A three-dimensional model-based intelligent design method for garden landscape water tank

Abstract

The application discloses a kind of based on three-dimensional model's garden landscape water tank intelligent design method, it is related to garden landscape design technical field, including: based on constraint compilation space set, extract water tank key position and allow space, obtain center line initial path, carry out equidistance resampling and tangential continuity, generate center line sampling sequence list;From center line sampling sequence list, extract curvature information and longitudinal slope change information, carry out joint discrimination, generate segmented boundary list, and configure section parameter, carry out geometric transition adjustment, generate segmented parameter assembly table;According to segmented parameter assembly table, execute along line sweep forming and transition section suturing, obtain water tank three-dimensional entity, and carry out interface geometry locking, to illegal interval trigger local partial segment recalculation and local sweep reconstruction, generate water tank three-dimensional design scheme.The application realizes transition section intelligent segmentation, avoids manual repeated adjustment, improves garden landscape water tank design efficiency.

Description

Technical Field

[0001] This invention relates to the field of landscape design technology, and in particular to an intelligent design method for landscape water features based on a three-dimensional model. Background Technology

[0002] With the deep penetration and innovative application of digital technology, 3D modeling technology in the field of landscape design is undergoing a critical stage of evolution from basic modeling to intelligent and automated processes. The deep integration of BIM and GIS has driven the leapfrog development of 3D models from static representation to dynamic intelligent analysis, making it a core technology platform for landscape site analysis, scheme derivation, and visualization. Currently, mainstream design software has fully integrated high-precision 3D modeling functions, capable of accurately capturing the spatial characteristics of terrain elevation, vegetation distribution, and water morphology, achieving multi-dimensional digital expression of complex landscape environments. This not only enhances the intuitiveness and spatial coordination of the design but also supports real-time interaction and optimization of design parameters, providing solid technical support for the refined and personalized creation of landscapes.

[0003] However, existing landscape water feature design technologies still have limitations: First, when integrating 3D models with water feature designs, there is a lack of automatic repair and semantic annotation capabilities for complex site topological relationships, resulting in low coupling between the design object and the site environment at the geometric and attribute levels, making it difficult to achieve parametric driving and real-time response to changes; Second, the geometric constraint processing mechanism is weak, making it difficult to efficiently execute spatial clipping, overlap resolution, and uniform coding, leading to frequent problems such as abrupt curvature changes and discontinuous longitudinal slopes when generating the water feature centerline, requiring repeated manual adjustments to meet smooth transition requirements and extending the design cycle. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a smart design method for garden landscape water features based on a three-dimensional model to solve the problems of low site coupling and weak geometric constraint processing mechanism.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] This invention provides an intelligent design method for garden landscape water features based on a 3D model, comprising: collecting garden site data, performing topological repair, and generating an object semantic attribute table by performing semantic annotation and attribute attachment through the 3D model; inputting the object semantic attribute table into the 3D model, performing 3D geometric constraint compilation to obtain constraint space objects, and performing spatial clipping, overlap resolution, and uniform encoding to generate a constraint compilation space set; based on the constraint compilation space set, extracting key locations and allowable spaces of the water feature, obtaining the initial path of the centerline, performing equidistant resampling and tangential continuity to generate a centerline sampling sequence table; extracting curvature information and longitudinal slope change information from the centerline sampling sequence table, performing joint discrimination to generate a segment boundary list, configuring cross-sectional parameters, performing geometric transition adjustments, and generating a segment parameter assembly table; according to the segment parameter assembly table, performing sweep forming along the line and transition segment stitching to obtain a 3D entity of the water feature, performing interface geometric locking, triggering local segment recalculation and local sweep reconstruction for non-compliant sections, and generating a 3D design scheme for the water feature.

[0008] As a preferred embodiment of the intelligent design method for garden landscape water features based on a three-dimensional model as described in this invention, the specific steps of collecting garden site data, performing topology repair, and generating an object semantic attribute table by performing semantic annotation and attribute attachment through a three-dimensional model are as follows:

[0009] Collect garden site data, perform coordinate benchmark unification and topological defect self-check and repair, and generate a three-dimensional basic element dataset;

[0010] Based on a 3D basic feature dataset, semantic annotation, adjacency verification, and object category correction are performed through a 3D model to generate an object semantic annotation table.

[0011] Based on the object semantic annotation table, attribute attachment is performed, and attribute completion and conflict resolution are carried out for objects with missing attributes, generating an object semantic attribute table.

[0012] As a preferred embodiment of the intelligent design method for garden landscape water features based on a three-dimensional model according to the present invention, the specific steps of inputting the object semantic attribute table into the three-dimensional model and performing three-dimensional geometric constraint compilation to obtain the constraint space object are as follows:

[0013] Input the object semantic attribute table into the 3D model, and perform object identification alignment, coordinate benchmark verification and object category comparison verification to generate an object index table;

[0014] Based on the object index table, perform 3D geometric constraint compilation to obtain constraint space objects, and attach boundary condition labels and mapping indexes to generate a list of constraint space objects.

[0015] As a preferred embodiment of the intelligent design method for garden landscape water features based on a three-dimensional model according to the present invention, the specific steps for generating the constraint compilation space set are as follows:

[0016] Perform spatial clipping and topological closure processing on the list of constraint space objects to generate a clipped constraint object set;

[0017] Perform overlapping boundary positioning, priority locking, and consistent encoding writing on the set of clipping constraint objects to generate a constraint compilation space set.

[0018] As a preferred embodiment of the intelligent design method for garden landscape water features based on a three-dimensional model as described in this invention, the specific steps for extracting key locations and allowable spaces of the water feature based on a constraint-compiled space set to obtain the initial path of the centerline are as follows:

[0019] Based on the constrained compilation space set and 3D model, the candidate locations of the water tank's start and end points, inlet and overflow outlets are locked, and the legality check of key location anchor points is performed to generate a list of key locations of the water tank.

[0020] By combining the list of key locations of the water tank with the constraint compilation space set, the feasible channel patches are expanded and the obstacle boundary volume is removed to generate the allowable space description set;

[0021] Projection anchor points are extracted from the allowed space description set and connected to form the initial connection segment of the centerline. Boundary condition checks and boundary setbacks are performed to generate the initial path of the centerline.

[0022] As a preferred embodiment of the intelligent design method for garden landscape water features based on a three-dimensional model according to the present invention, the specific steps for generating the centerline sampling sequence table are as follows:

[0023] Perform arc-length uniformity resampling, duplicate point removal, and cusp neighborhood resampling correction on the initial path of the centerline to generate an equidistant resampled centerline point list;

[0024] Perform tangential smoothing on the equidistant resampled centerline point series to obtain a continuous tangential centerline point series, and perform constrained neighbor labeling to generate a centerline sampling sequence list.

[0025] As a preferred embodiment of the intelligent design method for garden landscape water features based on a three-dimensional model as described in this invention, the specific steps for extracting curvature information and longitudinal slope change information from the centerline sampling sequence list, performing joint discrimination, and generating a segmented boundary list are as follows:

[0026] Extract the directional and elevation changes of adjacent sampling points from the centerline sampling sequence list, and write them into the curvature information labeling field and the longitudinal slope change information labeling field to generate a curved slope labeling sequence;

[0027] The curved slope annotation sequence and the centerline sampling sequence table are jointly judged, the boundary is merged and the continuity is checked, and segment numbers are added to generate a segment boundary list.

[0028] As a preferred embodiment of the intelligent design method for garden landscape water features based on a three-dimensional model according to the present invention, the specific steps for generating the segmented parameter assembly table are as follows:

[0029] Based on the segmented boundary list, locate the section landing point, obtain the section attitude record, and complete the section parameter integrity to generate a section parameter configuration list.

[0030] Extract the differences between adjacent segments from the cross-section parameter configuration list, and perform geometric transition adjustment, contour smoothing trimming and interface parameter solidification to generate a segment parameter assembly table.

[0031] As a preferred embodiment of the intelligent design method for garden landscape water troughs based on three-dimensional models described in this invention, the specific steps for obtaining the three-dimensional solid water trough by performing sweeping forming along the line and transition section stitching according to the segmented parameter assembly table are as follows:

[0032] Based on the segmented parameter assembly table, the sweep path segment is extracted from the centerline sampling sequence table, sweeping and shaping along the line is performed, and a sweeping and shaping record set is generated.

[0033] Based on the sweep forming record set, perform same-parameter alignment, contour smoothing trimming, and transition segment stitching repair to obtain a three-dimensional solid water tank.

[0034] As a preferred embodiment of the intelligent design method for garden landscape water features based on a three-dimensional model according to the present invention, the specific steps for generating the three-dimensional design scheme of the water feature are as follows:

[0035] The interface geometry locking mark is applied to the three-dimensional entity of the water tank. The spatial intersection search and close-range verification are combined with the constraint compilation space set to locate the violation interval and generate a list of affected segments.

[0036] Based on the list of affected segments, partial segment recalculation, local sweep reconstruction and transition segment re-stitching are performed, and the interface geometry is re-locked to generate a three-dimensional design scheme for the water tank.

[0037] The beneficial effects of this invention are as follows: by equidistant resampling and tangential continuity of the three-dimensional model, geometric smoothness of the centerline path is achieved, improving the natural smoothness and geometric accuracy of the water channel streamline; by jointly judging the curvature and longitudinal slope of the three-dimensional model, intelligent segmentation of the transition section is achieved, avoiding manual repeated adjustments and improving the design efficiency of garden landscape water channels. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart of a smart design method for garden landscape water features based on a 3D model.

[0040] Figure 2 A bar chart comparing the sampling point spacing with the running time.

[0041] Figure 3 This is a scatter plot of the interval deviation quantiles and the variance of the angle between adjacent tangents.

[0042] Figure 4 A pie chart showing the time spent on the center line and the key processing stages in each segment. Detailed Implementation

[0043] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0044] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0045] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0046] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for intelligent design of garden landscape water features based on a three-dimensional model, including the following steps:

[0047] S1: Collect garden site data, perform topology repair, and perform semantic annotation and attribute attachment through the 3D model to generate an object semantic attribute table;

[0048] S1.1: Collect garden site data, perform coordinate benchmark unification and topological defect self-check and repair, and generate a three-dimensional basic element dataset;

[0049] Specifically, the process involves collecting garden site data, compiling it into a continuous geometric element set according to the collection scope and order, and reading the coordinate reference information carried by the garden site data during the compilation process. The coordinate reference information is then checked against a unified coordinate reference item by item. For locations with inconsistent coordinate references, coordinate transformation is performed and the transformed coordinate values ​​are written back to the garden site data. For locations with inconsistent unit systems, scale conversion is performed and the converted values ​​are written back to the garden site data. For locations with inconsistent elevation references, elevation offset correction is performed and the corrected elevation values ​​are written back to the garden site data. The garden site data with unified coordinate references then enters the topology defect self-inspection and repair stage. The topology defect self-inspection and repair stage performs the following checks on the geometric element set: duplicate point detection and merging, broken endpoint detection and snap-connection, self-intersection segment detection and splitting and reconnection, non-closed boundary detection and closing edge repair, hole detection and filling, and overlapping boundary detection and overlap resolution. During the repair process, the endpoint snap-connection distance and boundary closure error are constrained according to the tolerance threshold. The repaired garden site data is then packaged into a three-dimensional basic element dataset.

[0050] It should be noted that the collection scope refers to the spatial area enclosed by the boundary line of the garden site and mapped to a unified coordinate reference after the coordinate reference is unified.

[0051] The tolerance threshold is determined by the accuracy of the collected point cloud and the repeated measurement deviation. The point accuracy value and the distance deviation statistics formed by repeated collection at the same location are read. The larger value between the range corresponding to the point accuracy value and the range corresponding to the distance deviation statistics value is taken as the tolerance threshold, and the tolerance threshold is limited to 0.005m to 0.02m. When the 3D basic feature dataset uses centimeters or millimeters as the scale unit, it is first converted to metric according to the unified scale conversion relationship before being written into the corresponding field.

[0052] S1.2: Based on the 3D basic feature dataset, semantic annotation, adjacency verification and object category correction are performed through the 3D model to generate an object semantic annotation table;

[0053] Specifically, after importing the 3D basic feature dataset into the 3D model, the 3D model breaks down polygon, line, and point features into objects and writes an object identifier and geometric boundary index for each object. The 3D model reads the feature attributes from the 3D basic feature dataset at the object identifier dimension and compares them against the object category mapping rules one by one. The matched object category labels and semantic annotation field sets are written into the object semantic annotation table. The 3D model extracts object boundary contours using the object identifier as the key and performs shared boundary retrieval, nearest boundary distance verification, and intersection state verification between object boundary contours. The 3D model registers object pairs whose nearest boundary distance is less than the adjacency verification threshold as adjacency relationships and writes them into the adjacency relationship field of the object semantic annotation table. The 3D model uses the object category labels in the object semantic annotation table as the constraint source and compares each adjacency relationship field against the object category constraint rules. The 3D model performs object category correction at constraint conflict locations. Object category correction achieves consistency closure by replacing object category labels, writing back the semantic annotation field set, and rewriting the adjacency relationship field. The 3D model summarizes the records after semantic annotation, adjacency verification, and object category correction to generate the object semantic annotation table.

[0054] It should be noted that the object category mapping rule is used to map the feature attributes and geometric feature types in the 3D basic feature dataset to object category labels according to a preset correspondence, and write the object category labels into the object semantic annotation table (for example, the polygon feature and the paving attribute correspond to the paving object category label).

[0055] Object category constraint rules are used to establish constraints on the object category labels and adjacency fields in the object semantic annotation table to allow and prohibit adjacent relationships, and to trigger object category correction when the constraints are not met (for example, allowing boundary object category labels to be adjacent to paving object category labels).

[0056] The adjacency verification threshold is jointly determined by the object boundary positioning deviation, the boundary sampling interval, and the minimum feature width of the object. After reading the object boundary positioning deviation and the boundary sampling interval, the adjacency verification threshold is selected within the multiple range of the boundary positioning deviation, and the adjacency verification threshold is limited to not exceeding a predetermined proportion of the minimum feature width of the object. The example range of the adjacency verification threshold is 0.05m to 0.2m. When the object semantic attribute table uses a non-metric scale, it is first converted according to the unified scale conversion relationship before being used for adjacency relationship verification.

[0057] S1.3: Based on the object semantic annotation table, perform attribute attachment, and perform attribute completion and conflict resolution for objects with missing attributes to generate an object semantic attribute table.

[0058] Specifically, based on the object identifier, object category label, geometric boundary index, and adjacency relationship fields in the object semantic annotation table, the corresponding feature attribute record is located in the 3D basic feature dataset according to the geometric boundary index. The attribute fields in the feature attribute record are written into the attribute attachment fields of the object semantic annotation table according to the object identifier. The completeness of the attribute attachment fields is checked for each object in the object semantic annotation table, and attribute missing markers are registered. The adjacent object identifiers corresponding to the adjacency relationship fields are retrieved according to the object identifier, and the attribute attachment fields associated with the adjacent object identifiers (such as material attribute fields, elevation attribute fields, connectivity direction fields, and functional purpose fields) are extracted as the completion candidate attribute set. Field consistency checks are performed on the completion candidate attribute set, and consistent fields are written back to the fields corresponding to the attribute missing markers. At the same time, the completion source object identifier is written into the attribute completion source field. The multiple source attribute values ​​under the same object identifier are checked for each field in the object semantic annotation table, and conflict markers are registered. Conflicting fields are sorted by priority and written back according to the object category label. The overwritten attribute values ​​are written into the conflict retention field, generating the object semantic attribute table.

[0059] S2: Input the object semantic attribute table into the 3D model, perform 3D geometric constraint compilation to obtain the constraint space object, and perform space clipping, overlap resolution and uniform encoding to generate the constraint compilation space set;

[0060] S2.1: Input the object semantic attribute table into the 3D model, and perform object identifier alignment, coordinate benchmark verification and object category comparison verification to generate an object index table;

[0061] Furthermore, after the object semantic attribute table is input into the 3D model, the object identifier, object category label, geometric boundary index, and attribute field set in the object semantic attribute table are read line by line. The corresponding object record is located in the 3D model according to the geometric boundary index. The object identifier in the object semantic attribute table is compared with the object identifier of the corresponding object record line by line. Duplicate positions of object identifiers are merged and a duplicate mark is registered. For positions where there are object records in the 3D model but no object records in the object semantic attribute table, a missing mark is registered. The coordinate values ​​after the object identifiers are aligned are checked for consistency with the coordinate reference of the 3D model. For inconsistent coordinate values, coordinate transformation is performed and the transformed coordinate values ​​are written back and a coordinate correction mark is registered. The 3D model generates object category labels according to the object category mapping rules and compares them line by line with the object category labels in the object semantic attribute table. For inconsistent positions, a category inconsistency mark is registered and the comparison record is retained. The 3D model summarizes the object identifier, geometric boundary index, duplicate mark, missing mark, coordinate correction mark, and category inconsistency mark into an object index table.

[0062] It should be noted that the coordinate reference of the 3D model is formed by the unified coordinate reference system, origin position and axial direction when importing the 3D basic element dataset. It is used to ensure that the coordinates of each object in the object semantic attribute table fall under the same spatial reference, which facilitates the execution of object positioning, coordinate transformation and boundary consistency verification.

[0063] S2.2: Based on the object index table, perform 3D geometric constraint compilation to obtain constraint space objects, and attach boundary condition labels and mapping indexes to generate a list of constraint space objects;

[0064] Specifically, based on the 3D model, the object identifier, object category label, geometric boundary index, and attribute field set are read from the object index table one by one. Geometric elements within the 3D model are extracted according to the geometric boundary index. The 3D model breaks down the geometric elements into compilable geometric fragments according to surface boundaries, line boundaries, and point anchors, and performs boundary connectivity checks and closure surface completion. The closed geometric fragments are then solidified into constraint space objects. Boundary condition labels are generated based on the object category label and attribute field set and bound to the constraint space objects. At the same time, constraint space object numbers are assigned to the constraint space objects and written into the mapping index. The mapping index establishes a one-to-one association between the object identifier, geometric boundary index, and constraint space object number, and retains duplicate, missing, coordinate correction, and category inconsistency markers. The 3D model summarizes the constraint space objects, boundary condition labels, and mapping index to generate a list of constraint space objects.

[0065] S2.3: Perform spatial clipping and topological closure processing on the list of constraint space objects to generate a clipped constraint object set;

[0066] Furthermore, after the list of constrained space objects enters the spatial clipping and topology closure process, the geometric boundaries, boundary condition labels, and mapping indexes of each constrained space object are read one by one according to their object numbers. The 3D model extracts the clipping boundary field set from the boundary condition labels and generates clipping boundary surfaces under the 3D model coordinate reference. The 3D model performs intersection cutting on the constrained space objects, retaining the geometric fragments inside the clipping boundary surfaces and deleting the geometric fragments outside the clipping boundary surfaces. The new boundary edges generated by the cutting enter the boundary sorting and breakpoint repair process and complete the cross-section patching. The 3D model performs topology closure processing on the clipped constrained space objects. The topology closure processing extracts closed loops from the open boundaries and processes them according to the closed loops. The boundary vertices are sequentially connected to adjacent boundary vertices to form a patch boundary. Connecting patches are written in the empty areas covered by the patch boundary. The boundary edges of the connecting patches are then stitched together with the boundary edges of adjacent geometric segments. Duplicate vertices and overlapping edges are merged and deduplicated. Non-manifold connection positions are split and reconnected. The orientation marks of adjacent patches are read one by one according to the shared edges. For patches with inconsistent orientations, the vertex arrangement order is flipped. The uniform orientation marks are passed along the adjacent patches to perform uniformization correction on the normal direction of the patches. The 3D model encapsulates and aggregates the constraint space objects after topological closure, constraint space object numbers, boundary condition labels and mapping indices to generate a set of clipped constraint objects.

[0067] S2.4: Perform overlapping boundary positioning, priority locking, and consistent encoding writing on the set of clipping constraint objects to generate a constraint compilation space set.

[0068] Furthermore, the geometric boundaries, boundary condition labels, and mapping indices of the constraint space objects in the clipping constraint object set are read one by one according to the constraint space object number. The set of boundary edges is extracted from the geometric boundaries of the constraint space objects and a boundary edge index table is established. In the boundary edge index table, a comparison is performed according to the geometric position and direction of the boundary edges. Boundary edges with overlapping spatial positions are registered as overlapping boundary entries and written into the overlapping boundary list. The priority rule set is read according to the object category label in the boundary condition label and the mapping index. A retention flag and a mask flag are written for each overlapping boundary entry and bound to the constraint space object number. According to the mask flag, the boundary edges corresponding to the overlapping boundary entries are deduplicated and merged and vertex merged. The merged boundary loops are closed and stitched to maintain the boundary connectivity of the clipping constraint object set. The clipping constraint object set enters the uniform encoding writing stage, where a unified encoding field is written for the constraint space object number, boundary condition label, mapping index, and overlapping boundary entries. The unified encoding field is written back to each constraint space object record and then collected and encapsulated into a constraint compilation space set.

[0069] S3: Based on the constraint-compiled space set, extract the key positions and allowable space of the water tank, obtain the initial path of the centerline, perform equidistant resampling and tangential continuity, and generate the centerline sampling sequence list;

[0070] S3.1: Based on the constraint compilation space set and 3D model, lock the candidate positions of the water tank's start and end points, inlet and overflow outlets, and perform key position anchor point legality verification to generate a list of key positions of the water tank;

[0071] Specifically, the constraint space object numbers are read sequentially according to the unified coding field, and the geometric boundaries, boundary condition labels, and mapping indexes of the constraint space objects are extracted. In the boundary condition labels, the boundary edges and boundary vertices corresponding to the start constraint, end constraint, inlet constraint, and overflow constraint are filtered. The boundary vertex coordinates are bound to the constraint space object number and the unified coding field and registered as candidate position records. The candidate position records are subjected to key position anchor point legality verification. The key position anchor point legality verification of the candidate position records includes geometric boundary inclusion verification, clipping boundary crossing verification, overlapping boundary masking mark verification, candidate position duplicate merging verification, and mapping index consistency verification. The candidate position records that pass the verification are written into the key position type field and the corresponding constraint space object number and unified coding field are retained. The start and end candidate positions, inlet candidate positions, and overflow candidate positions are collected and packaged into a water tank key position list.

[0072] It should be noted that a critical anchor point refers to a candidate location that simultaneously satisfies boundary condition constraints, connectivity requirements, and mapping index consistency within the constraint compilation space set, and directly determines the location of the water tank's start point, end point, inlet, or overflow outlet.

[0073] S3.2: Combine the list of key locations of the water tank with the constraint compilation space set, unfold the feasible channel patches and remove the obstacle boundary volume to generate the allowable space description set;

[0074] Furthermore, after combining the list of key locations of the water tank with the constraint compilation space set, the candidate locations of the water tank's start and end points, inlet, and overflow outlet are read one by one according to the key location type field in the list of key locations of the water tank. Based on the mapping index and unified encoding field of the constraint compilation space set, the landing point is located on the geometric boundary of the constraint space object. Initial feasible channel patches are generated around the landing point within the geometric boundary of the constraint space object, and the patches are expanded along the boundary connectivity relationship. During the patch expansion process, the channel patch number is written for each expansion and bound to the constraint space object number and boundary edge index to maintain the traceability of the channel patch source. The geometric boundary corresponding to the obstacle marker is extracted from the boundary condition label of the constraint compilation space set and the obstacle boundary volume is generated. The obstacle boundary volume intersection search is performed on the initial feasible channel patches, and the intersection area is clipped and the boundary is trimmed. The opening boundary generated after clipping is closed and the edge is supplemented to maintain the continuity of the channel patches. The retained feasible channel patches, obstacle boundary volume index, key location landing point index, and channel patch number are collected to generate the allowable space description set.

[0075] S3.3: Extract projection anchor points from the allowed space description set, connect them to form the initial connection segment of the centerline, perform boundary condition checks and boundary setbacks, and generate the initial path of the centerline;

[0076] Furthermore, the feasible channel patch, channel patch number, and key location landing point index are read. The landing point coordinates associated with the key location landing point index are projected onto the corresponding feasible channel patch and the projection anchor point number is registered. The projection anchor point numbers are arranged into a connection sequence according to the key location type field. The connectivity relationship between adjacent projection anchor points is traced along the feasible channel patch, and turning points are written at the patch boundaries. The projection anchor points and turning points form the initial connection segment of the centerline. After the initial connection segment of the centerline enters the boundary condition check, the obstacle boundary body index intersection search and feasible channel patch containment verification are performed on the initial connection segment of the centerline to locate the crossing position and write the crossing mark. The nearest boundary edge is extracted according to the boundary edge index associated with the crossing mark. The boundary retreat displacement is performed on the crossing position along the inside direction of the feasible channel patch and the corresponding turning point is replaced. After the crossing mark is cleared, the initial connection segment of the centerline is collected and encapsulated to generate the initial path of the centerline.

[0077] It should be noted that the boundary crossing condition is formed by extracting the obstacle boundary volume index, boundary edge index and feasible channel patch coverage relationship from the allowable space description set. It is used to limit the initial connection segment of the centerline from crossing the boundary edge corresponding to the obstacle boundary volume index and from leaving the coverage area of ​​the feasible channel patch.

[0078] Boundary retreat refers to shifting the corresponding position towards the safe area along the inside of the feasible channel patch when the initial connection segment of the centerline touches the boundary restriction position, so as to avoid boundary crossing and keep the initial path of the centerline within the allowable range.

[0079] S3.4: Perform arc length uniformity equidistant resampling, duplicate point removal and cusp neighborhood resampling correction on the initial path of the centerline to generate an equidistant resampled centerline point list;

[0080] Furthermore, the path point sequence of the initial path of the centerline is sequentially connected to form a set of line segments, and the cumulative arc length sequence of the line segment set is obtained. The cumulative arc length sequence determines the equidistant sampling interval (e.g., 0.2m) based on the resampling parameter set, and generates a sampling arc length sequence according to the equidistant sampling interval. The sampling arc length sequence locates the corresponding line segment in the cumulative arc length sequence and performs interpolation within the corresponding line segment to generate resampling points. The resampling points are written into a temporary point list in the order of the sampling arc length sequence. The temporary point list is subjected to duplicate point removal. For adjacent points with completely identical coordinates, duplicate point removal retains the previous point and deletes the subsequent point while maintaining the continuity of the point number. The point list after duplicate point removal is subjected to cusp neighborhood resampling correction. The cusp neighborhood resampling correction locates the cusp at the position of abrupt change in direction of adjacent line segments and extracts the neighboring point segments before and after the cusp. Within the neighboring point segments, points are re-interpolated according to the resampling parameter set to supplement the points and replace the point number of the neighboring point segments. The points are then encapsulated to generate an equidistant resampling centerline point list.

[0081] S3.5: Perform tangential smoothing adjustment on the equidistant resampled centerline point list to obtain a continuous tangential centerline point list, and perform constrained neighbor labeling to generate a centerline sampling sequence list.

[0082] Furthermore, the coordinates of each resampled point are read sequentially according to the point number, and adjacent point pairs are constructed. The tangential direction vector is obtained by using the coordinate difference of adjacent point pairs and normalized and written into the tangential direction field. The tangential direction is smoothly adjusted by performing neighborhood aggregation on the tangential direction field according to the neighborhood span determined by the resampled parameter set. The tangential direction vectors in the neighborhood are weighted and summed, then normalized and replaced with the original tangential direction field. The tangential direction field after replacement is checked for tangential consistency of adjacent points, and a second neighborhood aggregation is performed on the abrupt change position until the tangential direction field is continuous. The resampled point coordinates and the tangential direction field after continuity are encapsulated together into a tangential continuous centerline point column. After the tangential continuous centerline point column enters the constraint neighbor label, the nearest boundary retrieval is performed on each resampled point coordinate by combining the obstacle boundary volume index of the allowed space description set and the constraint space object number and unified encoding field of the constraint compilation space set, and the nearest constraint space object number, the nearest unified encoding field and the neighbor type field are written. The tangential continuous centerline point column together with the constraint neighbor label field is summarized into a centerline sampling sequence table.

[0083] It should be noted that the bar chart uses the sampling point spacing (m) as the horizontal axis and the running time (ms) as the vertical axis to compare the time changes of "A2_Equal-distance resampling" and "A3_Equal-distance resampling_Tangential continuity" under different sampling point spacings. The bar chart shows that as the sampling point spacing increases, the overall running time decreases, indicating that the processing cost of "arc-length consistent equal-distance resampling" changes synchronously with the sampling density under changes in point series size, and provides a quantifiable efficiency boundary for "smooth adjustment of tangential direction". The bar chart also shows that the time of "A3_Equal-distance resampling_Tangential continuity" remains at the same order of magnitude after the introduction of tangential continuity constraints, indicating that the conversion from "equal-distance resampling centerline point series" to "centerline sampling sequence list" is controllable in terms of efficiency, which is conducive to supporting interactive iteration and batch scheme evaluation in the landscape water feature design process.

[0084] S4: Extract curvature information and longitudinal slope change information from the centerline sampling sequence list, perform joint discrimination, generate a segment boundary list, configure cross-sectional parameters, perform geometric transition adjustment, and generate a segment parameter assembly table;

[0085] S4.1: Extract the directional and elevation changes of adjacent sampling points from the centerline sampling sequence list, and write them into the curvature information labeling field and the longitudinal slope change information labeling field to generate the curved slope labeling sequence;

[0086] Furthermore, the coordinates and tangential direction fields of adjacent sampling points are read sequentially according to the point number, and an adjacent point pair index is established for each pair of adjacent sampling points to keep the tangential direction vector corresponding to the tangential direction field in a normalized state. The angle change is calculated and written into the curvature information labeling field and bound to the point number and the adjacent point pair index. The denominator corresponding to the angle change is composed of the L2 norm of the sum of the tangential direction vectors of the two adjacent points and a small positive stable term to prevent the denominator from being zero. The adjacent point pair index is further used to extract the elevation component of the coordinates of adjacent sampling points and form the elevation difference. The elevation difference is associated with the distance between adjacent sampling points and written into the longitudinal slope change information labeling field and bound to the point number and the adjacent point pair index. The points are collected and encapsulated according to the point number to form a curved slope labeling sequence.

[0087] The formula for calculating the change in the included angle is:

[0088] ;

[0089] in, This indicates the change in the included angle. Indicates the first in the centerline sampling sequence list The tangential direction vector corresponding to the tangential direction field of each sampling point Indicates the first in the centerline sampling sequence list The tangential direction vector corresponding to the tangential direction field of each sampling point The L2 norm of a vector. This represents a small positive stable term to prevent the denominator from being zero.

[0090] It should be noted that the longitudinal slope change information refers to the degree of undulation of the centerline along the path, characterized by the elevation difference between adjacent sampling points and the distance between corresponding points. It is used to reflect the magnitude and location of the slope change of the centerline in the forward direction.

[0091] The scatter plot, with the 90% quantile of the spacing deviation on the horizontal axis and the variance of the adjacent tangential angle on the vertical axis, compares the difference in geometric stability between "A2_Equal-distance resampling" and "A3_Equal-distance resampling_Tangential continuity". The scatter distribution shows that, under similar 90% spacing deviation quantile conditions, "A3_Equal-distance resampling_Tangential continuity", corresponding to "Tangential direction smoothing adjustment", exhibits a lower value range in the adjacent tangential angle variance dimension. This indicates that the tangential continuity constraint can suppress the fluctuation amplitude of the directional changes of adjacent sampling points, making the local turning of the centerline path smoother. The scatter plot corresponds to the process of "extracting the directional changes of adjacent sampling points from the centerline sampling sequence list and writing them into the curvature information annotation field, then compiling and encapsulating them to form a curvature slope annotation sequence." The decreasing variance trend given by the scatter plot directly supports the description of the effect of "geometric smoothing of the centerline path and improvement of geometric accuracy".

[0092] S4.2: Perform joint discrimination, boundary merging and continuity verification on the curved slope annotation sequence and the centerline sampling sequence table, and add segment numbers to generate a segment boundary list;

[0093] Furthermore, the curvature information annotation field, the longitudinal slope change information annotation field, and the sampling point coordinates are read point by point. Curvature trigger markers and longitudinal slope trigger markers are written at each adjacent point pair. The curvature trigger markers are categorized according to the angle change based on the segmented discrimination parameter set, and the longitudinal slope trigger markers are categorized according to the ratio change of adjacent elevation difference to adjacent point distance based on the segmented discrimination parameter set. Joint discrimination combines the curvature trigger markers and longitudinal slope trigger markers into a joint status code. When the joint status code simultaneously satisfies both the curvature categorization and longitudinal slope categorization conditions, the current point number is written into the boundary candidate number. If only one of the criteria is met, a continuity check is performed on the trigger markers of the same type for consecutive point numbers, and the current point number is written into the boundary candidate number when the continuity is satisfied; boundary merging sorts the boundary candidate numbers by point number and merges the boundary candidate numbers with adjacent intervals that are too small, retaining the point numbers with higher trigger marker grades; continuity verification checks the start and end point coverage and segment length of the merged boundary candidate numbers, and if the segment length does not meet the requirement, the boundary candidate number is deleted or moved to the adjacent point number, and a segment boundary list is generated by appending segment numbers according to the final boundary candidate number order.

[0094] It should be noted that the curvature grading condition means that the segmented discrimination parameter set classifies the angle change in the curvature information annotation field into different levels and specifies the trigger level, which is used to determine whether the curvature trigger flag meets the requirements when the boundary candidate number is written.

[0095] The longitudinal slope classification condition indicates that the segmented discrimination parameter set classifies the changes in the ratio of adjacent elevation difference to adjacent point distance in the longitudinal slope change information labeling field and specifies the trigger level, which is used to determine whether the longitudinal slope trigger mark meets the requirements when the boundary candidate number is written.

[0096] The pie chart illustrates the time composition of key processing steps in the centerline and segment generation chain, presented as a percentage of time spent at each stage. The pie chart sectors correspond to "equidistant resampling," "tangential continuity," "slope annotation sequence writing," and "joint discrimination and boundary merging." The pie chart shows that "slope annotation sequence writing" and "tangential continuity" account for the majority of the time, indicating that writing about changes in the direction of adjacent sampling points, writing about changes in the elevation of adjacent sampling points, and smoothing adjustments in the tangential direction are the main sources of processing load in the process, consistent with the continuous generation relationship of the "centerline sampling sequence list," "slope annotation sequence," and "segment boundary list." The pie chart also shows that "joint discrimination and boundary merging" has a relatively lower percentage, indicating that the formation of the segment boundary list relies more on the quality of the preceding slope annotations and tangential continuity. This stage percentage structure supports the efficiency claims of "intelligent segmentation of transition sections, reduced manual repetitive adjustments, and improved efficiency in landscape water feature design," and can be used for subsequent targeted optimization of bottleneck locations in the process.

[0097] S4.3: Based on the segmented boundary list, locate the section landing point, obtain the section attitude record, complete the section parameter integrity, and generate a section parameter configuration list;

[0098] Specifically, the starting point number and ending point number of each segment are read according to the segment number, and the corresponding sampling point coordinates are located in the centerline sampling sequence table. The sampling point coordinates corresponding to the starting point number, the ending point number, and the midpoint number are registered as the cross-section landing points. The tangential direction field and the constraint adjacent annotation field are read from the centerline sampling sequence table for the cross-section landing points. The tangential direction field is used to constrain the cross-section normal direction, and the 3D model coordinate reference is used to constrain the cross-section rotation direction and write it into the cross-section attitude record. Null value verification is performed, and the null value positions are copied and filled from the cross-section parameter field set corresponding to the adjacent cross-section landing points within the same segment number. After the cross-section parameter field set is filled, a cross-section parameter configuration list is generated.

[0099] S4.4: Extract the differences between adjacent segments from the cross-section parameter configuration list, and perform geometric transition adjustment, contour smoothing trimming and interface parameter solidification to generate a segment parameter assembly table.

[0100] Furthermore, the cross-sectional landing point records of adjacent segments are read in sequence according to the segment number. Cross-sectional parameter values ​​are extracted at the end point of a segment and the beginning point of the next segment for field-by-field comparison. Inconsistent fields are written into the difference entries of adjacent segments and bound to the segment number and cross-sectional landing point number. A transition interval is selected along the range of cross-sectional landing point numbers near the segment boundary (centered on the cross-sectional landing point at the junction of adjacent segments, considering the number of inconsistent fields, the magnitude of the difference, and the requirement for contour continuity in the difference entries of adjacent segments, selecting a range of cross-sectional landing point numbers on both sides of the segment boundary that can fully accommodate parameter changes). Within the transition interval, the data is transferred according to the point number. The process involves updating the cross-sectional parameter values ​​and simultaneously updating the cross-sectional attitude records to ensure a smooth transition from the previous segment's cross-sectional parameter values ​​to the next segment's cross-sectional parameter values. In the contour smoothing and trimming stage, the point numbers corresponding to the contour boundaries of each cross-sectional parameter value within the transition interval are rearranged and the spacing between contour points is made consistent. Furthermore, the contour turning points are trimmed with polyline segments and connected to curve segments to maintain the tangential continuity of adjacent cross-sectional contours. In the interface parameter solidification stage, the cross-sectional parameter values ​​and attitude records of the segment's starting and ending cross-sectional landing points are fixedly written into the interface parameter field and summarized together with the segment number and the cross-sectional landing point number into a segment parameter assembly table.

[0101] S5: Based on the segmented parameter assembly table, perform sweep forming and transition section stitching along the line to obtain the three-dimensional solid of the water tank, and perform interface geometry locking. Trigger local segment recalculation and local sweep reconstruction for the violation section to generate the three-dimensional design scheme of the water tank.

[0102] S5.1: Based on the segmented parameter assembly table, extract the sweep path segment from the centerline sampling sequence table, perform sweeping shaping along the line, and generate a sweeping shaping record set;

[0103] Specifically, the starting and ending section landing point numbers of each segment are read according to the segment number. The corresponding sampling point coordinates and tangential direction fields are extracted from the centerline sampling sequence table according to the point number to form a sweep path segment. The sweep path segment is bound to the segment number and written into the sweep path index. In the sweep forming stage, the sweep path segments are read segment by segment according to the sweep path index. The cross-sectional parameter values ​​and cross-sectional attitude records corresponding to the segment number are read from the segment parameter assembly table. At each sampling point coordinate of the sweep path segment, the cross-sectional contour is placed according to the cross-sectional attitude record, and the cross-sectional contour dimensions are updated according to the cross-sectional parameter values. The cross-sectional contours are sequentially connected along the sweep path segment according to the point number to generate sweep patches within the segment. Boundary stitching is performed on adjacent patches to form a continuous shell. The continuous shell, along with the segment number, sweep path index, section landing point number, and interface parameter fields, is registered as a sweep forming record. The sweep forming records are compiled to generate a sweep forming record set.

[0104] S5.2: Based on the sweep forming record set, perform same-parameter alignment, contour smoothing trimming and transition segment stitching repair to obtain the three-dimensional solid of the water tank;

[0105] Specifically, the cross-sectional parameter values ​​at the corresponding interface positions of adjacent segments are compared field by field. Inconsistent fields are replaced and written back according to the interface parameter fields in the segment parameter assembly table. The contour point numbers at the interface positions of adjacent segments are aligned one-to-one to form a pair of interface contours with the same parameters. In the contour smoothing and trimming step, the contour point spacing of the pair of interface contours with the same parameters is made consistent and the turning position is trimmed. The boundary curves of the shell boundaries near the interface are refitted to maintain the tangential continuity of the adjacent shells. In the transition section stitching and repair step, a transition patch is generated with the pair of interface contours with the same parameters as the boundary and stitched with the adjacent shell boundaries. The stitch gap is retrieved and filled at the stitching point. The normal inconsistency position is corrected to obtain the three-dimensional solid of the water tank.

[0106] S5.3: Mark the interface geometry lock on the three-dimensional entity of the water tank, perform spatial intersection search and close-range verification to locate the violation interval in combination with the constraint compilation space set, and generate a list of affected segments;

[0107] Furthermore, the interface contour lines and interface patches are extracted along the interface boundaries of the 3D entity of the water tank and written with interface numbers. Interface geometry locking marks are written to the interface contour lines and interface patches, and the association between interface numbers and segment numbers is registered. The constraint compilation space set reads the geometric boundaries and boundary condition labels of constraint space objects one by one according to the constraint space object number. Spatial intersection retrieval is performed between the 3D entity of the water tank and the geometric boundaries of constraint space objects, and the numbers of intersecting constraint space objects, the unified encoding field, and the index of intersecting patches are recorded. The index of intersecting patches is traced back to the segment number and written as a violation candidate entry. Adjacent faces that have not triggered intersection are... The location of the segment is checked for close proximity. The close proximity check obtains the minimum distance between the outer surface of the three-dimensional solid of the water tank and the geometric boundary of the constrained space object and compares it with the close proximity check distance (which refers to the minimum interval used to determine whether the outer surface of the three-dimensional solid of the water tank and the geometric boundary of the constrained space object have approached the location that needs to be marked as a violation candidate when no spatial intersection occurs, for example, 0.05m). Locations with a minimum distance less than the close proximity check distance are written into violation candidate entries and bound with segment numbers. The segment numbers associated with the interface geometry lock mark and the segment numbers associated with the violation candidate entries are collected and deduplicated to generate a list of affected segments.

[0108] S5.4: Based on the list of affected segments, perform partial segment recalculation, local sweep reconstruction and transition segment re-stitching, and re-lock the interface geometry to generate a 3D design scheme for the water tank.

[0109] Specifically, the point number intervals corresponding to the centerline sampling sequence table are located according to the segment numbering, and the corresponding continuous shells are matched with the swept forming record set. Interface contour lines, interface patches, and interface geometric locking marks are extracted, while the interface contour lines and interface patches remain unchanged. For the point number intervals, boundary retreat correction is performed under the geometric boundary constraints of the constraint compilation space set, and the initial centerline path is written back. The initial centerline path after writing back is then subjected to arc length consistency equidistant resampling and tangential direction smoothing adjustment to generate an updated centerline sampling sequence table. Based on the updated centerline sampling sequence table, the curved slope annotation sequence is rewritten, and the segmented discrimination parameter set is updated. The section boundary list is updated, and the section parameter configuration list is updated according to the updated section boundary list. The interface parameter fields are fixed at the section landing points corresponding to the interface numbers. The section parameter configuration list is summarized to generate an updated section parameter assembly table. The swept path segments are extracted according to the updated section parameter assembly table, and swept forming is performed along the line to generate a new swept forming record set and replace the corresponding continuous shell of the affected section list. The replaced boundaries are aligned with the same parameters, the contour is smoothed and trimmed, and the transition segment is stitched and repaired to obtain an updated water tank 3D solid. The interface geometry locking mark is rewritten at the interface contour line and interface surface to generate the water tank 3D design scheme.

[0110] In summary, this invention achieves geometric smoothness of the centerline path and improves the natural flow and geometric accuracy of the water trough by: isometric resampling and tangential continuity of the three-dimensional model; and achieves intelligent segmentation of the transition section by jointly judging the curvature and longitudinal slope of the three-dimensional model, avoiding repeated manual adjustments and improving the design efficiency of garden landscape water troughs.

[0111] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent design of garden landscape water features based on a three-dimensional model, characterized in that, include: Collect garden site data, perform topology repair, and generate object semantic attribute tables by performing semantic annotation and attribute attachment through 3D models. Input the object semantic attribute table into the 3D model, perform 3D geometric constraint compilation to obtain the constraint space object, and perform space clipping, overlap resolution and uniform encoding to generate the constraint compilation space set; Based on the constrained compilation space set, the key locations and allowable spaces of the water tank are extracted to obtain the initial path of the centerline. Iso-sampling and tangential continuity are then performed to generate the centerline sampling sequence list. Curvature information and longitudinal slope change information are extracted from the centerline sampling sequence list, and joint discrimination is performed to generate a segment boundary list. Section parameters are configured, geometric transition adjustments are made, and a segment parameter assembly table is generated. Based on the segmented parameter assembly table, perform sweep forming along the line and transition section stitching to obtain the three-dimensional solid of the water tank, and perform interface geometry locking. Trigger local segment recalculation and local sweep reconstruction for the non-compliant section to generate the three-dimensional design scheme of the water tank.

2. The intelligent design method for garden landscape water features based on a three-dimensional model as described in claim 1, characterized in that, The process of collecting garden site data, performing topology repair, and generating an object semantic attribute table by performing semantic annotation and attribute attachment through a 3D model is as follows: Collect garden site data, perform coordinate benchmark unification and topological defect self-check and repair, and generate a three-dimensional basic element dataset; Based on a 3D basic feature dataset, semantic annotation, adjacency verification, and object category correction are performed through a 3D model to generate an object semantic annotation table. Based on the object semantic annotation table, attribute attachment is performed, and attribute completion and conflict resolution are carried out for objects with missing attributes, generating an object semantic attribute table.

3. The intelligent design method for garden landscape water features based on a three-dimensional model as described in claim 2, characterized in that, The specific steps for inputting the object semantic attribute table into the 3D model and performing 3D geometric constraint compilation to obtain the constraint space object are as follows: Input the object semantic attribute table into the 3D model, and perform object identification alignment, coordinate benchmark verification and object category comparison verification to generate an object index table; Based on the object index table, perform 3D geometric constraint compilation to obtain constraint space objects, and attach boundary condition labels and mapping indexes to generate a list of constraint space objects.

4. The intelligent design method for garden landscape water features based on a three-dimensional model as described in claim 3, characterized in that, The specific steps for generating the constraint compilation space set are as follows: Perform spatial clipping and topological closure processing on the list of constraint space objects to generate a clipped constraint object set; Perform overlapping boundary positioning, priority locking, and consistent encoding writing on the set of clipping constraint objects to generate a constraint compilation space set.

5. The intelligent design method for garden landscape water features based on a three-dimensional model as described in claim 4, characterized in that, The constraint-based compilation space set is used to extract key locations and allowable spaces in the water tank, and obtain the initial path of the centerline. The specific steps are as follows: Based on the constrained compilation space set and 3D model, the candidate locations of the water tank's start and end points, inlet and overflow outlets are locked, and the legality check of key location anchor points is performed to generate a list of key locations of the water tank. By combining the list of key locations of the water tank with the constraint compilation space set, the feasible channel patches are expanded and the obstacle boundary volume is removed to generate the allowable space description set; Projection anchor points are extracted from the allowed space description set and connected to form the initial connection segment of the centerline. Boundary condition checks and boundary setbacks are performed to generate the initial path of the centerline.

6. The intelligent design method for garden landscape water features based on a three-dimensional model as described in claim 5, characterized in that, The specific steps for generating the centerline sampling sequence table are as follows: Perform arc-length uniformity resampling, duplicate point removal, and cusp neighborhood resampling correction on the initial path of the centerline to generate an equidistant resampled centerline point list; Perform tangential smoothing on the equidistant resampled centerline point series to obtain a continuous tangential centerline point series, and perform constrained neighbor labeling to generate a centerline sampling sequence list.

7. The intelligent design method for garden landscape water features based on a three-dimensional model as described in claim 6, characterized in that, The specific steps for extracting curvature information and longitudinal slope change information from the centerline sampling sequence list, performing joint discrimination, and generating a segmented boundary list are as follows: Extract the directional and elevation changes of adjacent sampling points from the centerline sampling sequence list, and write them into the curvature information labeling field and the longitudinal slope change information labeling field to generate a curved slope labeling sequence; The curved slope annotation sequence and the centerline sampling sequence table are jointly judged, the boundary is merged and the continuity is checked, and segment numbers are added to generate a segment boundary list.

8. The intelligent design method for garden landscape water features based on a three-dimensional model as described in claim 7, characterized in that, The specific steps for generating the segmented parameter assembly table are as follows: Based on the segmented boundary list, locate the section landing point, obtain the section attitude record, and complete the section parameter integrity to generate a section parameter configuration list. Extract the differences between adjacent segments from the cross-section parameter configuration list, and perform geometric transition adjustment, contour smoothing trimming and interface parameter solidification to generate a segment parameter assembly table.

9. The intelligent design method for garden landscape water features based on a three-dimensional model as described in claim 8, characterized in that, The process of performing sweep forming along the line and stitching the transition section according to the segmented parameter assembly table to obtain the three-dimensional solid of the water tank is as follows: Based on the segmented parameter assembly table, the sweep path segment is extracted from the centerline sampling sequence table, sweeping and shaping along the line is performed, and a sweeping and shaping record set is generated. Based on the sweep forming record set, perform same-parameter alignment, contour smoothing trimming, and transition segment stitching repair to obtain a three-dimensional solid water tank.

10. The intelligent design method for garden landscape water features based on a three-dimensional model as described in claim 9, characterized in that, The specific steps for generating the three-dimensional design scheme of the water tank are as follows: The interface geometry locking mark is applied to the three-dimensional entity of the water tank. The spatial intersection search and close-range verification are combined with the constraint compilation space set to locate the violation interval and generate a list of affected segments. Based on the list of affected segments, partial segment recalculation, local sweep reconstruction and transition segment re-stitching are performed, and the interface geometry is re-locked to generate a three-dimensional design scheme for the water tank.

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