively. Fit the parameters of the set of coupled differential equations to obtain the cross-influence coefficient as a symbiotic quantitative indicator. Step S7: Based on the spatiotemporal distribution proxy data of socioeconomic activity intensity and the ecological service value assessment dataset, calculate the environmental Gini coefficient and green contribution coefficient respectively, set at least five urban green space coverage gradient scenarios, simulate the response trajectory of environmental Gini coefficient, green contribution coefficient and symbiotic quantitative indicators under each scenario, and determine the green space coverage interval in which environmental Gini coefficient, green contribution coefficient and symbiotic quantitative indicators simultaneously meet the conditions of coordinated symbiosis as the coordinated balance threshold interval. Step S8: Arrange the multi-period ecosystem service value assessment datasets in time series, combine them with the multi-period landscape pattern characteristic parameter datasets, calculate the periodic change rate and direction of ecosystem service value of each typical green space during the assessment period, and output the dynamic change trend analysis results.

2. The method for dynamic evaluation of urban green space ecosystem services based on multi-source data fusion as described in claim 1, characterized in that, In step S2, at least two types of spatiotemporal distribution proxy data of socioeconomic activity intensity are selected from the following data types: Geographic point of interest data, including geographic coordinates, type labels, and recording time information; Location service signaling data, including anonymized user location records and timestamps; Online map travel data includes origin and destination coordinates and travel time information.

3. The method for dynamic evaluation of urban green space ecosystem services based on multi-source data fusion as described in claim 1, characterized in that, The local spatial correlation statistical test method in step S3 is performed according to the following sub-steps: Using a regular grid with a predetermined spatial resolution or administrative units as the basic analysis units, the density values ​​of the spatiotemporal distribution proxy data of socioeconomic activity intensity within each unit are statistically analyzed to generate a spatial density distribution matrix of socioeconomic activity intensity. The spatial weight matrix between basic analysis units is constructed based on the inverse distance weighting method or the fixed distance band method. The Getis-Ord local spatial correlation statistic is used to conduct a local spatial autocorrelation test on the spatial density distribution matrix of socio-economic activity intensity, and the standardized statistic Z value and its corresponding significance probability p value of each unit are obtained. Spatial cells with Z-values ​​greater than 1.96 and p-values ​​less than 0.05 were identified as high-activity hotspot cells, and the corresponding spatial cells passed the positive spatial clustering significance test; spatial cells with Z-values ​​less than -1.96 and p-values ​​less than 0.05 were identified as low-activity coldspot cells, and the corresponding spatial cells passed the negative spatial clustering significance test. Urban green spaces whose geometric boundaries intersect with high-activity-intensity hotspot units and whose intersecting area accounts for more than 50% of the total area of ​​the green space are identified as typical high-activity-intensity green spaces; urban green spaces whose geometric boundaries intersect with low-activity-intensity coldspot units and whose intersecting area accounts for more than 50% of the total area of ​​the green space are identified as typical low-activity-intensity green spaces. An equal number of samples were selected from both high-activity-intensity and low-activity-intensity typical green spaces to serve as typical green spaces.

4. The method for dynamic evaluation of urban green space ecosystem services based on multi-source data fusion as described in claim 1, characterized in that, In step S1, the landscape pattern characteristic parameters are calculated using the following indicators across the three dimensions of spatial composition, morphological structure, and spatial configuration: Spatial composition dimensions include: patch area ratio index and maximum patch area index; Morphological and structural dimensions include: landscape shape index and patch fractal dimension; Spatial configuration dimensions include: clustering index, spread index, and Shannon diversity index.

5. The method for dynamic evaluation of urban green space ecosystem services based on multi-source data fusion as described in claim 1, characterized in that, The methods for assessing the value of ecosystem services in step S4 include: Using land use data, meteorological data, and soil data as inputs, the system simulates ecological processes such as water production, carbon sequestration, habitat quality, and soil conservation, and outputs the service value for each period. And / or, The total value for each period was calculated after localizing the equivalent service value per unit area of ​​various types of green spaces based on the biomass correction coefficient of the study area. And / or, The questionnaire survey quantifies residents' willingness to pay for specific ecological services in monetary form.

6. The method for dynamic assessment of urban green space ecosystem services based on multi-source data fusion as described in claim 1, characterized in that, In step S5, multiple buffer analysis units with different radii are generated using buffer technology based on typical green space boundaries. These include a first-level buffer, a second-level buffer, and a third-level buffer. The radii of the three-level buffers are determined based on the walkability standard or public transportation service radius standard of the study area, so that the three-level buffers correspond to the accessibility range of three travel modes: walking, non-motorized vehicles, and public transportation, respectively. The comparative indicators involved in spatial response relationship analysis include: green area, landscape shape index, vegetation species composition and vertical structure, patch connectivity, aggregation degree and species diversity index. The values ​​of the above indicators for typical green spaces with high activity intensity and typical green spaces with low activity intensity in each level of buffer zone were calculated respectively. Nonparametric statistical test methods were used to compare the differences of the two types of typical green spaces in each indicator, and key landscape pattern indicators that have a differentiating effect on the intensity of socio-economic activities were identified.

7. The method for dynamic evaluation of urban green space ecosystem services based on multi-source data fusion as described in claim 1, characterized in that, The specific form of the coupled differential equation system in step S6 is as follows: dN1(t) / dt=r1*N1(t)*(1-N1(t) / K1)+α 12 *N1(t)*N2(t); dN2(t) / dt=r2*N2(t)*(1-N2(t) / K2)+α 21 *N1(t)*N2(t); Where N1(t) is the state variable value of the first symbiotic unit at time t; N2(t) is the state variable value of the second symbiotic unit at time t; r1 is the natural growth rate parameter of the first symbiotic unit; r2 is the natural growth rate parameter of the second symbiotic unit; K1 is the environmental carrying capacity limit parameter when the first symbiotic unit grows independently; K2 is the environmental carrying capacity limit parameter when the second symbiotic unit grows independently; α 12 α is the influence coefficient of the second symbiotic unit on the growth rate of the first symbiotic unit. 21 α is the influence coefficient of the growth rate of the first symbiotic unit on the second symbiotic unit. 12 and α 21 These are two cross-influence coefficients, which together constitute a symbiotic quantitative indicator.

8. The method for dynamic evaluation of urban green space ecosystem services based on multi-source data fusion as described in claim 1, characterized in that, The environmental Gini coefficient in step S7 is calculated as follows: The spatial units within the study area are sorted by population from smallest to largest. The Lorenz curve is plotted with the proportion of the cumulative population of each spatial unit to the total population as the horizontal axis and the proportion of the cumulative green space ecological service value of the corresponding spatial unit to the total value as the vertical axis. The ratio of the enclosed area between the Lorenz curve and the line of absolute fairness to the area of ​​the triangle below the line of absolute fairness is used as the environmental Gini coefficient, with a value range of 0 to 1. The green contribution coefficient is calculated as follows: the ratio of the green area of ​​each spatial unit to the total green area of ​​the study area is used as the numerator, and the ratio of the construction land area of ​​the spatial unit to the total construction land area of ​​the study area is used as the denominator. The ratio of the two is the green contribution coefficient.

9. The method for dynamic evaluation of urban green space ecosystem services based on multi-source data fusion as described in claim 1, characterized in that, The criteria for determining whether the environmental Gini coefficient, green contribution coefficient, and symbiotic quantitative indicators simultaneously meet the conditions for coordinated symbiosis in step S7 are as follows: Condition 1: The environmental Gini coefficient is less than the minimum environmental Gini coefficient for each period within the assessment period; Condition 2: The green contribution coefficient is greater than 1; Condition 3: Both cross-influence coefficients in the symbiotic quantitative indicators are positive.

10. A dynamic evaluation system for urban green space ecosystem services based on multi-source data fusion, characterized in that, include: The first data acquisition module is used to acquire patch data of urban green space landscape composition types and construct a dataset of urban green space landscape pattern characteristic parameters. The second data acquisition module is used to acquire at least two types of spatiotemporal distribution proxy data of socioeconomic activity intensity, and to construct a spatiotemporal feature dataset of socioeconomic activity intensity. The typical green space identification module is used to conduct spatial clustering tests on urban green spaces across the entire region based on a dataset of spatiotemporal characteristics of socioeconomic activity intensity, and to identify typical green spaces with high activity intensity and typical green spaces with low activity intensity. The assessment module is used to conduct multi-period ecosystem service value assessments of typical green spaces and to construct an ecosystem service value assessment dataset. The spatial response relationship analysis module is used to delineate multiple buffer analysis units with different radii based on typical green spaces, analyze the spatial response relationship between landscape pattern characteristic parameters and ecosystem service value within each buffer, and output spatial response relationship characteristic data. The symbiotic quantitative index acquisition module is used to define the intensity of urban green space ecological service and the intensity of socio-economic activities as the first symbiotic unit and the second symbiotic unit, respectively. It constructs a symbiotic analysis model based on a set of coupled differential equations containing cross-influence coefficients. The values ​​of each period of the ecological service value assessment dataset and the spatiotemporal characteristic dataset of socio-economic activity intensity are used as the state variables of the two symbiotic units, respectively. The parameters of the coupled differential equations are fitted to obtain the cross-influence coefficients as symbiotic quantitative indicators. The coordination and balance threshold interval determination module is used to calculate the environmental Gini coefficient and green contribution coefficient based on the spatiotemporal distribution proxy data of socioeconomic activity intensity and the ecological service value assessment dataset, respectively. It sets at least five urban green space coverage gradient scenarios, simulates the response trajectory of environmental Gini coefficient, green contribution coefficient and symbiotic quantitative indicators under each scenario, and determines the green space coverage interval in which environmental Gini coefficient, green contribution coefficient and symbiotic quantitative indicators simultaneously meet the coordination and symbiotic conditions as the coordination and balance threshold interval. The dynamic trend analysis output module is used to arrange the multi-period ecosystem service value assessment datasets in time series, combine them with multi-period landscape pattern characteristic parameter datasets, calculate the periodic change rate and direction of ecosystem service value of each typical green space during the assessment period, and output the dynamic trend analysis results.

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