Urban mining waste land utilization optimization method and system based on cooperation of space conflict and ecological barrier

Abstract

The invention discloses an urban mining waste land utilization optimization method and system based on cooperation of space conflict and ecological barrier, and relates to the technical field of land utilization, and the method comprises the steps: collecting and processing urban mine waste land utilization data; carrying out geospatialization transformation on the traditional PSO model, calculating a three-generation space land utilization conflict, and designing an optimization target of the PSO model; constructing an ecological network around the mining area; identifying ecological obstacle points and pinch points based on an ecological network, designing a weighted roulette mechanism, and coupling an ecological resistance value and an ecological flow density into a PSO model; and outputting a three-generation space conflict optimization result and a land utilization space layout. According to the method, on the basis of pattern spot scale land utilization space conflict evaluation indexes of complexity, vulnerability and competitiveness, quantitative obstacle points and pinch points of an ecological network around a mining area are constructed, the quantitative obstacle points and pinch points are integrated into an iteration process of an optimization model through a weighted wheel disc mechanism, and land utilization layout is optimized.

Description

Technical Field

[0001] The present invention relates to the field of land utilization technology, and more particularly to a method and system for optimizing land utilization of urban mining wasteland based on the coordination of spatial conflict and ecological barrier. Background Art

[0002] Resource-based cities have made significant contributions to economic development and social well-being, but mineral resource extraction has significantly damaged urban surface land cover and ecosystems. In particular, the scarcity of land has led to an increasingly prominent interweaving, misaligned, and competitive landscape of "production, life, and ecology" within resource-based cities. Furthermore, mining activities have caused severe ecological degradation in localized areas of mines within resource-based cities, leading to fragile coupling with surrounding ecosystems and jeopardizing ecological security. Therefore, optimizing the spatial allocation of abandoned land resources within urban mines, coordinating the spatial layout of "production, life, and ecology," and enhancing connections with surrounding ecosystems are crucial approaches to achieving the transformation of resource-based cities and avoiding the obstacles to sustainable development following resource depletion.

[0003] Spatial optimization of land resources can provide a solution to the conflicts between land use and ecological degradation in abandoned urban mine land, promoting the sustainable use of land resources. Implementing rational and effective land optimization requires addressing two key issues: 1. Characterizing the spatial conflicts between production, life, and ecology in abandoned urban mine land at the patch scale; and 2. Designing a land-use optimization model that synergistically considers both spatial conflicts and ecological barriers. Optimizing the use layout of abandoned resource-based urban mine land is a complex problem involving the interactions of social, economic, and natural systems. Most methods quantify the spatial conflicts between production, life, and ecology by constructing an indicator system. Hierarchical multi-criteria evaluation methods are also commonly used to assess the competitiveness of different land-use types. However, existing assessment methods based on macro-indicators struggle to account for the impact of land-use change on spatial conflicts at the patch scale, thus failing to provide specific guidance for optimizing the optimization model. Furthermore, existing methods often divide the study area into fixed-size evaluation units, which significantly limits the flexibility and accuracy of the measurement results and makes them difficult to integrate into optimization models as global optimization objectives. When it comes to land optimization models, cellular automata-based methods have gradually become the mainstream approach for geospatial layout optimization simulations due to their accuracy and reliability in analyzing patch-level land use evolution. However, the surface cover of resource-based urban abandoned mine sites is significantly influenced by human activities, making its evolution difficult to simply deduce from historical patterns. Even when adjusting the quantitative structure of land use based on different development scenarios, using simulation models to collaboratively optimize the quantitative structure and spatial layout remains irrational and incompatible with the complex land use conditions of abandoned mine sites.

[0004] Therefore, how to propose a land use optimization method and system for urban mining wastelands based on the coordination of spatial conflict and ecological barriers, design a "production-life-ecology" spatial conflict measurement method at the patch scale, use the quantified "production-life-ecology" spatial conflict and ecological barrier as target guidance, carry out dynamic land use optimization at the patch scale, thereby coordinating spatial conflict and ecological connectivity protection, and providing guidance for land use planning and ecological restoration strategies in areas experiencing drastic land cover changes, is an urgent problem that technicians in this field need to solve. Summary of the Invention

[0005] In view of this, the present invention provides a land use optimization method and system for urban mining wasteland based on the coordination of spatial conflict and ecological barrier. It designs a "production-life-ecology" spatial conflict measurement method at the patch scale. Taking the quantified "production-life-ecology" spatial conflict and ecological barrier as target guidance, it conducts dynamic land use optimization at the patch scale, thereby coordinating spatial conflict and ecological connectivity protection, and providing guidance for land use planning and ecological restoration strategies in areas experiencing drastic land cover changes. To achieve the above objectives, the present invention adopts the following technical solutions:

[0006] A land use optimization method for urban mining wasteland based on the synergy of spatial conflict and ecological barrier, including:

[0007] Collect and process urban mine abandoned land use data;

[0008] The traditional PSO model is transformed into a geospatial model to calculate the land use conflicts in the three-life space and design the optimization objectives of the PSO model;

[0009] Identify the ecological source areas of the ecosystem around the mining area, calculate the ecological resistance surface, and then construct the ecological network around the mining area;

[0010] Based on the ecological network, ecological obstacles and pinch points are identified, a weighted roulette mechanism is designed, and the ecological resistance value and ecological flow density are coupled into the PSO model.

[0011] According to the PSO model, the optimization results of the three-life spatial conflicts and the land use spatial layout are output.

[0012] Optionally, the geospatial transformation of the traditional PSO model includes:

[0013] Each pixel unit is regarded as a particle, the position solution space of each particle in the particle swarm is set to the number of land use types D, the particle speed is regarded as the probability of selecting different land use types, and the position update strategy of each particle is set to be guided by the objective function, that is, the objective function is compared in each iteration. If the objective function improves, the particle position is updated to the new land use type, otherwise the particle does not move;

[0014] The particle at pixel i selects the speed of different land use types during the tth iteration. The update calculation formula is as follows:

[0015]

[0016] Where, The probability selection vector for the previous round, X i is the suitability of the current land use type, which is calculated based on the objective function. Pbest is the suitability of the optimal land use type corresponding to the particle at the current position. w is the inertia weight, and c is the learning factor.

[0017] Optionally, calculating the land use conflict in the three-life space includes:

[0018] The production-life-ecology spatial conflict index (PLEI) of resource-based cities is constructed from the three aspects of land use system complexity (SCI), vulnerability (SVI) and competitiveness (SPI), as follows:

[0019] PLEI=SCI+SVI-SPI.

[0020] Optionally, it also includes quantifying the spatial complexity of land patches based on the area-weighted mean patch fractal dimension (AWMPFD) indicator, using the following formula:

[0021]

[0022] Where, P ij and a ij are the perimeter and area of ​​the patch, respectively. A is the total area of ​​all patches. m and n are the total number of patches and the total number of patch types, respectively. The value of AWMPFD ranges from 1 to 2. The larger the value, the higher the complexity of the patch and the greater the possibility of land use spatial conflict.

[0023] Optionally, the patch density PD is used to calculate spatial vulnerability, using the following formula:

[0024]

[0025] Where n j is the number of patches of type n, and the PD value range is from 0 to 1. The larger the value, the higher the degree of spatial fragmentation and the greater the possibility of land use conflict.

[0026] Optionally, the landscape superiority index (LPI) is selected to reflect spatial competitiveness. The formula is as follows:

[0027]

[0028] Where a maxIt represents the area of ​​the largest patch among all patch types. The LPI value ranges from 0 to 1. The larger the value, the more the space is dominated by specific land use activities and the less likely spatial conflict is to occur.

[0029] Optionally, the optimization objectives of the PSO model include:

[0030] After measuring the land use conflicts of the three-dimensional space of each pixel unit, the economic development development and ecological protection protection The objective function is constructed in terms of the optimization goal of the PSO model:

[0031] U=-w1×f conflict +w2×f development +w3×f protection ;

[0032] w1+w2+w3=1;

[0033] In the formula, w1, w2, and w3 are the weights corresponding to the three targets, f conflict is the land use conflict index PLEI,

[0034] Economic development goals are measured using the bisquare function, as follows:

[0035]

[0036] Where x and μ are the current construction land area and the maximum allowable construction land area, respectively. origin is the initial construction land area, x origin is the minimum construction land area in the iterative process, σ is the economic density coefficient;

[0037] Ecological protection is expressed as the proportion of ecological land in the Moore neighborhood, and the formula is as follows:

[0038]

[0039] Where Ω refers to the Moore neighborhood of the current unit, N Ω is the number of pixel units in the neighborhood, x ij If it is 1, the current land use type is ecological land, otherwise it is 0.

[0040] Optionally, the identifying of ecological sources of the ecosystem surrounding the mining area, calculating the ecological resistance surface, and then constructing the ecological network surrounding the mining area includes:

[0041] The selection of ecological source areas for the ecosystem around the mining area was comprehensively considered from three aspects: service function, morphology, and connectivity. Service function was measured using the habitat quality function in the InVEST model, morphological types were obtained based on the Guidos tool, and connectivity was obtained using the Conefor2.6 tool. Ultimately, ecological patches with high habitat quality, core area morphology, and high connectivity were selected as ecological source areas.

[0042] The ecological resistance surface was derived using a weighted summation method. Three resistance factors, DEM, land use type, and nighttime light intensity, were selected, with equal weights. Using the reclassification tool in ArcGIS, the DEM and nighttime light intensity resistance factors were classified into five levels using the natural breakpoint method. Land use types were divided into five types: forest land, water bodies, cultivated land and gardens, unused land and bare land, and construction land. Scores were assigned to each level of the three resistance factors, and then the weighted summation was used to generate the ecological resistance surface.

[0043] Finally, the ecological source area and ecological resistance surface were input into Linkage Mapper software, and the ecological corridor was obtained based on the minimum cost distance path calculation of the software;

[0044] The ecological source areas are regarded as nodes and the ecological corridors as edges. The edges and nodes together construct the ecological network around the mining area.

[0045] Optionally, the identifying of ecological obstacles and pinch points based on the ecological network, designing a weighted roulette mechanism, and coupling the ecological resistance value and ecological flow density into the PSO model includes:

[0046] The ecological resistance value and ecological flow density value were calculated by BarrierMapper and PinchpointMapper tools respectively;

[0047] After calculating the ecological resistance value and ecological flow density value, a weighted wheel mechanism is designed to guide the PSO model to focus on areas that affect ecological connectivity during the iteration process. That is, the probability of selecting different land use types in each iteration is determined by the particle speed. The ecological resistance value and ecological flow density value are incorporated into this process. The formula is as follows:

[0048]

[0049] Where, is the probability of selecting ecological land for pixel i in the tth iteration after modification, which is used to construct the weighted wheel. is the selection probability of original ecological land for pixel unit i, RES ij and EFL ij are the ecological resistance value and ecological flow density value of pixel unit (i, j), ∑ x RES x and ∑x EFL x are the total values ​​of regional ecological resistance value and ecological flow density value respectively, and N is the total number of pixel units.

[0050] Optionally, a land use optimization system for urban mining wasteland based on the synergy of spatial conflict and ecological barrier includes:

[0051] Collection module: used to collect and process urban mine abandoned land use data;

[0052] Transformation module: used to transform the traditional PSO model into geospatial, calculate the land use conflicts in the three-life space, and design the optimization objectives of the PSO model;

[0053] Network construction module: used to identify the ecological source areas of the ecosystem around the mining area, calculate the ecological resistance surface, and then construct the ecological network around the mining area;

[0054] Optimization module: used to identify ecological obstacles and pinch points based on the ecological network, design a weighted roulette mechanism, and couple the ecological resistance value and ecological flow density into the PSO model;

[0055] Output module: used to output the optimization results of the three-life spatial conflict and the land use spatial layout according to the PSO model.

[0056] It can be seen from the above technical solutions that, compared with the prior art, the present invention provides a method and system for optimizing land use of urban mining wasteland based on the synergy of spatial conflict and ecological barrier, which has the following beneficial effects:

[0057] This paper proposes a method for optimizing land use in urban mining wastelands based on the synergy of spatial conflict and ecological barriers. The method includes the following steps: collecting and processing urban mining wasteland land use data; geospatializing the traditional PSO model, calculating the three-dimensional spatial land use conflicts, and designing the optimization objectives of the PSO model; identifying the ecological source areas of the ecosystem surrounding the mining area, calculating the ecological resistance surface, and then constructing an ecological network around the mining area; identifying ecological obstacles and pinch points based on the ecological network, designing a weighted roulette wheel mechanism, and coupling the ecological resistance value and ecological flow density into the PSO model; and outputting the three-dimensional spatial conflict optimization results and land use spatial layout based on the PSO model. Based on the patch-scale land use spatial conflict evaluation index of complexity, vulnerability, and competitiveness, the method constructs an ecological network around the mining area to quantify obstacle points and pinch points. These are then integrated into the iterative process of the optimization model through the weighted roulette wheel mechanism. By optimizing the land use layout, the method achieves the synergistic optimization of land use spatial conflict and ecological barriers in mining wastelands.

[0058] This method, based on patch-scale land-use spatial conflict, effectively characterizes spatial conflicts in terms of complexity, vulnerability, and competitiveness, and uses these as optimization targets to improve the effectiveness of land-use conflict optimization. A weighted wheel mechanism also incorporates ecological barriers and flow density into the local iterative optimization of the optimization model, guiding the model's discovery of potential ecological sources and corridors. This method can serve as an effective tool for land-use planning and layout in abandoned mine sites, and the results can support the coordinated development of "production-living-ecology" spaces and ecological restoration efforts. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0060] Figure 1 A flow chart of a method for optimizing land use of urban mining wasteland based on the synergy of spatial conflict and ecological barrier provided by the present invention.

[0061] FIG2( a ) is a land use conflict-spatial conflict complexity distribution diagram provided by the present invention.

[0062] FIG2( b ) is a land use conflict-landscape system vulnerability distribution map provided by the present invention.

[0063] FIG2( c ) is a distribution diagram of land use conflict-spatial conflict competition provided by the present invention.

[0064] FIG2( d ) is a land use conflict-land use conflict level distribution map provided by the present invention.

[0065] Figure 3 This is the spatial distribution map of the ecological source and corridor provided by the present invention.

[0066] FIG4( a ) is a distribution map of ecological barrier areas provided by the present invention.

[0067] FIG4( b ) is a distribution diagram of ecological pinch points and ecological flow density provided by the present invention.

[0068] FIG5( a ) is a distribution map of land use conflicts after optimization provided by the present invention.

[0069] FIG5( b ) is a graph showing the change in land use spatial conflict targets after optimization provided by the present invention.

[0070] Figure 6 This is the spatial hotspot distribution map of the optimized ecological land provided by the present invention. DETAILED DESCRIPTION

[0071] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0072] The embodiment of the present invention discloses a method for optimizing land use of urban mining wasteland based on the coordination of spatial conflict and ecological barrier, comprising:

[0073] Collect and process urban mine abandoned land use data;

[0074] The traditional PSO model is transformed into a geospatial model to calculate the land use conflicts in the three-life space and design the optimization objectives of the PSO model;

[0075] Identify the ecological source areas of the ecosystem around the mining area, calculate the ecological resistance surface, and then construct the ecological network around the mining area;

[0076] Based on the ecological network, ecological obstacles and pinch points are identified, a weighted roulette mechanism is designed, and the ecological resistance value and ecological flow density are coupled into the PSO model.

[0077] According to the PSO model, the optimization results of the three-life spatial conflicts and the land use spatial layout are output.

[0078] In a specific embodiment, a land use optimization method for urban mining wasteland based on the synergy of spatial conflict and ecological barrier is provided. Figure 1 The specific steps are as follows:

[0079] Step 1: Collect and process urban mine abandoned land use data.

[0080] Step 2: The traditional particle swarm optimization (PSO) model is geospatialized to adapt it to the land use optimization problem of resource-based urban mine wasteland.

[0081] Step 3: Calculate the land use conflicts in the “production-life-ecology” space from the three aspects of complexity, vulnerability and competitiveness, and design the optimization objectives of the PSO model.

[0082] Step 4: Identify the ecological source areas of the ecosystem around the mining area, calculate the ecological resistance surface, and then construct the ecological network around the mining area.

[0083] Step 5: Based on the ecological network obtained in step 4, identify ecological barrier points and pinch points, and design a weighted roulette mechanism to couple the ecological resistance value and ecological flow density into the PSO model.

[0084] Step 6: Output the optimization results of the three-life spatial conflicts and the land use spatial layout.

[0085] In a specific embodiment, the geospatial transformation process of the PSO model in step 2 is as follows:

[0086] The PSO model mimics the natural process of birds flocking to find food. Its core design principle is that birds share optimal food locations (flocking behavior) and update their individual speed and position (migratory behavior). It effectively solves nonlinear, multi-peak optimization problems in numerical space. To prevent the PSO model from being trapped in local optima due to excessive randomness when searching for optimal results in geographic space, the model's solution space and search strategy are adjusted to produce optimization results that are more consistent with geographic spatial characteristics and land planning policies.

[0087] First, each pixel unit is regarded as a particle, and the position solution space of each particle in the particle swarm is set to D (i.e., the number of land use types). The speed of the particle is regarded as the probability of selecting different land use types. The position update strategy of each particle is set to be guided by the objective function, that is, the objective function is compared in each iteration. If the objective function is improved, the position of the particle is updated to the new land use type, otherwise the particle does not move. The particle at pixel i chooses the speed of different land use types in the tth iteration. The update calculation formula is as follows:

[0088]

[0089] Where, The probability selection vector for the previous round, X i is the suitability of the current land-use type, calculated based on the objective function. Pbest is the suitability of the optimal land-use type for the particle at the current position. w is the inertia weight, and c is the learning factor. The final selected land-use type is determined through a roulette wheel. Because a new geospatial mapping relationship is established and the solution space of each particle is modified, the particle swarm update process is not considered. After multiple iterations, the probability of selecting a dominant position at each pixel unit will dominate, indicating that the particle is more likely to choose this land-use type, thereby outputting a stable land-use layout solution.

[0090] In a specific embodiment, the definition of the "production-life-ecology" three-life space land use conflict index in step 3 is as follows:

[0091] Land use conflicts in resource-based urban abandoned mine sites primarily focus on competition for land resources across three main areas: production, living, and ecology. Production land includes cultivated land, orchards, transportation, and industrial and mining land; living land includes urban land and rural residential land; and ecological land includes forests, grasslands, water bodies, and other land. To characterize the probability of spatial conflict, a "production-living-ecology" spatial conflict index (PLEI) for resource-based urban abandoned mine sites was constructed based on the complexity (SCI), vulnerability (SVI), and competitiveness (SPI) of the land use system. This index can be expressed as follows:

[0092] PLEI = SCI + SVI - SPI;

[0093] The complexity of spatial conflict is reflected in the fragmented and disordered nature of land patches under the influence of mining activities and urbanization. Fractal theory is an important tool for explaining the spatial patterns of nature and the spatial complexity of geographical phenomena. The area-weighted mean patch fractal dimension (AWMPFD) is a commonly used indicator for quantifying the spatial complexity of land patches. Its calculation formula is as follows:

[0094]

[0095] Where, P ij and a ij are the perimeter and area of ​​the patch, A is the total area of ​​all patches, m is the total number of patches, and n is the total number of patch types. AWMPFD values ​​range from 1 to 2. Larger values ​​indicate greater patch complexity and a greater likelihood of land use spatial conflict.

[0096] The vulnerability of spatial conflict is reflected in the stability of the regional landscape system. Within a certain range, the more fragmented the spatial separation of different types of patches, the more unstable the landscape system in the region and the stronger the spatial conflict. Therefore, patch density (PD) is used to calculate spatial vulnerability, and its expression is as follows:

[0097]

[0098] Where n j is the number of patches of type n, and the PD value range is from 0 to 1. The larger the value, the higher the degree of spatial fragmentation and the greater the possibility of land use conflict.

[0099] The competitiveness of spatial conflict refers to the dominance of different types of land use activities within a certain spatial range. When a certain land use activity is dominant, the possibility of spatial competition is low. The landscape dominance index (LPI) is selected to reflect spatial competitiveness and is calculated as follows:

[0100]

[0101] Where a max Represents the area of ​​the largest patch among all patch types. The LPI value ranges from 0 to 1. The larger the value, the more dominant the space is due to a specific land use activity, and the less likely spatial conflict is to occur.

[0102] In a specific embodiment, determining the area, perimeter, and number of patches in the spatial conflict index requires a predefined spatial range. Traditional methods often perform global grid division or use community or county administrative units as the basic unit of calculation. In the iterative optimization process, the present invention constructs a neighborhood of size s for each pixel unit. This neighborhood perceives land use conflicts in the surrounding space and quantifies the surrounding spatial conflicts as the PLEI of the grid center pixel, avoiding the loss of flexibility and reliability of fixed units when measuring spatial conflicts. After calculating the PLEI for each pixel, it is set as one of the objective functions for calculating suitability in the PSO model.

[0103] In a specific embodiment, after measuring the land use conflicts of the “production-life-ecology” three-dimensional space of each pixel unit, the economic development (f development ) and ecological protection (f protection ) construct the objective function from two aspects, which together constitute the optimization goal of the PSO model:

[0104] U=-w1×f conflict +w2×f development +w3×f protection ;

[0105] w1+w2+w3=1;

[0106] In the formula, w1, w2, and w3 are the weights corresponding to the three targets, f conflict It is the land use conflict index PLEI.

[0107] Economic development goals are measured using the bisquare function, calculated as follows:

[0108]

[0109] Where x and μ are the current construction land area and the maximum allowable construction land area, respectively. origin is the initial construction land area. Since construction land cannot be changed to other types of land, x origin is the minimum construction land area in the iterative process, and σ is the economic density coefficient.

[0110] Ecological protection is expressed as the percentage of ecological land within the Moore neighborhood, calculated as follows:

[0111]

[0112] Where Ω refers to the Moore neighborhood of the current unit, N Ω is the number of pixel units in the neighborhood, x ij If it is 1, the current land use type is ecological land, otherwise it is 0.

[0113] In a specific embodiment, the ecological source of the ecosystem around the mine wasteland in step 4 is selected from the three aspects of service function, morphology, and connectivity. The service function is measured by the habitat quality function in the InVEST model, the morphological type is obtained based on the Guidos tool, and the connectivity is obtained using the Conefor2.6 tool. Finally, the ecological patches with high habitat quality, core area morphology and high connectivity are selected as the ecological source. The ecological resistance surface adopts the method of weighted summation of indicators, selects three resistance factors: DEM, land use type, and night light intensity, and the weights are set to equal weight. In ArcGIS software, the reclassification tool is used to divide the two resistance factors of DEM and night light intensity into 5 levels according to the natural breakpoint method. The land use type is divided into five types: forest land, water body, cultivated land and garden land, unused land and bare land, and construction land. Each level of the three resistance factors is scored, and then the ecological resistance surface is obtained by equal weight addition. Finally, the ecological source and ecological resistance surface are input into the Linkage Mapper software, and the ecological corridor is obtained based on the minimum cost distance path calculation of the software. The ecological source areas are regarded as nodes and the ecological corridors as edges. The edges and nodes together construct the ecological network around the mining area.

[0114] In a specific embodiment, the ecological barrier points in step 5 are areas with higher resistance values ​​in the ecological network, and the ecological pinch points are parts of the ecological network with higher ecological flow density values. Both are key factors affecting the overall connectivity of the ecosystem and can be obtained by the Barrier Mapper and Pinchpoint Mapper tools, respectively. Areas with higher ecological resistance values ​​(barrier points) or higher ecological flow density values ​​(pinch points) indicate higher ecological risks, and maintaining ecological land in these areas is crucial to ecological security. After calculating the ecological resistance value and the ecological flow density value, the present invention designs a weighted roulette mechanism to guide the PSO model to focus on areas that affect ecological connectivity during the iteration process. Specifically, the probability of selecting different land use types in each iteration is determined by the speed of the particles. The present invention incorporates the ecological resistance value and the ecological flow density value into this process to increase the possibility of selecting ecological land, namely:

[0115]

[0116] Where, is the probability of selecting ecological land for pixel i in the tth iteration after modification, which is used to construct the weighted wheel. is the selection probability of original ecological land for pixel unit i, RESij and EFL ij are the ecological resistance value and ecological flow density value of pixel unit (i, j), ∑ x RES x and Σ x EFL x are the total values ​​of regional ecological resistance value and ecological flow density value respectively, and N is the total number of pixel units.

[0117] In terms of optimizing land use spatial conflicts and ecological barriers in resource-based urban mine wastelands, this paper designs a patch-level index to evaluate land use conflicts in the "production-living-ecological" space, and constructs a weighted roulette mechanism to guide the bionic intelligent optimization model to improve the connectivity between the study area and the surrounding ecosystem during iteration. Compared with traditional land use spatial conflict quantification and optimization methods, this paper reveals the spatial differences of fine-grained land use conflicts from three aspects: land use system complexity, landscape system vulnerability, and spatial competitiveness, by constructing dynamic micro-neighborhoods, and flexibly compares land use conflicts under different scenarios. In addition, the weighted roulette takes into account external ecological connectivity during optimization iterations in a locally guided manner, achieving coordinated optimization of land use spatial conflicts and ecological barriers, and can effectively solve land use problems caused by mining activities in resource-based urban mine wastelands.

[0118] In a specific embodiment, a land use optimization method for urban mining wasteland based on the synergy of spatial conflict and ecological barrier is provided, and the specific steps are as follows:

[0119] Step 11: Taking the Xubei mining area in Xuzhou, a typical resource-based city in China, as the study area, collect urban mine abandoned land use data of the study area and unify the coordinate system and raster spatial resolution.

[0120] Step 22: The traditional particle swarm optimization (PSO) model is geospatialized to adapt it to the resource-based urban land use optimization problem. First, each pixel unit is regarded as a particle, the position solution space of each particle in the particle swarm is set to D (i.e., the number of land use types), the speed of the particle is regarded as the probability of selecting different land use types, and the position update strategy of each particle is set to be objective function-oriented, that is, the objective function is compared in each iteration. If the objective function is improved, the position of the particle is updated to the new land use type, otherwise the particle does not move. The particle at pixel i selects the speed of different land use types in the tth iteration. The update calculation formula is as follows:

[0121]

[0122] Where, The probability selection vector for the previous round, X iis the suitability of the current land-use type, calculated based on the objective function; Pbest is the suitability of the optimal land-use type for the particle at the current location; w is the inertia weight, set to 0.8 in this example; c is the learning factor, set to 1.5 in this example; the final selected land-use type is determined through a roulette wheel. Because a new geospatial mapping relationship is established and the solution space of each particle is modified, the particle swarm update process is not considered. After multiple iterations, the probability of selecting the dominant position at each pixel unit will become dominant, indicating that the particle is more likely to choose this land-use type, thereby outputting a stable land-use layout solution.

[0123] Step 33: The “production-life-ecology” spatial conflict index (PLEI) of resource-based urban mine wasteland was constructed based on the complexity (SCI), vulnerability (SVI), and competitiveness (SPI) of the land use system, which can be expressed as follows:

[0124] PLEI = SCI + SVI - SPI;

[0125] The complexity of spatial conflict is reflected in the fragmented and disordered nature of land patches under the influence of mining activities and urbanization. Fractal theory is an important tool for explaining the spatial patterns of nature and the spatial complexity of geographical phenomena. The area-weighted mean patch fractal dimension (AWMPFD) is a commonly used indicator for quantifying the spatial complexity of land patches. Its calculation formula is as follows:

[0126]

[0127] Where, P ij and a ij are the perimeter and area of ​​the patch, A is the total area of ​​all patches, m is the total number of patches, and n is the total number of patch types. AWMPFD values ​​range from 1 to 2. Larger values ​​indicate greater patch complexity and a greater likelihood of land use spatial conflict.

[0128] The vulnerability of spatial conflict is reflected in the stability of the regional landscape system. Within a certain range, the more fragmented the spatial separation of different types of patches, the more unstable the landscape system in the region and the stronger the spatial conflict. Therefore, patch density (PD) is used to calculate spatial vulnerability, and its expression is as follows:

[0129]

[0130] Where n j is the number of patches of type n, and the PD value range is from 0 to 1. The larger the value, the higher the degree of spatial fragmentation and the greater the possibility of land use conflict.

[0131] The competitiveness of spatial conflict refers to the dominance of different types of land use activities within a certain spatial range. When a certain land use activity is dominant, the possibility of spatial competition is low. The landscape dominance index (LPI) is selected to reflect spatial competitiveness and is calculated as follows:

[0132]

[0133] Where a max Represents the area of ​​the largest patch among all patch types. The LPI value ranges from 0 to 1. The larger the value, the more dominant the space is due to a specific land use activity, and the less likely spatial conflict is to occur.

[0134] During the iterative optimization process, a neighborhood of size s is constructed for each pixel unit. This neighborhood perceives the land use conflict in the surrounding space and quantifies the surrounding space conflict as the PLEI of the central pixel of the grid. In this embodiment, the size of the perception neighborhood is set to 20. The calculated land use conflicts are shown in Figures 2(a), 2(d), 2(c), and 2(d). The statistical results of the land use spatial conflict levels are shown in Table 1.

[0135] Table 1

[0136]

[0137] The land use conflict of “production-life-ecology” in each pixel unit is obtained by measuring the land use conflict of “production-life-ecology” in each pixel unit. conflict Afterwards, from the perspective of economic development (f development ) and ecological protection (f protection ) construct the objective function from two aspects, which together constitute the optimization goal of the PSO model:

[0138] U=-w1×f conflict +w2×f development +w3×f protection ;

[0139] w1+w2+w3=1;

[0140] In this embodiment, the three optimization objectives are considered to be equally important, that is, have equal weights.

[0141] Step 4: Ecological source sites were selected based on comprehensive consideration of service function, morphology, and connectivity. The thresholds for high habitat quality and high connectivity were set at 0.71 and 0.05, respectively. Finally, 116 ecological source sites and 286 ecological corridors were obtained. The results of the ecological network construction around the study area are shown in the figure below. Figure 3 As shown in the figure, the ecological network presents a distribution pattern of dense center and sparse east-west.

[0142] Step 5: Use the BarrierMapper and PinchpointMapper tools to calculate areas with high barrier values ​​(barrier points) and high ecological flow (pinch points), such as Figure 4(a)-Figure 4(b) As shown in the figure, as a typical urban industrial and mining area, the study area passes through two ecological corridors, including a barrier area with high resistance value and two obvious ecological pinch points. The weighted wheel mechanism is designed to guide the PSO model to focus on the areas that affect ecological connectivity during the iteration process. The probability of selecting ecological land after modification is The calculation is as follows:

[0143]

[0144] Where, is the probability of selecting ecological land for pixel i in the tth iteration after modification, which is used to construct the weighted wheel. is the selection probability of original ecological land for pixel unit i, RES ij and EFL ij are the ecological resistance value and ecological flow density value of pixel unit (i, j), Σ x RES x and Σ x EFL x are the total values ​​of regional ecological resistance value and ecological flow density value respectively, and N is the total number of pixel units.

[0145] Step 6: Output the optimization results of the three-life spatial conflict and the land use spatial layout. The changes of the three-life spatial conflict index during the optimization process are as follows: Figure 5(a)-Figure 5(b) As shown in Table 2, the statistical results of the three-life spatial conflicts after optimization are shown. When the land use type changes by only 0.7%, the area of ​​high land use conflict areas decreases by 7.75%, and the area of ​​non-conflict areas increases by 5.42%. The reduction of high land use conflict areas mainly occurs at the edges of patches, which is also the area where the most land use type conversion occurs. The area of ​​medium land use conflict areas increases, because the optimization of high conflict areas will lead to higher optimization target improvements. Therefore, the model tends to optimize high conflict areas to medium conflict areas. By comparing the impact of the optimization results before and after considering the land use conflict target, it can be found that after considering the land use conflict target, the spatial conflict decreases faster, more significantly, and with smaller fluctuations. In addition, Figure 6 The hotspots optimized for ecological land use after considering ecological barrier values ​​and ecological flow density are shown. (a) is a schematic diagram combined with the weighted roulette mechanism, and (b) is a schematic diagram without the weighted roulette mechanism. It can be seen that the local roulette mechanism guides the model to focus on potentially improveable areas near pinch points and high barrier areas. These areas are the optional spatial ranges for new ecological sources and corridors, providing a clear reference for ecological connectivity and ecological protection planning.

[0146] Table 2

[0147]

[0148]

[0149] In a specific embodiment, a land use optimization system for urban mining wasteland based on the synergy of spatial conflict and ecological barrier includes:

[0150] Collection module: used to collect and process urban mine abandoned land use data;

[0151] Transformation module: used to transform the traditional PSO model into geospatial, calculate the land use conflicts in the three-life space, and design the optimization objectives of the PSO model;

[0152] Network construction module: used to identify the ecological source areas of the ecosystem around the mining area, calculate the ecological resistance surface, and then construct the ecological network around the mining area;

[0153] Optimization module: used to identify ecological obstacles and pinch points based on the ecological network, design a weighted roulette mechanism, and couple the ecological resistance value and ecological flow density into the PSO model;

[0154] Output module: used to output the optimization results of the three-life spatial conflict and the land use spatial layout according to the PSO model.

[0155] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0156] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A land use optimization method for urban mining wasteland based on the synergy of spatial conflict and ecological barrier, It is characterized in that include: Collect and process urban mine abandoned land use data; The traditional PSO model is transformed into geospatial, the land use conflicts of the three living spaces are calculated, and the optimization objectives of the PSO model are designed; Identify the ecological source areas of the ecosystem around the mining area, calculate the ecological resistance surface, and then construct the ecological network around the mining area; Based on the ecological network, ecological obstacles and pinch points are identified, and a weighted roulette mechanism is designed to couple the ecological resistance value and ecological flow density into the PSO model. According to the PSO model, the optimization results of the three-life spatial conflicts and the spatial layout of land use are output.

2. According to claim 1, a land utilization optimization method for urban mining wasteland based on the synergy of spatial conflict and ecological barrier, It is characterized in that The geospatial transformation of the traditional PSO model includes: Each pixel unit is regarded as a particle, and the position solution space of each particle in the particle swarm is set to the number of land use types D. The speed of the particle is regarded as the probability of selecting different land use types. The position update strategy of each particle is set to be guided by the objective function, that is, the objective function is compared in each iteration. If the objective function is improved, the position of the particle is updated to the new land use type, otherwise the particle does not move. The particle at pixel i selects the speed of different land use types in the tth iteration. The update calculation formula is as follows: In the formula, is the probability selection vector for the previous round, X i is the suitability of the current land use type, which is calculated based on the objective function, Pbest is the suitability corresponding to the optimal land use type of the particle at the current position, w is the inertia weight, and c is the learning factor.

3. According to claim 1, a land utilization optimization method for urban mining wasteland based on spatial conflict and ecological barrier synergy, It is characterized in that The calculation of land use conflicts in the three-life space includes: The production-life-ecology spatial conflict index (PLEI) of resource-based cities is constructed from the three aspects of land use system complexity (SCI), vulnerability (SVI) and competitiveness (SPI), as follows: PLEI=SCI+SVI-SPI.

4. According to claim 3, a land use optimization method for urban mining wasteland based on the synergy of spatial conflict and ecological barrier, It is characterized in that It also includes quantifying the spatial complexity of land patches based on the area-weighted mean patch fractal dimension (AWMPFD) indicator, as follows: Where P ij and a ij are the perimeter and area of ​​the patch, respectively, A is the total area of ​​all patches, m and n are the total number of patches and the total number of patch types, respectively. The value of AWMPFD ranges from 1 to 2. The larger the value, the higher the complexity of the patch and the greater the possibility of land use spatial conflict.

5. According to claim 3, a land use optimization method for urban mining wasteland based on the synergy of spatial conflict and ecological barrier, It is characterized in that It also includes using patch density PD to calculate spatial vulnerability, the formula is as follows: Where n j is the number of patches of type n, and the PD value range is from 0 to 1. The larger the value, the higher the degree of spatial fragmentation and the greater the possibility of land use conflict.

6. According to claim 3, a land use optimization method for urban mining wasteland based on the synergy of spatial conflict and ecological barrier, It is characterized in that It also includes selecting landscape advantage LPI to reflect spatial competitiveness, the formula is as follows: In the formula, a max It represents the area of ​​the largest patch among all patch types. The LPI value ranges from 0 to 1. The larger the value, the more space is dominated by specific land use activities and the less likely spatial conflict is to occur.

7. The land utilization optimization method of urban mining wasteland based on the synergy of spatial conflict and ecological barrier according to claim 1, It is characterized in that The optimization objectives of the designed PSO model include: After measuring the land use conflicts of the three living spaces of each pixel unit, the economic development development and ecological protection protection The objective function is constructed in terms of the optimization goal of the PSO model: U=-w 1 ×f conflict +w 2 ×f development +w 3 ×f protection ; In 1 +in 2 +in 3 =1; In the formula, w 1 、w 2 、w 3 are the weights corresponding to the three objectives, f conflict The PLEI is the land use conflict index. Economic development goals are measured using the bi-square function, with the following formula: In the formula, x and μ are the current construction land area and the maximum allowable construction land area, respectively. origin is the initial construction land area, x origin is the minimum construction land area in the iteration process, σ is the economic density coefficient; Ecological protection is expressed by the proportion of ecological land in the Moore neighborhood, and the formula is as follows: Where Ω refers to the Moore neighborhood of the current unit, N Ω is the number of pixel units in the neighborhood, x ij If it is 1, the current land use type is ecological land, otherwise it is 0.

8. The land utilization optimization method of urban mining wasteland based on the synergy of spatial conflict and ecological barrier according to claim 1, It is characterized in that The identification of the ecological source of the ecosystem around the mining area, calculation of the ecological resistance surface, and construction of the ecological network around the mining area include: The selection of ecological source areas of the ecosystem around the mining area was comprehensively considered from three aspects: service function, morphology, and connectivity; the service function was measured by the habitat quality function in the InVEST model, the morphological type was obtained based on the Guidos tool, and the connectivity was obtained using the Conefor 2.6 tool. Finally, ecological patches with high habitat quality, core area morphology, and high connectivity were selected as ecological source areas; The ecological resistance surface was obtained by the weighted summation method of indicators. Three resistance factors, namely DEM, land use type, and night light intensity, were selected, and the weights were set to be equal. In ArcGIS software, the reclassification tool was used to divide the two resistance factors, namely DEM and night light intensity, into five levels according to the natural breakpoint method. The land use type was divided into five types, namely forest land, water body, cultivated land and garden land, unused land and bare land, and construction land. Each level of the three resistance factors was scored, and then the ecological resistance surface was obtained by equal weight addition. Finally, the ecological source and ecological resistance surface were input into Linkage Mapper software, and the ecological corridor was obtained based on the software’s minimum cost distance path calculation; The ecological source areas are regarded as nodes, and the ecological corridors are regarded as edges. The edges and nodes together construct the ecological network around the mining area.

9. The land utilization optimization method of urban mining wasteland based on the synergy of spatial conflict and ecological barrier according to claim 1, It is characterized in that The method of identifying ecological obstacles and pinch points based on ecological network, designing weighted roulette mechanism, and coupling ecological resistance value and ecological flow density into PSO model includes: The ecological resistance value and ecological flow density value were calculated by the BarrierMapper and PinchpointMapper tools respectively; After calculating the ecological resistance value and ecological flow density value, a weighted roulette mechanism is designed to guide the PSO model to focus on the areas that affect ecological connectivity during the iteration process. That is, the probability of selecting different land use types in each iteration is determined by the speed of the particles. The ecological resistance value and ecological flow density value are incorporated into this process. The formula is as follows: In the formula, is the probability of selecting ecological land for pixel i in the tth iteration after modification, which is used to construct the weighted wheel. is the selection probability of the original ecological land of pixel unit i, RES ij and EFL ij are the ecological resistance value and ecological flow density value of pixel unit (i, j), ∑ x RES x and∑ x EFL x are the total values ​​of regional ecological resistance value and ecological flow density value respectively, and N is the total number of pixel units.

10. A land use optimization system for urban mining wasteland based on spatial conflict and ecological barrier synergy, It is characterized in that include: Collection module: used to collect and process urban mine abandoned land use data; Transformation module: used to transform the traditional PSO model into geospatial, calculate the land use conflicts in the three-life space, and design the optimization objectives of the PSO model; Network construction module: used to identify the ecological source of the ecosystem around the mining area, calculate the ecological resistance surface, and then construct the ecological network around the mining area; Optimization module: used to identify ecological obstacles and pinch points based on ecological networks, design weighted roulette mechanisms, and couple ecological resistance values ​​and ecological flow density into the PSO model; Output module: used to output the optimization results of the three-life spatial conflict and the spatial layout of land use according to the PSO model.

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