Three-dimensional space site selection method and system for pumped storage power station based on multi-model fusion

Abstract

This disclosure provides a three-dimensional spatial site selection method and system for pumped storage power stations based on multi-model fusion, relating to the field of data processing technology. The method includes: constructing a digital elevation model (DEM) and a building information model (BIM); mapping the BIM to the spatial coordinate system of the DEM using a spatial transformation matrix to obtain a fused spatial model; using the fused spatial model to perform slope calculations, spatial intersection calculations, and burial depth calculations to determine the preliminary site selection range; obtaining a set of constraints using the preliminary site selection range, and using the fused spatial model to perform spatial mapping and constraint calculations on the set of constraints to determine candidate construction areas; determining multiple candidate construction locations based on the candidate construction areas, and using the fused spatial model to evaluate each candidate construction location, and selecting the target construction location corresponding to the pumped storage power station based on the evaluation calculation results. The technical solution in this disclosure can improve the accuracy and reliability of pumped storage power station site selection.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and more specifically, to a three-dimensional spatial site selection method and system for pumped storage power stations based on multi-model fusion. Background Technology

[0002] As an important infrastructure for peak shaving, frequency regulation and energy storage in the power system, the site selection process of pumped storage power stations is directly related to the safety, economy and operational stability of the project.

[0003] However, in existing technologies, the site selection of pumped storage power stations typically relies on two-dimensional topographic maps, local geological profiles, and manual comparative analysis methods, using basic indicators such as topographic elevation differences, geological conditions, and transportation conditions for preliminary screening. Because these methods primarily rely on the static overlay of planar information, the spatial information is incomplete, and the relationship between the undulations and engineering layout under complex terrain conditions is difficult to consistently depict. This results in a strong dependence on human experience in the screening results, making it difficult to guarantee the accuracy and reliability of the site selection conclusions. Therefore, existing methods for selecting pumped storage power stations still have shortcomings in terms of accuracy and reliability, and are insufficient to meet the site selection requirements under complex terrain conditions.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this disclosure is to provide a three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion, a three-dimensional spatial site selection system for pumped storage power stations based on multi-model fusion, electronic equipment, and computer-readable storage medium. By constructing a digital elevation model and a building information model to obtain a fused spatial model, the preliminary site selection range, candidate construction areas, and target construction locations are determined based on the fused spatial model, thereby improving the accuracy and reliability of pumped storage power station site selection to at least a certain extent.

[0006] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0007] According to a first aspect of the present disclosure, a three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion is provided. The method includes: constructing a digital elevation model (DEM) based on elevation data of the site selection area, and constructing a building information model (BIM) using the spatial range determined by the DEM and the engineering construction data corresponding to the site selection area; determining the spatial transformation matrix corresponding to the DEM and the BIM, and mapping the BIM to the spatial coordinate system of the DEM using the spatial transformation matrix to obtain a fused spatial model; performing slope calculation, spatial intersection calculation, and burial depth calculation using the fused spatial model, and determining a preliminary site selection range based on the calculation results; obtaining a set of constraint conditions using the preliminary site selection range, and performing spatial mapping and constraint calculation on the set of constraint conditions using the fused spatial model to determine candidate construction areas; determining multiple candidate construction locations based on the candidate construction areas, evaluating each candidate construction location using the fused spatial model, and selecting a target construction location corresponding to the pumped storage power station from the multiple candidate construction locations based on the evaluation calculation results.

[0008] According to a second aspect of the present disclosure, a three-dimensional spatial site selection system for pumped storage power stations based on multi-model fusion is provided to implement the aforementioned three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion. The system includes: a model building module, used to build a digital elevation model (DEM) based on elevation data of the site selection area, and to build a building information model (BIM) using the spatial range determined by the DEM and the engineering construction data corresponding to the site selection area; and a model fusion module, used to determine the spatial transformation matrix corresponding to the DEM and the BIM, and to map the BIM to the spatial coordinate system of the DEM using the spatial transformation matrix to obtain... The system comprises: a fusion spatial model; a site selection module for performing slope calculation, spatial intersection calculation, and burial depth calculation using the fusion spatial model, and determining a preliminary site selection range based on the calculation results; a constraint overlay module for obtaining a set of constraint conditions using the preliminary site selection range, and performing spatial mapping and constraint calculation on the set of constraint conditions using the fusion spatial model to determine candidate construction areas; and a location determination module for determining multiple candidate construction locations based on the candidate construction areas, evaluating each candidate construction location using the fusion spatial model, and selecting the target construction location corresponding to the pumped storage power station from the multiple candidate construction locations based on the evaluation calculation results.

[0009] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory storing computer-readable instructions, which, when executed by the processor, implement the three-dimensional spatial location method for pumped storage power stations based on multi-model fusion as described in the first aspect.

[0010] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the three-dimensional spatial location method for pumped storage power stations based on multi-model fusion as described in the first aspect.

[0011] The technical solutions provided in this disclosure may have the following beneficial effects: This embodiment of the three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion firstly constructs a digital elevation model (DEM) based on the elevation data of the site selection area. Then, a building information model (BIM) is constructed using the spatial range determined by the DEM and the corresponding engineering construction data of the site selection area. This ensures that the input data uses the same spatial range as a benchmark, reducing discrepancies caused by inconsistencies in the range. On one hand, the spatial transformation matrices corresponding to the DEM and BIM are determined, and the BIM is mapped to the spatial coordinate system of the DEM using these matrices to obtain a fused spatial model. This aligns the topographic data and engineering data under the same spatial coordinate system, reducing misalignment caused by inconsistencies in coordinates. On the other hand, the fused spatial model is used to calculate slope, spatial intersection, and burial depth. Based on the calculation results, a preliminary site selection range is determined, ensuring that the preliminary site selection range is formed from calculable results and maintains a consistent calculation benchmark. Furthermore, a set of constraints is obtained using the preliminary site selection range, and the fused spatial model is used to perform spatial mapping and constraint calculations on this set of constraints to determine candidate construction areas. This ensures that candidate construction areas are screened under the same spatial reference. Furthermore, multiple candidate construction locations are determined based on the candidate construction area, and a fusion spatial model is used to evaluate and calculate each candidate construction location. Based on the evaluation and calculation results, the target construction location corresponding to the pumped storage power station is selected, thus providing a unified spatial benchmark for the determination of the target construction location. This improves the accuracy and reliability of pumped storage power station site selection.

[0012] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0013] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0014] Figure 1The illustration shows a flowchart of a three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion, according to some embodiments of the present disclosure.

[0015] Figure 2 The illustration shows a schematic diagram of a process for constructing a digital elevation model according to some embodiments of the present disclosure.

[0016] Figure 3 The illustration shows a flowchart of determining an initial site selection range according to some embodiments of the present disclosure.

[0017] Figure 4 The illustration shows a schematic diagram of the composition of a three-dimensional spatial location system for a pumped storage power station based on multi-model fusion, according to some embodiments of the present disclosure.

[0018] Figure 5 The schematic diagram illustrates the structural schematic of a computer system of an electronic device according to some embodiments of the present disclosure.

[0019] Figure 6 A schematic diagram of a computer-readable storage medium according to some embodiments of the present disclosure is shown.

[0020] In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed Implementation

[0021] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this specification. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this specification as detailed in the appended claims.

[0022] The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of this specification. The singular forms “a,” “the,” and “the” as used in this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0023] It should be understood that although the terms first, second, third, etc., may be used in this specification to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this specification, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0024] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art.

[0025] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0026] Furthermore, the accompanying drawings are for illustrative purposes only and are not necessarily drawn to scale. The block diagrams shown in the drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0027] In this example embodiment, a three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion is first provided. Figure 1 The illustration schematically depicts a flowchart of a three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion, according to some embodiments of the present disclosure. (Reference) Figure 1 As shown, the three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion may include the following steps: Step S110: Construct a digital elevation model based on the elevation data of the site selection area, and construct a building information model using the spatial range determined by the digital elevation model and the engineering construction data corresponding to the site selection area. Step S120: Determine the spatial transformation matrices corresponding to the digital elevation model and the building information model, and use the spatial transformation matrices to map the building information model to the spatial coordinate system of the digital elevation model to obtain the fused spatial model; Step S130: Use the fusion spatial model to calculate the slope, spatial intersection and burial depth, and determine the preliminary site selection range based on the calculation results; Step S140: Obtain the set of constraints using the preliminary site selection range, and use the fusion spatial model to perform spatial mapping and constraint calculation on the set of constraints to determine the candidate construction area; Step S150: Based on the candidate construction area, determine multiple candidate construction locations, use the fusion spatial model to evaluate and calculate each candidate construction location, and select the target construction location corresponding to the pumped storage power station from the multiple candidate construction locations based on the evaluation and calculation results.

[0028] According to the three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion in this example embodiment, firstly, a digital elevation model (DEM) is constructed based on the elevation data of the site selection area. Then, a building information model (BIM) is constructed using the spatial range determined by the DEM and the corresponding engineering construction data of the site selection area. This ensures that the input data uses the same spatial range as a benchmark, reducing discrepancies caused by inconsistencies in the range. On one hand, the spatial transformation matrices corresponding to the DEM and BIM are determined, and the BIM is mapped to the spatial coordinate system of the DEM using these matrices to obtain a fused spatial model. This aligns the topographic data and engineering data under the same spatial coordinate system, reducing misalignment caused by inconsistencies in coordinates. On the other hand, the fused spatial model is used to calculate slope, spatial intersection, and burial depth. Based on the calculation results, a preliminary site selection range is determined, ensuring that the preliminary site selection range is formed from calculable results and maintains a consistent calculation benchmark. Furthermore, a set of constraints is obtained using the preliminary site selection range, and the fused spatial model is used to perform spatial mapping and constraint calculations on this set of constraints to determine candidate construction areas. This ensures that candidate construction areas are screened under the same spatial reference. Furthermore, multiple candidate construction locations are determined based on the candidate construction area, and a fusion spatial model is used to evaluate and calculate each candidate construction location. Based on the evaluation and calculation results, the target construction location corresponding to the pumped storage power station is selected, thus providing a unified spatial benchmark for the determination of the target construction location. This improves the accuracy and reliability of pumped storage power station site selection.

[0029] The following will further explain the three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion in this example embodiment.

[0030] In step S110, a digital elevation model is constructed based on the elevation data of the site selection area, and a building information model is constructed using the spatial range determined by the digital elevation model and the engineering construction data corresponding to the site selection area.

[0031] The site selection area can represent the target geographical range for three-dimensional spatial site selection analysis of pumped storage power stations. This area can be defined by any of the following: a preset administrative boundary, a preset catchment area boundary, or a preset coordinate bounding box. Elevation data can represent elevation information data related to the site selection area, used to characterize the surface elevation distribution. This can include one or more of raster elevation values, contour line elevation values, and discrete measurement point elevation values, and the elevation information data can carry corresponding plane coordinate information and vertical reference information. The Digital Elevation Model (DEM) can represent a digital terrain model generated based on elevation data, used to characterize the spatial distribution of surface elevation in the site selection area. The DEM can adopt any of the following forms: a regular raster elevation matrix, a triangular mesh terrain surface, or a point cloud terrain surface. The DEM can provide a surface elevation query interface to output the elevation value corresponding to any spatial location. The spatial extent can represent the spatial boundary range covering the site selection area as represented by the DEM, which can include both planar coverage and elevation coverage. Engineering construction data can represent engineering data related to the site selection area, used to characterize the geometric and attribute information of engineering structures. This data can include one or more of the following: structure outline dimensions, control point coordinates, axis path data, control elevation data, and burial depth parameters. The engineering data can also carry structure category identifiers and parameter fields. Building Information Modeling (BIM) can represent a three-dimensional engineering model generated based on engineering construction data, used to characterize the spatial geometry and engineering attribute information of engineering structures. It provides information on the geometric boundaries, spatial location, and engineering parameters of engineering structures during spatial analysis.

[0032] In this step, a digital elevation model is constructed based on the elevation data of the site selection area. A building information model is then constructed using the spatial range determined by the digital elevation model and the corresponding engineering construction data of the site selection area. This establishes a spatial correspondence between the digital elevation model and the building information model with the same spatial range as a constraint. As a result, the topographic elevation information and engineering construction information are expressed within the same spatial boundary and participate in subsequent calculations, reducing the accumulation of deviations caused by inconsistencies in spatial range. This, in turn, helps to improve the accuracy and reliability of pumped storage power station site selection.

[0033] In step S120, the spatial transformation matrices corresponding to the digital elevation model and the building information model are determined, and the building information model is mapped to the spatial coordinate system of the digital elevation model using the spatial transformation matrices to obtain the fused spatial model.

[0034] The spatial transformation matrix represents a mathematical mapping relationship describing the correspondence between different spatial coordinate expressions. It is used to transform spatial position and direction to achieve spatial correspondence between different models. The spatial coordinate system represents a coordinate reference system describing spatial positional relationships. It is used to uniformly express the position of spatial objects and serves as a benchmark for spatial calculation and mapping. The fused spatial model represents a comprehensive spatial expression model formed by spatially aligning the digital elevation model and the building information model under a unified spatial coordinate system. It is used to simultaneously represent topographic spatial information and engineering spatial information. In this step, by generating the fused spatial model, the building information model and the digital elevation model form a corresponding spatial expression relationship under a unified spatial coordinate system. This allows engineering spatial information and topographic spatial information to participate in spatial calculation and spatial determination under the same spatial benchmark, reducing spatial misalignment and calculation deviation caused by inconsistent coordinate benchmarks. This provides a consistent spatial basis for subsequent slope calculation, spatial intersection calculation, and depth calculation based on the fused spatial model.

[0035] In step S130, the slope, spatial intersection and burial depth are calculated using the fusion spatial model, and the preliminary site selection range is determined based on the calculation results.

[0036] Among these, slope calculation can represent the process of calculating and processing the relationship of surface elevation changes based on topographic spatial information in the fused spatial model to obtain the slope value or slope distribution result for the corresponding spatial location. Spatial intersection calculation can represent the spatial operation process of calculating and processing the spatial overlap, intersection, or inclusion relationship between spatial objects based on the geometric boundary relationship of different spatial objects in the fused spatial model. Burial depth calculation can represent the spatial calculation process of calculating and processing the elevation difference relationship between the engineering spatial location and the surface spatial location based on topographic spatial information and engineering spatial information in the fused spatial model. The preliminary site selection range can represent the spatial area range determined based on the slope calculation results, spatial intersection calculation results, and burial depth calculation results. This spatial area range is used to limit the area of ​​application for subsequent site selection analysis and spatial screening. In this step, the fused spatial model is used to perform slope calculation, spatial intersection calculation, and burial depth calculation, and the preliminary site selection range is determined based on the calculation results. This ensures that the preliminary site selection range is formed by the joint constraints of multiple types of spatial calculation results, so that the determination of the site selection range is based on a unified spatial model and calculable results, reducing the instability caused by single-factor judgment.

[0037] In step S140, the set of constraints is obtained using the preliminary site selection range, and the set of constraints is spatially mapped and constrained using the fusion spatial model to determine the candidate construction area.

[0038] The constraint set represents a set of constraint information used to limit the spatial scope of site selection. This set includes spatial constraints that restrict the construction location, which may include one or more of geological, water body, traffic, and land use constraints. Spatial mapping represents the process of converting the constraint information in the constraint set into a spatial representation consistent with the fused spatial model, ensuring that the constraint information and the fused spatial model are under the same spatial reference. Constraint calculation represents the computational process of performing spatial operations and judgments on the spatially mapped constraint information based on the fused spatial model, resulting in the spatial outcome corresponding to the constraint effect. The candidate construction area represents the spatial region within the initial site selection area that meets the constraint requirements after constraint calculation, serving as the spatial basis for determining subsequent candidate construction locations. In this step, the fused spatial model is used to perform spatial mapping and constraint calculations on the constraint set to determine the candidate construction area. This ensures that the candidate construction area is calculated from the constraint conditions under a unified spatial model, providing a consistent spatial benchmark and computational basis for determining the candidate construction area.

[0039] In step S150, multiple candidate construction locations are determined based on the candidate construction area, and the fusion spatial model is used to evaluate and calculate each candidate construction location. Based on the evaluation and calculation results, the target construction location corresponding to the pumped storage power station is selected from the multiple candidate construction locations.

[0040] Here, candidate construction locations refer to spatial points selected within the candidate construction area for feasibility evaluation, serving as the spatial objects for subsequent evaluation calculations. Evaluation calculations represent the process of processing the spatial information corresponding to candidate construction locations based on a fused spatial model and outputting evaluation results. Target construction locations refer to spatial locations determined from multiple candidate construction locations based on the evaluation calculation results, serving as the selected location for the pumped storage power station. In this step, the target construction location for the pumped storage power station is selected based on the evaluation calculation results, ensuring that the determination of the target construction location is based solely on the evaluation calculation results, reducing the involvement of human experience and thus improving the accuracy and reliability of pumped storage power station site selection.

[0041] The technical content of the above embodiments will be described in detail below.

[0042] In some embodiments, reference Figure 2As shown, the construction of a digital elevation model based on the elevation data of the selected area includes the following technical steps: Step S210: Obtain multiple types of elevation data corresponding to the selected area, and unify the coordinate datum and vertical datum of the multiple types of elevation data to obtain unified elevation data.

[0043] Among them, multi-type elevation data can represent a set of elevation information data used to describe the surface elevation distribution of the site selection area. This elevation information data can include one or more of the following: raster elevation data, contour line elevation data, discrete measurement point elevation data, and point cloud elevation data. It reflects the elevation characteristics corresponding to different spatial locations within the site selection area. Coordinate datum unification can represent the process of standardizing the spatial coordinate references used for multi-type elevation data, enabling the multi-type elevation data to be expressed under the same plane coordinate reference. Vertical datum unification can represent the process of standardizing the elevation reference datums used for multi-type elevation data, enabling the multi-type elevation data to be expressed under the same elevation reference. Unified elevation data can represent the set of elevation data after coordinate datum unification and vertical datum unification, used as input elevation data for subsequent digital elevation model construction.

[0044] Specifically, multiple types of elevation data corresponding to the selected area are acquired, and coordinate and vertical references are unified for these multiple types of elevation data. When obtaining unified elevation data, the coordinate reference information and vertical reference information carried by the multiple types of elevation data can be read separately, and the multiple types of elevation data can be converted to a unified coordinate reference. For vertical reference unification, the elevation values ​​of the multiple types of elevation data can be corrected based on known elevation reference transformation relationships or corresponding elevation control data, and outlier removal and null value identification processing can be performed on the corrected data to form unified elevation data.

[0045] Step S220: Determine the grid coverage area and establish a unified grid using unified elevation data. Perform full-domain fusion calculation on the unified elevation data using the unified grid to obtain the basic elevation grid.

[0046] The grid coverage area can represent the spatial extent used to define the boundaries of the gridded processing space, clarifying the coverage area of ​​the unified grid in both the planar and elevation directions. The unified grid can represent the regularized spatial division structure established within the grid coverage area, used for spatial indexing and computational processing of the unified elevation data. Global fusion computation can represent the computational process of comprehensively fusing unified elevation data within the grid coverage area based on the unified grid, used to generate elevation representation results at the unified grid scale. The base elevation grid can represent the gridded elevation data results obtained through global fusion computation, representing the surface elevation distribution at the unified grid scale.

[0047] Specifically, the grid coverage area is determined and a unified grid is established using unified elevation data. When the unified elevation data is fused across the entire domain using the unified grid to obtain the basic elevation grid, the grid coverage area can be determined based on the spatial bounding boundary of the unified elevation data, and a unified grid index can be generated within the grid coverage area according to the preset grid resolution. The fusion calculation can retrieve the elevation samples falling into the corresponding grid grid cell by cell according to the unified grid, and fuse the elevation samples within the grid to form the grid elevation value. For grids that lack elevation samples, interpolation can be performed based on adjacent grid samples to complete the grid and the completion mark can be recorded to output the basic elevation grid.

[0048] Step S230: Calculate the terrain complexity using the basic elevation grid, and use the terrain complexity to determine the high-complexity sub-regions.

[0049] Among them, terrain complexity can be represented by an index that quantifies the characteristics of surface elevation changes based on a basic elevation grid, and is used to reflect the degree of terrain undulation and the intensity of elevation changes within a spatial region. High-complexity sub-regions can be represented by spatial regions in the basic elevation grid whose corresponding terrain complexity reaches a preset judgment condition, and are used to identify local areas with significant terrain undulation changes.

[0050] Specifically, when calculating terrain complexity using a basic elevation grid and determining high-complexity sub-regions using terrain complexity, local elevation change features can be extracted from the grid neighborhood on the basic elevation grid, and terrain complexity can be calculated based on the local elevation change features. Subsequently, grid indices that meet the preset judgment conditions for terrain complexity are aggregated to obtain a spatially continuous set of grids, and the spatial range corresponding to this set of grids is determined as the high-complexity sub-region.

[0051] Step S240: A refined grid is established within the grid coverage area of ​​the basic elevation grid using a high-complexity sub-region. The refined grid is then used to resample and perform sub-domain fusion calculations on the unified elevation data to obtain the refined elevation grid.

[0052] Among these, refined grid refers to a high-resolution grid structure established within the grid coverage of the base elevation grid for a highly complex sub-region, used to provide a more refined spatial representation of elevation information within the highly complex sub-region. Resampling refers to the process of spatially extracting and reconstructing uniform elevation data based on the refined grid, used to convert the original elevation data into an elevation representation that matches the resolution of the refined grid. Subdomain fusion computation refers to the computational process of fusing resampled elevation data within a sub-region defined by the refined grid, used to form consistent elevation calculation results within the sub-region. Refined elevation grid refers to the gridded elevation data result obtained through subdomain fusion computation, representing the surface elevation distribution of a highly complex sub-region at the refined grid scale, used to supplement the elevation representation of the base elevation grid within the highly complex region.

[0053] Specifically, a refined grid is established within the grid coverage of the basic elevation grid in a high-complexity sub-region. The refined grid is then used to resample and perform subdomain fusion calculations on the unified elevation data to obtain the refined elevation grid. In the high-complexity sub-region, the grid resolution can be set to a refinement scale smaller than the unified grid resolution, and a refined grid index covering the high-complexity sub-region can be generated. Resampling can extract elevation samples falling into the refined grid from the unified elevation data grid cell by cell, or the elevation samples can be rasterized to generate a sample set at the refined grid scale. Subdomain fusion calculations can fuse the sample set according to the refined grid and complete the missing grids in the neighborhood, outputting the refined elevation grid.

[0054] Step S250: Multi-resolution stitching is performed using the basic elevation grid and the refined elevation grid to obtain a digital elevation model.

[0055] Multi-resolution stitching refers to the process of combining and connecting gridded elevation data at different spatial resolutions according to their spatial relationships, thereby forming a continuous elevation representation within the same spatial range. Specifically, when obtaining a digital elevation model by multi-resolution stitching of a base elevation grid and a refined elevation grid, the spatial location corresponding to the refined elevation grid can be mapped to the grid coverage area of ​​the base elevation grid, and the corresponding elevation value of the base elevation grid can be replaced with the elevation value of the refined elevation grid in the overlapping area. For the boundary area between the base elevation grid and the refined elevation grid, boundary connection processing can be performed based on spatial adjacency to eliminate abrupt changes and generate a digital elevation model containing both a unified grid scale and a refined grid scale.

[0056] In this embodiment, firstly, coordinate and vertical references of multiple types of elevation data are unified to obtain unified elevation data, enabling subsequent gridding and fusion calculations to be carried out based on a consistent spatial reference. On one hand, the unified grid is used to perform global fusion calculations on the unified elevation data to obtain a basic elevation grid, making the terrain elevation representation a calculable gridded result. On the other hand, the terrain complexity is calculated using the basic elevation grid to determine high-complexity sub-regions, and a refined grid is established within the high-complexity sub-regions to resample and perform sub-domain fusion calculations on the unified elevation data to obtain a refined elevation grid, providing more detailed support for the elevation representation of areas with significant undulations. Furthermore, the basic elevation grid and the refined elevation grid are stitched together at multiple resolutions to obtain a digital elevation model, ensuring that the digital elevation model maintains coordination between spatial continuity and local detail representation, providing a more stable terrain input for subsequent three-dimensional spatial site selection calculations, thereby improving the accuracy and reliability of pumped storage power station site selection.

[0057] In some embodiments, a building information model is constructed using the engineering construction data corresponding to the spatial range and site selection area determined by the digital elevation model. Specifically, this can be achieved through the following technical steps: extracting spatial range boundary parameters based on the digital elevation model, and using these parameters to spatially trim the engineering construction data to obtain target engineering construction data; generating three-dimensional construction geometric objects using the target engineering construction data, and locating and arranging these objects using the spatial position parameters in the target engineering construction data to obtain a construction geometry set; writing engineering attribute parameters from the target engineering construction data into the construction geometry set to obtain an attributed construction set; and constructing a building information model using the attributed construction set.

[0058] Among them, spatial extent boundary parameters can represent parameter information used to describe the spatial coverage boundary of the digital elevation model, which characterizes the boundary position of the spatial extent in both the planar and elevation directions. Engineering construction data can represent engineering data used to describe the spatial characteristics and engineering attributes of engineering structures within the selected site area, including the spatial location information and engineering parameter information corresponding to the engineering structures. Spatial clipping can represent the process of filtering the spatial extent of engineering construction data based on spatial extent boundary parameters, used to determine whether the engineering construction data is within the spatial extent. Target engineering construction data can represent the set of engineering construction data obtained after spatial clipping, corresponding to engineering construction data within the range defined by the spatial extent boundary parameters. Construction geometry set can represent the set of geometric objects generated based on the target engineering construction data to describe the spatial morphology of engineering structures, reflecting the geometric position and shape characteristics of the engineering structures in space. Attributed construction set can represent the construction set formed by adding engineering attribute parameters to the construction geometry set, containing both geometric and engineering attribute information of the engineering structures.

[0059] In some embodiments, the spatial transformation matrix corresponding to the digital elevation model and the building information model is determined, which can be done through the following technical steps: The first step is to obtain the set of terrain benchmarks in the digital elevation model and the set of engineering benchmarks in the building information model.

[0060] The topographic benchmark set can represent the set of benchmarks selected from the digital elevation model (DEM) to characterize the spatial location of the terrain, reflecting the geometric reference relationship of the DEM in the spatial coordinate system. The engineering benchmark set can represent the set of benchmarks selected from the building information model (BIM) to characterize the spatial location of the engineering structure, reflecting the geometric reference relationship of the BIM in its own coordinate system.

[0061] The second step is to determine the set of point pairs using the set of terrain reference points and the set of engineering reference points, and then solve the initial spatial transformation matrix based on the set of point pairs.

[0062] The point pair set represents a set of points formed by the correspondence between benchmark points in the topographic benchmark set and benchmark points in the engineering benchmark set. This set is used to establish the spatial correspondence between the digital elevation model (DEM) and the building information model (BIM). The initial spatial transformation matrix represents the matrix parameters calculated based on the point pair set, used to describe the initial spatial mapping relationship between the BIM and the DEM.

[0063] In the specific implementation process, after obtaining the set of topographic benchmark points and the set of engineering benchmark points, the sets of topographic benchmark points and engineering benchmark points are matched according to their spatial positional relationships. Topographic benchmark points and engineering benchmark points that correspond to each other in spatial position are selected to form a set of point pairs. After forming the set of point pairs, the three-dimensional coordinate values ​​corresponding to each pair of topographic benchmark points and engineering benchmark points are extracted. The three-dimensional coordinates of the engineering benchmark points are used as the source coordinates, and the three-dimensional coordinates of the corresponding topographic benchmark points are used as the target coordinates, establishing a spatial correspondence between the source coordinates and the target coordinates. Subsequently, based on the spatial correspondence, a set of coordinate transformation equations is constructed with the parameters of the spatial transformation matrix as unknowns. Each pair of point pairs in the set corresponds to a set of coordinate constraints. After constructing the set of coordinate transformation equations, the entire set of coordinate transformation equations is solved. By fitting all the coordinate constraints in the set of point pairs, a spatial transformation matrix is ​​obtained to map the coordinates of the engineering benchmark points to the coordinates of the topographic benchmark points, serving as the initial spatial transformation matrix.

[0064] The third step involves using the initial spatial transformation matrix to perform coordinate mapping on the building information model to generate an initial fused spatial model, and then using the initial fused spatial model to determine the residual parameters corresponding to the set of point pairs.

[0065] The initial fused spatial model represents the spatial model that, after coordinate mapping of the Building Information Model (BIM) using the initial spatial transformation matrix, is in the same spatial coordinate system as the Digital Elevation Model (DEM). The residual parameter represents the spatial deviation between corresponding points in the point pair set within the initial fused spatial model; it characterizes the degree of alignment deviation of the BIM after spatial mapping.

[0066] In the specific implementation process, after obtaining the initial spatial transformation matrix, coordinate transformation operations are performed on the three-dimensional coordinates of each engineering benchmark point in the Building Information Model (BIM) based on the initial spatial transformation matrix. The transformed engineering benchmark point coordinates are mapped to the spatial coordinate system corresponding to the Digital Elevation Model (DEM), thereby completing the unified mapping of the overall coordinates of the BIM and generating the initial fused spatial model. After generating the initial fused spatial model, for each pair of engineering benchmark points and terrain benchmark points in the point pair set, the mapped coordinates of the engineering benchmark points in the initial fused spatial model are extracted, and the original coordinates of the corresponding terrain benchmark points in the DEM are extracted. Subsequently, based on the coordinate difference between the mapped coordinates and the original coordinates, the coordinate deviation corresponding to the point pair set is calculated point by point, and the coordinate deviations of each point pair are summarized to obtain the residual parameter used to characterize the mapping accuracy of the initial spatial transformation matrix. For example, the summary calculation can include any one of the following: average calculation, weighted calculation, and statistical calculation based on deviation distribution.

[0067] The fourth step involves iteratively updating the initial spatial transformation matrix using the residual parameters to generate an iterative spatial transformation matrix. After each iteration, the building information model is mapped to coordinates using the iterative spatial transformation matrix to update the residual parameters until the residual parameters meet the preset termination conditions.

[0068] The iterative spatial transformation matrix can be represented as the spatial transformation matrix obtained after updating based on the residual parameters, based on the initial spatial transformation matrix. It is used to progressively correct the spatial mapping relationship between the Building Information Model (BIM) and the Digital Elevation Model (DEM). The termination condition can be a criterion used to determine whether the spatial transformation matrix update process has ended, based on the changes in the residual parameters. For example, the termination condition could be that the change in the residual parameters before and after two consecutive iterations is less than a preset threshold; or, the termination condition could be that the statistical value corresponding to the residual parameters remains stable in multiple consecutive iterations; or, the termination condition could be that the change in the parameters of the iterative spatial transformation matrix in multiple consecutive iterations is less than a preset threshold; or, the termination condition could be that the number of iterations reaches a preset maximum number of iterations. In the above examples, both the preset threshold and the preset maximum number of iterations can be set according to the spatial scale and data accuracy of the DEM and BIM.

[0069] In the specific implementation process, after obtaining the residual parameters, the update step size is determined based on the comparison between the residual parameters and the preset termination condition, and a parameter correction amount is constructed based on the coordinate deviation of each point pair in the point pair set. Subsequently, the parameter correction amount is scaled using the update step size, and the scaled parameter correction amount is superimposed on the matrix parameters of the initial spatial transformation matrix to update the initial spatial transformation matrix and generate an iterative spatial transformation matrix. After generating the iterative spatial transformation matrix, coordinate mapping is performed on the building information model using the iterative spatial transformation matrix to update the fused spatial model, and the residual parameters corresponding to the point pair set are recalculated based on the updated fused spatial model in the same manner as in step three. When the recalculated residual parameters do not meet the preset termination condition, the current iterative spatial transformation matrix is ​​used as the new initial spatial transformation matrix, and the process of determining the update step size, constructing the parameter correction amount, and updating the matrix parameters is repeated until the residual parameters meet the preset termination condition.

[0070] The fifth step is to use the iterative spatial transformation matrix corresponding to the residual parameters that satisfy the termination condition as the spatial transformation matrix. By using the iterative spatial transformation matrix corresponding to the residual parameters that satisfy the termination condition as the spatial transformation matrix, the Building Information Model (BIM) is stably mapped in the spatial coordinate system of the Digital Elevation Model (DEM). This ensures a consistent spatial correspondence during subsequent fusion spatial model construction and spatial calculation, reducing the impact of mapping deviations on the calculation results.

[0071] In some embodiments, reference Figure 3 As shown, the slope, spatial intersection, and burial depth are calculated using a fusion spatial model, and the preliminary site selection area is determined based on the calculation results. The specific technical steps include the following: Step S310: Use the fused spatial model to calculate the slope and obtain the slope calculation result.

[0072] Among them, the slope calculation results can represent the set of slope values ​​calculated based on the elevation change relationship of the terrain surface at a spatial location in the fusion spatial model.

[0073] Step S320: Use the fused spatial model to perform spatial intersection calculation on the boundary of the building information model and the terrain surface of the digital elevation model to obtain the spatial intersection calculation result.

[0074] Specifically, the Building Information Model (BIM) boundary represents the boundary representation data used in the BIM to express the geometric shape of engineering objects. The Digital Elevation Model (DEM) terrain surface represents continuous surface representation data reconstructed from the elevation data of the DEM. Continuous surface representation data includes one or more of the following: regular raster surfaces, triangular mesh surfaces, or surfaces reconstructed based on contour lines. Spatial intersection calculation represents the calculation process of solving the intersection relationship between the BIM boundary and the DEM terrain surface in the same spatial coordinate system of the fused spatial model. The intersection relationship solution includes one or more of the following: intersection state determination, intersection line extraction, intersection surface extraction, and intersection volume solution. The spatial intersection calculation result represents the intersection relationship data output by the spatial intersection calculation.

[0075] In the specific implementation process, after obtaining the fused spatial model, the boundaries of the Building Information Model (BIM) and the terrain surface of the Digital Elevation Model (DEM) are read separately under the same spatial coordinate system of the fused spatial model, and the geometric representation of the BIM boundaries and the DEM terrain surface is unified. Specifically, when the BIM boundary is in the form of a boundary mesh, the boundary mesh is normalized using triangular patches; when the DEM terrain surface is in the form of a regular raster, the regular raster is converted into a triangular mesh surface, and a topological consistency check is performed on the converted triangular mesh surface. Subsequently, a spatial retrieval structure is established based on the BIM boundary and the DEM terrain surface, and the spatial retrieval structure is used to perform neighborhood filtering on the boundary fragments of the BIM boundary and the surface fragments of the DEM terrain surface, obtaining a set of candidate intersecting fragment pairs. The neighborhood filtering is based on the spatial positional relationship of the fragments, and the retrieval radius of the neighborhood filtering is determined according to the resolution parameters of the DEM. After obtaining the set of candidate intersecting fragment pairs, an exact intersection solution is performed on each candidate intersecting fragment pair in the set to obtain intersection relationship data. The exact intersection solution includes fragment intersection state determination and intersection segment calculation. The fragment intersection state determination is based on the spatial positional relationship between boundary fragments and surface fragments, and the intersection segment calculation is based on the intersection lines of boundary fragments and surface fragments. After obtaining the intersection relationship data, the intersection segments are connected and deduplicated using the data to obtain a set of intersection lines. When the set of intersection lines has a closed structure, an intersection surface set is generated using the closed structure, and the intersection volume values ​​are calculated based on the intersection surface set and the boundary of the Building Information Model (BIM). Finally, one or more of the following—intersection state identifier, intersection line set, intersection surface set, and intersection volume values—are output as the spatial intersection calculation results.

[0076] Step S330: Use the fusion spatial model to generate a set of discrete sampling points along the axis of the underground project, and use the set of discrete sampling points to calculate the burial depth and obtain the burial depth calculation result.

[0077] The underground engineering axis can represent the centerline data used in the Building Information Model (BIM) to express the spatial orientation of the underground engineering project. The discrete sampling point set can represent a collection of multiple sampling points generated along the underground engineering axis according to preset sampling rules, where each sampling point contains three-dimensional coordinate parameters in the spatial coordinate system of the fused spatial model. The burial depth calculation result can represent the set of soil cover thickness values ​​calculated in the fused spatial model based on the discrete sampling point set.

[0078] Step S340: Based on the slope calculation results, spatial intersection calculation results, and burial depth calculation results, a joint judgment is made to determine the preliminary site selection range.

[0079] Specifically, after obtaining the slope calculation results, spatial intersection calculation results, and burial depth calculation results, a unified judgment index is first selected in the same spatial coordinate system of the fused spatial model, and a set of judgment objects is established based on the unified judgment index. Then, slope parameters are written to the set of judgment objects using the slope calculation results, intersection identifier parameters are written to the set of judgment objects using the spatial intersection calculation results, and burial depth parameters are written to the set of judgment objects using the burial depth calculation results. After completing the parameter writing, joint judgment rules are obtained, and the set of judgment objects is judged one by one using the joint judgment rules. The joint judgment rules include slope interval judgment, intersection state judgment, and burial depth interval judgment, and judgment objects that simultaneously satisfy the slope interval judgment, intersection state judgment, and burial depth interval judgment are assigned a pass identifier. Finally, spatial aggregation and boundary extraction are performed in the fused spatial model using the judgment objects corresponding to the pass identifiers to obtain the preliminary site selection range.

[0080] In some embodiments, the slope calculation using the fused spatial model in step S310 above, to obtain the slope calculation result, can be performed through the following technical steps: The first step is to obtain the basic terrain surface using the fused spatial model, and then use the fused spatial model to obtain the refined terrain surface corresponding to the mesh refinement region.

[0081] The basic terrain surface can represent the surface representation data reconstructed from the basic elevation grid corresponding to the digital elevation model in the fused spatial model. This surface representation data reflects the overall terrain undulation characteristics within the selected area and describes continuous terrain morphology with a uniform spatial resolution. The refined grid region can represent local areas in the basic elevation grid that require higher spatial resolution representation based on terrain variation characteristics. Spatially, this region corresponds to a sub-region in the basic terrain surface. The refined terrain surface can represent the surface representation data reconstructed from the refined elevation grid corresponding to the refined grid region. This surface representation data has a higher spatial resolution than the basic terrain surface and is used to characterize more detailed terrain variations within local areas.

[0082] In the specific implementation process, the basic elevation grid corresponding to the digital elevation model is read under the unified spatial coordinate system of the fused spatial model. A continuous surface representation is reconstructed based on the elevation values ​​of each grid node in the basic elevation grid, forming a basic terrain surface. This basic terrain surface is obtained by interpolating adjacent elevation grid nodes, maintaining a spatial reference relationship consistent with the fused spatial model. After obtaining the basic terrain surface, based on the determined grid refinement region index in the fused spatial model, the spatial subdomain corresponding to the grid refinement region in the basic elevation grid is located, and refined elevation grid data is called within this subdomain. The refined elevation grid consists of elevation data at a smaller grid size and is located in the same spatial coordinate system as the basic elevation grid. Subsequently, using the elevation values ​​of each elevation node in the refined elevation grid, a continuous surface representation is reconstructed within the spatial range corresponding to the grid refinement region, forming a refined terrain surface.

[0083] The second step is to calculate the basic slope result using the basic terrain surface and to calculate the refined slope result using the refined terrain surface.

[0084] The basic slope result can represent a set of slope values ​​calculated based on the elevation variation relationship of the basic terrain surface at its spatial location. This set of slope values ​​is used to describe the slope distribution at the overall scale within the selected site area. The refined slope result can represent a set of slope values ​​calculated based on the elevation variation relationship of the refined terrain surface at its spatial location. This set of slope values ​​is used to describe the slope variation at the local scale within the refined grid area.

[0085] In the specific implementation process, firstly, under the unified spatial coordinate system of the fused spatial model, the basic terrain surface is spatially discretized to determine the set of computational nodes corresponding to the basic terrain surface. This set of computational nodes consists of grid nodes from the basic elevation grid or surface sampling points reconstructed from the basic terrain surface, and each computational node corresponds to a unique spatial coordinate position. Then, for each computational node in the set, the slope value at that node is calculated based on the elevation difference and spatial distance between that node and its adjacent computational nodes. The elevation difference is obtained by subtracting the elevation values ​​of adjacent computational nodes, and the spatial distance is determined by the planar distance between adjacent computational nodes in the fused spatial model. By performing the above calculations on each computational node one by one, a basic slope result covering the basic terrain surface is formed. After obtaining the basic slope result, the slope calculation steps corresponding to the above process are performed on the refined terrain surface. Specifically, under the unified spatial coordinate system of the fused spatial model, the refined terrain surface is spatially discretized to determine the set of refined computational nodes corresponding to the refined terrain surface. The set of refined computational nodes has a higher spatial density than the set of computational nodes, and each refined computational node corresponds to elevation data in the refined elevation grid. Subsequently, for each refined computational node in the set of refined computational nodes, the slope value at that refined computational node is calculated based on the elevation difference and spatial distance between that refined computational node and its adjacent refined computational nodes. By performing slope calculations on each of the refined computational nodes in the set, a refined slope result covering the refined terrain surface is formed.

[0086] The third step involves fusing the basic slope results with the refined slope results to obtain the slope calculation result. Specifically, after obtaining the basic and refined slope results, they are first aligned based on their spatial correspondence, allowing slope results at different resolutions to be compared in the same spatial location. Then, for spatial locations within the refined grid area, the corresponding slope values ​​are selected from the refined slope results; for spatial locations outside the refined grid area, the corresponding slope values ​​are selected from the basic slope results. Finally, the selected slope values ​​are combined to form a unified set of slope values, which is output as the slope calculation result.

[0087] In this embodiment, by calculating the corresponding slope results based on the basic terrain surface and the refined terrain surface in the fusion spatial model, and then fusing the basic slope results and the refined slope results, the slope calculation results can maintain global consistency and local detail depiction capabilities at the same time, reducing the oversmoothing or noise amplification caused by single-resolution slope calculation, thereby reducing the dependence on human experience in slope determination.

[0088] In some embodiments, step S330 above, which involves generating a set of discrete sampling points along the underground engineering axis using a fused spatial model and then using the set of discrete sampling points to calculate the burial depth, specifically includes the following technical steps: The first step is to use the fusion spatial model to generate a first set of discrete sampling points along the underground engineering axis at a first sampling interval, and then use the first set of discrete sampling points to calculate the first burial depth to obtain the initial burial depth sequence.

[0089] The first sampling interval can represent a preset distance parameter used to control the axial distance between adjacent sampling points along the underground engineering axis in a spatial coordinate system. This distance parameter is used to limit the generation interval of discrete sampling points in the axial direction. The first discrete sampling point set can represent a set of multiple sampling points generated along the underground engineering axis according to the first sampling interval, where each sampling point in the set contains corresponding three-dimensional spatial coordinate information. The first burial depth calculation can represent the calculation process of calculating the vertical distance from each sampling point in the first discrete sampling point set to the terrain surface of the digital elevation model based on the position parameters of each sampling point in the first discrete sampling point set. The initial burial depth sequence can represent the burial depth numerical sequence formed by arranging the first burial depth calculation point by point in the first discrete sampling point set in the order of the underground engineering axis direction.

[0090] In the specific implementation process, after obtaining the fused spatial model and the underground engineering axis, the axis parameter expression of the underground engineering axis is first read in the spatial coordinate system of the fused spatial model, and the axis length parameter between the starting point and the ending point of the axis is determined based on the axis parameter expression. Subsequently, the axis length parameter is processed by equal segmentation based on the first sampling interval to generate multiple axis sampling position parameters, and a first discrete sampling point set is generated based on the corresponding axis coordinates of each axis sampling position parameter on the underground engineering axis. After generating the first discrete sampling point set, for each sampling point in the first discrete sampling point set, the three-dimensional spatial coordinates of the sampling point are extracted, and the elevation value of the terrain surface of the digital elevation model corresponding to the plane position of the sampling point is extracted in the fused spatial model; then, the vertical difference between the terrain surface elevation value and the elevation coordinates of the sampling point is calculated as the first burial depth calculation result of the sampling point. Finally, the first burial depth calculation results of each sampling point are arranged according to the order of the axis sampling position parameters to generate an initial burial depth sequence.

[0091] The second step is to calculate the change in burial depth using the initial burial depth sequence, and then use the change in burial depth and a preset change threshold to determine the encrypted section.

[0092] The burial depth variation can represent the difference or variation parameter between adjacent burial depth values ​​in the initial burial depth sequence. This variation parameter reflects the magnitude of burial depth change with the axis position. The variation threshold can represent a preset numerical parameter used to compare and judge the burial depth variation, limiting whether the burial depth variation reaches a level requiring further processing. The densified section can represent the axial interval within the underground engineering axis where the corresponding burial depth variation meets the variation threshold condition. This axial interval is used to limit the spatial range for subsequent sampling point generation.

[0093] In the specific implementation process, after obtaining the initial burial depth sequence, the burial depth values ​​in the initial burial depth sequence are first indexed and sorted according to the sampling order of the underground engineering axis, forming an ordered burial depth value sequence corresponding one-to-one with the first discrete sampling point set. Subsequently, based on the ordered burial depth value sequence, differential calculation is performed on the burial depth values ​​corresponding to adjacent sampling points to obtain the burial depth change between each adjacent sampling point; the burial depth change is calculated from the numerical difference or absolute difference between two adjacent burial depth values ​​and is associated with the corresponding axis position parameters. After obtaining the burial depth change, each burial depth change is compared with a preset change threshold, and the burial depth change positions that meet the preset change threshold conditions are marked; the preset change threshold is used to limit the burial depth change to the numerical condition requiring further densification processing. Finally, based on the continuity of the marked burial depth change positions on the underground engineering axis, the corresponding axis intervals are merged to obtain at least one densified segment, where the densified segment is a continuous interval in the axis direction where the corresponding burial depth change meets the preset change threshold conditions.

[0094] The third step involves generating a second set of discrete sampling points along the underground engineering axis in the encrypted section at a second sampling interval less than the first sampling interval, and then using the second set of discrete sampling points to calculate the second burial depth, thus obtaining the burial depth calculation result.

[0095] The second sampling interval can represent the sampling interval parameter used to control the axial distance between adjacent sampling points along the underground engineering axis within the densified section. The second sampling interval is smaller than the first sampling interval. The second discrete sampling point set can represent the set of multiple sampling points generated along the underground engineering axis within the densified section according to the second sampling interval. The sampling point set has a higher spatial density than the first discrete sampling point set. The second burial depth calculation can represent the process of calculating the vertical distance from each sampling point in the second discrete sampling point set to the terrain surface of the digital elevation model.

[0096] In the specific implementation process, after determining the encrypted sections, the starting and ending position parameters of each encrypted section on the underground engineering axis are first located in the spatial coordinate system of the fused spatial model. Then, based on the second sampling interval, the axis interval corresponding to each encrypted section is divided into equal segments to generate multiple axis sampling position parameters. A second discrete sampling point set is then generated based on the corresponding axis coordinates of each axis sampling position parameter on the underground engineering axis; the second sampling interval is smaller than the first sampling interval. After generating the second discrete sampling point set, for each sampling point in the second discrete sampling point set, the three-dimensional spatial coordinates of the sampling point are extracted, and the elevation value of the terrain surface corresponding to the plane position of the sampling point in the digital elevation model is extracted in the fused spatial model. Then, the vertical difference between the terrain surface elevation value and the elevation coordinates of the sampling point is calculated as the second burial depth calculation result for that sampling point. Finally, based on the second burial depth calculation results in the second discrete sampling point set, the burial depth values ​​are summarized according to the direction of the underground engineering axis to form the burial depth calculation result.

[0097] In this embodiment, a discrete sampling point set is generated in stages along the underground engineering axis, and sampling is carried out in the encrypted section after determining the encrypted section based on the initial burial depth sequence. This makes the burial depth calculation results have higher calculation resolution in the section with obvious changes, reduces the missed detection and misjudgment caused by the fixed sampling interval, and thus reduces the dependence of burial depth determination on human experience.

[0098] In some embodiments, a fusion spatial model is used to perform spatial mapping and constraint calculation on the set of constraints to determine candidate construction regions, specifically including the following technical steps: The first step is to perform coordinate mapping in the fusion space model using the set of constraints, and to convert the constraints into constraint range values, which include buffer distance and height range.

[0099] Among them, the constraint range value can represent the set of spatial influence range parameters corresponding to the constraint condition in the fused spatial model, used to describe the scale and coverage of the constraint condition in space. The buffer distance can represent the minimum safe distance parameter in the constraint range value used to limit the constraint condition in the horizontal or planar direction, used to describe the extent of the constraint condition's expansion relative to the target spatial object in the planar position. The height range can represent the parameter in the constraint range value used to limit the range of the constraint condition's effect in the vertical direction, used to describe the upper and lower boundary range of the constraint condition in the elevation direction.

[0100] In the specific implementation process, after obtaining the set of constraints, the spatial data corresponding to each constraint is read one by one. Based on the spatial coordinate system of the fused spatial model, coordinate transformation and coordinate positioning are performed on the spatial data to obtain the three-dimensional position record of each constraint in the fused spatial model. After obtaining the three-dimensional position record, the constraint parameters associated with the three-dimensional position record are read one by one, and the constraint parameters are converted into constraint range values ​​according to preset conversion rules. Among them, the buffer distance is converted from the planar distance parameter in the constraint parameters, and the height range is converted from the upper and lower elevation limits or vertical interval parameters in the constraint parameters. When the constraint parameter is given by a level or type identifier, the level or type identifier is converted into the corresponding buffer distance and height range according to a preset mapping table.

[0101] The second step is to generate three-dimensional constraint objects in the fusion space model using constraint range values.

[0102] The three-dimensional constraint object represents a three-dimensional spatial entity with spatial location, geometric shape, and spatial scale generated in the fused spatial model based on constraint range values. It is used to characterize the actual area occupied by the constraint conditions in three-dimensional space. Specifically, after obtaining the constraint range values, for each constraint range value in the fused spatial model, the corresponding three-dimensional position record is read and used as a spatial reference. Subsequently, based on the buffer distance, a distance expansion operation is performed on the three-dimensional position record in the horizontal direction in the fused spatial model to generate the corresponding planar constraint region; simultaneously, based on the height range, a height limitation operation is performed on the three-dimensional position record in the vertical direction in the fused spatial model to generate the corresponding vertical constraint interval. After determining the planar constraint region and the vertical constraint interval, the two are combined in the fused spatial model to form a three-dimensional constraint object with a clearly defined spatial location, planar range, and vertical range.

[0103] The third step involves performing three-dimensional overlay operations on the fusion space model based on the three-dimensional constraint objects to obtain the constraint partitioning results.

[0104] The 3D overlay operation represents the spatial computation process of uniformly overlaying multiple 3D constraint objects according to their spatial positional relationships within the fused spatial model, forming a comprehensive distribution of constraints in 3D space. The constraint partitioning result represents the spatial partitioning data obtained through the 3D overlay operation, reflecting the distribution of the degree of constraint influence on different spatial locations within the fused spatial model. Specifically, each 3D constraint object is uniformly loaded according to its spatial coordinates in the fused spatial model, and spatial overlap determination is performed on any two or more 3D constraint objects with overlapping spatial positions. When spatial overlap is determined, the overlapping area is marked. Subsequently, the overlay marking results are summarized for each spatial region in the fused spatial model. Based on the number of overlaid 3D constraint objects in each spatial region and their corresponding relationships, the fused spatial model is divided into regions, forming spatial partitions with different overlay states. The overlay state of each spatial partition is then output as the constraint partitioning result.

[0105] The fourth step is to use the constrained zoning results to spatially filter the preliminary site selection area and obtain candidate construction areas.

[0106] Specifically, based on the integrated spatial model, the corresponding partition identifier of each spatial unit in the preliminary site selection range is determined in the constraint partitioning results, and the constraint status information associated with the partition identifier is obtained. Subsequently, according to the constraint status information, spatial filtering processing is performed on the spatial units in the preliminary site selection range, wherein: spatial units in the allowed construction partition are retained; spatial units in the restricted construction partition are further judged whether their corresponding buffer distance and height range meet the preset spatial conditions, and are retained if they meet them, and removed if they do not; spatial units in the prohibited construction partition are directly removed. After completing the above spatial filtering processing, the retained spatial units are spatially merged in the integrated spatial model to obtain continuous spatial regions as candidate construction areas.

[0107] Furthermore, when obtaining the set of constraints using the preliminary site selection range, the corresponding spatial range parameters are extracted based on the preliminary site selection range, and the spatial range parameters are used to limit the acquisition range of constraints in the fused spatial model. Within the limited acquisition range, spatial queries are performed on the data corresponding to geological constraints, water constraints, traffic constraints, and land use constraints to obtain constraint data that spatially overlaps or is spatially related to the preliminary site selection range. The obtained constraint data are organized according to the constraint type, and one or more of the organized geological constraints, water constraints, traffic constraints, and land use constraints are aggregated to form a constraint set.

[0108] Among them, geological constraints can represent constraint information reflecting the geological safety and engineering suitability of the site selection area. The corresponding data includes one or more of the following: fault distribution, fracture zone range, distribution of adverse geological bodies, lithological zoning, and geological stability classification information. This describes the spatial constraints imposed by underground or near-surface geological structures on the project layout. Water body constraints can represent constraint information reflecting hydrological elements and their influence range within the site selection area. The corresponding data includes one or more of the following: the influence range of rivers, reservoirs, lakes, wetlands, groundwater, and water protection control lines. This describes the spatial constraints imposed by water body elements on the project site selection. Transportation constraints can represent constraint information reflecting transportation facilities and their safety control range within the site selection area. The corresponding data includes one or more of the following: the spatial location and control range of highways, railways, tunnels, bridges, and their ancillary facilities. This describes the spatial constraints imposed by transportation facilities on the project layout. Land use constraints can represent constraint information reflecting land use attributes and planning control requirements within the site selection area. The corresponding data includes one or more of the following: land use type, planned use zoning, basic farmland area, construction land boundary, and related control line information. This describes the spatial constraints imposed by land use attributes on project construction.

[0109] In this embodiment, geological constraints, water constraints, traffic constraints, and land use constraints are uniformly mapped into the fusion spatial model. Three-dimensional constraint objects are generated through constraint range values, and constraint partitioning results are obtained through superposition operations. This allows the screening of the initial site selection range to be completed by calculable spatial constraint relationships, reducing subjective biases caused by manual interpretation and manual superposition.

[0110] In some embodiments, determining multiple candidate construction locations based on a candidate construction region specifically includes the following technical steps: determining a first sampling interval and a second sampling interval based on the candidate construction region; generating a first sampling point within the candidate construction region using the first sampling interval, and generating a second sampling point within the candidate construction region using the second sampling interval; merging the first sampling point and the second sampling point to generate multiple initial candidate locations, and determining the nearest neighbor distance corresponding to each initial candidate location based on the location distance between different initial candidate locations; filtering out the initial candidate locations using the nearest neighbor distance and the minimum spacing threshold to obtain multiple candidate construction locations.

[0111] The first sampling interval can represent the first spatial interval parameter used when spatially sampling within the candidate construction area, used to define the spatial spacing scale between adjacent sampling locations. The second sampling interval can represent the second spatial interval parameter used when spatially sampling within the candidate construction area, with a value different from the first sampling interval, used to form sampling distributions with different spatial densities. The first sampling point can represent a spatial location point generated within the candidate construction area according to a preset spatial rule based on the first sampling interval, used to characterize the first type of discrete sampling location within the candidate construction area. The second sampling point can represent a spatial location point generated within the candidate construction area according to a preset spatial rule based on the second sampling interval, used to characterize the second type of discrete sampling location within the candidate construction area. The initial candidate location can represent a single location point in the set of spatial location points formed by merging the first and second sampling points, used as the basic location unit for subsequent spatial screening and evaluation calculations. The nearest neighbor distance can represent the minimum distance value selected from the spatial distances calculated for any initial candidate location, used to characterize the spatial proximity of the initial candidate location. The minimum spacing threshold can be represented as a threshold parameter used to limit the minimum allowable spatial interval between candidate construction locations. It is used as a criterion for filtering out initial candidate locations that are too close in space.

[0112] For example, when determining the first sampling interval and the second sampling interval based on the candidate construction region, the spatial range and geometric dimension parameters of the candidate construction region in the fusion spatial model can be read first, and the first sampling interval and the second sampling interval can be set respectively within the spatial range of the candidate construction region; wherein, the first sampling interval and the second sampling interval are two sets of spatial interval parameters with different values, and the first sampling interval and the second sampling interval can correspond to different sampling density configurations respectively.

[0113] For example, when generating a first sampling point within a candidate construction area using a first sampling interval, a sampling coordinate reference can be established within the spatial range of the candidate construction area, and the first sampling interval can be used as the step interval between adjacent sampling positions to perform traversal sampling to generate the first sampling point within the candidate construction area; correspondingly, when generating a second sampling point within a candidate construction area using a second sampling interval, traversal sampling can be performed within the same candidate construction area using the second sampling interval as the step interval to generate the second sampling point; wherein, traversal sampling may include generating position points in a row-column step within the planar range of the candidate construction area, and performing a region inclusion determination on the generated position points to retain position points falling within the candidate construction area as sampling points.

[0114] For example, when generating multiple initial candidate locations by merging the first and second sampling points, the first and second sampling points can be merged into a set, and duplicate locations can be deduplicated to obtain a set of initial candidate locations. After obtaining the set of initial candidate locations, the location distance between each initial candidate location and the other initial candidate locations can be calculated, where the location distance can be directly calculated based on the spatial coordinates in the fusion spatial model. The minimum value among the location distances corresponding to the initial candidate location is selected as the nearest neighbor distance corresponding to the initial candidate location.

[0115] For example, when filtering out initial candidate positions using the nearest neighbor distance and the minimum spacing threshold, the nearest neighbor distance and the minimum spacing threshold corresponding to each initial candidate position can be compared and determined; for initial candidate positions whose nearest neighbor distance is less than the minimum spacing threshold, the initial candidate position is marked as a position to be eliminated and removed from the set of initial candidate positions; for initial candidate positions whose nearest neighbor distance is greater than or equal to the minimum spacing threshold, the initial candidate position is retained as a candidate construction position; thus, multiple candidate construction positions are obtained.

[0116] In this embodiment, by introducing two sampling intervals within the candidate construction area to generate sampling points of different densities and merging them to form initial candidate positions, the initial candidate positions are then screened out using the nearest neighbor distance and minimum spacing threshold. This allows the generation and screening process of candidate construction positions to be executed based on computable rules, thereby reducing the subjectivity of manual point selection and improving the spatial distribution stability of candidate construction positions.

[0117] In some embodiments, the fusion spatial model is used to evaluate and calculate each candidate construction location, specifically including the following technical steps: The first step is to determine the layout parameters in the integrated spatial model based on each candidate construction location. The layout parameters include the reservoir area control elevation, the passage direction, and the underground engineering axis.

[0118] The layout parameters, within the integrated spatial model, represent a set of parameters characterizing the spatial layout of the engineering structures corresponding to candidate construction locations. This set of parameters defines the positional and geometric relationships of the engineering structures in three-dimensional space. The reservoir control elevation represents elevation parameters describing the corresponding water level or boundary height position of the reservoir area within the integrated spatial model. The channel orientation represents directional parameters describing the extension of the channel along a planar or spatial direction within the integrated spatial model; these directional parameters characterize the channel's orientation characteristics in three-dimensional space. The underground engineering axis represents centerline parameters describing the spatial extension of the underground engineering structure within the integrated spatial model.

[0119] In the specific implementation process, when determining the layout parameters in the integrated spatial model, for each candidate construction location, the spatial coordinates of that candidate construction location can be read in the integrated spatial model and used as the positioning reference for the layout parameters. In the integrated spatial model, with the positioning reference as a constraint, the available elevation intervals corresponding to the reservoir area are retrieved along the vertical direction, and the control elevation of the reservoir area is determined from the available elevation intervals. In the integrated spatial model, with the control elevation of the reservoir area and the positioning reference as constraints, the spatial direction vector or azimuth parameter corresponding to the passage direction is determined. In the integrated spatial model, the centerline geometry of the underground engineering axis is generated based on the passage direction and the positioning reference, and the centerline geometry is written into the underground engineering axis.

[0120] The second step is to use the layout parameters to perform three-dimensional calculations in the fusion space model to obtain the basic evaluation quantities, which include elevation difference, passage length, burial depth, and earthwork balance.

[0121] Among these, 3D calculation can represent the spatial operation process performed on relevant geometric objects and spatial relationships based on candidate construction locations and their corresponding layout parameters within the integrated spatial model. Basic evaluation quantities can represent a set of spatial quantification results directly obtained through 3D calculation, which characterize the basic spatial features of the engineering layout corresponding to the candidate construction locations. Elevation difference can represent the vertical distance between different control elevations within the integrated spatial model. Passage length can represent the spatial path length calculated along the passage direction or underground engineering axis within the integrated spatial model. Burial depth can represent the vertical distance between the corresponding location of the underground engineering axis and the terrain surface within the integrated spatial model. Earthwork balance can represent the difference or combination of earthwork volumes calculated based on the spatial relationship between the engineering structure geometry and the terrain geometry within the integrated spatial model.

[0122] Specifically, the three-dimensional calculations performed in the integrated spatial model using layout parameters can include the following technical processes: Based on the reservoir area control elevation, extract the elevation values ​​corresponding to the upstream and downstream reservoir areas in the integrated spatial model, and calculate the difference between the elevation values ​​to obtain the elevation difference; Based on the channel orientation and the underground engineering axis, calculate the spatial length of the underground engineering axis in the integrated spatial model to obtain the channel length; Based on the underground engineering axis, extract the corresponding topographic surface elevation point by point along the underground engineering axis in the integrated spatial model, calculate the difference between the elevation and the axial elevation of the underground engineering axis, summarize the difference results to obtain the burial depth; Based on the underground engineering axis and the corresponding structural geometry of the reservoir area, calculate the excavation volume and backfill volume related to the structural geometry in the integrated spatial model, and perform a balance calculation on the excavation volume and backfill volume to obtain the earthwork balance.

[0123] The third step is to calculate the comprehensive evaluation quantity using the basic evaluation quantity. The comprehensive evaluation quantity includes the elevation difference utilization rate and the terrain disturbance coefficient.

[0124] Among them, the comprehensive evaluation quantity can represent the composite quantitative result calculated further based on the basic evaluation quantity, which is used to reflect the comprehensive relationship between multiple basic spatial quantities. The elevation difference utilization rate can represent the quantitative ratio calculated from the functional relationship between elevation difference and related spatial parameters. The terrain disturbance coefficient can represent the quantitative parameter calculated from the degree of spatial change of the terrain surface in the fused spatial model caused by the engineering construction geometry. Specifically, the calculation of the comprehensive evaluation quantity using the basic evaluation quantity can include the following technical processes: calculating the elevation difference utilization rate based on the comparison between the elevation difference and the preset reference elevation difference, wherein the preset reference elevation difference is the maximum elevation difference corresponding to the candidate construction location set or the preset design elevation difference; calculating the terrain disturbance coefficient based on the channel length, burial depth, and earthwork balance, wherein the terrain disturbance coefficient is a weighted combination of the normalized value of the channel length, the statistical value of the burial depth, and the normalized value of the earthwork balance, and the normalization benchmark is the maximum value corresponding to the candidate construction location set or the preset upper limit value.

[0125] The fourth step involves weighted summation of the basic evaluation metrics and the comprehensive evaluation metrics to obtain the location score for each candidate construction location. The location score represents the numerical result obtained after weighted calculation based on the basic and comprehensive evaluation metrics; this numerical result is used to characterize the relative evaluation results between different candidate construction locations.

[0126] Furthermore, after obtaining the location scores corresponding to each candidate construction location, the location scores corresponding to multiple candidate construction locations are sorted, and the candidate construction location with the maximum location score is selected as the target construction location corresponding to the pumped storage power station.

[0127] Furthermore, this disclosure also provides a three-dimensional spatial site selection system for pumped storage power stations based on multi-model fusion. (Refer to...) Figure 4 As shown, the three-dimensional spatial site selection system 400 for pumped storage power stations based on multi-model fusion may include: a model building module 410, a model fusion module 420, a site initial selection module 430, a constraint overlay module 440, and a location determination module 450. Wherein: The model building module 410 can be used to build a digital elevation model based on the elevation data of the site selection area, and to build a building information model using the spatial range determined by the digital elevation model and the engineering construction data corresponding to the site selection area.

[0128] The model fusion module 420 can be used to determine the spatial transformation matrix corresponding to the digital elevation model and the building information model, and use the spatial transformation matrix to map the building information model to the spatial coordinate system of the digital elevation model to obtain the fused spatial model.

[0129] The initial site selection module 430 can be used to perform slope calculation, spatial intersection calculation and burial depth calculation using the fusion spatial model, and determine the preliminary site selection range based on the calculation results.

[0130] The constraint overlay module 440 can be used to obtain a set of constraint conditions using the preliminary site selection range, and to perform spatial mapping and constraint calculation on the set of constraint conditions using the fusion spatial model to determine the candidate construction area.

[0131] The location determination module 450 can be used to determine multiple candidate construction locations based on the candidate construction area, evaluate and calculate each candidate construction location using a fusion spatial model, and select the target construction location corresponding to the pumped storage power station from multiple candidate construction locations based on the evaluation and calculation results.

[0132] The specific details of each module in the above-mentioned three-dimensional spatial location system for pumped storage power stations based on multi-model fusion have been described in detail in the corresponding three-dimensional spatial location method for pumped storage power stations based on multi-model fusion, so they will not be repeated here.

[0133] It should be noted that although several modules or units of the three-dimensional spatial site selection system for pumped storage power stations based on multi-model fusion have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0134] Furthermore, in an exemplary embodiment of this disclosure, an electronic device is also provided that can implement the above-described method for three-dimensional spatial site selection of pumped storage power stations based on multi-model fusion.

[0135] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be embodied in the following forms: a completely hardware embodiment, a completely software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0136] The following reference Figure 5 To describe an electronic device 500 according to an embodiment of the present disclosure. Figure 5 The electronic device 500 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0137] like Figure 5As shown, the electronic device 500 is presented in the form of a general-purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different system components (including storage unit 520 and processing unit 510), and a display unit 540.

[0138] The storage unit stores program code, which can be executed by the processing unit 510, causing the processing unit 510 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure. The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 521 and / or a cache memory unit 522, and may further include a read-only memory unit (ROM) 523.

[0139] Storage unit 520 may also include a program / utility 524 having a set (at least one) program module 525, such program module 525 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0140] Bus 530 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0141] Electronic device 500 can also communicate with one or more external devices 570 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 500, and / or with any device that enables electronic device 500 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 550. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 560. As shown, network adapter 560 communicates with other modules of electronic device 500 via bus 530. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0142] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware.

[0143] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of this disclosure may also be implemented as a program product including program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0144] refer to Figure 6 As shown, a program product 600 for implementing the above-described three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion, according to embodiments of the present disclosure, is described. It may employ a portable compact disk read-only memory (CD-ROM) and include program code, and can run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0145] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0146] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0147] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this disclosure and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0148] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion, characterized in that, include: A digital elevation model is constructed based on the elevation data of the site selection area, and a building information model is constructed using the spatial range determined by the digital elevation model and the engineering construction data corresponding to the site selection area. Determine the spatial transformation matrices corresponding to the digital elevation model and the building information model, and use the spatial transformation matrices to map the building information model to the spatial coordinate system of the digital elevation model to obtain a fused spatial model; The fused spatial model is used to calculate slope, spatial intersection, and burial depth, and the preliminary site selection range is determined based on the calculation results; The set of constraints is obtained using the preliminary site selection range, and the set of constraints is spatially mapped and constrained using the fusion spatial model to determine the candidate construction area; Based on the candidate construction area, multiple candidate construction locations are determined, and the fusion spatial model is used to evaluate and calculate each of the candidate construction locations. Based on the evaluation and calculation results, the target construction location corresponding to the pumped storage power station is selected from the multiple candidate construction locations.

2. The three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion according to claim 1, characterized in that, The construction of a digital elevation model based on the elevation data of the site selection area includes: Obtain multiple types of elevation data corresponding to the selected site area, and unify the coordinate reference and vertical reference of the multiple types of elevation data to obtain unified elevation data; The grid coverage area is determined and a unified grid is established using the unified elevation data. The unified grid is then used to perform a full-domain fusion calculation on the unified elevation data to obtain the basic elevation grid. The terrain complexity is calculated using the aforementioned basic elevation grid, and the high-complexity sub-regions are determined using the terrain complexity. A refined grid is established within the grid coverage area of ​​the basic elevation grid using the highly complex sub-region, and the unified elevation data is resampled and fused using the refined grid to obtain the refined elevation grid; The digital elevation model is obtained by stitching the basic elevation grid and the refined elevation grid together at multiple resolutions.

3. The three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion according to claim 1, characterized in that, Determining the spatial transformation matrix corresponding to the digital elevation model and the building information model includes: Obtain the set of terrain benchmark points in the digital elevation model and the set of engineering benchmark points in the building information model; A set of point pairs is determined using the set of terrain reference points and the set of engineering reference points, and an initial spatial transformation matrix is ​​solved based on the set of point pairs; The initial spatial transformation matrix is ​​used to perform coordinate mapping on the building information model to generate an initial fused spatial model, and the initial fused spatial model is used to determine the residual parameters corresponding to the set of point pairs; The initial spatial transformation matrix is ​​iteratively updated using the residual parameters to generate an iterative spatial transformation matrix. After each iteration, the building information model is mapped to coordinates using the iterative spatial transformation matrix to update the residual parameters until the residual parameters meet the preset termination condition. The iterative space transformation matrix corresponding to the residual parameter that satisfies the termination condition is taken as the space transformation matrix.

4. The three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion according to claim 1, characterized in that, The process of using the fused spatial model to calculate slope, spatial intersection, and burial depth, and determining the preliminary site selection range based on the calculation results, includes: The slope is calculated using the fused spatial model to obtain the slope calculation results; The spatial intersection calculation between the boundary of the building information model and the terrain surface of the digital elevation model is performed using the fused spatial model to obtain the spatial intersection calculation result; The fusion spatial model is used to generate a set of discrete sampling points along the axis of the underground project, and the burial depth is calculated using the set of discrete sampling points to obtain the burial depth calculation result. The preliminary site selection range is determined by jointly judging the slope calculation results, the spatial intersection calculation results, and the burial depth calculation results.

5. The three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion according to claim 4, characterized in that, The slope calculation using the fused spatial model, to obtain the slope calculation result, includes: The basic terrain surface is obtained using the fused spatial model, and the refined terrain surface corresponding to the mesh refinement region is obtained using the fused spatial model. The basic slope result is calculated using the basic terrain surface, and the refined slope result is calculated using the refined terrain surface; The slope calculation result is obtained by fusing the basic slope result with the refined slope result.

6. The three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion according to claim 4, characterized in that, The process involves generating a set of discrete sampling points along the underground engineering axis using the fused spatial model, and then using this set of discrete sampling points to calculate the burial depth, yielding the burial depth calculation results, including: Using the fusion spatial model, a first discrete sampling point set is generated along the underground engineering axis at a first sampling interval, and the first burial depth is calculated using the first discrete sampling point set to obtain an initial burial depth sequence; The initial burial depth sequence is used to calculate the burial depth change, and the burial depth change is used to determine the encrypted section with a preset change threshold; A second set of discrete sampling points is generated along the underground engineering axis using the encrypted section at a second sampling interval less than the first sampling interval. The second set of discrete sampling points is then used to calculate the second burial depth, and the burial depth calculation result is obtained.

7. The three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion according to claim 1, characterized in that, The step of using the fused spatial model to perform spatial mapping and constraint calculation on the set of constraints to determine candidate construction regions includes: The set of constraints is used to perform coordinate mapping in the fusion space model, and the constraints are converted into constraint range values, which include buffer distance and height range. The constraint range values ​​are used to generate three-dimensional constraint objects in the fusion space model; Based on the three-dimensional constraint objects, a three-dimensional superposition operation is performed in the fusion space model to obtain the constraint partitioning result; The preliminary site selection range is spatially filtered using the constrained partitioning results to obtain the candidate construction area; The constraints in the set of constraints include one or more of the following: geological constraints, water body constraints, traffic constraints, and land use constraints.

8. The three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion according to claim 1, characterized in that, The process of determining multiple candidate construction locations based on the candidate construction area includes: The first sampling interval and the second sampling interval are determined based on the candidate construction area; A first sampling point is generated within the candidate construction area using the first sampling interval, and a second sampling point is generated within the candidate construction area using the second sampling interval; Multiple initial candidate locations are generated by merging the first sampling point and the second sampling point, and the nearest neighbor distance corresponding to each initial candidate location is determined based on the location distance between different initial candidate locations. The initial candidate locations are filtered out using the nearest neighbor distance and the minimum spacing threshold to obtain the multiple candidate construction locations.

9. The three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion according to claim 1, characterized in that, The evaluation and calculation of each candidate construction location using the fusion spatial model includes: Based on the candidate construction locations, layout parameters are determined in the integrated spatial model, including reservoir area control elevation, passageway orientation, and underground engineering axis. Using the aforementioned layout parameters, three-dimensional calculations are performed in the fusion space model to obtain basic evaluation quantities, which include elevation difference, passage length, burial depth, and earthwork balance. The comprehensive evaluation quantity is calculated using the basic evaluation quantity, which includes the elevation difference utilization rate and the terrain disturbance coefficient. The basic evaluation quantity and the comprehensive evaluation quantity are weighted and summed to obtain the location score corresponding to each candidate construction location.

10. A three-dimensional spatial site selection system for pumped storage power stations based on multi-model fusion, used to implement the three-dimensional spatial site selection method for pumped storage power stations based on multi-model fusion as described in any one of claims 1 to 9, characterized in that, The system includes: The model building module is used to build a digital elevation model based on the elevation data of the site selection area, and to build a building information model using the spatial range determined by the digital elevation model and the engineering construction data corresponding to the site selection area. The model fusion module is used to determine the spatial transformation matrix corresponding to the digital elevation model and the building information model, and to use the spatial transformation matrix to map the building information model to the spatial coordinate system of the digital elevation model to obtain a fused spatial model. The initial site selection module is used to perform slope calculation, spatial intersection calculation and burial depth calculation using the fused spatial model, and determine the preliminary site selection range based on the calculation results; The constraint overlay module is used to obtain a set of constraint conditions using the preliminary site selection range, and to perform spatial mapping and constraint calculation on the set of constraint conditions using the fusion spatial model to determine the candidate construction area; The location determination module is used to determine multiple candidate construction locations based on the candidate construction area, evaluate and calculate each of the candidate construction locations using the fusion spatial model, and select the target construction location corresponding to the pumped storage power station from the multiple candidate construction locations based on the evaluation and calculation results.

Images