A three-dimensional property modeling and integrated registration method and system for water flow type natural resources

Abstract

The application discloses a kind of water flow class natural resources three-dimensional property body modeling and integrated registration method and system, belong to natural resource right registration and three-dimensional geographic information technical field.Method includes: obtaining multi-source heterogeneous data and establishing standardized spatial database by preprocessing;Three-dimensional property body modeling is carried out for surface water and groundwater resources respectively, and ownership unit is expanded from two-dimensional boundary to three-dimensional property body;Surface water and groundwater property body model are integrated under unified three-dimensional space reference system;Three-dimensional space conflict detection identifies spatial superposition relationship.Irregular triangle net is used to construct water bottom DEM for surface water, and generalized three-prism and multi-layer Delaunay triangle net hybrid algorithm is used for modeling for groundwater;Conflict detection uses the framework of fusing concave polyhedron segmentation, Minkowski difference and simplex cycle judgment.The application realizes the change of water flow class natural resources from two-dimensional boundary to three-dimensional property body, and provides technical support for stereoscopic, fine right and integrated registration.

Description

Technical Field

[0001] This invention belongs to the field of natural resource ownership registration and three-dimensional geographic information technology, specifically involving a method and system for modeling and integrating the ownership of water flow-type natural resources in three dimensions. Background Technology

[0002] Clear definition and unified registration of natural resources are crucial spatial foundations for achieving sustainable resource governance, ecological protection, and coordinated land-water management. Water-related natural resources (including surface water and groundwater), as an important component of the natural resource system, exhibit significant spatial heterogeneity, a clear vertical stratification structure, and multi-scale dynamic interactions. Therefore, accurately representing the spatial scope and ownership boundaries of water-related natural resources is a key issue in resource allocation, regulatory enforcement, and conflict resolution. However, in current management practices, the definition and registration of water-related natural resources still primarily rely on two-dimensional boundary representations. While the two-dimensional boundary method offers certain operational convenience, it essentially simplifies a three-dimensional natural system into a planar geometric object, making it difficult to effectively characterize the vertical structure, spatial layering relationships, and underground connectivity of water resources. This structural mismatch between the three-dimensional natural attributes of the water system and the two-dimensional registration model is gradually becoming a significant factor restricting refined resource management, particularly prominent in areas with complex land-water relationships and well-developed underground structures.

[0003] In recent years, the development of 3D geographic information technology has provided new technical means for the registration and confirmation of natural resource rights. For example, Chinese invention patent CN122288953A discloses a method for the registration and confirmation of natural resource rights based on a 3D model. This method discretizes the 3D terrain model into a set of voxels and introduces a time axis to construct a four-dimensional spatiotemporal framework. By constructing a dynamic boundary surface function that changes with time, the resource attributes of the voxels at different times are determined in real time, and then the ownership subject and share of rights are determined according to the duration. This method, by introducing the time dimension, solves the problem of confirming rights to dynamic natural resources such as intertidal zones and seasonal wetlands to a certain extent. However, this method still has the following shortcomings: First, this method discretizes the 3D terrain model with regular voxels (cube meshes), and the spatial resolution of the voxels is fixed and globally uniform. Although this regular voxel discretization method is convenient for calculation, it cannot adapt to the irregular shape and non-straight boundary characteristics of water-type natural resources. For meandering river shorelines, irregular lake boundaries, and complex wetland terrain, regular voxels will produce serious step-like errors at the boundaries, resulting in a systematic deviation between the confirmed boundary and the actual resource boundary. Second, this method only focuses on the switching of resource attributes for surface water (water surface / tidal flats), and does not address the three-dimensional confirmation and registration of groundwater resources. Groundwater has complex three-dimensional structural characteristics, such as vertical stratification of aquifers and recharge / discharge relationships with surface water. The method in patent CN122288953A is completely unsuitable for the three-dimensional confirmation of groundwater resources. Third, after discretizing the three-dimensional terrain model into regular voxels, this method independently determines the attributes and calculates ownership for each voxel, severing the spatial relationships between voxels. The regular voxel discretization method is essentially still a discretized approximation of continuous three-dimensional space, rather than a direct modeling of the natural resource property rights unit itself, and cannot express the complete property rights boundaries of holistic water flow resource units such as rivers, lakes, and wetlands. Fourth, this method does not address three-dimensional spatial conflict detection. In the practice of confirmation and registration, spatial overlap and ownership conflicts often exist between different resource types and between resources and construction utilization, requiring effective conflict identification and quantitative analysis methods.

[0004] In summary, there is currently a lack of a modeling and registration method that can transform water-related natural resources from two-dimensional boundaries to three-dimensional property rights, simultaneously cover both surface water and groundwater, and possess three-dimensional spatial conflict detection capabilities. Summary of the Invention

[0005] Purpose of the invention: To solve the above-mentioned technical problems, the present invention provides a method and system for three-dimensional property rights modeling and integrated registration of water flow-type natural resources.

[0006] Technical solution: The present invention provides a method for modeling and integrating the ownership of three-dimensional water flow natural resources, comprising the following steps: S1: Acquire multi-source heterogeneous data of the study area, including spatial geometric data, hydrogeological attribute data and dynamic monitoring data; preprocess the multi-source heterogeneous data and establish a standardized spatial database. S2: Three-dimensional property rights modeling is carried out for the property rights of surface water resources and groundwater resources respectively, and the ownership units of water flow natural resources are expanded from two-dimensional boundaries to three-dimensional property rights with clear volume and spatial structure. S3: Integrate the surface water property rights model and the groundwater property rights model under a unified three-dimensional spatial reference system to construct an integrated three-dimensional property rights model of surface water and groundwater; S4: Based on the integrated three-dimensional property rights model, perform three-dimensional spatial conflict detection to identify and locate the spatial overlapping relationship between the property rights entity and related management elements. This generates three-dimensional registration result data to assist in registration and confirmation of rights.

[0007] The three-dimensional spatial conflict detection results output in step S4 include the spatial location, extent, and corresponding property rights number of the conflict area. These conflict detection results are then associated and stored with each property rights unit in the integrated three-dimensional property rights model to form three-dimensional registration result data containing the spatial extent of the property rights, ownership information, and conflict detection results. This data is used to verify the legality and compliance of the spatial extent of the property rights, providing a technical basis for the registration, confirmation, and unified management of water-related natural resource assets.

[0008] Furthermore, the three-dimensional modeling of the surface water resource asset ownership body mentioned in step S2 includes: For river and lake ownership bodies, underwater topographic data and water surface boundary information are integrated to construct an underwater digital elevation model (DEM) based on a triangular network of irregularities (TIN). The underwater DEM is then fused with the polygonal water surface area to generate a continuous, closed 3D river ownership body model; where the lake volume is 𝑉 l𝑎𝑘𝑒 Calculated using the integral formula: , In the formula, 𝑉 l𝑎𝑘𝑒 S represents the volume of the lake. lake The surface area of ​​the lake, 𝐻 𝑠𝑢𝑟𝑓𝑎𝑐𝑒 This represents the water surface elevation, 𝐷𝐸𝑀 𝑏𝑜𝑡𝑡𝑜𝑚 Digital elevation model of the lakebed; A water balance equation is introduced for dynamic simulation. The water balance equation is as follows: , In the formula, V is the volume of water, t is time, and t is the volume of water. 𝑖𝑛 and 𝑄 𝑜𝑢𝑡Here, denoted as inflow and outflow, denoted as precipitation rate, E as evaporation rate, and denoted as water surface area; this model supports parametric dynamic simulation and can accurately reflect the impact of seasonal water level fluctuations on lake storage. For wetland property rights, multi-layer information on water depth, vegetation layer thickness, and base elevation was collected. A three-dimensional wetland property rights model was constructed using a grid overlay and layer stretching algorithm. , In the formula, 𝑀 𝑤𝑒𝑡l𝑎𝑛d This is a three-dimensional property rights model of the wetland, where m is the total number of structural layers, 𝐷𝐸𝑀 𝑏𝑎𝑠𝑒 For the digital elevation model of the wetland underwater, ∆ℎ 𝑘 The thickness of the nth layer (e.g., water, vegetation, topsoil), K 𝑘 This represents the spatial extent of the corresponding layer. This model can clearly express the three-dimensional structure and spatial distribution of ecological functions of wetlands.

[0009] Furthermore, the three-dimensional modeling of the groundwater resource asset ownership described in step S2 includes: Groundwater property rights boundaries are delineated using hydrogeological units as basic registration units. Water level fluctuations are tracked in real time through a monitoring network. Boundary maps are updated according to a preset cycle, and water level fluctuation zones are marked as flexible management areas. The preset cycle can be set to quarterly and can be adjusted according to hydrogeological conditions. A hybrid algorithm combining generalized triangular prism (GTP) and multi-layer Delaunay triangulation (MD) was used to model groundwater in three dimensions. The generalized triangular prism volume elements were used to simulate the spatial distribution of strata, and a three-dimensional property body model of the aquifer was constructed.

[0010] Furthermore, the hybrid algorithm of generalized triangular prism and multi-layer Delaunay triangulation includes the following steps: (1) Obtain the Delaunay triangulation network of the study area; (2) Extract a triangle from the ground Delaunay triangulation and set it as the upper triangle of the first generalized triangular prism; (3) Based on the stratum numbers of the three vertices of the upper triangle, extend downward along the borehole to generate the lower triangle; (4) Based on the correspondence between the upper triangle and the lower triangle and the drilling point chain, construct a generalized triangular prism and set the lower triangle as the new upper triangle; (5) Repeat steps (3) to (4) until all vertices of the upper triangle are the bottom points of their respective drill holes; (6) Repeat steps (2) to (5) until all triangles of the ground Delaunay triangulation have been traversed; (7) When new hydrogeological borehole data is added, only the Delaunay triangulation is modified locally on the ground and then extended downward along the borehole to generate a new generalized triangular prism, thus avoiding global mesh reconstruction.

[0011] Furthermore, the three-dimensional spatial collision detection described in step S4 employs a Fusion Collision Detection Framework (FCDF), which includes the following steps: S41: Perform concave polyhedron determination on the 3D property model participating in conflict detection. If concave points exist, perform concave polyhedron segmentation to decompose the concave polyhedron into a set of convex polyhedrons. S42: Calculate the Minkowski difference for a set of convex polyhedra, whereby the Minkowski difference is defined as: , In the formula, A and B are two polyhedra, a∈A, b∈B, and a and b are points in polyhedra A and B, respectively; S43: Use the simplex cycle judgment method (SCDM) to determine whether the origin is located within the Minkowski difference set. If the origin is located within the difference set, it is determined that there is a spatial conflict between the two property entities.

[0012] Furthermore, the concave polyhedral segmentation in step S41 includes: Traverse the vertices of the polyhedron, calculate the interior angle of each vertex, and mark vertices with interior angles greater than 180° as concave points; For each concave point, calculate the bisector of its concave angle, and find the point with the closest geometric distance to the bisector among the remaining non-nearby vertices; The concave polyhedron is divided into two sub-polyhedra by connecting the nearest point to the concave point. Recursively perform concave point detection and segmentation operations on the sub-polyhedra generated by segmentation until all sub-polyhedra are convex polyhedra.

[0013] Furthermore, the simplex cycle determination method described in step S43 includes: S431: Select the initial search direction, find the support point of the search in the Minkowski difference set as the farthest point, and add the farthest point to the simplex point set; S432: Update the search direction based on the current geometric features of the simplex, search for new support points in the Minkowski difference set along the updated direction, and add the new support points to the simplex point set to update the simplex. S433: Determine whether the current simplex contains the origin. If it does, determine that the two polyhedra intersect and return True. S434: Iteratively execute steps S432 and S433. If a support point that crosses the origin cannot be found in a certain search direction, it is determined that the two polyhedra do not intersect, and False is returned.

[0014] Furthermore, the method also includes a model accuracy verification step: for the surface water property rights model, using high-resolution remote sensing imagery and / or field measurement data as the baseline, the planar mean square error and elevation mean square error are calculated to perform a quantitative accuracy assessment. ,

[0015] In the formula, 𝑅𝑀𝑆𝐸 𝑝l𝑎𝑛𝑎𝑟 For plane standard error, 𝑅𝑀𝑆𝐸 𝑣𝑒𝑟𝑡𝑖𝑐𝑎l Here, is the elevation error, 𝑛 is the number of sampling points, and 𝑋 is the standard error. 𝑜𝑏𝑠,𝑖 , 𝑡𝑟𝑢𝑒,𝑖 These are the observed and true coordinates of the i-th sampling point, respectively. 𝑜𝑏𝑠,𝑖 , 𝑍 𝑡𝑟𝑢𝑒,𝑖 These are the observed and true elevation values ​​of the i-th sampling point, respectively; this evaluation method can comprehensively reflect the model's ability to reproduce the water-land interface, underwater topography, and ecological spatial structure.

[0016] For the groundwater ownership model, independent hydrogeological boreholes that were not involved in the model construction were used as validation samples. The corresponding model prediction profiles were extracted from the 3D model at the borehole plane location. The burial depth error of the aquifer top plate and the layer thickness error were calculated, and the mean absolute error (MAE) and root mean square error (RMSE) were calculated as evaluation indicators.

[0017] In the evaluation of the accuracy of the burial depth of the aquifer top plate, let the burial depth of the aquifer top plate corresponding to the i-th virtual borehole be . The measured burial depth of the top plate is Then the error in the burial depth of its top plate Defined as: , In the accuracy evaluation of aquifer thickness, let the aquifer layer thickness corresponding to the nth virtual borehole be denoted as . The actual measured layer thickness is Then its layer thickness error Defined as: , To comprehensively reflect the model error characteristics, the mean absolute error (MAE) and root mean square error (RMSE) are calculated as the main evaluation indicators. Specifically, the MAE and RMSE for the roof burial depth error are as follows: , , Correspondingly, the MAE and RMSE of the aquifer thickness error are as follows: , , Among them, MAE D RMSE is the average absolute error of the burial depth of the roof. D The root mean square error (RMS) of the burial depth of the roof is represented by the MAE. H RMSE is the mean absolute error of the layer thickness. H denoted as Root Mean Square Error (RMSE), where n is the total number of valid corresponding layers included in the statistics. Compared to using only a single mean index, MAE can reflect the overall deviation of the model's prediction error, while RMSE is more sensitive to larger errors. Combining the two can more comprehensively characterize the vertical accuracy of the WNRP groundwater model.

[0018] Furthermore, the data preprocessing described in step S1 includes: cleaning and removing outliers from multi-source heterogeneous data; filling in missing attributes through spatial interpolation or data from neighboring regions; uniformly converting all spatial data into the CGCS2000 coordinate system; and establishing a standardized spatial database by managing attribute data and spatial data respectively using MySQL and Geodatabase.

[0019] This invention also provides a three-dimensional property rights modeling and integrated registration system for water flow-type natural resources, comprising: The data acquisition and preprocessing module is used to acquire and process multi-source heterogeneous data from the study area and establish a standardized spatial database. The surface water 3D property rights modeling module is used to construct underwater digital elevation models based on irregular triangular networks, and generate 3D property rights models of rivers, lakes and wetlands; The groundwater 3D property rights modeling module is used to construct a 3D property rights model of an aquifer using a hybrid algorithm of generalized triangular prisms and multi-layer Delaunay triangulation. An integrated module is used to integrate surface water property rights models and groundwater property rights models under a unified three-dimensional spatial reference system; A three-dimensional spatial conflict detection module is used to identify and locate the spatial overlapping relationship between property entities based on a fusion collision detection framework; The 3D registration and visualization module is used to realize the 3D visualization of the property rights model, property rights information query, and spatial measurement.

[0020] Furthermore, the three-dimensional spatial conflict detection module includes: The concave polyhedron segmentation unit is used to identify concave points in a 3D property model and recursively segment concave polyhedra into a set of convex polyhedra. Minkowski difference calculation unit, used to calculate the Minkowski difference set of two convex polyhedra; The simplex cyclic judgment unit is used to iteratively construct a simplex to determine whether the origin is located within the Minkowski difference set, and outputs the conflict detection results and the spatial location and range of the conflict region.

[0021] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following significant advantages: (1) This invention takes “property rights body” as the basic spatial unit, and expands the ownership object of water flow resources from a two-dimensional surface domain to a three-dimensional entity with a clear volume and spatial structure, realizing the paradigm shift of ownership expression from “volume element attribute marking” to “direct modeling of property rights body”.

[0022] (2) This invention is the first to integrate surface water and groundwater resources into the same three-dimensional spatial framework for expression and management: surface water uses irregular triangular mesh to finely express underwater topographic undulations, while groundwater uses a hybrid algorithm of generalized triangular prism and multi-layer Delaunay triangular mesh to characterize the aquifer structure, realizing "heterogeneous modeling and unified expression", filling the technical gap in three-dimensional property modeling of groundwater.

[0023] (3) This invention uses irregular units such as triangular meshes (TINs) and generalized triangular prisms (GTPs) for modeling. The size and shape of the units can be adaptively adjusted according to the complexity of the terrain and strata. With the same accuracy requirements, fewer units are required, and with the same number of units, the boundary fitting accuracy is higher. Verification by examples shows that the planar boundary error of the surface water property rights body is controlled within 0.1m, which is far superior to the boundary representation accuracy of the regular volume element discretization method at the same resolution.

[0024] (4) The Fusion Collision Detection Framework (FCDF) proposed in this invention solves the problem that irregular property rights bodies are difficult to directly participate in collision detection by dividing concave polyhedra. By introducing the Minkowski difference and simplex cyclic judgment method, the complex three-dimensional intersection judgment is transformed into a stable and efficient geometric calculation process, so that spatial conflicts can be transformed from subjective interpretation into calculable objects, providing a quantitative basis for the mediation of ownership disputes.

[0025] (5) This invention realizes the dynamic simulation of the volume of lake property by introducing the water balance equation, which can accurately reflect the impact of seasonal water level fluctuations on lake storage; through the local update mechanism of the generalized triangular prism model, it avoids the problem of global grid reconstruction caused by data addition and deletion in traditional modeling, and is particularly suitable for dynamically changing aquifer systems.

[0026] (6) This invention establishes a systematic accuracy evaluation system and proposes quantitative accuracy verification methods for surface water and groundwater property rights models, providing an objective basis for the reliability and applicability of the models. Attached Figure Description

[0027] Figure 1 This is an overall flowchart of the method for modeling and integrating the ownership of water-related natural resources according to the present invention; Figure 2 This is a flowchart of the three-dimensional modeling process for the groundwater property rights of this invention; Figure 3 This is a flowchart of the three-dimensional polyhedron collision detection process of the present invention; Figure 4 This is a schematic diagram of the concave polyhedron segmentation steps of the present invention; Figure 5 This is a schematic diagram illustrating the Minkowski difference and simplex determination method of the present invention, wherein, Figure 5 (a) represents polyhedra A and B that do not intersect. Figure 5 (b) are intersecting polyhedra A and B. Figure 5 (c) represents the Minkowski difference in the case of non-intersection. Figure 5 (d) represents the Minkowski difference in the case of intersection. Figure 5 (e) is a simplex constructed from the Minkowski difference vertices; Figure 6 This is a schematic diagram illustrating the comparative analysis of thinning borehole data in this invention; Figure 7 This is a schematic diagram of the three-dimensional property rights model of the lake in this invention; Figure 8 This is a schematic diagram of the three-dimensional property rights model of the river in this invention; Figure 9 This is a schematic diagram of the three-dimensional property rights model of the wetland of the present invention; Figure 10 This is a schematic diagram of the three-dimensional property rights model of groundwater according to the present invention, wherein, Figure 10 (a) is a complete 3D hydrogeological model. Figure 10 (b) is a partial excavation view. Figure 10 (c) represents all aquifers. Figure 10 (d) is a porous unconfined aquifer. Figure 10 (e) represents the first confined aquifer with porous structure. Figure 10 (f) is the second confined aquifer with porous structure. Figure 10 (g) is a porous, confined aquifer of type III; Figure 11 To compare the borehole stratification information and 3D model cross-sections in different sub-regions within the groundwater modeling and verification area of ​​this invention, wherein... Figure 11 (a) Subregion A, Figure 11 (b) Subregion B, Figure 11 (c) Subregion C, Figure 11 (d) Subregion D; Figure 12This is a distribution diagram of the burial depth error and layer thickness error of the groundwater model of this invention. Detailed Implementation

[0028] The following specific embodiments are combined with Figure 1-12 The technical solutions described in this invention will be further explained in detail. However, it should be noted that the following embodiments are only used to describe the content of the invention and do not constitute a limitation on the scope of protection of this invention.

[0029] Example 1

[0030] This embodiment uses the typical Yangtze River Delta region as an example to explain in detail the method for three-dimensional property rights modeling and integrated registration of water-related natural resources according to the present invention. The process is as follows: Figure 1 As shown.

[0031] I. Data Acquisition and Preprocessing in the Study Area The study area is located in the alluvial plain of the lower Yangtze River estuary, with a well-developed surface water system and obvious water-land interaction, making it a typical region with strong surface water-groundwater interaction. The terrain of the study area is mainly low hills and plains, with relatively small landform undulations but a complex hydrological structure, with various water body types such as rivers, lakes, and wetlands coexisting in the region.

[0032] Acquiring multi-source heterogeneous data specifically includes: Spatial geometric data: high-resolution remote sensing images, digital orthophotos, digital elevation models, cadastral survey results, water distribution data, 1:10000 basic geographic data, land use data, digital line maps, and Nantong City road dataset.

[0033] Hydrogeological attribute data: hydrogeological borehole data, aquifer stratification information, groundwater level monitoring records, surface water bottom topographic data, hydrological dynamic monitoring data, water resource survey data, wetland resource survey data, water quality monitoring data, and water resource distribution data.

[0034] Dynamic monitoring data includes: underwater topographic survey points, UAV aerial survey images, 3D lidar scanning data, continuous hydrological monitoring records, panoramic shooting data, and actual measurement coordinate information.

[0035] Data preprocessing includes the following three core steps: First, data cleaning was performed to remove outliers (such as negative river lengths and abnormal water level records). Missing attributes (such as wetland vegetation type and borehole depth) were filled in using spatial interpolation or data from neighboring areas. Paper maps were scanned and vectorized, with planar errors controlled within 0.5 meters.

[0036] Subsequently, all spatial data were uniformly converted to the CGCS2000 coordinate system and stored in Geodatabase format. Control point registration ensured spatial matching of multi-source data, and overlay analysis was performed on surface water and groundwater data to identify logical inconsistencies and mark areas requiring supplementary measurements.

[0037] Finally, based on MySQL and Geodatabase to manage attribute and spatial data respectively, metadata and data dictionary structures were designed, and automated quality inspection tools were developed to perform rule-based checks on data integrity, accuracy, topological consistency, and validity. All processed data was uniformly organized into a standardized spatial database.

[0038] II. 3D Modeling of Surface Water Property Rights (a) Delineation of property rights boundaries According to the regulations on delineating the management scope of rivers and lakes issued by the water resources department, rivers with and without dikes are distinguished, and their management boundaries are determined accordingly. The management scope of rivers with dikes includes the dike itself and an extension of 30-50 meters beyond the toe of its back slope, with a minimum of 10 meters in urban areas; for rivers without dikes, the boundary is the design flood level or the highest historical flood level, extending 30-50 meters beyond the design flood level in plain areas.

[0039] In the process of defining the spatial boundaries of property rights, when the management scope of rivers and lakes overlaps with construction land units, the spatial integrity of the construction land units is maintained by adjusting the boundaries of the registration units. If there is overlap with permanent basic farmland, priority is given to maintaining the continuity of water body management boundaries, keeping the original management scope line unchanged, and prioritizing the protection of the ecological functions of aquatic areas.

[0040] (II) Modeling of Lake Property Rights Taking a certain lake as an example, this lake was formed by an external water diversion project. Its surface area is 133,892.56 m², with an average depth of 1.5-3.0 m and a maximum depth of 4.2 m. The three-dimensional property model of the lake is as follows: Figure 7 As shown.

[0041] High-precision underwater topographic data was acquired using UAV oblique photogrammetry and 3D LiDAR scanning technology. A digital elevation model (DEM) of the underwater surface was constructed based on a triangular network of irregularities (TIN). The underwater DEM was then fused with polygons representing the water surface area to generate a continuous 3D water body model. Lake volume 𝑉 l𝑎𝑘𝑒 Calculated using the integral formula: , In the formula, 𝑉 l𝑎𝑘𝑒 S represents the volume of the lake. lake The surface area of ​​the lake, 𝐻 𝑠𝑢𝑟𝑓𝑎𝑐𝑒 This represents the water surface elevation, 𝐷𝐸𝑀 𝑏𝑜𝑡𝑡𝑜𝑚 Digital elevation model of the lakebed; To reflect the dynamic characteristics of the modern model, a water balance equation is introduced: , In the formula, V is the volume of water, t is time, and t is the volume of water. 𝑖𝑛 and 𝑄 𝑜𝑢𝑡 Here, denoted as inflow and outflow, denoted as precipitation rate, E as evaporation rate, and denoted as water surface area; this model supports parametric dynamic simulation and can accurately reflect the impact of seasonal water level fluctuations on lake storage.

[0042] Based on the current area and average water depth, the estimated volume of the lake's three-dimensional ownership volume is approximately (2.0-4.0) × 10⁻¹⁰. 5 m³. Model accuracy verification results show that the planar boundary error of the lake's ownership body is 0.085m, and the relative error in volume calculation is less than 3.2%.

[0043] (III) Modeling of River Property Rights Taking a certain river as an example, the river channel is 5,024.34m long, with an average width of 8.97m and a water surface area of ​​approximately 4.51 × 10⁻⁶ m. 4 m². A three-dimensional property rights model of the river, as shown below. Figure 8 As shown.

[0044] By fusing underwater topographic data at a resolution of 0.2m with water surface boundary information, a digital elevation model (DEM) of the underwater surface is constructed based on an irregular triangular network. The underwater DEM is then fused with the polygonal area of ​​the water surface to generate a continuous, closed three-dimensional river ownership model. The model results clearly express the changes in riverbed depth, the gradual change in channel width, and the overall flow direction characteristics.

[0045] Accuracy verification shows that the planar boundary error of the river property is 0.079m, and the riverbed elevation error is less than 0.15m.

[0046] (iv) Modeling of wetland property rights Taking a specific wetland as an example, this wetland covers an area of ​​15,515.12 m², has a total volume of 134,050.63 m³, and an average thickness of 8.64 m. The three-dimensional property model of the wetland is shown below. Figure 9 As shown.

[0047] Wetland modeling integrates UAV aerial survey and field sampling data to obtain multi-layered information such as water depth, vegetation layer thickness, and base elevation. Using raster overlay and layered stretching algorithms, topographic DEM data and data from various structural layers are integrated to construct a three-dimensional wetland model. , In the formula, 𝑀 𝑤𝑒𝑡l𝑎𝑛d This is a three-dimensional property rights model of the wetland, where m is the total number of structural layers, 𝐷𝐸𝑀 𝑏𝑎𝑠𝑒 For the digital elevation model of the wetland underwater, ∆ℎ𝑘 The thickness of the nth layer (e.g., water, vegetation, topsoil), K 𝑘 This represents the spatial extent of layer X. The model clearly expresses the three-dimensional structure and spatial distribution of ecological functions of wetlands. The model construction process comprehensively utilized water depth data at a resolution of 0.3m, vegetation layer thickness data (average 1.2m), and soil cover layer thickness data (average 2.3m).

[0048] The accuracy verification results show that the planar boundary error of the wetland property is 0.051m and the elevation error is less than 0.25m.

[0049] III. Three-dimensional modeling of groundwater property rights (a) Delineation of property rights boundaries The core of groundwater property rights delineation is to use hydrogeological units (such as groundwater basins and aquifer structures) as the basic registration units to ensure the integrity of the aquifer system. Given the dynamic nature of groundwater levels, a monitoring network is used to track water level fluctuations in real time, and boundary maps are updated quarterly. Water level fluctuation zones are marked as flexible management areas to adapt to seasonal changes and the impact of human exploitation.

[0050] (II) Modeling of Groundwater Property Rights To address the complex structure and dynamic characteristics of groundwater systems, a hybrid algorithm combining Generalized Triangular Prism (GTP) and Multilayer Delaunay Triangulation (MD) is employed for 3D modeling. GTP Voxels are capable of deformation, simulating complex geological structures such as faults, bifurcations, and pinch-outs. This method not only accurately reflects the spatial distribution characteristics of strata but also ensures the consistency of the model's data structure. The model is filled with triangular prism Voxels, which not only guarantees the simulation accuracy of the top and bottom interfaces but also reduces the model's data volume. The 3D modeling process for groundwater ownership bodies is as follows: Figure 2 As shown.

[0051] The specific modeling process is as follows: Step 1: Extract a triangle from the ground Delaunay triangulation and set it as the upper triangle of the first generalized triangular prism; Step 2: Based on the stratigraphic numbers of the three vertices of the upper triangle, a new triangle (called the lower triangle) is generated by extending downwards along the borehole according to the reasoning rules of three-dimensional structural knowledge. Step 3: Based on the correspondence between the upper and lower triangles in the triangular doubly linked list and the drilling point chain, construct a generalized triangular prism, record its description information, and set the lower triangle as the new upper triangle; Step 4: Repeat steps 2 and 3 until all vertices of the upper triangle are the bottom points of their respective drill holes; Step 5: Repeat steps 1 to 4 until all triangles of the ground Delaunay triangulation have been traversed.

[0052] When new hydrogeological borehole data is added, only the Delaunay triangulation needs to be locally modified on the surface. Then, the locally modified triangles are extended downwards along the borehole according to knowledge reasoning rules to generate new generalized triangular prisms. This process avoids the global mesh reconstruction problem caused by data addition and deletion in traditional modeling. It only adjusts the local area and extends along the borehole to build the new prism, making it particularly suitable for dynamically changing aquifer systems.

[0053] This embodiment focuses on the three-dimensional property modeling of the III confined aquifer. This area covers a section of the main urban area along the river where borehole data is relatively concentrated, and can comprehensively reflect the spatial variation characteristics of the underground aquifer in the planar and vertical directions. The III confined aquifer exists in the form of pore water in the Quaternary sedimentary layer, with a relatively continuous spatial distribution, and its hydrological conditions are significantly affected by surface water recharge and precipitation infiltration. The three-dimensional model constructed using the GTP-MD hybrid modeling method covers an area of ​​approximately 8.6 km² and contains 12,358 generalized triangular prism elements.

[0054] Three-dimensional property rights model of groundwater, such as Figure 10 As shown, the distribution of burial depth error and layer thickness error in the groundwater model is as follows: Figure 12 As shown, the modeling results indicate that the thickness of the III confined aquifer is mainly distributed between 1.7 and 2.5 m, with an average thickness of approximately 2.1 m, and the top burial depth ranges from 28.5 to 33.2 m. The model can relatively completely reflect the spatial variation characteristics of the aquifer in both the planar and vertical directions. After removing 5% of samples with large errors, the truncated mean values ​​of the top burial depth error and the layer thickness error are 1.74 m and 1.30 m, respectively; after removing 10% of samples with large errors, these values ​​further decrease to 1.40 m and 1.03 m. It can be seen that the model can recover the variations in the top burial depth and layer thickness of the aquifer well in most borehole locations, but there are still significant deviations in areas with drastic local strata changes or relatively insufficient borehole control. It should be noted that the truncated mean values ​​are only used to help describe the robustness of the error distribution and do not replace the overall error statistics based on all samples. Based on the combined mean, median, standard deviation, and truncated mean, the constructed groundwater WNRP model can be considered to reflect the regional geometric structure and vertical variation trend of the III confined aquifer well, and is suitable for three-dimensional representation and registration analysis at the regional scale.

[0055] IV. Three-dimensional spatial conflict detection Using the proposed Fusion Collision Detection Framework (FCDF), a systematic spatial conflict analysis was conducted on all water-related natural resource assets and related planning elements within the study area. The 3D polyhedral collision detection process is as follows: Figure 3As shown.

[0056] (a) Concave polyhedron segmentation Concave polyhedron segmentation, such as Figure 4 As shown, some water flow property models (such as meandering channels and irregular wetlands) are geometrically represented as concave polyhedra, while collision detection methods based on Minkowski differences are only applicable to convex polyhedra. Therefore, the fusion collision detection framework first performs convex decomposition on the concave polyhedra: Step 1: Concave Point Identification; Traverse the vertices of the polyhedron and calculate the interior angle of each vertex. If the interior angle of a vertex is greater than 180°, mark it as a concave point; Step 2: Calculation of bisectors and search for the nearest point; For each concave point, calculate the bisector of its concave angle; Among the remaining non-nearby vertices, find the point with the closest geometric distance to the bisector; Step 3: Segmentation operation; Connect the point closest to the dividing line found in Step 2 with the concave point found in Step 1 to form the dividing line, dividing the concave polyhedron into two sub-polyhedra; At this time, the interior angle of the concave point in this segmentation must be less than 180°, and after the segmentation operation, the concave point will be transformed into a convex point; Step 4: Recursively perform concave point judgment and segmentation operations on the sub-polyhedra generated by segmentation until all sub-polyhedra are convex polyhedra; concave polyhedra are transformed into a set of convex polyhedra after segmentation. (II) Minkowski difference calculation like Figure 5 As shown, for two polyhedra A and B, their Minkowski difference is defined as: , In the formula, A and B are two polyhedra, a∈A, b∈B, where a and b are points in polyhedra A and B, respectively, as shown in 5(a) and Figure 5 As shown in (b). This difference set has important geometric significance: as Figure 5 As shown in (d), if two polyhedra intersect, then the Minkowski difference set must contain the origin; conversely, as shown in (d), Figure 5 As shown in (c), if the two are separated, the origin is outside the difference set. Therefore, the collision detection problem is transformed into determining whether the origin is in the difference set.

[0057] (III) Simplex Cycle Detection To efficiently determine whether the origin lies within the Minkowski difference set, the Simplex Iterative Determination Method (SCDM) is employed. This method iteratively constructs simplexes (such as line segments, triangles, tetrahedrons, etc.) to progressively approximate the geometry of the Minkowski difference set, thereby quickly determining the inclusion relationship of the origin. Figure 5 (e) shows the simplex constructed using the Minkowski difference vertices. The specific steps are as follows: Step 1: Select the initial search direction, search for the support point along this direction in the Minkowski difference set as the farthest point, and add the farthest point to the simplex point set; Step 2: Update the search direction based on the current geometric features of the simplex, search for new support points in the Minkowski difference set along the updated direction, and add the new support points to the simplex point set to update the simplex. Step 3: Determine whether the current simplex contains the origin. If it does, determine that the two polyhedra intersect and return True. Step 4: Iterate through steps 2 and 3. If a support point that crosses the origin cannot be found in a certain search direction, then the two polyhedra are determined not to intersect and False is returned.

[0058] (iv) Conflict detection results A total of 12 conflict areas with spatial overlap were identified, mainly involving conflicts between surface water rights and ecological protection red lines, as well as spatial intersections between groundwater rights and construction land. Regarding surface water resources, a certain river rights area partially overlaps with the ecological protection red line, with an overlap area of ​​approximately 235 m².

[0059] Regarding groundwater resources, there is an overlap between the groundwater ownership and the construction land of a certain commercial service area. The overlapping area between a certain commercial complex project and the groundwater ownership is approximately 0.3 hm², accounting for 5.2% of the project's land area.

[0060] The FCDF framework can output the spatial location, extent, and corresponding property rights number of the conflict area, providing technical support for the quantitative analysis and subsequent mediation of ownership conflicts.

[0061] V. Model Accuracy Verification (I) Surface water model accuracy verification Stratified sampling was conducted on 121 surface water property rights, resulting in 1,050 verification points, including 550 rivers, 250 lakes, and 250 wetlands. The model-generated results were compared and analyzed using 0.2m resolution remote sensing imagery, total station measured data, and underwater topographic survey results as baseline values.

[0062] For the river ownership model, in addition to comparing boundary data, ridgeline data in the river channel are also introduced into the analysis; for the lake ownership model, several feature point data evenly distributed within the lake are selected to assist in the accuracy assessment; for the wetland ownership model, representative inflection points on the wetland boundary and feature point data of different topographic and geomorphic areas within the wetland are selected for analysis.

[0063] Quantitative accuracy assessment is performed by calculating the mean square error and the mean square error of elevation. , , In the formula, 𝑅𝑀𝑆𝐸 𝑝l𝑎𝑛𝑎𝑟 For plane standard error, 𝑅𝑀𝑆𝐸 𝑣𝑒𝑟𝑡𝑖𝑐𝑎l Here, is the elevation error, 𝑛 is the number of sampling points, and 𝑋 is the standard error. 𝑜𝑏𝑠,𝑖 , 𝑡𝑟𝑢𝑒,𝑖 These are the observed and true coordinates of the i-th sampling point, respectively. 𝑜𝑏𝑠,𝑖 , 𝑍 𝑡𝑟𝑢𝑒,𝑖 These are the observed and true elevation values ​​for the i-th sampling point, respectively.

[0064] The validation results show that the planar boundary errors of rivers, lakes, and wetlands are 0.079m, 0.085m, and 0.051m, respectively; the corresponding elevation errors are 0.13m, 0.15m, and 0.21m, respectively. The errors for each type of surface water property are all below the predetermined accuracy thresholds (planar accuracy ≤ 1.0-1.5m, elevation accuracy ≤ 0.3-0.5m), indicating that the constructed model can accurately represent the location of water body boundaries, underwater topographic relief, and the vertical structural characteristics of wetlands.

[0065] (II) Accuracy Verification of Groundwater Model One hundred and nine independent hydrogeological boreholes not involved in the model construction were used as validation samples. The model predictions were compared and analyzed with the measured hydrogeological borehole data. The evaluation focused on two geometric indicators: the error in the burial depth of the aquifer top and the error in the aquifer thickness.

[0066] For each verification borehole, the corresponding model-predicted profile is extracted from the constructed 3D model at its planar location to obtain the aquifer top depth and layer thickness information at the same location. Only when the model-predicted layer corresponds one-to-one with the measured borehole layer is that layer included in the error statistics. Figure 6 As shown, in terms of evaluating the accuracy of the burial depth of the aquifer top plate, a comparative analysis of the burial depth error of the aquifer top plate between real boreholes and virtual boreholes at the same location was carried out.

[0067] In the evaluation of the accuracy of the burial depth of the aquifer top plate, let the burial depth of the aquifer top plate corresponding to the i-th virtual borehole be . The measured burial depth of the top plate is Then the error in the burial depth of its top plate Defined as: , In the accuracy evaluation of aquifer thickness, let the aquifer layer thickness corresponding to the nth virtual borehole be denoted as . The actual measured layer thickness is Then its layer thickness error Defined as: , To comprehensively reflect the model error characteristics, this invention calculates the mean absolute error (MAE) and root mean square error (RMSE) as the main evaluation indicators. Specifically, the MAE and RMSE for the roof burial depth error are as follows: , , Correspondingly, the MAE and RMSE of the aquifer thickness error are as follows: , .

[0068] like Figure 11 As shown, the model-predicted profile and the measured borehole stratification information were compared layer by layer. Validation results show that the MAE and RMSE of the aquifer top burial depth error were 2.84m and 3.63m, respectively, with a median of 2.37m and a standard deviation of 2.26m; the MAE and RMSE of the stratification thickness error were 2.39m and 3.12m, respectively, with a median of 1.56m and a standard deviation of 2.01m. After removing 10% of the samples with large errors, the truncated mean values ​​of the top burial depth error and the stratification thickness error decreased to 1.40m and 1.03m, respectively.

[0069] Based on the combined mean, median, standard deviation, and truncated mean, the constructed groundwater property rights model can be considered to reflect the regional geometric structure and vertical variation trend of the III confined aquifer well, and is suitable for three-dimensional representation and registration analysis at the regional scale.

[0070] VI. Implementation of the Three-Dimensional Registration System Based on multi-source spatial and attribute data of the study area, a three-dimensional registration and visualization management system for water flow-related property rights was constructed and implemented. The system adopts a browser / server (B / S) architecture, integrates surface water and groundwater property rights models on a unified three-dimensional spatial base, uses JavaScript as the core development language, relies on 3ds Max 2021 to construct the three-dimensional property rights models, and integrates OpenLayers and Cesium engines to realize functions such as property rights information query, three-dimensional visualization display, spatial measurement, conflict detection, and data update.

[0071] The system can simultaneously support the unified management of different types of property rights. During system operation, the 3D property rights model can be smoothly loaded and interactively displayed within a unified coordinate framework, and the spatial extent, volume, and attribute information of property rights units can be quickly retrieved through multi-dimensional queries. The system operates stably in actual testing and can meet the basic requirements for 3D registration and management of property rights.

[0072] The aforementioned 3D spatial conflict detection results are associated and stored with each property unit in the integrated 3D property rights model. Specifically, the spatial location, extent, and involved property unit number of the conflict area output by the FCDF framework are synchronously written into the attribute information table of each property unit, forming complete registration result data together with the spatial geometric information and ownership information of the property unit. Registration personnel can intuitively view whether there is spatial overlap between each property unit and the specific location and extent of the overlap based on the above registration result data. This allows for the identification of potential ownership conflicts during the registration review stage, preventing the inclusion of property units with spatial conflicts in the registration process and improving the legality and enforceability of the registration results. This seamless connection in the registration process achieves data integration from 3D property rights modeling to registration application, providing complete technical support for the integrated registration and unified management of water flow-type natural resource assets. The system operates stably in actual testing and can meet the basic requirements for 3D registration and management of property rights.

[0073] To further illustrate the technical effects of this invention, a comparative analysis is conducted between this invention and the method disclosed in patent publication number CN122288953A: (1) Differences in modeling objects. CN122288953A discretizes the 3D terrain model using regular voxels (cube meshes). Voxels are computational units rather than ownership units, and the registration of ownership is based on the statistical results of voxel attributes. This invention uses "ownership bodies" as the basic modeling unit and directly performs 3D geometric modeling of water resources entities such as rivers, lakes, wetlands, and groundwater aquifers. The ownership body itself is the carrier of ownership. This difference determines that this invention is more direct in the logic of registration of ownership—ownership is directly attached to the resource entity, rather than indirectly derived through the statistical analysis of spatial grid attributes. (2) Differences in surface water modeling accuracy. CN122288953A uses regular voxels of fixed size, which inevitably produces stepped errors at the boundaries. The magnitude of the error is directly related to the size of the voxels. This invention uses triangular irregular meshes (TINs), and the shape and size of the units can be adaptively adjusted according to the complexity of the terrain. Sub-decimeter accuracy can be achieved at the boundaries. The implementation examples demonstrate that the planar boundary error of the river property rights body of the present invention is 0.079m, which is far superior to the boundary representation accuracy of the regular volume element discretization method under the same computing resources.

[0074] (3) Differences in resource coverage. CN122288953A only involves the determination of resource attributes of surface water (water surface / tidal flats) and does not involve groundwater. This invention covers both surface water and groundwater, unifying them into the same three-dimensional spatial framework, filling the technical gap in the three-dimensional land rights confirmation method for groundwater.

[0075] (4) Differences in conflict detection capabilities. CN122288953A does not provide any spatial conflict detection methods. The FCDF framework proposed in this invention can automatically identify three-dimensional spatial conflicts between property owners and between property owners and planning elements, and output the location, scope and property owner number of the conflict area, providing a quantitative basis for the mediation of ownership disputes.

[0076] (5) Differences in data update efficiency. CN122288953A uses regular voxel discretization, which requires re-dividing all voxels and recalculating the attribute sequence of each voxel when updating data, resulting in a large amount of computation. The GTP model of this invention adopts a local update mechanism. When adding borehole data, it is only necessary to modify the local triangulation and construct new voxels along the borehole extension, avoiding global reconstruction and making it more suitable for dynamic update scenarios.

[0077] Example 2

[0078] This embodiment provides a three-dimensional property rights modeling and integrated registration system for water flow-type natural resources, including: The data acquisition and preprocessing module is used to acquire and process multi-source heterogeneous data of the study area, including spatial geometric data, hydrogeological attribute data and dynamic monitoring data. The module performs data cleaning, coordinate system unification and data storage on the multi-source heterogeneous data to establish a standardized spatial database. The surface water 3D property rights modeling module is used to construct underwater digital elevation models based on irregular triangular networks, generating 3D property rights models for rivers, lakes and wetlands respectively. The groundwater 3D property rights modeling module is used to construct a 3D property rights model of an aquifer using a hybrid algorithm of generalized triangular prisms and multi-layer Delaunay triangulation. An integrated module is used to integrate surface water property rights models and groundwater property rights models under a unified three-dimensional spatial reference system; The three-dimensional spatial conflict detection module includes a concave polyhedron segmentation unit, a Minkowski difference calculation unit, and a simplex cycle judgment unit, which are used to identify and locate the spatial overlapping relationship between property entities based on the fusion collision detection framework. The 3D registration and visualization module adopts a browser / server architecture and integrates OpenLayers and Cesium 3D map engines to realize the 3D visualization of property model, property information query and spatial measurement; The accuracy verification module is used to perform error statistical analysis on the aquifer top burial depth and layer thickness of the groundwater property model using independent hydrogeological boreholes as verification samples. It calculates the mean absolute error, root mean square error, median absolute error, and truncated mean as accuracy evaluation indicators.

[0079] The specific implementation methods of each module of the system described in this embodiment correspond to the method steps in Embodiment 1, and will not be repeated here.

[0080] Example 3

[0081] The difference between this embodiment and Embodiment 1 is that this embodiment is applied to the modeling and registration of three-dimensional property rights of water-related natural resources in karst areas.

[0082] In karst regions, groundwater aquifers exhibit highly complex structures, including unique geological formations such as caves and fissures. To address this application scenario, higher-density geophysical data (such as high-density electrical resistivity tomography and ground-penetrating radar data) is introduced, borehole control points are densified, and the generalized triangular prism element construction strategy is adjusted to enhance the model's ability to characterize complex structures.

[0083] Specifically, in the hybrid algorithm of generalized triangular prism and multi-layer Delaunay triangulation, the identification and processing of special geological structures such as karst caves and fissures are added: when karst caves or fissures are identified in the borehole data, internal constraint surfaces are added to the corresponding generalized triangular prism element, and the karst caves or fissures are expressed as independent sub-elements, thereby realizing fine three-dimensional modeling of complex karst aquifer structures.

[0084] The remaining steps are the same as in Example 1.

[0085] Example 4

[0086] The difference between this embodiment and Embodiment 1 is that this embodiment is applied to the modeling and registration of three-dimensional property rights of water-related natural resources in areas with highly dynamic surface water morphology (such as seasonal rivers, floodplains, etc.).

[0087] For regions with highly dynamic surface water morphology, the parameterized expression of water level changes and flow processes is further enhanced.

[0088] Specifically: In lake property rights modeling, the time step of the water balance equation is refined from monthly to daily scale, and real-time hydrological monitoring data (including daily water level, flow, precipitation and evaporation data) is integrated to achieve daily updates of the property rights volume.

[0089] In the modeling of river property rights, a one-dimensional hydrodynamic model is introduced. Based on the upstream inflow conditions and downstream boundary conditions, the water level and water surface width along the river are dynamically calculated to generate a dynamic river property rights model that changes over time.

[0090] At the level of property registration, a "dynamic property boundary" labeling function has been added. In the three-dimensional model, different colors or transparency are used to represent the spatial range of the property body under different water level frequencies (such as the range during the wet season, the dry season, and the normal season), providing a visual basis for the ownership definition of dynamic water flow resources.

[0091] The remaining steps are the same as in Example 1.

Claims

1. A method for modeling and integrating the registration of three-dimensional property rights of water-related natural resources, characterized in that, Includes the following steps: S1: Acquire multi-source heterogeneous data of the study area, including spatial geometric data, hydrogeological attribute data and dynamic monitoring data; preprocess the multi-source heterogeneous data and establish a standardized spatial database. S2: Three-dimensional property rights modeling is carried out for the property rights of surface water resources and groundwater resources respectively, and the ownership units of water flow natural resources are expanded from two-dimensional boundaries to three-dimensional property rights with clear volume and spatial structure. S3: Integrate the surface water property rights model and the groundwater property rights model under a unified three-dimensional spatial reference system to construct an integrated three-dimensional property rights model of surface water and groundwater; S4: Based on the integrated three-dimensional property rights model, perform three-dimensional spatial conflict detection to identify and locate the spatial overlapping relationship between the property rights and related management elements.

2. The method according to claim 1, characterized in that, The three-dimensional modeling of the surface water resource asset ownership body mentioned in step S2 includes: For river and lake ownership bodies, underwater topographic data and water surface boundary information are integrated. An underwater digital elevation model is constructed based on an irregular triangular mesh. This underwater digital elevation model is then fused with the polygonal water surface area to generate a continuous, closed 3D river ownership body model; where the lake volume is 𝑉 l𝑎𝑘𝑒 Calculated using the integral formula: ， In the formula, 𝑉 l𝑎𝑘𝑒 S represents the volume of the lake. lake The surface area of ​​the lake, 𝐻 𝑠𝑢𝑟𝑓𝑎𝑐𝑒 This represents the water surface elevation, 𝐷𝐸𝑀 𝑏𝑜𝑡𝑡𝑜𝑚 Digital elevation model of the lakebed; A water balance equation is introduced for dynamic simulation. The water balance equation is as follows: ， In the formula, V is the volume of water, t is time, and t is the volume of water. 𝑖𝑛 and 𝑄 𝑜𝑢𝑡 Here, denoted as inflow and outflow rates, 𝑃 as precipitation rate, E as evaporation rate, and 𝐴 as water surface area; For wetland property rights, multi-layer information on water depth, vegetation layer thickness, and base elevation was collected. A three-dimensional wetland property rights model was constructed using a grid overlay and layer stretching algorithm. ， In the formula, 𝑀 𝑤𝑒𝑡l𝑎𝑛d This is a three-dimensional property rights model of the wetland, where m is the total number of structural layers, 𝐷𝐸𝑀 𝑏𝑎𝑠𝑒 For the digital elevation model of the wetland underwater, ∆ℎ 𝑘 Let H be the thickness of the nth layer, and K be the thickness of the nth layer. 𝑘 This refers to the spatial range of the corresponding layer.

3. The method according to claim 1, characterized in that, The three-dimensional modeling of the groundwater resource asset ownership body in step S2 includes: delineating the groundwater ownership boundary using hydrogeological units as basic registration units, tracking water level fluctuations in real time using a monitoring network, updating the boundary map according to a preset cycle, and marking the water level change zone as a flexible management area; using a hybrid algorithm of generalized triangular prism and multi-layer Delaunay triangulation to perform three-dimensional groundwater modeling, using generalized triangular prism elements to simulate the spatial distribution of strata, and constructing a three-dimensional ownership body model of the aquifer.

4. The method according to claim 3, characterized in that, The hybrid algorithm of generalized triangular prism and multi-layer Delaunay triangulation includes the following steps: (1) Obtain the Delaunay triangulation network of the study area; (2) Extract a triangle from the ground Delaunay triangulation and set it as the upper triangle of the first generalized triangular prism; (3) Based on the stratum numbers of the three vertices of the upper triangle, extend downward along the borehole to generate the lower triangle; (4) Based on the correspondence between the upper triangle and the lower triangle and the drilling point chain, construct a generalized triangular prism and set the lower triangle as the new upper triangle; (5) Repeat steps (3) to (4) until all vertices of the upper triangle are the bottom points of their respective drill holes; (6) Repeat steps (2) to (5) until all triangles of the ground Delaunay triangulation have been traversed; (7) When new hydrogeological borehole data is added, only the Delaunay triangulation is modified locally on the ground and then extended downward along the borehole to generate a new generalized triangular prism, thus avoiding global mesh reconstruction.

5. The method according to claim 1, characterized in that, The three-dimensional spatial conflict detection described in step S4 employs a fusion collision detection framework, which includes the following steps: S41: Perform concave polyhedron determination on the 3D property model participating in conflict detection. If concave points exist, perform concave polyhedron segmentation to decompose the concave polyhedron into a set of convex polyhedrons. S42: Calculate the Minkowski difference for a set of convex polyhedra, wherein the Minkowski difference is defined as: ， In the formula, A and B are two polyhedra, a∈A, b∈B; S43: Use the simplex cyclic judgment method to determine whether the origin is located within the Minkowski difference set. If the origin is located within the difference set, it is determined that there is a spatial conflict between the two property entities.

6. The method according to claim 5, characterized in that, The concave polyhedral segmentation in step S41 includes: Traverse the vertices of the polyhedron, calculate the interior angle of each vertex, and mark vertices with interior angles greater than 180° as concave points; For each concave point, calculate the bisector of its concave angle, and find the point with the closest geometric distance to the bisector among the remaining non-nearby vertices; The concave polyhedron is divided into two sub-polyhedra by connecting the nearest point to the concave point. Recursively perform concave point detection and segmentation operations on the sub-polyhedra generated by segmentation until all sub-polyhedra are convex polyhedra.

7. The method according to claim 5, characterized in that, The simplex cycle detection method described in step S43 includes: S431: Select the initial search direction, find the support point of the search in the Minkowski difference set as the farthest point, and add the farthest point to the simplex point set; S432: Update the search direction based on the current geometric features of the simplex, search for new support points in the Minkowski difference set along the updated direction, and add the new support points to the simplex point set to update the simplex. S433: Determine whether the current simplex contains the origin. If it does, determine that the two polyhedra intersect and return True. S434: Iteratively execute steps S432 and S433. If a support point that crosses the origin cannot be found in a certain search direction, it is determined that the two polyhedra do not intersect, and False is returned.

8. The method according to claim 1, characterized in that, It also includes a model accuracy verification step: for the surface water property rights model, using high-resolution remote sensing imagery and / or field measurement data as the baseline, the horizontal and vertical mean square errors are calculated to quantitatively assess the accuracy. ， ， In the formula, 𝑅𝑀𝑆𝐸 𝑝l𝑎𝑛𝑎𝑟 For plane standard error, 𝑅𝑀𝑆𝐸 𝑣𝑒𝑟𝑡𝑖𝑐𝑎l为 Elevation error, where 𝑛 is the number of sampling points, 𝑋 𝑜𝑏𝑠,𝑖 , 𝑡𝑟𝑢𝑒,𝑖 These are the observed and true coordinates of the i-th sampling point, respectively. 𝑜𝑏𝑠,𝑖 , 𝑍 𝑡𝑟𝑢𝑒,𝑖 These are the observed and true elevation values ​​of the i-th sampling point, respectively; For the groundwater ownership model, independent hydrogeological boreholes that were not involved in the model construction were used as verification samples. The corresponding model prediction profiles were extracted from the 3D model at the borehole plane location. The burial depth error and layer thickness error of the aquifer top plate were calculated, and the mean absolute error and root mean square error were calculated as evaluation indicators.

9. A three-dimensional property rights modeling and integrated registration system for water flow-type natural resources, characterized in that, include: The data acquisition and preprocessing module is used to acquire and process multi-source heterogeneous data from the study area and establish a standardized spatial database. The system includes the following modules: a surface water 3D property rights modeling module for constructing underwater digital elevation models based on irregular triangular meshes, generating 3D property rights models for rivers, lakes, and wetlands; a groundwater 3D property rights modeling module for constructing aquifer 3D property rights models using a hybrid algorithm combining generalized triangular prisms and multi-layer Delaunay triangular meshes; an integrated module for integrating surface water and groundwater property rights models within a unified 3D spatial reference system; a 3D spatial conflict detection module for identifying and locating spatial overlay relationships between property rights based on a fusion collision detection framework; and a 3D registration and visualization module for enabling 3D visualization of property rights models, property rights information queries, and spatial measurement.

10. The system according to claim 9, characterized in that, The three-dimensional spatial conflict detection module includes: a concave polyhedron segmentation unit, used to identify concave points on the three-dimensional property model and recursively segment the concave polyhedron into a set of convex polyhedra; a Minkowski difference calculation unit, used to calculate the Minkowski difference set between two convex polyhedra; and a simplex iterative judgment unit, used to iteratively construct a simplex to determine whether the origin is located within the Minkowski difference set, and output the conflict detection result and the spatial location and spatial extent of the conflict region.

Images