Optimal configuration method and system for blue-green space

Abstract

The invention relates to an optimal configuration method and system for a blue-green space. The optimal configuration method of the blue-green space comprises the following steps: acquiring acquisition data of a to-be-evaluated area; performing landscape pattern quantitative analysis on the to-be-evaluated area based on the collected data; carrying out fusion degree analysis on the to-be-evaluated area based on a landscape pattern quantitative analysis result; performing landscape optimization evaluation on the to-be-evaluated area based on a landscape pattern quantitative analysis result and a fusion degree analysis result to obtain an optimal fusion degree; and optimizing the to-be-evaluated region based on the optimal fusion degree. According to the method, accurate evaluation of the blue-green space can be realized, and space optimization configuration can be effectively carried out, so that the space distribution of the blue-green space is reasonable, and the problems of fragmentation, poor connectivity and the like do not exist.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological assessment, and in particular to a method and system for optimizing the configuration of blue-green spaces. Background Art

[0002] With the acceleration of urbanization, urban ecological environments face severe challenges. Blue-green spaces, as crucial urban ecological infrastructure, play a vital role in regulating urban microclimates, improving environmental quality, and enhancing ecosystem functions. However, due to a lack of effective spatial optimization methods during urban construction, the spatial distribution of blue-green spaces is often irrational, with technical issues such as severe fragmentation and poor connectivity. These issues severely restrict the full realization of the ecological functions of urban blue-green spaces.

[0003] Currently, there is an increasingly urgent need both domestically and internationally for optimized blue-green space configurations. Practice has demonstrated that the ecological benefits of blue-green spaces can be significantly enhanced through rational spatial planning and layout optimization. However, existing technical solutions still face numerous challenges in practical application: first, the evaluation index system is simplistic or redundant, making it difficult to reflect the structural and functional synergy of blue-green spaces; second, a lack of systematic optimization path design makes it difficult to effectively guide actual spatial layout adjustments; and third, unclear configuration grading standards and poor operability make it difficult to support refined, locally adapted management. In particular, key technical aspects such as quantitative assessment of blue-green space integration, identification of response mechanisms, and optimal spatial configuration still lack a universal, integrated technical solution with verifiable effectiveness.

[0004] However, with the increasing refinement and digitization of urban ecological space management, higher requirements are being placed on the integration accuracy of blue-green space allocation schemes, the interpretability of optimization paths, and the degree of technical integration. In practical applications, a fusion assessment method with a clear indicator system, a response modeling mechanism, and the ability to determine optimization intervals is urgently needed to adapt to diverse urban spatial patterns and planning objectives. Summary of the Invention

[0005] Based on this, it is necessary to provide a method and system for optimizing the configuration of blue-green space.

[0006] To achieve the above objectives, in a first aspect, the present invention provides a method for optimizing configuration of a blue-green space, the method comprising:

[0007] Obtaining collected data for the area to be assessed;

[0008] Based on the collected data, quantitative analysis of the landscape pattern of the area to be assessed is performed;

[0009] Performing a fusion analysis on the area to be assessed based on the results of the quantitative analysis of the landscape pattern;

[0010] Based on the results of the landscape pattern quantitative analysis and the results of the fusion degree analysis, a landscape optimization assessment is performed on the area to be assessed to obtain an optimal fusion degree;

[0011] The area to be evaluated is optimized based on the optimal fusion degree.

[0012] The above-mentioned method for optimizing the configuration of blue-green space includes: obtaining collected data of the area to be evaluated; conducting a quantitative analysis of the landscape pattern of the area to be evaluated based on the collected data; conducting a fusion analysis of the area to be evaluated based on the results of the quantitative landscape pattern analysis; conducting a landscape optimization assessment of the area to be evaluated based on the results of the quantitative landscape pattern analysis and the fusion analysis to obtain an optimal fusion degree; and optimizing the area to be evaluated based on the optimal fusion degree. The present invention can achieve accurate assessment of blue-green space and effectively optimize spatial configuration, ensuring a reasonable spatial distribution of blue-green space without problems such as fragmentation and poor connectivity.

[0013] In some embodiments, the quantitative analysis of the landscape pattern of the area to be assessed based on the collected data includes:

[0014] Acquire landscape component indicators of the area to be assessed based on the collected data;

[0015] Obtaining landscape structure indicators of the area to be assessed;

[0016] Obtain a landscape connectivity index for the area to be assessed.

[0017] In some embodiments, obtaining the landscape component index of the area to be assessed based on the collected data includes:

[0018] Obtaining the blue space coverage of the area to be evaluated;

[0019] Obtaining the green space coverage of the area to be assessed;

[0020] The area to be evaluated is divided into a plurality of statistical units, and the blue-green space coverage rate of each statistical unit is obtained.

[0021] In some embodiments, obtaining the landscape structure index of the area to be assessed includes:

[0022] Obtain the number of blue patches and the number of green patches in the area to be evaluated;

[0023] Obtain the average area of ​​blue patches and the average area of ​​green patches in the area to be evaluated;

[0024] The maximum patch index is obtained based on the percentage of the area of ​​the largest patch among all patches to the area of ​​the area to be evaluated; the landscape structure indicators of the area to be evaluated include: the number of blue patches, the number of green patches, the average area of ​​the blue patches, the average area of ​​the green patches and the maximum patch index.

[0025] In some embodiments, obtaining the landscape connectivity index of the area to be assessed includes:

[0026] The aggregation index COHESION is obtained based on the following formula:

[0027]

[0028] Among them, p ij is the perimeter of the jth patch in the i-th land type, a ij is the area of ​​the jth patch in the i-th land category, A is the total area of ​​the area to be assessed; m is the total number of patches in the i-th land category;

[0029] The effective mesh size MESH is obtained based on the following formula:

[0030]

[0031] Among them, a ij is the area of ​​the jth patch in the i-th land class, A is the total area of ​​the landscape; m is the total number of patches in the i-th land class;

[0032] In some embodiments, performing a fusion analysis on the area to be assessed based on the results of the landscape pattern quantitative analysis includes:

[0033] Create buffer zones for each blue and green patch;

[0034] The fusion degree I of each plaque is obtained based on the following formula:

[0035]

[0036] Where Ab∩Ag is the intersection area of ​​the buffer zone and the heterogeneous patch; A b+g is the area of ​​the buffer zone; K is the adjustable coefficient;

[0037] Establish a grading standard for integration level.

[0038] In some embodiments, the landscape optimization assessment of the area to be assessed based on the results of the landscape pattern quantitative analysis and the results of the fusion degree analysis to obtain the optimal fusion degree includes:

[0039] The landscape multifunctional index MLI is constructed based on the following formula:

[0040] MLI=w1×COHESION'+w2×MESH';

[0041] Where CHOESIN' is the normalized clustering index, ranging from [0, 1]; MESH' is the normalized effective mesh size, ranging from [0, 1]; w1 and w2 are weight coefficients, and w1 + w2 = 1;

[0042] The generalized additive model was used to analyze the nonlinear relationship between integration and landscape indicators: f = s(I, PLAND) + A; where f is the landscape multifunctional index (MLI), I is the integration, PLAND is the coverage of blue-green space, A is the area of ​​the area to be evaluated, and S() is a smooth function used to fit the nonlinear relationship.

[0043] Constructing an optimization objective function based on the nonlinear relationship;

[0044] Set constraints;

[0045] The optimization objective function is solved using a grid search method based on the constraint conditions to obtain the optimal fusion degree.

[0046] In some embodiments, solving the optimization objective function based on the constraint conditions using a grid search method to obtain an optimal fusion degree includes:

[0047] Dividing the fusion degree and the blue-green space coverage into multiple value intervals as model input parameters;

[0048] Traverse all possible combinations of integration degree and coverage rate, and calculate the landscape multifunctionality index under each set of parameters;

[0049] According to the response result of the landscape multifunctional index, the corresponding fusion degree value when the landscape multifunctional index reaches the maximum value is determined, and the fusion degree is the optimal fusion degree.

[0050] In some embodiments, optimizing the area to be evaluated based on the optimal fusion degree and the fusion degree grading standard includes:

[0051] If the area to be assessed is a low-integration area, increase waterfront green space, build a wetland park, or restore riverside vegetation;

[0052] If the area to be assessed is a moderately integrated area, the border area between the existing water body and the green space will be expanded, a blue-green composite node space will be constructed, or an ecological buffer zone will be added around the existing water body;

[0053] If the area to be evaluated is a high-integration area, the spatial layout is optimized, the patch shape is adjusted, or corridor connections are increased.

[0054] In a second aspect, the present invention further provides a blue-green space optimization configuration system, the blue-green space optimization configuration system comprising:

[0055] Data acquisition equipment, used to obtain data from the area to be evaluated;

[0056] A landscape pattern quantitative analysis module, configured to perform a landscape pattern quantitative analysis on the area to be assessed based on the collected data;

[0057] A fusion degree analysis module is used to perform fusion degree analysis on the area to be evaluated based on the results of the landscape pattern quantitative analysis;

[0058] A landscape optimization assessment module is used to perform landscape optimization assessment on the area to be assessed based on the results of the landscape pattern quantitative analysis and the results of the fusion degree analysis to obtain an optimal fusion degree;

[0059] An optimization module is used to optimize the area to be evaluated based on the optimal fusion degree.

[0060] The aforementioned blue-green space optimization system includes data acquisition equipment, a landscape pattern quantitative analysis module, a fusion analysis module, a landscape optimization assessment module, and an optimization module. This blue-green space optimization method and system is applicable to a variety of data sources, including remote sensing imagery, land use maps, and urban planning maps. It boasts excellent platform adaptability and scalability, providing standardized, operational, and replicable fusion optimization solutions for new urban development, ecological corridor layout, and waterfront area improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the conventional technology, the following briefly introduces the drawings required for use in the embodiments or the conventional technology descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0062] Figure 1 This is a flow chart of a method for optimizing configuration of blue-green spaces provided in one embodiment of the present invention;

[0063] Figure 2 A schematic diagram of a method for calculating fusion degree provided in one embodiment of the present invention, illustrating the construction of a blue-green patch buffer zone and the spatial relationship between its overlapping areas;

[0064] Figure 3 This is a response relationship diagram between the integration degree and the landscape multifunctional index provided in one embodiment of the present invention, drawn based on the generalized additive model (GAM) fitting results;

[0065] Figure 4 This is a heat map of the landscape multifunctional index (MLI) response in the parameter space of integration degree and blue-green space coverage provided by one embodiment of the present invention, where the dark area corresponds to the local maximum area of ​​the MLI value;

[0066] Figure 5 A comparison chart of the integration degree and landscape multifunctional index of a typical coastal area before and after optimization provided in one embodiment of the present invention is used to verify the improvement effect of the optimization method of the present invention;

[0067] Figure 6 This is a structural block diagram of a blue-green space optimization configuration system provided in another embodiment of the present invention.

[0068] Explanation of reference numerals: 10. Data acquisition equipment; 20. Landscape pattern quantitative analysis module; 30. Fusion degree analysis module; 40. Landscape optimization assessment module; 50. Optimization module. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0070] The blue-green space evaluation method mainly relies on a single indicator for evaluation, such as green space ratio, water surface ratio, etc. This evaluation method lacks a systematic evaluation method for the overall pattern of blue-green space. The optimization of blue-green space mostly stays at the qualitative description level, and lacks a quantifiable optimization method; especially in terms of spatial layout, it is difficult to accurately evaluate the pros and cons of different configuration schemes, resulting in the optimization effect being difficult to guarantee. Although the industry generally recognizes the importance of blue-green space integration, there is a lack of quantitative evaluation standards for the degree of integration. When optimizing blue-green space, multiple goals need to be considered simultaneously, such as landscape connectivity, spatial balance, etc. There may be conflicts between these goals. How to establish a quantitative evaluation system with multi-objective balance and formulate optimization strategies based on this is an important problem that the present invention needs to solve.

[0071] The main technical solutions for optimizing the allocation of urban blue-green spaces include the following: 1) Landscape index-based evaluation methods: This is the most widely used technical solution. This method establishes a landscape measurement index system and calculates indicators such as landscape area ratio, fragmentation, and connectivity to assess the spatial pattern of blue-green spaces. While this method can quantitatively characterize landscape characteristics, its evaluation index system is overly simplistic and fails to reflect the interactions between spatial elements. Furthermore, its calculation method is limited to analyzing a single type of spatial feature and lacks a quantitative expression of the degree of integration of spatial elements, making it difficult to provide effective technical support for the optimal allocation of blue-green spaces. 2) GIS-based spatial analysis methods: GIS-based spatial analysis methods are another important technical approach. These methods utilize geographic information systems for spatial data processing and analysis, including techniques such as buffer analysis and overlay analysis, and can effectively identify the distribution characteristics and changing trends of blue-green spaces. However, the spatial analysis techniques used in these methods are relatively simple, primarily confined to the descriptive analysis level, and lack standardized analytical processes and technical specifications. Existing GIS analysis methods often fail to meet practical needs, especially when conducting multi-scale and multi-level comprehensive analyses. 3) Evaluation methods based on ecosystem services: This approach attempts to construct an evaluation system from the perspective of ecosystem functions to assess the ecological benefits of blue-green spaces. Although this approach considers the integrity of the ecosystem, it faces many difficulties in its technical implementation: evaluation indicators are difficult to quantify, the spatial scale conversion mechanism is unclear, and there is a lack of effective integration with actual planning and design. This makes it difficult for this approach to achieve the expected results in practical applications. 4) Design methods based on urban planning: Design-oriented optimization methods are a highly practical technical solution that achieves the optimal allocation of blue-green spaces through planning and design measures such as increasing water forests and restoring riparian zones. However, this approach has significant technical deficiencies: optimization objectives are difficult to quantify, there is a lack of standardized technical processes, optimization results are difficult to objectively evaluate, and the verification mechanism for the feasibility of the scheme is still imperfect. These technical deficiencies seriously restrict the effectiveness of this approach in actual engineering applications.

[0072] In one embodiment, see Figure 1 The present invention provides a method for optimizing the configuration of a blue-green space. The method for optimizing the configuration of a blue-green space may include the following steps: S10 to S50.

[0073] S10: Acquire collected data of the area to be evaluated.

[0074] S20: Based on the collected data, quantitative analysis of the landscape pattern of the area to be assessed is performed.

[0075] S30: performing a fusion degree analysis on the area to be evaluated based on the results of the quantitative analysis of the landscape pattern.

[0076] S40: Based on the results of the landscape pattern quantitative analysis and the results of the fusion degree analysis, a landscape optimization evaluation is performed on the area to be evaluated to obtain an optimal fusion degree.

[0077] S50: Optimizing the area to be evaluated based on the optimal fusion degree.

[0078] The above-mentioned method for optimizing the configuration of blue-green spaces includes: obtaining collected data from the area to be evaluated; conducting a quantitative landscape pattern analysis of the area to be evaluated based on the collected data; conducting a fusion analysis of the area to be evaluated based on the results of the quantitative landscape pattern analysis; conducting a landscape optimization assessment of the area to be evaluated based on the results of the quantitative landscape pattern analysis and the fusion analysis to obtain an optimal fusion degree; and optimizing the area to be evaluated based on the optimal fusion degree. The above-mentioned method for optimizing the configuration of blue-green spaces can achieve accurate assessment of blue-green spaces and effectively optimize spatial configuration, ensuring a reasonable spatial distribution of blue-green spaces without problems such as fragmentation and poor connectivity.

[0079] In step S10, refer to Figure 1 In step S10, collected data of the area to be evaluated is obtained.

[0080] For example, the collected data may include multi-source remote sensing data; specifically, optical remote sensing images, radar, or drone images, etc. The spatial resolution range of the area to be assessed is adjustable from 10 to 100 meters. The temporal requirements for the collected data may include at least two periods of data. The collected data can be reported in mainstream formats such as GeoTIFF or IMG.

[0081] As an example, after acquiring the collected data, a feature classification system can be constructed, specifically as follows: primary classification: blue space, green space, and other land uses; secondary classification: blue space: including water bodies (rivers or lakes) and wetlands; green space: woodland, grassland, farmland, and green land; other land uses: construction land and transportation land, etc. The above classification system can be adjusted according to actual application needs.

[0082] As an example, after the land feature classification system is constructed, it also includes classification accuracy control, specifically: using a stratified random sampling method to select verification samples; the sample size is not less than 0.1% of the total number of pixels in each category, the overall classification accuracy is controlled within the range of 80% to 90%, and the Kappa system is not less than 0.75.

[0083] As an example, after obtaining the collected data, the step of performing data standardization on the collected data is also included, specifically including: unifying the projection coordinate system; using the nearest neighbor method, bilinear interpolation method or cubic convolution method for grid sampling, and the specific selection can be determined according to the data characteristics; the output resolution can be selected within the range of 10-100 meters.

[0084] In step S20, refer to Figure 1 In step S20, based on the collected data, a quantitative analysis of the landscape pattern of the area to be assessed is performed.

[0085] As an example, in step S20, the quantitative analysis of the landscape pattern of the area to be assessed based on the collected data may include the following steps: S201 to S203.

[0086] S201: Obtaining landscape component indicators of the area to be evaluated based on the collected data.

[0087] S202: Obtain landscape structure indicators of the area to be evaluated.

[0088] S203: Obtaining a landscape connectivity index of the area to be evaluated.

[0089] As an example, in step S201, obtaining the landscape component indicators of the area to be evaluated based on the collected data may include the following steps: obtaining the blue space coverage (PLAND-blue) of the area to be evaluated; obtaining the green space coverage (PLAND-green) of the area to be evaluated; dividing the area to be evaluated into multiple statistical units, and obtaining the blue-green space coverage of each statistical unit.

[0090] Specifically, the blue space coverage rate can be calculated as the percentage of the blue space in the area of ​​the area to be evaluated. The green space coverage rate can be calculated as the percentage of the blue space area in the area of ​​the area to be evaluated. Specifically, the size of each statistical unit can be set according to actual needs. For example, the statistical unit can be 1 km × 1 km.

[0091] As an example, in step S202, obtaining the landscape structure index of the area to be evaluated may include the following steps: obtaining the number of patches (NP), that is, obtaining the number of blue patches and the number of green patches in the area to be evaluated; obtaining the average patch area (MPS), that is, obtaining the average area of ​​blue patches and the average area of ​​green patches in the area to be evaluated; obtaining the maximum patch index (LPI) based on the percentage of the area of ​​the largest patch among all patches to the area of ​​the area to be evaluated; the landscape structure index of the area to be evaluated includes: the number of blue patches, the number of green patches, the average area of ​​the blue patches, the average area of ​​the green patches and the maximum patch index.

[0092] As an example, in step S203, obtaining the landscape connectivity index of the area to be evaluated may include:

[0093] The aggregation index COHESION is obtained based on the following formula:

[0094]

[0095] Among them, p ij is the perimeter of the jth patch in the i-th land type, a ij is the area of ​​the jth patch in the i-th land category, A is the total area of ​​the area to be assessed; m is the total number of patches in the i-th land category;

[0096] The effective mesh size MESH is obtained based on the following formula:

[0097]

[0098] Among them, a ij is the area of ​​the jth patch in the i-th land class, A is the total area of ​​the landscape; m is the total number of patches in the i-th land class;

[0099] As an example, the effective grid size quantifies the degree of fragmentation of the calculated landscape, with lower values ​​representing more fragmented landscapes. When the effective grid size reaches its maximum value, i.e., the landscape area, it represents a landscape consisting of one complete patch.

[0100] In step S30, refer to Figure 1 In step S30, a fusion degree analysis is performed on the area to be evaluated based on the results of the quantitative analysis of the landscape pattern.

[0101] As an example, in step S30, performing a fusion analysis on the area to be evaluated based on the result of the landscape pattern quantitative analysis may include:

[0102] Create buffer zones for each blue and green patch;

[0103] The fusion degree I of each plaque is obtained based on the following formula:

[0104]

[0105] Where Ab∩Ag is the intersection area of ​​the buffer zone and the heterogeneous patch; A b+g is the area of ​​the buffer zone; K is an adjustable coefficient, and the value range of K can be 80 to 120;

[0106] Establish a grading standard for integration level.

[0107] Specifically, the width of each buffer zone may be 0.5-2 m; however, the width may be adjusted according to actual application scenarios. Figure 2 A schematic diagram of a fusion degree calculation method provided in one embodiment of the present invention illustrates the construction of a blue-green patch buffer zone and the spatial relationship between their overlapping areas. The diagram illustrates the calculation logic for the fusion degree I: a buffer zone of a certain width is established around each blue or green patch, and the overlapping area within the buffer zone with heterogeneous patches is calculated. The fusion degree is then calculated using the fusion degree calculation formula.

[0108] Specifically, based on the natural breakpoint method, the fusion degree grading standard can include the following three levels: low fusion degree: I∈[0,2%), moderate fusion degree: I∈[2%,8%), and high fusion degree: I∈[8%,100%). The fusion degree thresholds can be adjusted by ±1% based on actual application needs.

[0109] In step S40, refer to Figure 1 In step S40, a landscape optimization evaluation is performed on the area to be evaluated based on the results of the landscape pattern quantitative analysis and the results of the fusion degree analysis to obtain an optimal fusion degree.

[0110] As an example, in step S40, performing landscape optimization assessment on the area to be assessed based on the results of the landscape pattern quantitative analysis and the results of the fusion degree analysis to obtain the optimal fusion degree may include the following steps:

[0111] Construct a landscape multifunctional index (MLI). Specifically, the landscape multifunctional index (MLI) can be constructed based on the following formula:

[0112] MLI=w1×COHESION'+w2×MESH';

[0113] Where CHOESIN' is the normalized clustering index, ranging from [0, 1]; MESH' is the normalized effective mesh size, ranging from [0, 1]; w1 and w2 are weight coefficients, and w1 + w2 = 1;

[0114] Establish a fusion optimization model; specifically, the generalized additive model (GAM) can be used to analyze the nonlinear relationship between the fusion degree and landscape indicators: f = s(I, PLAND) + A; where f is the landscape multifunctional index MLI, I is the fusion degree, PLAND is the blue-green space coverage rate; A is the area of ​​the area to be evaluated; S() is a smooth function used to fit the nonlinear relationship;

[0115] Specifically, the GAM model training can adopt the following parameter settings: smooth parameter selection based on the GCV criterion; using the cubic spline function as the basis function; automatically selecting the number of nodes, and the number of nodes can range from [10, 20].

[0116] Specifically, an optimization objective function is constructed based on the nonlinear relationship, and constraints are set, including: 1) fusion index range: I∈[0,20%]; 2) coverage range: PLAND∈[actual value±5%]; 3) region area: A=actual value.

[0117] As an example, the optimization objective function is solved using a grid search method based on the constraint conditions to obtain the optimal fusion degree; specifically, the method includes:

[0118] Dividing the fusion degree and the blue-green space coverage into multiple value intervals as model input parameters;

[0119] Traverse all possible combinations of integration degree and coverage rate, and calculate the landscape multifunctionality index under each set of parameters;

[0120] According to the response result of the landscape multifunctional index, the corresponding fusion degree value when the landscape multifunctional index reaches the maximum value is determined, and the fusion degree is the optimal fusion degree.

[0121] Specifically, set the fusion degree parameter I∈[I min ,I max ] and blue-green space coverage PLAND∈[PLAND min ,PLAND max ], setting the step size to 0.1% and 0.5% respectively. The objective function is constructed as follows:

[0122]

[0123] Where I is the integration degree, PLAND is the coverage of blue-green space, and MLI(I, PLAND) represents the landscape multifunctional index predicted by the generalized additive model (GAM) under different combinations of integration degree and coverage. By traversing the parameter combination space, the MLI response value under different combinations of integration degree and coverage is obtained, and the integration degree value I corresponding to the maximum value of MLI is determined. *, that is, calculate the MLI value corresponding to each pair (I, PLAND) in turn, and finally determine the corresponding fusion value I when MLI reaches the maximum value * The obtained I* is the optimal integration degree of regional integration optimization. In the above process, green space coverage is used as an input variable of the generalized additive model to participate in response modeling, which is used to improve the prediction accuracy of the landscape multifunctional index, but is not used as the final optimization output result.

[0124] In some implementations, the grid search process may further include: calling the GAM model for each combination point, outputting a nonlinear response value based on a smooth function; recording the fusion degree value corresponding to the maximum value and outputting a fusion degree response heat map to assist in fusion level recommendation and visualization. Figures 3 to 5 The data shown are from more than 500 typical newly built areas in the coastal area of ​​the Yangtze River Delta. Figure 4 This method demonstrates the nonlinear response of the fusion degree under varying coverage conditions, assisting in determining the optimal fusion degree I*. Blue-green space coverage is included as a modeling input but not as the final optimization output. To ensure the applicability and stability of the method, the fusion degree, landscape pattern index, and coverage data used were standardized based on remote sensing interpretation and landscape pattern analysis results to construct a fusion optimization modeling and validation dataset. The associated maps demonstrate the typical response trends and optimization effects of this method across large, complex areas, demonstrating strong representativeness and applicability.

[0125] In step S50, refer to Figure 1 In step S50, the area to be evaluated is optimized based on the optimal fusion degree.

[0126] As an example, in step S50, the area to be evaluated is optimized based on the optimal integration degree and the integration degree grading standard, which may include: if the area to be evaluated is a low integration degree area, increasing waterfront green space, constructing a wetland park or repairing riverbank vegetation; if the area to be evaluated is a medium integration degree area, expanding the border area between the existing water body and the green space, constructing a blue-green composite node space or adding an ecological buffer zone around the existing water body; if the area to be evaluated is a high integration degree area, optimizing the spatial layout, adjusting the patch shape or increasing corridor connections.

[0127] As an example, when the area to be evaluated is a high-integration area, optimizing the spatial layout may be further optimizing the spatial layout by adjusting the spatial distribution and service radius of blue and green patches.

[0128] As an example, in step S50, for each evaluation unit, a target fusion degree can be calculated based on its actual coverage PLAND. The calculation results are divided into implementation priorities: Priority I: the difference between the current fusion degree and the target fusion degree is greater than 5%; Priority II: the difference between the current fusion degree and the target fusion degree is between 2-5%; Priority III: the difference between the current fusion degree and the target fusion degree is less than 2%.

[0129] In another embodiment, see Figure 6 The present invention also provides a blue-green space optimization configuration system, which includes: a data acquisition device 10, a landscape pattern quantitative analysis module 20, a fusion degree analysis module 30, a landscape optimization evaluation module 40 and an optimization module; wherein the data acquisition device 10 is used to obtain collected data of the area to be evaluated; the landscape pattern quantitative analysis module 20 is used to perform a landscape pattern quantitative analysis on the area to be evaluated based on the collected data; the fusion degree analysis module 30 is used to perform a fusion degree analysis on the area to be evaluated based on the results of the landscape pattern quantitative analysis; the landscape optimization evaluation module 40 is used to perform a landscape optimization evaluation on the area to be evaluated based on the results of the landscape pattern quantitative analysis and the results of the fusion degree analysis to obtain an optimal fusion degree; and the optimization module 50 is used to optimize the area to be evaluated based on the optimal fusion degree.

[0130] The above-mentioned blue-green space optimization configuration system includes: a data acquisition device 10, a landscape pattern quantitative analysis module 20, a fusion analysis module 30, a landscape optimization assessment module 40, and an optimization module 50. The above-mentioned blue-green space optimization configuration system can achieve accurate assessment of blue-green space and effectively optimize spatial configuration, ensuring that the spatial distribution of blue-green space is reasonable and does not suffer from problems such as fragmentation and poor connectivity.

[0131] The present invention innovatively proposes a set of urban blue-green space integration assessment methods. Unlike the existing technology that only uses a single indicator for evaluation, the present invention develops a spatial evaluation technology based on buffer zone analysis. Specifically, by setting a controllable buffer zone and combining it with an innovative fusion quantification calculation formula, an accurate assessment of the blue-green space configuration is achieved. At the same time, the method also integrates the three-dimensional indicator system of spatial components, structural characteristics and connectivity, making the evaluation results more comprehensive and objective. The blue-green space optimization configuration method of the present invention can achieve accurate quantification and systematic optimization of urban blue-green space, effectively improving the scientific nature of spatial configuration and the synergy of ecological functions. Among them, the integration index is calculated based on the superimposed area of ​​the blue-green patch buffer zone, has a clear spatial physical meaning, and can be adapted to different regional scales and data sources through parameter adjustment. The landscape optimization assessment part introduces a generalized additive model to model the nonlinear response relationship between integration and landscape pattern indicators (such as aggregation, effective grid size, etc.), thereby enhancing the interpretability of the fusion mechanism and the fitting ability of the model. On this basis, the present invention constructs a parameter optimization objective function based on the optimal fusion degree, and uses the grid search method to traverse the parameter combination space within the constraint range to determine the optimal fusion parameters that maximize the landscape multifunctional index (MLI). Combining the fusion classification standards with regional characteristics, differentiated spatial optimization strategies can also be proposed to enhance the adaptability and promotion value of the scheme. The above-mentioned blue-green space optimization configuration method has good applicability and versatility, can effectively reduce spatial fragmentation, and improve the level of coordinated optimization of ecological connectivity and system functions.

[0132] Secondly, the present invention employs a technologically advanced generalized additive model as its analytical method. This model offers significant technical advantages: it can simultaneously address the synergistic relationships between multiple variables, possesses adaptive smoothing capabilities, and can effectively handle complex nonlinear relationships. Practical applications have demonstrated that the model's fitting accuracy can reach over 0.70, significantly outperforming the simple linear analysis methods commonly used in existing technologies.

[0133] Third, this invention provides a comprehensive set of technical parameter systems, significantly improving the operability of optimization strategies. By establishing a grading standard for the degree of integration, it provides clear parameter optimization ranges for different application scenarios. Furthermore, the developed iterative parameter optimization algorithm automatically adjusts optimization parameters based on specific circumstances, ensuring the optimal configuration solution.

[0134] Fourth, the present invention possesses strong technical versatility. This method can be implemented on mainstream GIS platforms such as ArcGIS and QGIS, and provides a standardized data interface that supports processing of multiple data formats, including raster and vector formats. Furthermore, the present invention can process data of varying spatial resolutions, making it suitable for analysis at multiple spatial scales, from block to city.

[0135] Fifth, the present invention establishes a standardized technical implementation process. This includes clear data quality requirements (accuracy must reach above 85%), unified spatial analysis units, and standardized indicator calculation steps. This standardized processing flow ensures the comparability and repeatability of analysis results.

[0136] Finally, the present invention utilizes a modular program structure design, which offers excellent scalability. The system supports parameter customization, allowing the indicator system to be adjusted according to actual needs. This design enables the present invention to flexibly respond to diverse application requirements, and has broad application prospects.

[0137] In summary, the present invention has achieved innovations in multiple aspects, including evaluation methods, analysis techniques, parameter systems, versatility, standardization, and scalability, overcoming the limitations of existing technologies and providing a set of scientific, reliable, and practical technical solutions for the optimization of urban blue-green spaces.

[0138] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features of the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0139] The above embodiments merely illustrate several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A method for optimizing the configuration of blue-green space, characterized in that: The optimization configuration method of the blue-green space includes: Obtaining collected data for the area to be assessed; Based on the collected data, quantitative analysis of the landscape pattern of the area to be assessed is performed; Performing a fusion analysis on the area to be assessed based on the results of the quantitative analysis of the landscape pattern; Based on the results of the landscape pattern quantitative analysis and the results of the fusion degree analysis, a landscape optimization assessment is performed on the area to be assessed to obtain an optimal fusion degree; The area to be evaluated is optimized based on the optimal fusion degree.

2. The method for optimizing the configuration of blue-green space according to claim 1, characterized in that: The quantitative analysis of the landscape pattern of the area to be assessed based on the collected data includes: Acquire landscape component indicators of the area to be evaluated based on the collected data; Obtaining landscape structure indicators of the area to be assessed; Obtain a landscape connectivity index for the area to be assessed.

3. The method for optimizing the configuration of blue-green space according to claim 2, characterized in that: The obtaining of landscape component indicators of the area to be assessed based on the collected data includes: Obtaining the blue space coverage of the area to be evaluated; Obtaining the green space coverage of the area to be assessed; The area to be evaluated is divided into a plurality of statistical units, and the blue-green space coverage rate of each statistical unit is obtained.

4. The method for optimizing the configuration of blue-green space according to claim 3, characterized in that: The obtaining of the landscape structure index of the area to be assessed includes: Obtain the number of blue patches and the number of green patches in the area to be evaluated; Obtain the average area of ​​blue patches and the average area of ​​green patches in the area to be evaluated; The maximum patch index is obtained based on the percentage of the area of ​​the largest patch among all patches to the area of ​​the area to be evaluated; the landscape structure indicators of the area to be evaluated include: the number of blue patches, the number of green patches, the average area of ​​the blue patches, the average area of ​​the green patches and the maximum patch index.

5. The method for optimizing the configuration of blue-green space according to claim 4, characterized in that: The obtaining of the landscape connectivity index of the area to be assessed includes: The aggregation index COHESION is obtained based on the following formula: Among them, p ij is the perimeter of the jth patch in the i-th land type, a ij is the area of ​​the jth patch in the i-th land category, A is the total area of ​​the area to be assessed; m is the total number of patches in the i-th land category; The effective mesh size MESH is obtained based on the following formula: Among them, a ij is the area of ​​the jth patch in the i-th land class, A is the total area of ​​the landscape; and m is the total number of patches in the i-th land class.

6. The method for optimizing the configuration of blue-green space according to claim 5, characterized in that: The performing of a fusion degree analysis on the area to be assessed based on the results of the quantitative analysis of the landscape pattern includes: Create buffer zones for each blue and green patch; The fusion degree I of each plaque is obtained based on the following formula: Where Ab∩Ag is the intersection area of ​​the buffer zone and the heterogeneous patch; A b+g is the area of ​​the buffer zone; K is the adjustable coefficient; Establish a grading standard for integration level.

7. The method for optimizing the configuration of blue-green space according to claim 6, characterized in that: The performing of landscape optimization assessment on the area to be assessed based on the results of the landscape pattern quantitative analysis and the results of the fusion degree analysis to obtain an optimal fusion degree includes: The landscape multifunctional index MLI is constructed based on the following formula: MLI=w1×COHESION'+w2×MESH'; Where CHOESIN' is the normalized clustering index, ranging from [0, 1]; MESH' is the normalized effective mesh size, ranging from [0, 1]; w1 and w2 are weight coefficients, and w1 + w2 = 1; The generalized additive model was used to analyze the nonlinear relationship between integration degree and landscape indicators: f = s(I, PLAND) + A; where f is the landscape multifunctional index (MLI), I is the integration degree, PLAND is the blue-green space coverage, A is the area of ​​the area to be assessed, and S() is a smooth function used to fit nonlinear relationships. Constructing an optimization objective function based on the nonlinear relationship; Set constraints; The optimization objective function is solved using a grid search method based on the constraint conditions to obtain the optimal fusion degree.

8. The method for optimizing the configuration of blue-green space according to claim 7, characterized in that: Solving the optimization objective function based on the constraint conditions using a grid search method to obtain the optimal fusion degree includes: Dividing the fusion degree and the blue-green space coverage into multiple value intervals as model input parameters; Traverse all possible combinations of integration degree and coverage rate, and calculate the landscape multifunctionality index under each set of parameters; According to the response result of the landscape multifunctional index, the corresponding fusion degree value when the landscape multifunctional index reaches the maximum value is determined, and the fusion degree is the optimal fusion degree.

9. The method for optimizing the configuration of blue-green space according to claim 8, characterized in that: Optimizing the area to be evaluated based on the optimal fusion degree and the fusion degree grading standard, including: If the area to be assessed is a low-integration area, waterfront green space will be increased, wetland parks will be constructed, or riverbank vegetation will be restored; if the area to be assessed is a medium-integration area, the bordering area between the existing water body and green space will be expanded, a blue-green composite node space will be constructed, or an ecological buffer zone will be added around the existing water body; if the area to be assessed is a high-integration area, the spatial layout will be optimized, the patch shape will be adjusted, or corridor connections will be increased.

10. A blue-green space optimization configuration system, characterized in that: The blue-green space optimization configuration system includes: Data acquisition equipment, used to obtain data from the area to be evaluated; A landscape pattern quantitative analysis module, configured to perform a landscape pattern quantitative analysis on the area to be assessed based on the collected data; A fusion degree analysis module is used to perform fusion degree analysis on the area to be evaluated based on the results of the landscape pattern quantitative analysis; A landscape optimization assessment module is used to perform landscape optimization assessment on the area to be assessed based on the results of the landscape pattern quantitative analysis and the results of the fusion degree analysis to obtain an optimal fusion degree; An optimization module is used to optimize the area to be evaluated based on the optimal fusion degree.

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