Optimized configuration method and system for blue-green space

Abstract

This invention relates to a method and system for optimizing the configuration of blue-green spaces. The method includes: acquiring collected data of the area to be evaluated; performing quantitative landscape pattern analysis of the area based on the collected data; performing integration analysis of the area based on the results of the quantitative landscape pattern analysis; conducting landscape optimization evaluation of the area based on the results of the quantitative landscape pattern analysis and the integration analysis to obtain the optimal integration degree; and optimizing the area based on the optimal integration degree. This invention enables accurate evaluation of blue-green spaces and effectively optimizes their configuration, resulting in a rational spatial distribution that avoids fragmentation and poor connectivity issues.

Description

Technical Field

[0001] This invention relates to the field of ecological assessment technology, and in particular to a method and system for optimizing the allocation of blue-green space. Background Technology

[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, during urban construction, the lack of effective spatial optimization methods often results in an irrational spatial distribution of blue-green spaces, exhibiting severe fragmentation and poor connectivity. These problems seriously restrict the realization of the ecological functions of urban blue-green spaces.

[0003] Currently, the demand for optimizing the configuration of blue-green spaces is increasingly urgent both domestically and internationally. Practice has shown that rational spatial planning and layout optimization can significantly improve the ecological benefits of blue-green spaces. However, existing related technical solutions still face many problems in practical application: First, the evaluation index system is either too simplistic or redundant, making it difficult to reflect the synergistic characteristics of the structure and function of blue-green spaces; second, there is a lack of systematic optimization path design, making it difficult to effectively guide actual spatial layout adjustments; and third, the configuration grading standards are unclear, lacking operability and failing to support refined management tailored to local conditions. In particular, in key technical areas such as the quantitative assessment of blue-green space integration, the identification of response mechanisms, and the optimized configuration of spaces, there is still a lack of universally applicable and verifiable integrated technical solutions.

[0004] However, with the continuous improvement of the refinement and digitalization of urban ecological space management, higher requirements are placed on the accuracy of the integration assessment of blue-green space configuration schemes, the interpretability of optimization paths, and the degree of technological integration. In practical applications, there is an urgent need for an integrated assessment method with a clear indicator system, response modeling mechanism, and optimization interval determination capability, which can adapt to diverse urban spatial patterns and planning objectives. Summary of the Invention

[0005] Therefore, 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 the configuration of blue-green space, the method comprising:

[0007] Acquire the collected data from the area to be evaluated;

[0008] Based on the collected data, a quantitative analysis of the landscape pattern of the area to be evaluated is performed.

[0009] Based on the results of the quantitative analysis of the landscape pattern, a degree of integration analysis is performed on the area to be evaluated.

[0010] Based on the results of the quantitative analysis of the landscape pattern and the results of the integration analysis, the landscape optimization assessment of the area to be evaluated is carried out to obtain the optimal integration degree.

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

[0012] The aforementioned method for optimizing the configuration of blue-green spaces includes: acquiring collected data of the area to be evaluated; performing quantitative landscape pattern analysis on the area to be evaluated based on the collected data; performing integration analysis on the area to be evaluated based on the results of the quantitative landscape pattern analysis; performing landscape optimization evaluation on the area to be evaluated based on the results of the quantitative landscape pattern analysis and the integration analysis to obtain the optimal integration degree; and optimizing the area to be evaluated based on the optimal integration degree. This invention can achieve accurate evaluation of blue-green spaces and effectively optimize spatial configuration, resulting in a reasonable spatial distribution of blue-green spaces without problems such as fragmentation and poor connectivity.

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

[0014] Based on the collected data, landscape component indicators of the area to be evaluated are obtained;

[0015] Obtain the landscape structure indicators of the area to be evaluated;

[0016] Obtain the landscape connectivity index of the area to be evaluated.

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

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

[0019] Obtain the green space coverage rate of the area to be evaluated;

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

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

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

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

[0024] The maximum patch index is obtained as a percentage of the area of ​​the largest patch among all patches to the area of ​​the region to be evaluated. The landscape structure indicators of the region 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 evaluated includes:

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

[0027]

[0028] Where, p ij Let a be the perimeter of the j-th patch in the i-th land category. ij Let A be the area of ​​the j-th patch in the i-th land class, and let A be the total area of ​​the area to be evaluated; m is the total number of patches in the i-th land class.

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

[0030]

[0031] Among them, a ij Let be the area of ​​the j-th patch in the i-th land class, A be the total area of ​​the landscape, and m be the total number of patches in the i-th land class.

[0032] In some embodiments, the integration analysis of the area to be evaluated based on the results of the quantitative analysis of the landscape pattern includes:

[0033] Establish a buffer zone for each blue and green patch;

[0034] The fusion degree I of each patch 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 Let K be the area of ​​the buffer zone; K is an adjustable coefficient.

[0037] Establish a grading standard for integration degree.

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

[0039] The Landscape Multifunctionality Index (MLI) is constructed based on the following formula:

[0040] MLI=w1×COHESION'+w2×MESH';

[0041] Wherein, CHOESIN' is the standardized clustering index, with a value range of [0,1]; MESH' is the standardized effective grid size, with a value range of [0,1]; w1 and w2 are weighting coefficients, and w1+w2=1;

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

[0043] An optimization objective function is constructed based on the aforementioned nonlinear relationship;

[0044] Set constraints;

[0045] Based on the constraints, the optimization objective function is solved using a grid search method to obtain the optimal fusion degree.

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

[0047] The fusion degree and the blue-green space coverage are both divided into multiple value ranges and used as model input parameters.

[0048] Iterate through all possible combinations of integration and coverage, and calculate the landscape multifunctionality index for each set of parameters;

[0049] Based on the response results of the landscape multifunctionality index, the degree of integration corresponding to the maximum value is determined, and this degree of integration is the optimal degree of integration.

[0050] In some embodiments, the region to be evaluated is optimized based on the optimal fusion degree and the fusion degree grading standard, including:

[0051] If the area to be evaluated is a low-integration area, then increase waterfront green space, construct a wetland park, or restore riverbank vegetation;

[0052] If the area to be evaluated is a moderately integrated area, then the area where the existing water body and green space meet should be expanded, a blue-green composite node space should be constructed, or an ecological buffer zone should be added around the existing water body.

[0053] If the area to be evaluated is a highly integrated area, then optimize the spatial layout, adjust the shape of the patches, or increase corridor connections.

[0054] Secondly, the present invention also provides an optimized configuration system for blue-green space, the optimized configuration system for blue-green space comprising:

[0055] Data acquisition equipment is used to acquire data from the area to be evaluated.

[0056] The landscape pattern quantitative analysis module is used to perform a quantitative analysis of the landscape pattern of the area to be evaluated based on the collected data.

[0057] The integration analysis module is used to perform integration analysis on the area to be evaluated based on the results of the quantitative analysis of the landscape pattern.

[0058] The landscape optimization assessment module is used to conduct a landscape optimization assessment of the area to be assessed based on the results of the quantitative analysis of the landscape pattern and the results of the integration degree analysis, so as to obtain the optimal integration degree.

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

[0060] The aforementioned blue-green space optimization configuration system includes: data acquisition equipment, a landscape pattern quantitative analysis module, an integration analysis module, a landscape optimization assessment module, and an optimization module. The aforementioned blue-green space optimization configuration method and system are applicable to multiple data sources such as remote sensing imagery, land use maps, and urban planning maps. It possesses good platform adaptability and scale universality, and can provide standardized, operable, and replicable integrated optimization solutions for urban new area development, ecological corridor layout, and waterfront area remediation. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 This is a flowchart of an optimized configuration method for blue-green space provided in one embodiment of the present invention;

[0063] Figure 2 This is a schematic diagram of a fusion degree calculation method provided in an embodiment of the present invention, illustrating the spatial relationship between the construction of the blue-green patch buffer zone and its superposition region;

[0064] Figure 3 The response relationship diagram between the degree of integration and the landscape multifunctionality index provided in an embodiment of the present invention was drawn based on the fitting results of the generalized additive model (GAM);

[0065] Figure 4 This is a landscape multifunctionality index (MLI) response heatmap under the parameters of integration degree and blue-green space coverage rate provided in an embodiment of the present invention, wherein the dark area corresponds to the local maximum area of ​​MLI value;

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

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

[0068] Figure labeling: 10, data acquisition equipment; 20, landscape pattern quantitative analysis module; 30, integration degree analysis module; 40, landscape optimization evaluation module; 50, optimization module. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0070] Current methods for assessing blue-green spaces primarily rely on single indicators, such as green space ratio and water surface ratio. These methods lack a systematic evaluation approach for the overall pattern of blue-green spaces. Optimization of blue-green spaces often remains at the qualitative descriptive level, lacking quantifiable optimization methods. Particularly in terms of spatial layout, it is difficult to accurately assess the merits of different configuration schemes, leading to uncertainties in ensuring optimization effectiveness. While the industry generally recognizes the importance of integrating blue-green spaces, there is a lack of quantitative evaluation standards for the degree of integration. Optimizing blue-green spaces requires considering multiple objectives simultaneously, such as landscape connectivity and spatial balance. These objectives may conflict; therefore, establishing a quantitative evaluation system that balances multiple objectives and formulating optimization strategies accordingly is a crucial problem that this invention aims to address.

[0071] The main technical solutions for optimizing the configuration of urban blue-green spaces include the following: 1) Landscape index-based evaluation methods: This is currently the most widely used technical solution. This method assesses the blue-green spatial pattern by establishing a landscape measurement index system and calculating indicators such as landscape area ratio, fragmentation, and connectivity. While this method can quantitatively characterize landscape features, the evaluation index system is too simplistic and fails to reflect the interaction between spatial elements. Furthermore, its calculation method is limited to the analysis of single-type spatial features, lacking a quantitative expression of the degree of integration of spatial elements, making it difficult to provide effective technical support for the optimized configuration of blue-green spaces. 2) GIS-based spatial analysis methods: This is another important technical approach. This method utilizes geographic information systems for spatial data processing and analysis, including buffer analysis and overlay analysis, which can effectively identify the distribution characteristics and changing trends of blue-green spaces. However, the spatial analysis techniques of this type are relatively simple, mainly remaining at the descriptive analysis level, and lack standardized analysis processes and technical specifications. Especially when conducting multi-scale and multi-level comprehensive analysis, existing GIS analysis methods often fail to meet practical needs. 3) Ecosystem Service-Based Evaluation Methods: These methods attempt to construct an evaluation system from the perspective of ecosystem function to assess the ecological benefits of blue-green spaces. While considering the integrity of the ecosystem, this approach faces numerous difficulties in its technical implementation: evaluation indicators are difficult to quantify, spatial scale conversion mechanisms are unclear, and there is a lack of effective linkage mechanisms with actual planning and design. This often makes it difficult for this method to achieve the expected results in practical applications. 4) Urban Planning-Based Design Methods: Design-oriented optimization methods are a more practical technical solution that optimizes the allocation of blue-green spaces through planning and design measures such as increasing aquatic forests and restoring riverbanks. However, this method has significant technical shortcomings: optimization objectives are difficult to quantify, standardized technical processes are lacking, optimization effects are difficult to objectively evaluate, and the feasibility verification mechanism is still imperfect. These technical deficiencies severely restrict the application of this method in practical engineering.

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

[0073] S10: Obtain the collected data for the area to be evaluated.

[0074] S20: Based on the collected data, perform a quantitative analysis of the landscape pattern of the area to be evaluated.

[0075] S30: Based on the results of the quantitative analysis of the landscape pattern, perform a fusion analysis on the area to be evaluated.

[0076] S40: Based on the results of the quantitative analysis of the landscape pattern and the results of the integration analysis, the landscape optimization assessment of the area to be evaluated is carried out to obtain the optimal integration degree.

[0077] S50: Optimize the region to be evaluated based on the optimal fusion degree.

[0078] The aforementioned method for optimizing the configuration of blue-green spaces includes: acquiring collected data of the area to be evaluated; performing quantitative analysis of the landscape pattern of the area to be evaluated based on the collected data; performing a degree of integration analysis of the area to be evaluated based on the results of the quantitative analysis of the landscape pattern; conducting a landscape optimization evaluation of the area to be evaluated based on the results of the quantitative analysis of the landscape pattern and the degree of integration analysis to obtain the optimal degree of integration; and optimizing the area to be evaluated based on the optimal degree of integration. This method for optimizing the configuration of blue-green spaces can achieve accurate evaluation of blue-green spaces, effectively optimize spatial configuration, and ensure a reasonable spatial distribution of blue-green spaces, avoiding problems such as fragmentation and poor connectivity.

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

[0080] As an example, the acquired data may include multi-source remote sensing data; specifically, it may use optical remote sensing imagery, radar, or UAV imagery, etc. The spatial resolution range during the acquisition of the area to be evaluated is adjustable from 10 to 100 meters; the temporal requirements for the acquired data may include at least two data periods; the format of the acquired data may be a mainstream format such as GeoTIFF or IMG.

[0081] As an example, after acquiring the collected data, a land feature classification system can be constructed, 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 greening 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 cover classification system is built, it also includes classification accuracy control, specifically: using stratified random sampling to select validation 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 accuracy is not less than 0.75.

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

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

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

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

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

[0088] S203: Obtain the 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 area to be evaluated occupied by blue space. Similarly, the green space coverage rate can be calculated as the percentage of the area to be evaluated occupied by blue space. Furthermore, the size of each statistical unit can be set according to actual needs; for example, the statistical unit can be 1km × 1km.

[0091] As an example, in step S202, obtaining the landscape structure indicators 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 indicators of the area to be evaluated include: the number of blue patches, the number of green patches, the average area of ​​blue patches, the average area of ​​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 cohesion index is obtained based on the following formula:

[0094]

[0095] Where, p ij Let a be the perimeter of the j-th patch in the i-th land category. ij Let A be the area of ​​the j-th patch in the i-th land class, and let A be the total area of ​​the area to be evaluated; m is the total number of patches in the i-th land class.

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

[0097]

[0098] Among them, a ij Let be the area of ​​the j-th patch in the i-th land class, A be the total area of ​​the landscape, and m be 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; a lower value indicates a higher degree of fragmentation. When the effective grid size reaches its maximum value, i.e., the landscape area, it means that the landscape consists of a single, intact patch.

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

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

[0102] Establish a buffer zone for each blue and green patch;

[0103] The fusion degree I of each patch 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 denoted as the area of ​​the buffer zone; K is an adjustable coefficient, which can range from 80 to 120.

[0106] Establish a grading standard for integration degree.

[0107] Specifically, the width of each buffer can be 0.5-2m; however, the width can be adjusted according to the actual application scenario. Figure 2 This is a schematic diagram of a fusion degree calculation method provided in an embodiment of the present invention, illustrating the spatial relationship between the blue-green patch buffer zone and its superimposed area. The diagram shows the calculation logic of fusion degree I, namely, establishing a buffer zone of a certain width around each blue or green patch, calculating the superimposed area of ​​the buffer zone with the heterogeneous patch, and obtaining the fusion degree according to 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%), medium fusion degree: I∈[2%,8%), and high fusion degree: I∈[8%,100%). The grading thresholds for each of the above fusion degrees can be adjusted by ±1% according to actual application needs.

[0109] In step S40, please refer to Figure 1 In step S40, the landscape optimization assessment of the area to be evaluated is carried out based on the results of the quantitative analysis of the landscape pattern and the results of the integration analysis, so as to obtain the optimal integration degree.

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

[0111] Construct the Landscape Multifunctionality Index (MLI); specifically, the MLI can be constructed based on the following formula:

[0112] MLI=w1×COHESION'+w2×MESH';

[0113] Wherein, CHOESIN' is the standardized clustering index, with a value range of [0,1]; MESH' is the standardized effective grid size, with a value range of [0,1]; w1 and w2 are weighting coefficients, and w1+w2=1;

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

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

[0116] Specifically, an optimization objective function is constructed based on the aforementioned nonlinear relationship, and constraints are set, including: 1) Fusion index range: I∈[0,20%]; 2) Coverage range: PLAD∈[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 constraints to obtain the optimal fusion degree; specifically including:

[0118] The fusion degree and the blue-green space coverage are both divided into multiple value ranges and used as model input parameters.

[0119] Iterate through all possible combinations of integration and coverage, and calculate the landscape multifunctionality index for each set of parameters;

[0120] Based on the response results of the landscape multifunctionality index, the degree of integration corresponding to the maximum value is determined, and this degree of integration is the optimal degree of integration.

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

[0122]

[0123] Where I represents the integration degree, PLAD represents the blue-green space coverage, and MLI(I,PLAND) represents the landscape multifunctionality 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 values ​​under different combinations of integration degree and coverage are obtained, and the integration degree value I corresponding to the maximum value of MLI is determined. *That is, the MLI value corresponding to each pair (I,PLAND) is calculated sequentially, and finally the fusion degree value I corresponding to the maximum value of MLI is determined. * The obtained I* represents the optimal fusion degree of the regional fusion optimization. Green space coverage, in the above process, participates in response modeling as an input variable of the generalized additive model to improve the prediction accuracy of the landscape multifunctionality index, but is not included in the final optimization output.

[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 heatmap to assist in fusion level recommendation and visualization. Figures 3 to 5 The data shown comes from over 500 typical newly developed areas in the coastal region of the Yangtze River Delta. Figure 4 This method is used to illustrate the nonlinear response relationship of fusion degree under different coverage conditions, in order to assist in determining the optimal fusion degree I*. Blue-green space coverage is used as a modeling input in the analysis, 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 are all standardized based on remote sensing interpretation and landscape pattern analysis results, and a fusion optimization modeling and validation dataset is constructed. The relevant maps reflect the typical response trends and optimization effects of this invention in large-scale complex areas, and are highly representative and applicable.

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

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

[0127] As an example, when the area to be evaluated is a highly integrated area, optimizing the spatial layout can be achieved by further optimizing the spatial layout through methods such as adjusting the spatial distribution of blue-green patches and service radius.

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

[0129] In another embodiment, please refer to Figure 6 The present invention also provides an optimization configuration system for blue-green spaces, comprising: 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 acquire data of the area to be evaluated; the landscape pattern quantitative analysis module 20 is used to perform a quantitative analysis of the landscape pattern of the area to be evaluated based on the acquired data; the fusion degree analysis module 30 is used to perform a fusion degree analysis of 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 of the area to be evaluated based on the results of the landscape pattern quantitative analysis and the fusion degree analysis to obtain the 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 aforementioned blue-green space optimization configuration system includes: a data acquisition device 10, a landscape pattern quantitative analysis module 20, a integration analysis module 30, a landscape optimization evaluation module 40, and an optimization module 50. This system enables accurate assessment of blue-green spaces and effective spatial optimization, resulting in a rational spatial distribution that avoids fragmentation and poor connectivity issues.

[0131] This invention innovatively proposes a method for assessing the integration of urban blue-green spaces. Unlike existing technologies that use only a single indicator for evaluation, this invention develops a spatial evaluation technique based on buffer zone analysis. Specifically, by setting a controllable buffer zone and combining it with an innovative quantitative calculation formula for integration, a precise assessment of blue-green space configuration is achieved. Simultaneously, this method integrates an indicator system encompassing three dimensions: spatial components, structural characteristics, and connectivity, making the evaluation results more comprehensive and objective. The blue-green space optimization configuration method of this invention enables precise quantification and systematic optimization of urban blue-green spaces, effectively improving the scientific nature of spatial configuration and the synergy of ecological functions. The integration index, calculated based on the superimposed area of ​​blue-green patch buffer zones, has a clear spatial physical meaning and can be adapted to different regional scales and data sources through parameter adjustment. The landscape optimization assessment section introduces a generalized additive model to model the nonlinear response relationship between integration and landscape pattern indicators (such as clustering degree and effective grid size), thereby enhancing the interpretability of the integration mechanism and the model's fitting ability. Building upon this foundation, this invention constructs a parameter optimization objective function based on optimal integration degree. A grid search method is employed to traverse the parameter combination space within constraints, determining the optimal integration parameters that maximize the Landscape Multifunctionality Index (MLI). By combining integration grading standards with regional characteristics, differentiated spatial optimization strategies can also be proposed, enhancing the adaptability and promotional value of the scheme. The aforementioned method for optimizing the allocation of blue-green spaces possesses good applicability and versatility, effectively reducing spatial fragmentation and improving the synergistic optimization level of ecological connectivity and system functions.

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

[0133] Third, this invention provides a complete set of technical parameters, significantly improving the operability of the optimization strategy. By establishing a hierarchical standard for integration degree, it provides a clear range of parameter optimization for different application scenarios. Simultaneously, the developed iterative parameter optimization algorithm can automatically adjust the optimization parameters according to specific circumstances, ensuring the acquisition of the optimal configuration scheme.

[0134] Fourth, this invention has strong technical versatility. The method can be implemented on mainstream GIS platforms such as ArcGIS and QGIS, and provides standardized data interfaces, supporting the processing of various data formats such as raster and vector. Furthermore, this invention can process data with different spatial resolutions, making it suitable for analysis at multiple spatial scales, from street blocks to cities.

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

[0136] Finally, this invention employs a modular program structure design, offering excellent scalability. The system supports customizable parameters, allowing adjustments to the indicator system based on actual needs. This design enables the invention to flexibly address diverse application requirements, thus possessing broad application prospects.

[0137] In summary, this invention achieves innovation in multiple aspects, including evaluation methods, analysis techniques, parameter systems, universality, standardization, and scalability, overcoming the limitations of existing technologies and providing a scientific, reliable, and practical technical solution for optimizing urban blue-green spaces.

[0138] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method for optimizing the allocation of blue-green space, characterized in that, The optimized configuration method for the blue-green space includes: Acquire the collected data from the area to be evaluated; Based on the collected data, a quantitative analysis of the landscape pattern of the area to be evaluated is performed, including: obtaining landscape component indicators of the area to be evaluated based on the collected data; obtaining landscape structure indicators of the area to be evaluated; and obtaining landscape connectivity indicators of the area to be evaluated. Specifically, obtaining the landscape structure indicators of the area to be evaluated includes: obtaining the number of blue patches and the number of green patches within the area to be evaluated; obtaining the average area of ​​blue patches and the average area of ​​green patches within the area to be evaluated; and obtaining a maximum patch index 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 ​​blue patches, the average area of ​​green patches, and the maximum patch index. Based on the results of the quantitative analysis of the landscape pattern, a blending degree analysis was performed on the area to be evaluated, including: establishing a buffer zone for each blue patch and green patch; and obtaining the blending degree I of each patch based on the following formula: ,in, This represents the intersection area between the buffer zone and the heterogeneous patch; Let K be the area of ​​the buffer zone; K be an adjustable coefficient; establish a grading standard for the degree of integration; Based on the results of the quantitative analysis of the landscape pattern and the results of the integration analysis, the landscape optimization assessment of the area to be evaluated is carried out to obtain the optimal integration degree, including: constructing the landscape multifunctionality index MLI based on the following formula: MLI=w1×COHESION'+w2×MESH'; where, COHESION' is the standardized aggregation index, with a value range of [0,1]; MESH' is the standardized effective grid size, with a value range of [0,1]; w1 and w2 are weight coefficients, and w1+w2=1; using a generalized additive model to analyze the nonlinear relationship between integration degree and landscape indicators: f=s(I,PLAND)+A; where, f is the landscape multifunctionality index MLI, I is the integration degree, PLAD is the blue-green space coverage rate; A is the area of ​​the area to be evaluated; S() is a smoothing function used to fit the nonlinear relationship; constructing an optimization objective function based on the nonlinear relationship; setting constraints; and solving the optimization objective function using a grid search method based on the constraints to obtain the optimal integration degree. The region to be evaluated is optimized based on the optimal fusion degree.

2. The method for optimizing the allocation of blue-green space according to claim 1, characterized in that, The process of obtaining landscape component indicators for the area to be evaluated based on the collected data includes: Obtain the blue space coverage of the area to be evaluated; Obtain the green space coverage rate of the area to be evaluated; The area to be evaluated is divided into multiple statistical units, and the blue-green space coverage rate of each statistical unit is obtained.

3. The method for optimizing the allocation of blue-green space according to claim 1, characterized in that, The process of obtaining the landscape connectivity index of the area to be evaluated includes: The cohesion index is obtained based on the following formula: , where p ij Let a be the perimeter of the j-th patch in the i-th land category. ij Let A be the area of ​​the j-th patch in the i-th land class, and let A be the total area of ​​the area to be evaluated; m is the total number of patches in the i-th land class. The effective mesh size MESH is obtained based on the following formula: , where a ij Let A be the area of ​​the j-th patch in the i-th land category, A be the total area of ​​the landscape, and m be the total number of patches in the i-th land category.

4. The method for optimizing the allocation of blue-green space according to claim 1, characterized in that, The step of solving the optimization objective function using a grid search method based on the constraints to obtain the optimal fusion degree includes: The fusion degree and the blue-green space coverage are both divided into multiple value ranges and used as model input parameters. Iterate through all possible combinations of integration and coverage, and calculate the landscape multifunctionality index for each set of parameters; Based on the response results of the landscape multifunctionality index, the degree of integration corresponding to the maximum value is determined, and this degree of integration is the optimal degree of integration.

5. The method for optimizing the allocation of blue-green space according to claim 4, characterized in that, The region to be evaluated is optimized based on the optimal fusion degree and the fusion degree grading standard, including: If the area to be evaluated is a low-integration area, then increase waterfront green space, construct wetland parks, or restore riverbank vegetation; if the area to be evaluated is a medium-integration area, then expand the boundary area between existing water bodies and green spaces, construct blue-green composite node spaces, or add ecological buffer zones around existing water bodies; if the area to be evaluated is a high-integration area, then optimize the spatial layout, adjust the shape of patches, or add corridor connections.

6. An optimized allocation system for blue-green space, characterized in that, The blue-green space optimization configuration system is used to execute the blue-green space optimization configuration method as described in any one of claims 1 to 5, wherein the blue-green space optimization configuration system comprises: Data acquisition equipment is used to acquire data from the area to be evaluated. The landscape pattern quantitative analysis module is used to perform a quantitative analysis of the landscape pattern of the area to be evaluated based on the collected data. The integration analysis module is used to perform integration analysis on the area to be evaluated based on the results of the quantitative analysis of the landscape pattern. The landscape optimization assessment module is used to conduct a landscape optimization assessment of the area to be assessed based on the results of the quantitative analysis of the landscape pattern and the results of the integration degree analysis, so as to obtain the optimal integration degree. An optimization module is used to optimize the region to be evaluated based on the optimal fusion degree.

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