Spatial analysis-based station site selection and leveling design collaborative decision-making method and system

Abstract

The invention discloses a station site selection and leveling design collaborative decision-making method and system based on spatial analysis, and belongs to the technical field of constructional engineering.According to the method, through topographic data processing, candidate station range screening, station and slope collaborative design, optimization adjustment and iterative optimization, and through combination of high-precision topographic data, topographic classification and a topographic analysis algorithm, the site selection and leveling design collaborative decision-making efficiency is improved. Areas suitable for construction stations can be accurately screened out, unavailable lands can be effectively avoided, and risks are reduced; according to the method, the automatic collaborative optimization of the station position and the slope design is realized, the mutual influence among the station parameters, the slope parameters and various constraint conditions is fully considered, the iterative optimization is carried out by taking the minimum leveling cost as the target, and the station construction cost is effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of building engineering technology, and in particular to a collaborative decision-making method and system for site selection and leveling design based on spatial analysis. Background Technology

[0002] Traditional site selection and leveling design methods often rely on manual experience and simple geographic information analysis, which has many limitations. In the site selection stage, the analysis of topography is not accurate and comprehensive enough, making it difficult to quickly select suitable sites from complex terrain data. It is also difficult to efficiently utilize contour lines, DEM, or LAS point cloud data to quantitatively analyze elevation and slope, resulting in a time-consuming and labor-intensive site selection process that easily overlooks potential adverse terrain factors.

[0003] In terms of leveling design, previous methods lacked systematicity and coordination. Typically, site design and slope design were carried out independently after initial site selection, without fully considering the mutual influence between the two. During the site selection phase, engineers could only make assessments based on rough terrain conditions, lacking a detailed evaluation of leveling costs. In the design phase, slope design was passively based on the selected location, often leading to repeated adjustments due to large excavation and filling volumes and slope boundary conflicts, sometimes even requiring relocation. Furthermore, when checking whether slope boundaries exceeded limits, there was a lack of effective hierarchical optimization strategies. Operations such as shrinking or adjusting site boundary shapes, dynamically iterating site design elevations, adjusting slope forms, or even setting up retaining walls were often not performed according to the actual situation and in a priority order. This resulted in a lack of coordinated optimization between site location and slope design, increasing construction costs and project risks.

[0004] Compared with some existing related technologies, although some technologies can use geographic information systems (GIS) to perform simple terrain analysis, they are significantly lacking in the collaborative decision-making of site selection and leveling design. They cannot achieve full automation from terrain data processing to final optimization design, and are unable to meet the needs of modern engineering construction for efficient and accurate site design. Summary of the Invention

[0005] This invention proposes a collaborative decision-making method and system for site selection and leveling design based on spatial analysis, in order to solve the problem of poor efficiency of traditional decision-making methods.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A collaborative decision-making method for site selection and leveling design based on spatial analysis includes:

[0008] Terrain data processing: Constructing terrain models and performing automatic terrain classification through elevation and slope analysis;

[0009] Candidate site selection: Digitize terrain requirements and traverse terrain regions to select suitable areas;

[0010] Coordinated design of station and slope: A collaborative mathematical model is established to set the number of vertices and slope parameters in the corresponding candidate area. The slope parameters are fixed parameters, including slope gradient, slope level, slope height of each level and platform width, and variable parameters, including the coordinates of the station center point, the distance from each vertex to the center point, the angle of each vertex relative to the center point and the station elevation. The objective function related to leveling cost is established with the user-specified station area and candidate area boundary as constraints.

[0011] Optimization and Adjustment: Using spatial overlay analysis, we detect whether the slope boundary exceeds the allowable range. If the slope boundary exceeds the limit, we optimize it according to the graded adjustment strategy.

[0012] Iterative optimization: Optimize the parameters of the collaborative mathematical model.

[0013] Furthermore, the candidate site range screening includes: traversing the region of the terrain model based on a specified size of squares; if the average difference in elevation within the current square meets a preset requirement, then the region is marked as a flat region; using a connected component analysis algorithm, merging adjacent flat regions to form a continuous flat region; if the continuous flat region is smaller than a specified threshold, then merging continues or the region is discarded.

[0014] Furthermore, the candidate site range screening also includes: excluding areas that cannot be used as sites by combining terrain features; the terrain model is a DEM digital elevation model; the size of the block is a 10-meter × 10-meter square; and the preset requirement for the average difference is less than 5 meters.

[0015] Furthermore, the optimization according to the hierarchical adjustment strategy includes: Priority 1 - Prioritize shrinking or adjusting the shape of the site boundary: Based on the actual situation, appropriately shrink or adjust the shape of the site boundary to reduce the slope range and make the slope boundary meet the requirements; Priority 2 - Elevation adjustment: If adjusting the shape of the site boundary cannot completely solve the problem, dynamically iterate the site design elevation and optimize the slope boundary by changing the height of the site, while ensuring that the function and use of the site are less affected by the preset value; Priority 3 - Slope adjustment: If the strategies of Priority 1 and 2 still cannot meet the requirements, adjust the slope form; Priority 4 - Set up retaining walls: For the part that still exceeds the limit after the strategies of Priority 1, 2 and 3, set up retaining walls to solve the problem of the slope boundary exceeding the limit, and ensure that the site location and the slope design are optimized in a coordinated manner.

[0016] Furthermore, optimizing the parameters of the collaborative mathematical model includes: calculating the optimized and adjusted site leveling cost; iterating the site parameters step by step through a preset optimization algorithm to determine the optimal site design scheme under various conditions; and obtaining the optimal site range, slope form, and land use boundary.

[0017] A collaborative decision-making system for site selection and leveling design based on spatial analysis includes:

[0018] The first module is for terrain data processing: building terrain models and performing automatic terrain classification through elevation and slope analysis;

[0019] The second module is used for candidate site range selection: digitize the terrain requirements and traverse the terrain area to select the matching area;

[0020] The third module is used for the collaborative design of the site and the slope: a collaborative mathematical model is established to set the number of vertices and slope parameters in the corresponding candidate area. The slope parameters are fixed parameters, including the slope gradient, the number of slope levels, the height of each slope level and the width of the platform, and variable parameters, including the coordinates of the site center point, the distance from each vertex to the center point, the angle of each vertex relative to the center point and the site elevation. The objective function related to the leveling cost is established with the site area and the boundary of the candidate area specified by the user as constraints.

[0021] The fourth module is used for optimization and adjustment: it uses spatial overlay analysis to detect whether the slope boundary exceeds the allowable range. If the slope boundary exceeds the limit, it is optimized according to the graded adjustment strategy.

[0022] The fifth module is used for iterative optimization: optimizing the parameters of the collaborative mathematical model.

[0023] Furthermore, the candidate site range screening includes: traversing the region of the terrain model based on a specified size of squares; if the average difference in elevation within the current square meets a preset requirement, then the region is marked as a flat region; using a connected component analysis algorithm, merging adjacent flat regions to form a continuous flat region; if the continuous flat region is smaller than a specified threshold, then merging continues or the region is discarded.

[0024] Furthermore, the candidate site range screening also includes: excluding areas that cannot be used as sites by combining terrain features; the terrain model is a DEM digital elevation model; the size of the block is a 10-meter × 10-meter square; and the preset requirement for the average difference is less than 5 meters.

[0025] Furthermore, the optimization according to the hierarchical adjustment strategy includes: Priority 1 - Prioritize shrinking or adjusting the shape of the site boundary: Based on the actual situation, appropriately shrink or adjust the shape of the site boundary to reduce the slope range and make the slope boundary meet the requirements; Priority 2 - Elevation adjustment: If adjusting the shape of the site boundary cannot completely solve the problem, dynamically iterate the site design elevation and optimize the slope boundary by changing the height of the site, while ensuring that the function and use of the site are less affected by the preset value; Priority 3 - Slope adjustment: If the strategies of Priority 1 and 2 still cannot meet the requirements, adjust the slope form; Priority 4 - Set up retaining walls: For the part that still exceeds the limit after the strategies of Priority 1, 2 and 3, set up retaining walls to solve the problem of the slope boundary exceeding the limit, and ensure that the site location and the slope design are optimized in a coordinated manner.

[0026] Furthermore, optimizing the parameters of the collaborative mathematical model includes: calculating the optimized and adjusted site leveling cost; iterating the site parameters step by step through a preset optimization algorithm to determine the optimal site design scheme under various conditions; and obtaining the optimal site range, slope form, and land use boundary.

[0027] By adopting the above technical solution, the present invention has the following beneficial effects:

[0028] 1. This invention, through terrain data processing, candidate site range screening, site and slope collaborative design, optimization and adjustment, and iterative optimization, can accurately screen suitable areas for site construction by combining high-precision terrain data, terrain classification and terrain analysis algorithms, effectively avoiding unusable land and reducing risks; it realizes automatic collaborative optimization of site location and slope design, fully considers the mutual influence between site parameters, slope parameters and various constraints, and performs iterative optimization with the goal of minimizing leveling costs, effectively reducing site construction costs. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the collaborative decision-making method for site selection and leveling design based on spatial analysis proposed in this invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] like Figure 1 The following is a collaborative decision-making method for site selection and leveling design based on spatial analysis:

[0032] S1. Terrain Data Processing: Construct a terrain model and perform automatic terrain classification through elevation and slope analysis;

[0033] S2. Candidate site range screening: Digitize the terrain requirements and traverse the terrain regions to screen for suitable areas;

[0034] S3. Coordinated Design of Station and Slope: Coordinated Design of Station and Slope: Establish a collaborative mathematical model to set the number of vertices and slope parameters in the corresponding candidate area. The slope parameters are fixed parameters, including slope gradient, slope level, slope height of each level and platform width. The variable parameters are the coordinates of the station center point, the distance from each vertex to the center point, the angle of each vertex relative to the center point and the station elevation. The objective function related to leveling cost is established with the user-specified station area and candidate area boundary as constraints.

[0035] S4. Optimization and Adjustment: Using spatial overlay analysis, detect whether the slope boundary exceeds the allowable range. If the slope boundary exceeds the limit, optimize it according to the graded adjustment strategy.

[0036] S5. Iterative optimization: Optimize the parameters of the collaborative mathematical model.

[0037] The candidate site range filtering includes: traversing the region of the terrain model based on a specified size of squares; if the average difference in elevation within the current square meets a preset requirement, the region is marked as a flat region; merging adjacent flat regions using a connected component analysis algorithm to form a continuous flat region; if the continuous flat region is smaller than a specified threshold, merging continues or the site is discarded.

[0038] The candidate site selection process also includes: excluding areas that cannot be used as sites by combining terrain features; the terrain model is a DEM digital elevation model; the size of the block is a 10m × 10m square; and the preset requirement for the average difference is less than 5m.

[0039] The optimization according to the hierarchical adjustment strategy includes: Priority 1 - Prioritize shrinking or adjusting the shape of the site boundary: Based on the actual situation, appropriately shrink or adjust the shape of the site boundary to reduce the slope range and make the slope boundary meet the requirements; Priority 2 - Elevation adjustment: If adjusting the site boundary shape cannot completely solve the problem, dynamically iterate the site design elevation and optimize the slope boundary by changing the height of the site, while ensuring that the site's function and use are less affected by the preset value; Priority 3 - Slope adjustment: If the strategies of Priority 1 and 2 still cannot meet the requirements, adjust the slope form; Priority 4 - Set up retaining walls: For the part that still exceeds the limit after the strategies of Priority 1, 2 and 3, set up retaining walls to solve the problem of the slope boundary exceeding the limit, and ensure that the site location and the slope design achieve coordinated optimization.

[0040] The actual situation refers to the relevant data corresponding to the scene and state on site. Appropriate shrinking involves reducing boundary data (such as coordinates, length, width, etc.) according to preset standards. Adjusting its shape involves changing the shape formed by the boundary lines according to preset standards and updating the site boundary data. If adjusting the site boundary shape still does not meet the specified standard values ​​(i.e., cannot completely solve the problem), the site design elevation is dynamically iterated. The slope boundary is optimized by changing the site height, while ensuring that the site's function and use are less affected than preset values. "Still cannot meet the requirements" means that the specified data constraints are still not met after the above processing. The retaining wall is a set of proposed constraint data used to modify the slope boundary values.

[0041] The optimization of the parameters of the collaborative mathematical model includes: calculating the optimized and adjusted site leveling cost; iterating the site parameters step by step through a preset optimization algorithm to determine the optimal site design scheme under various conditions; and obtaining the optimal site range, slope form, and land use boundary.

[0042] A collaborative decision-making system for site selection and leveling design based on spatial analysis includes:

[0043] The first module is for terrain data processing: building terrain models and performing automatic terrain classification through elevation and slope analysis;

[0044] The second module is used for candidate site range selection: digitize the terrain requirements and traverse the terrain area to select the matching area;

[0045] The third module is used for the collaborative design of the site and the slope: a collaborative mathematical model is established to set the number of vertices and slope parameters in the corresponding candidate area. The slope parameters are fixed parameters, including the slope gradient, the number of slope levels, the height of each slope level and the width of the platform, and variable parameters, including the coordinates of the site center point, the distance from each vertex to the center point, the angle of each vertex relative to the center point and the site elevation. The objective function related to the leveling cost is established with the site area and the boundary of the candidate area specified by the user as constraints.

[0046] The fourth module is used for optimization and adjustment: it uses spatial overlay analysis to detect whether the slope boundary exceeds the allowable range. If the slope boundary exceeds the limit, it is optimized according to the graded adjustment strategy.

[0047] The fifth module is used for iterative optimization: optimizing the parameters of the collaborative mathematical model.

[0048] The candidate site range filtering includes: traversing the region of the terrain model based on a specified size of squares; if the average difference in elevation within the current square meets a preset requirement, the region is marked as a flat region; merging adjacent flat regions using a connected component analysis algorithm to form a continuous flat region; if the continuous flat region is smaller than a specified threshold, merging continues or the site is discarded.

[0049] The candidate site selection process also includes: excluding areas that cannot be used as sites by combining terrain features; the terrain model is a DEM digital elevation model; the size of the block is a 10m × 10m square; and the preset requirement for the average difference is less than 5m.

[0050] The optimization according to the hierarchical adjustment strategy includes: Priority 1 - Prioritize shrinking or adjusting the shape of the site boundary: Based on the actual situation, appropriately shrink or adjust the shape of the site boundary to reduce the slope range and make the slope boundary meet the requirements; Priority 2 - Elevation adjustment: If adjusting the site boundary shape cannot completely solve the problem, dynamically iterate the site design elevation and optimize the slope boundary by changing the height of the site, while ensuring that the site's function and use are less affected by the preset value; Priority 3 - Slope adjustment: If the strategies of Priority 1 and 2 still cannot meet the requirements, adjust the slope form; Priority 4 - Set up retaining walls: For the part that still exceeds the limit after the strategies of Priority 1, 2 and 3, set up retaining walls to solve the problem of the slope boundary exceeding the limit, and ensure that the site location and the slope design achieve coordinated optimization.

[0051] The optimization of the parameters of the collaborative mathematical model includes: calculating the optimized and adjusted site leveling cost; iterating the site parameters step by step through a preset optimization algorithm to determine the optimal site design scheme under various conditions; and obtaining the optimal site range, slope form, and land use boundary.

[0052] This invention provides a collaborative decision-making method for site selection and leveling design based on spatial analysis. Its core framework consists of "topographic data processing → candidate site range screening → site and slope coordination design → optimization and adjustment." The specific technical solution is as follows:

[0053] (a) Terrain Data Processing

[0054] Terrain Model Construction: Using contour lines, DEM, or LAS point cloud data, and employing professional modeling algorithms and software, a high-precision terrain model is constructed. This model accurately reflects the terrain's undulations, slope, and other information, providing a foundation for subsequent analysis.

[0055] Elevation and Slope Analysis: Based on the constructed terrain model, spatial analysis tools are used to accurately calculate the elevation and slope distribution of the terrain. Analysis of elevation data provides a clear understanding of the altitude of different areas; calculation of slope data identifies areas with steep or gentle slopes, providing a basis for terrain classification.

[0056] Automatic terrain classification: Based on elevation and slope analysis results, and combined with preset classification rules, the terrain is automatically classified. For example, the terrain is divided into different types such as mountains, hills, and plains, which facilitates the subsequent targeted selection of suitable areas for site construction.

[0057] (II) Screening of Candidate Sites

[0058] Digitalization of terrain requirements: Transforming the terrain requirements of building sites into digital models, clarifying specific requirements such as site flatness and slope restrictions.

[0059] Terrain Region Traversal: Based on the terrain-classified DEM digital elevation model, each region in the DEM is traversed using a 10m x 10m square, with a traversal step size set to 1m. During the traversal, the average elevation difference within the current square is calculated in real time. If the average difference meets a preset requirement, such as being less than 5m, the region is marked as a flat region.

[0060] Region merging and fragmentation: Using connected component analysis algorithms, adjacent flat regions are merged to form larger, continuous flat areas. Simultaneously, fragmented regions that are too small after merging are processed, for example, by merging them into adjacent larger regions or discarding them, to improve site usability.

[0061] Unusable land avoidance: Combining topographic features, such as the location information of unusable land like rivers, lakes, houses, and geological disaster areas, spatial analysis methods are used to exclude areas that do not meet the requirements.

[0062] Area analysis and screening: The area after the above processing is analyzed to screen out areas that meet the site requirements and form the candidate site range.

[0063] (III) Coordinated Design of Station and Slope Protection

[0064] Establish a mathematical model: Within the candidate area, set the number of vertices and slope parameters, such as slope gradient and slope type, as fixed parameters. Use the coordinates of the station center point, the distance from each vertex to the center point, the angle of each vertex relative to the center point, and the station elevation as variable parameters. Use the station area and candidate area boundary specified by the user as constraints to establish a leveling cost-related objective function.

[0065] (iv) Optimization and Adjustment

[0066] Slope boundary detection: A spatial overlay analysis method is used to detect whether the slope boundary exceeds the allowable range. If the slope boundary exceeds the limit, optimization is performed according to a tiered adjustment strategy.

[0067] Priority 1 - Shrink Boundary: Prioritize shrinking or adjusting the shape of the site boundary: Based on the actual situation, appropriately shrink or adjust the shape of the site boundary to reduce the slope range and make the slope boundary meet the requirements.

[0068] Priority 2 - Elevation Adjustment: If adjusting the site boundary shape cannot completely solve the problem, dynamically iterate the site design elevation, optimize the slope boundary by changing the height of the site, and ensure that the function and use of the site are not significantly affected.

[0069] Priority 3 - Slope Adjustment: If the above two methods still cannot meet the requirements, consider adjusting the slope form, such as changing from straight slope to broken slope or using graded slope, to adapt to site conditions and slope boundary requirements.

[0070] Priority 4 - Set up retaining walls: For the parts that still exceed the limits after the above optimization, set up retaining walls to solve the problem of exceeding the slope boundary, and ensure that the site location and slope design are optimized in a coordinated manner.

[0071] (5) Iterative optimization

[0072] Based on the mathematical model established in step (III), the cost of leveling the site after optimization and adjustment is calculated. Through mathematical optimization algorithm, the site parameters are iterated step by step to determine the optimal site design scheme under various conditions, and information such as the optimal site range, slope form and land boundary is obtained.

[0073] Compared with the prior art, the present invention has the following advantages:

[0074] 1) By combining high-precision terrain data, terrain classification and terrain analysis algorithms, it is possible to accurately screen out areas suitable for site construction, effectively avoid unusable land, reduce risks, and improve site selection effectiveness by more than 30% and site selection efficiency by 2 times compared with traditional methods.

[0075] 2) It realizes automatic collaborative optimization of site location and slope design, fully considers the mutual influence between site parameters, slope parameters and various constraints, and performs iterative optimization with the goal of minimizing leveling cost, effectively reducing site construction cost by about 20% and improving design efficiency by more than 3 times.

[0076] 3) By implementing a tiered adjustment strategy, unnecessary excavation and filling can be reduced while ensuring usable area and avoiding encroachment on unusable land. This improves the efficiency of land resource utilization, reduces station construction costs, and also reduces the impact of station construction on the surrounding environment.

[0077] Mountainous area station site selection and leveling design

[0078] (1) Terrain data processing: collect contour line data, DEM data and a small amount of LAS point cloud data of a mountainous area, and use professional geographic information modeling software to construct a high-precision terrain model of the area.

[0079] (2) Terrain analysis: Using the spatial analysis module, the elevation and slope of the terrain model are analyzed to calculate the elevation and slope of different locations in the area.

[0080] (3) Terrain classification: According to the preset terrain classification rules, the terrain of the region is divided into different types such as mountains, hills and plains.

[0081] (4) Flat Area Screening: The terrain requirements of the building site are converted into a digital model, specifying that the site flatness requirement is an average elevation difference of less than 5m and a slope limit within a certain range. A 5m × 5m square is used to traverse the DEM digital elevation model with a step size of 1m. The average elevation difference within each square area is calculated, and flat areas that meet the flatness requirements are marked. Adjacent flat areas are merged using a connected component analysis algorithm, and fragmented areas with too small an area are merged or discarded.

[0082] (5) Unusable land screening: Based on the location information of unusable land such as rivers, lakes, villages and geological disaster areas in the region, spatial analysis methods are used to exclude areas that do not meet the requirements.

[0083] (6) Area analysis: The remaining area is analyzed to select areas that meet the construction requirements of the site and form a candidate site range.

[0084] (7) Setting of station parameters: With the candidate station range boundary, station area, length-to-width ratio and rotation angle as constraints, the following station parameters are randomly set: station center point coordinates (N,E), length (L), width (W) and rotation angle (α), etc. The optimal elevation is automatically calculated by the cut-fill balance algorithm to obtain the preliminary station range boundary and slope boundary.

[0085] (8) Site optimization and adjustment: Based on the slope boundary of the site, spatial overlay analysis is used to detect whether the slope boundary exceeds the limit. If there is an area exceeding the limit, the site is optimized and adjusted according to the priority of shrinking the boundary, adjusting the elevation, adjusting the slope, and setting up retaining walls, according to the graded adjustment strategy.

[0086] (9) Leveling cost calculation: Calculate the leveling cost based on the optimized and adjusted station parameters.

[0087] (10) Iterative optimization: Using mathematical optimization algorithms, with the goal of minimizing leveling cost, repeat steps (7) to (9) for iterative optimization to calculate the station parameters that meet the station construction requirements and minimize leveling cost.

[0088] The above description is a detailed description of the preferred embodiments of the present invention. However, the embodiments are not intended to limit the scope of the patent application of the present invention. All equivalent changes or modifications made under the technical spirit of the present invention should fall within the patent scope covered by the present invention.

Claims

1. A collaborative decision-making method for site selection and leveling design based on spatial analysis, characterized in that, include: Terrain data processing: Constructing terrain models and performing automatic terrain classification through elevation and slope analysis; Candidate site selection: Digitize terrain requirements and traverse terrain regions to select suitable areas; Coordinated design of station and slope: A collaborative mathematical model is established to set the number of vertices and slope parameters in the corresponding candidate area. The slope parameters are fixed parameters, including slope gradient, slope level, slope height of each level and platform width, and variable parameters, including the coordinates of the station center point, the distance from each vertex to the center point, the angle of each vertex relative to the center point and the station elevation. The objective function related to leveling cost is established with the user-specified station area and candidate area boundary as constraints. Optimization and Adjustment: Using spatial overlay analysis, we detect whether the slope boundary exceeds the allowable range. If the slope boundary exceeds the limit, we optimize it according to the graded adjustment strategy. Iterative optimization: Optimize the parameters of the collaborative mathematical model.

2. The collaborative decision-making method for site selection and leveling design based on spatial analysis according to claim 1, characterized in that, The candidate site range filtering includes: Based on blocks of a specified size, the region of the terrain model is traversed. If the average difference in elevation within the current block meets the preset requirements, the region is marked as a flat region. Using a connected component analysis algorithm, adjacent flat regions are merged to form a continuous flat region. If the continuous flat region is smaller than a specified threshold, merging continues or the region is discarded.

3. The collaborative decision-making method for site selection and leveling design based on spatial analysis according to claim 2, characterized in that, The candidate site selection process also includes: excluding areas that cannot be used as sites by considering terrain features; The terrain model is a DEM (Digital Elevation Model); The square is 10 meters by 10 meters in size; The preset requirement for the average difference is less than 5m.

4. The collaborative decision-making method for site selection and leveling design based on spatial analysis according to claim 3, characterized in that, The optimization according to the hierarchical adjustment strategy includes: Priority 1 - Prioritize shrinking or adjusting the shape of the site boundary: Based on the actual situation, appropriately shrink or adjust the shape of the site boundary to reduce the slope range and make the slope boundary meet the requirements; Priority 2 - Elevation Adjustment: If adjusting the site boundary shape cannot completely solve the problem, dynamically iterate the site design elevation, optimize the slope boundary by changing the height of the site, and ensure that the site's function and use are less affected by the preset value. Priority 3 - Gradient Adjustment: If the strategies of Priority 1 and 2 still cannot meet the requirements, the gradient method is adjusted. Priority 4 - Set up retaining walls: For the portion that still exceeds the limit after using strategies of priorities 1, 2 and 3, set up retaining walls to solve the problem of exceeding the slope boundary, and ensure that the site location and slope design are optimized in a coordinated manner.

5. The collaborative decision-making method for site selection and leveling design based on spatial analysis according to claim 4, characterized in that, The parameters for optimizing the collaborative mathematical model include: The cost of leveling the station after optimization and adjustment is calculated. The station parameters are iterated step by step through a preset optimization algorithm to determine the optimal station design scheme under various conditions. The optimal site area, slope configuration, and land use boundary are obtained.

6. A collaborative decision-making system for site selection and leveling design based on spatial analysis, characterized in that, include: The first module is for terrain data processing: building terrain models and performing automatic terrain classification through elevation and slope analysis; The second module is used for candidate site range selection: digitize the terrain requirements and traverse the terrain area to select the matching area; The third module is used for the collaborative design of the site and the slope: a collaborative mathematical model is established to set the number of vertices and slope parameters in the corresponding candidate area. The slope parameters are fixed parameters, including the slope gradient, the number of slope levels, the height of each slope level and the width of the platform, and variable parameters, including the coordinates of the site center point, the distance from each vertex to the center point, the angle of each vertex relative to the center point and the site elevation. The objective function related to the leveling cost is established with the site area and the boundary of the candidate area specified by the user as constraints. The fourth module is used for optimization and adjustment: it uses spatial overlay analysis to detect whether the slope boundary exceeds the allowable range. If the slope boundary exceeds the limit, it is optimized according to the graded adjustment strategy. The fifth module is used for iterative optimization: optimizing the parameters of the collaborative mathematical model.

7. The site selection and leveling design collaborative decision-making system based on spatial analysis according to claim 6, characterized in that, The candidate site range filtering includes: Based on blocks of a specified size, the region of the terrain model is traversed. If the average difference in elevation within the current block meets the preset requirements, the region is marked as a flat region. Using a connected component analysis algorithm, adjacent flat regions are merged to form a continuous flat region. If the continuous flat region is smaller than a specified threshold, merging continues or the region is discarded.

8. The site selection and leveling design collaborative decision-making system based on spatial analysis according to claim 7, characterized in that, The candidate site selection process also includes: excluding areas that cannot be used as sites by considering terrain features; The terrain model is a DEM (Digital Elevation Model); The square is 10 meters by 10 meters in size; The preset requirement for the average difference is less than 5m.

9. The site selection and leveling design collaborative decision-making system based on spatial analysis according to claim 8, characterized in that, The optimization according to the hierarchical adjustment strategy includes: Priority 1 - Prioritize shrinking or adjusting the shape of the site boundary: Based on the actual situation, appropriately shrink or adjust the shape of the site boundary to reduce the slope range and make the slope boundary meet the requirements; Priority 2 - Elevation Adjustment: If adjusting the site boundary shape cannot completely solve the problem, dynamically iterate the site design elevation, optimize the slope boundary by changing the height of the site, and ensure that the site's function and use are less affected by the preset value. Priority 3 - Gradient Adjustment: If the strategies of Priority 1 and 2 still cannot meet the requirements, the gradient method is adjusted. Priority 4 - Set up retaining walls: For the portion that still exceeds the limit after using strategies of priorities 1, 2 and 3, set up retaining walls to solve the problem of exceeding the slope boundary, and ensure that the site location and slope design are optimized in a coordinated manner.

10. The site selection and leveling design collaborative decision-making system based on spatial analysis according to claim 9, characterized in that, The parameters for optimizing the collaborative mathematical model include: The cost of leveling the station after optimization and adjustment is calculated. The station parameters are iterated step by step through a preset optimization algorithm to determine the optimal station design scheme under various conditions. The optimal site area, slope configuration, and land use boundary are obtained.

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