A cim-based building setback distance review method

Abstract

The present application relates to the field of construction engineering planning, and provides a building setback distance examination method based on CIM, which is aimed at the single examination task of building setback road red line in the city information model (CIM) examination, compares the shortest distance between the building and the road with the theoretical minimum setback distance, and realizes the automatic verification of the spatial relationship between the building contour and the road red line through the construction of a two-three-dimensional collaborative examination mechanism. The present application takes the model passed by the building information model (BIM) examination as the basis input, combines the road red line data in the city information model (CIM) environment, and focuses on solving the problems of accurate calculation of building setback distance and three-dimensional violation judgment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent engineering planning, more particularly, to a building setback distance review method based on CIM. BACKGROUND

[0002] In the construction engineering planning permission review system, the building information model (BIM) and the city information model (CIM) technology have formed a clear division of labor: the BIM review mainly verifies the geometric and attribute compliance of the internal components of the building model, including the self-consistency check of the building height, floor height, building area and other indicators, which belongs to the internal review category of single building; the CIM review focuses on the interaction between the building model and the urban space environment, involving the verification of the external space relationship such as the building setback road red line and the building spacing control.

[0003] The road red line, as a statutory control line of urban planning (according to the "Construction Engineering Design Scheme Review Guidelines"), its setback distance is the core control indicator of construction engineering planning permission. In actual engineering, building setback review faces multiple technical challenges: modern building profiles are often irregular curves (such as arc facades, jagged roofs), increasing the difficulty of distance calculation; the road red line is often a segmented linear or curved element (such as urban main road intersections), which needs to be dynamically adapted to the local curvature; cantilever components, rain shelters and other three-dimensional elements may invade the red line range in the vertical direction, but the projection plane shows compliance.

[0004] Currently, the following technical solutions are mainly used for building setback road red line review:

[0005] Two-dimensional plane review method: export the BIM model as a two-dimensional contour line (usually in DXF format), superimpose it with the road red line CAD layer, and determine by calculating the shortest Euclidean distance between the building contour polygon and the road red line segment. This method only verifies the projection plane, ignoring the invasion risk in the building height direction (such as the bottom shop rain shelter entity exceeding the boundary but the projection is compliant); for non-convex polygon contours, traditional algorithms only check the distance of the vertices, and cannot accurately locate the possible shortest distance point of the midpoints of the edges; when the road red line is a segmented or curved element, additional data preprocessing is required, which simplifies the processing and causes calculation errors.

[0006] Three-dimensional space review method: extend the road red line to a three-dimensional buffer zone (such as a 5-meter-wide column), and perform full-volume collision detection with the BIM model to output the invasion volume or Boolean result. This method has obvious problems: the road red line is essentially a two-dimensional linear element, and full-model three-dimensional detection results in more than 90% of the space being an invalid detection area, causing serious waste of computing resources; the collision result (invasion volume) is difficult to directly convert into the plan-required plane setback distance (such as the "setback 5 meters" indicator requirement); the detection of small-scale violations (such as a 0.3-meter protruding air conditioner outdoor unit) requires high-precision grids, resulting in large calculation overhead and easy false positives. SUMMARY

[0007] The present application aims at the technical problems existing in the prior art, and provides a building retreat distance review method based on CIM, which realizes automatic verification of the spatial relationship between the building contour and the road red line by constructing a two-three-dimensional collaborative review mechanism.

[0008] The present application provides a building retreat distance review method based on CIM, comprising:

[0009] S1, obtaining building base data and road red line data;

[0010] S2, decomposing the sub-plot boundary to which each building belongs to obtain a plurality of line segments of the sub-plot boundary, and calculating the unit directional vector and normal vector of each line segment;

[0011] S3, emitting a query ray in the direction of the normal vector of each line segment of the corresponding sub-plot boundary with each building as the center, and establishing the association relationship between the building and the road through spatial intersection analysis;

[0012] S4, obtaining the height of each building, and obtaining the theoretical minimum retreat distance of each building relative to the road according to the height of each building and the associated road red line attribute information through query;

[0013] S5, respectively generating two point sets by using an adaptive high-density interpolation method for each building base contour line and road red line, performing nearest neighbor search on the two point sets through spatial index acceleration, calculating the shortest Euclidean distance of all point pairs in the two point sets, and positioning the shortest distance point pair;

[0014] S6, judging whether the shortest distance of each building to the road is within the warning distance range, if yes, ending; if not, generating a review report, and performing S9;

[0015] S7, determining the linear range of the road red line in the direction of the road based on the distance nearest point of each building to the road and the corresponding side line length, stretching the generated local road three-dimensional buffer model in the direction of the building based on the linear range of the road red line, so that the local road three-dimensional buffer model is equal in height to the building and equal in depth to the theoretical minimum retreat distance;

[0016] S8, detecting whether the building components invade the road buffer zone by using a fast collision detection algorithm based on a bounding box for the generated local road three-dimensional buffer model and the building BIM model;

[0017] S9, calculating and outputting the collision detection result, identifying the component ID, position coordinates and invasion depth value of the components in the building BIM model invading the road buffer zone, and outputting a three-dimensional sketch map identifying the collision area.

[0018] The application provides a building setback distance review method based on CIM, which is used for the building setback road red line single review task in the CIM review, realizes the automatic verification of the spatial relationship between the building contour and the road red line by constructing a two-three-dimensional collaborative review mechanism, and solves the problems of accurate calculation of the building setback distance and three-dimensional violation judgment. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 A flowchart of a building setback distance review method based on CIM is provided for an embodiment of the application. DETAILED DESCRIPTION

[0020] To make the purpose, technical solutions and advantages of the embodiments of the application clearer, the technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are some of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work are within the protection scope of the application. In addition, the technical features in each embodiment or in a single embodiment provided by the application can be combined with each other at will to form a feasible technical solution, and the combination is not restricted by the sequence of steps and / or the structure mode, but should be based on the fact that the technical solution can be realized. When the combination of technical solutions appears contradictory or cannot be realized, it should be considered that the combination of technical solutions does not exist and is not within the protection scope of the application.

[0021] The building setback distance review method based on CIM provided by the application is characterized in that the global compliance is quickly screened through high-precision two-dimensional calculation, and the targeted three-dimensional local verification is accurately triggered, so that the accuracy and efficiency of the review are considered. The two-dimensional plane distance calculation and the three-dimensional space collision analysis are organically combined in the whole process to form a complete automatic review closed loop.

[0022] The method first performs data processing and correlation construction in the two-dimensional plane, calculates the shortest distance between the building base and the road red line through a high-precision algorithm, and completes the preliminary screening. Then, the "suspected violation" area identified in the two-dimensional screening is dynamically generated to generate a corresponding local three-dimensional road buffer model, and the spatial collision analysis is performed between the building BIM model and the three-dimensional road buffer model. Finally, the three-dimensional collision result is reversely mapped back to the two-dimensional plane to generate a review report and a visual result that meet the planning specification requirements. The method effectively solves the core problems of missing three-dimensional violations in pure two-dimensional review, low calculation efficiency in pure three-dimensional review, and disconnection between the review result and the specification clauses.

[0023] Figure 1 A flow chart of a building setback review method based on CIM is shown in an embodiment of the present application, as shown in Figure 1 The method comprises the following steps:

[0024] S1, obtaining building base data and road red line data.

[0025] It can be understood that two key input data layers are obtained from the CIM platform: one is the building base layer in the building review graphics, which contains the projection plane contour (usually a closed polygon) of the building to be reviewed. The second is the road layer, which contains all the road red line information (usually linear features) within the review range. The data is read through a spatial database interface (such as OGC WFS service) or a file (such as SHP, DWG), and is converted into an internal unified coordinate reference system, laying a data foundation for subsequent spatial analysis.

[0026] S2, decomposing the sub-plot boundary to which each building belongs to obtain a plurality of line segments of the sub-plot boundary, and calculating a unit directional vector and a normal vector of each line segment.

[0027] It can be understood that after obtaining each building base polygon contour, all building base polygon contours are traversed, and first, geometric validity verification (checking whether there is self-intersection, repeated vertex, etc.) is performed to ensure the legality of geometric entities. Then, spatial relationship judgment (using ST_Within, ST_Contains, etc. Spatial predicate) is used to verify whether each building base is completely contained by the sub-plot boundary polygon to which it belongs, to ensure the spatial logical correctness of the "building-plot" relationship and exclude low-level errors such as buildings exceeding the land red line.

[0028] In order to facilitate directional setback analysis, the sub-plot boundary polygon to which each building belongs is decomposed into a series of connected and independent line segment units (Segments). For each line segment, a directional vector from the starting point to the ending point is calculated, and a unit normal vector (used to indicate the "setback direction" perpendicular to the boundary) is further obtained through vector operation. This step converts the surface spatial relationship problem into a discrete line relationship problem, laying an accurate spatial reference for subsequent directional sensitive setback analysis, and effectively solving the technical problem that the prior art cannot distinguish different boundary surfaces of buildings and their different setback requirements for roads.

[0029] The decomposition algorithm of the sub-plot polygon boundary comprises the following steps: (1) extracting the sequence of polygon vertices:

[0030] Reading the WKT or GeoJSON format data of the sub-plot boundary polygon, extracting the sequence of vertex coordinates of the sub-plot boundary polygon , wherein (Closed polygon). Verify the topological correctness of the vertex sequence, ensure no self-intersection or repeated vertices.

[0031] (2) Line segment unit generation:

[0032] Build a line segment unit set in vertex order:

[0033] Each line segment is defined by the start point and the end point , from 0 to , when , the end point is . Assign a unique identifier SID = [parcel ID]_[line segment number] to each line segment, preserve the topological relationship information of the original polygon, record the predecessor and successor line segments of each line segment.

[0034] (3) Special case processing:

[0035] For complex polygons containing island holes, only the outer ring boundary is processed, and the inner ring island hole does not participate in the retreat analysis. For self-intersecting polygons, first perform topological repair and then execute decomposition. For repeated vertices, automatically merge into a single vertex to avoid generating zero-length line segments.

[0036] Calculate the direction vector and unit normal vector of each line segment, including:

[0037] (1) Direction vector calculation:

[0038] For line segment , the start point coordinates are , and the end point coordinates are ;

[0039] Calculate the direction vector: ;

[0040] Calculate the length of the line segment: .

[0041] (2) Unit normal vector calculation:

[0042] Calculate the normal vector: or , depending on the retreat direction requirement.

[0043] The invention adopts the "building outward retreat" principle to determine the normal vector direction pointing to the outside of the building, and calculates the unit normal vector: , verify the length of the unit normal vector: .

[0044] (3) Retreat direction confirmation:

[0045] To verify whether the direction of the normal vector is correct, start from the midpoint of the line segment. Take a small distance along the direction of the normal vector checkpoint Whether it is located outside the building base polygon; if the point is located inside, take the opposite direction: .

[0046] For road segments near the boundaries of sub-plots, identify all road segments at the corners of the plot boundaries. Use an angle threshold filter (typically set between 60° and 160°) to calculate the angle between the direction vectors of the two road segments at the corner. If the angle falls within this range, the corner is considered close to a right angle and belongs to a normal road turn. Since the setback calculation has already been covered by the two adjacent road segments, the road segment at that corner is filtered out from the candidate set for this calculation to avoid duplicate calculations and result interference.

[0047] S3, with each building as the center, emits query rays in the direction of the normal vector of each line segment of the corresponding sub-plot boundary, and establishes the mapping relationship between buildings and roads through spatial intersection analysis.

[0048] Understandably, for each building base polygon, a query ray is cast from its geometric center or a specific feature point as the origin, radiating towards the normal direction of the sub-plot boundary obtained in step S2. Using the ray intersection function of the spatial database, the nearest intersection point between these rays and the road redline layer is calculated. Roads with intersection points have spatial relationships with buildings, and each valid intersection point establishes a spatial association from the building to a certain road, recording the setback direction and the target road.

[0049] This step ensures the validity and uniqueness of the setback direction. All ray paths are analyzed. If a ray originating from building A is occluded by the base polygon of building B before reaching the target road (i.e., the ray intersects with the polygon of building B), then building B is determined to be closer to the road in that direction, and the association of this ray from building A is invalid. All such occluded rays are removed, ensuring that each building retains only the nearest setback direction that allows unobstructed direct access to the road.

[0050] Furthermore, since road boundary data may have logical segments or be discontinuous, the same road segment may be simultaneously associated with rays from multiple buildings. To address this, spatial and attribute deduplication of the "building-road" mapping relationship is performed. Duplicate references to the same road segment are merged based on road ID and spatial location (e.g., using spatial clustering algorithms). This ensures that each road segment is processed only once in subsequent calculations, improving computational efficiency and generating a unique theoretical minimum setback reference value.

[0051] S4, get the height of each building, according to the height of each building and the associated road red line attribute information, through the query to get each building relative to the road theoretical minimum distance.

[0052] It can be understood that through S3, each building space is associated with multiple roads, and through the pre-defined unique building identifier (such as UUID), the association query is performed with the BIM review result database to accurately obtain the "building height" attribute value of each building.

[0053] Subsequently, according to the input of each road red line attribute information (such as road grade, design width), and the building height, the built-in "retreat rule configuration table" is queried to automatically match and output the theoretical minimum retreat distance value required to be met by the building adjacent to this section of road. This process converts the text specification into calculable structured data. Then, the theoretical minimum retreat distance of each building and the associated roads is found, and the theoretical minimum retreat distance corresponding to different types of roads and different building heights can be seen in Table 1.

[0054] Table 1 Theoretical minimum retreat distance of building and road

[0055]

[0056] S5, for each building base contour line and road red line, respectively, an adaptive high-density interpolation method is used to generate two point sets, and the nearest neighbor search is accelerated by spatial indexing to calculate the shortest Euclidean distance between all point pairs in the two point sets, and the shortest distance point pair is located.

[0057] It can be understood that for each building, the building and each associated road are extracted, and a high-density interpolation method is used to calculate the accurate shortest distance between the building and each road.

[0058] To solve the problem of calculating the shortest distance between complex building contours (such as curved, concave balconies) and irregular road red lines (such as curved segments, discontinuous segments), this step uses an adaptive high-density interpolation algorithm. This method breaks through the limitation of traditional algorithms that only check the distance of vertices, and achieves millimeter-level measurement accuracy by discretizing continuous line features into high-density point clouds, combined with spatial indexing technology, effectively solving the shortest distance positioning problem of complex contours such as concave hallways. The detailed implementation process is as follows:

[0059] (1) High-density interpolation algorithm implementation includes: first, parameter configuration and line feature preprocessing, parameter initialization: define the interpolation density parameter (default 0.5 meters, system administrator can configure). Get the geometric data of the building base contour line and the road red line , verify the topological correctness of the input line feature, and repair self-intersection problems.

[0060] Line element classification processing of building base contour line and road red line : The input line elements are classified into straight line segments and curve segments. A straight line segment is defined by two end points with a curvature κ = 0. A curve segment is defined by a parametric equation (such as a circular arc or a Bezier curve) and requires curvature calculation.

[0061] Special case processing: For zero-length line segments, the processing is automatically skipped. For discontinuous road red line data, spatial connection processing is performed first. For small holes (<0.1 meters) in the building contour, they are considered as noise and ignored.

[0062] Two point sets are generated for the building base contour line and the associated road red line using adaptive high-density interpolation, respectively. The specific steps include:

[0063] Defining the interpolation point density parameter and obtaining the building base contour line and the associated road red line;

[0064] Obtaining the straight line segments and curve segments in the building base contour line and the road red line, respectively;

[0065] For straight line segments, adaptive high-density interpolation is used to generate point sets, including:

[0066] Calculating the length L of the straight line segment to determine the number of interpolation points , which is rounded down;

[0067] Based on the number of interpolation points N, equally spaced interpolation points are inserted on the straight line segment from to , and the interpolation point positions are calculated;

[0068] Based on the two end points and the interpolation points of the straight line segment, a point set is generated;

[0069] For curve segments, adaptive high-density interpolation is used to generate point sets, including:

[0070] Calculating the curvature of the curve segment , where t is the parameter variable;

[0071] Adjusting the interpolation point density dynamically to calculate the interpolation step size , where α is the curvature influence coefficient;

[0072] Starting from t = 0, interpolation points are inserted at the dynamic step size until t ≥ 1;

[0073] Based on the two end points and the interpolation points of the curve segment, a point set is generated.

[0074] The building contour line and the road red line are respectively interpolated by an adaptive high-density interpolation algorithm to generate a building contour line point set and a road red line point set.

[0075] The generated point density is checked again to see whether it meets the accuracy requirement, and the generated points are verified to completely cover the original line elements, and the point density is automatically increased in key areas (such as concave structures) to ensure that the shortest distance points are not missed.

[0076] In some embodiments of the present application, the nearest neighbor search of the two point sets is accelerated by spatial indexing, the shortest Euclidean distance between all point pairs in the two point sets is calculated, and the shortest distance point pair is located, including:

[0077] Based on the building base contour line point set and the road red line point set, R-trees are respectively constructed, wherein the index items of the R-trees include point coordinates (x, y), the original line segment ID where the point is located, and the parameter position s of the original line segment;

[0078] The shortest distance of each building contour point to the road red line is searched through the R-trees corresponding to the two point sets;

[0079] According to the shortest distances of all building contour points to the road red line, the shortest distance of the building to the road red line is determined, and the building contour nearest point and the road red line nearest point corresponding to the shortest distance point are recorded to form the shortest distance point pair.

[0080] Specifically, after the building contour line point set and the road red line point set are generated, R-Tree spatial indexing is constructed, and first, the index structure is designed: R-Tree is used as the main spatial index structure, which is suitable for two-dimensional spatial data. R-Tree is constructed for the building contour point cloud P_b and the road red line point cloud P_r, and the index items include point coordinates (x, y), original line segment ID, and parameter position s.

[0081] The R*-Tree variant is used to optimize node overlap and area, set the node capacity M=100 (which can be adjusted according to memory), insert point data in the order of Hilbert curve, and optimize spatial locality.

[0082] After the R-tree of the building contour line point set and the R-tree of the road red line point set are constructed, an efficient nearest neighbor search algorithm is used for searching to determine the shortest distance of the building to each road.

[0083] The first step is to search the area close to the road in the building contour point cloud, use the convex hull feature to calculate the shortest distance between the building contour convex hull and the road red line, and terminate the search in advance for the area whose distance is greater than the theoretical minimum retreat distance.

[0084] For the shortest distance point pair found initially, the range of the neighborhood is expanded for secondary verification to exclude local extreme value interference. In the search process, multiple buildings can be processed using multi-threading and parallel computing to accelerate distance calculation. In addition, in the search process, to accelerate distance calculation, distance lower bound pruning is implemented: when the minimum possible distance of the current point to the road point cloud is greater than the current shortest distance, the search of the point is terminated in advance.

[0085] Accurate positioning of the searched distance includes global minimum value positioning. Specifically, by using the above search algorithm, the nearest distance of each point in the building contour point cloud to the road point cloud is recorded, all points are compared in terms of the nearest distance, the global minimum value is determined as the accurate shortest distance from the building to the road, and the shortest distance point pair of the building and the road is formed.

[0086] Then, the coordinates of the building contour point corresponding to the shortest distance point pair and the ID and parameter position t_b of the line segment to which it belongs are recorded, and the coordinates of the road red line point corresponding to the shortest distance point pair and the ID and parameter position t_r of the line segment to which it belongs are recorded. Through the line segment ID and the parameter position, the specific position of the shortest distance on the building contour and the road is accurately identified.

[0087] Finally, for the determined shortest distance point pair, neighborhood verification (checking points within ±d / 2 range) is performed to confirm that the point pair indeed represents the global shortest distance and avoid local extreme value interference. Traceable positioning information is generated to support subsequent three-dimensional mapping and visualization.

[0088] By searching, the shortest distance point pair between each building and each associated road is determined. Based on the determined shortest distance point pair, the actual width of the road can be determined. Specifically, taking the building center as the starting point and the found road nearest point as the direction, an extended ray is emitted. The ray will pass through the current road and intersect with the opposite road red line, thereby determining the complete range of the road. Connecting the two side boundary points of the same road can establish the complete mapping of the road. Based on this, the road median line can be calculated, and the actual width of the road can be verified or calculated in combination with the map scale or attribute data.

[0089] S6, determine whether the shortest distance between each building and the road is within the pre-warning distance range. If yes, end; if no, generate an audit report and perform S7.

[0090] It can be understood that after the shortest distance from the building to each road is searched, it is determined whether the shortest distance is within the pre-warning distance range (theoretical minimum retreat distance). If yes, no processing is performed. If the shortest distance is within the pre-warning distance range, the road width, the shortest retreat distance of the road, and the road red line configuration are returned to the front end, the front end generates a visual result and generates a compliant audit report, and performs subsequent S7.

[0091] S7, determining a linear range of the road red line along the road direction based on each building-to-road shortest distance point and the corresponding side line length, and generating a local road three-dimensional buffer model with the same height as the building and the same depth as the theoretical minimum setback distance by stretching in the building direction based on the linear range of the road red line.

[0092] In some embodiments of the application, S7, determining a linear range of the road red line along the road direction based on each building-to-road shortest distance point and the corresponding side line length, and generating a local road three-dimensional buffer model with the same height as the building and the same depth as the theoretical minimum setback distance by stretching in the building direction based on the linear range of the road red line, comprises:

[0093] Obtaining the building contour closest point in the shortest distance point pair and the building side line where it is located;

[0094] Calculating the building side line length, extending to both sides of the building contour closest point by half of the building side line length to form a building side influence range;

[0095] Mapping the building side influence range to the road red line space by normal vector projection technology to determine the linear range of the road red line;

[0096] Generating a local road three-dimensional buffer model by parameterized stretching in the building direction based on the determined linear range of the road red line, with the depth of the stretching being the theoretical minimum setback distance and the height of the stretching being the building height in the building BIM model.

[0097] Specifically, this step is a key technical link to realize two-dimensional and three-dimensional collaborative analysis, and innovatively solves the problem of serious waste of computing resources in traditional three-dimensional review. By accurately identifying the potential violation area (i.e. the building and road with the shortest distance to the road within the pre-warning distance range), a highly customized three-dimensional buffer model is generated only for this area, rather than modeling the entire road, which greatly improves the efficiency and accuracy of subsequent collision detection.

[0098] In determining the benchmark range, firstly, based on the precisely positioned building contour nearest point and its building edge, an accurate spatial correlation is constructed. Specifically, the actual length of the building edge where the building contour nearest point is located is calculated, and the nearest point is extended to both sides by half of the edge length (not more than 5 meters at most), forming the building side influence range. Subsequently, through the normal vector projection technology, this range is accurately mapped to the road red line space to determine the corresponding linear range on the road red line. For curved roads, the mapping parameters are dynamically adjusted according to the road curvature to ensure that the buffer range can be accurately defined in the curved area; at road intersections, according to the angle threshold of S2, the results are filtered to intelligently determine whether the buffer range needs to be expanded to the adjacent road, avoiding the risk of missed detection due to the complexity of road topology.

[0099] In the parameterized modeling process, the system adopts a highly accurate geometric generation strategy. With the determined road red line linear range as the benchmark, parameterized stretching is performed in the building direction, and the depth of stretching strictly matches the calculated theoretical minimum setback distance, ensuring that the model depth is completely consistent with the planning requirements. The height of stretching accurately corresponds to the "building height" attribute value in the building BIM model, ensuring the detection accuracy in the vertical direction. For road terrain changes, dynamic inclination processing is performed on the buffer model bottom surface according to the digital elevation model (DEM) data, making the model perfectly fit the actual ground form. For high-rise buildings, the wind load effect is also considered, and a tapered taper design is adopted in the top 5% height area of the model, more realistically reflecting the spatial relationship between the building and the road. In terms of geometric representation, NURBS surface is used to accurately describe the buffer boundary of the road curve segment, ensuring the geometric accuracy of the model, while intelligent grid simplification is implemented in non-critical areas to reduce data volume.

[0100] In the model optimization and verification link, topological checks are performed to ensure that the generated three-dimensional buffer body is a manifold geometry without self-intersection and holes. Through coordinate system conversion, the model is aligned to the local coordinate system consistent with the building BIM model. The bounding box technology is used to pre-calculate the spatial range of the model, and the final generated local three-dimensional road buffer model only contains the narrowest space where violations are most likely to occur, with a data volume of only 5-15% of the full road segment model, but retains the geometric details of the key areas, laying a foundation for three-dimensional collision detection.

[0101] According to the road red line linear range, the local three-dimensional road buffer model is generated.

[0102] S8, the generated local road three-dimensional buffer model and the building BIM model are detected for whether the building components invade the road buffer area using the fast collision detection algorithm based on the bounding box.

[0103] In some embodiments of the present application, the S8, the generated local road three-dimensional buffer model and the building BIM model are detected by a rapid collision detection algorithm based on bounding box to detect whether the building components invade the road buffer area, including:

[0104] The local road three-dimensional buffer model and the building BIM model are analyzed by a collision detection algorithm for spatial intersection, wherein the collision algorithm includes a coarse screening stage and a precise measurement stage.

[0105] The coarse screening stage includes:

[0106] The space is divided using a dynamic octree, the axial bounding box is calculated for each component in the building BIM model and the local road three-dimensional buffer model, the obviously non-overlapping object pairs are excluded by bounding box overlap test, and the remaining components in the building BIM model are taken as potential collision objects for precise measurement.

[0107] The precise measurement stage includes:

[0108] The potential collision objects are judged for geometric intersection, the depth and volume of each building component invading the road are calculated, the collision component ID, position coordinates and invasion depth are recorded, and the three-dimensional invasion depth is converted into a two-dimensional plane insufficient retreat distance value by a geometric projection algorithm.

[0109] It can be understood that this step performs spatial intersection analysis of the local road three-dimensional buffer model generated in S7 and the building BIM model. A two-stage collision detection algorithm is used to detect only the potential violation area, improving the calculation efficiency.

[0110] In the coarse screening stage, the system divides the space using a dynamic octree, calculates the axial bounding box (AABB) for each component in the building BIM model and the road buffer model. Through bounding box overlap test, obviously non-overlapping object pairs in space are quickly excluded. The remaining components are detected as potential collision objects, and the possible collision area is predicted using the previous detection result, and the processing efficiency is improved by parallel computing technology.

[0111] In the precise measurement stage, the system performs precise geometric intersection judgment on the potential collision object pairs that pass through the coarse screening, performs precise intersection test after quality inspection of the component triangular mesh, calculates the invasion depth and volume. A multi-resolution strategy is adopted to first determine the approximate collision position and then perform fine calculation to ensure measurement accuracy.

[0112] Record the key information such as collision component ID, position coordinates, intrusion depth, etc., and convert the three-dimensional intrusion depth into a two-dimensional plane retreat distance deficiency value through a geometric projection algorithm, and cross-verify with the two-dimensional calculation results of step S6. The final generated collision results are used to identify the illegal intrusion of cantilever components, rain shelters and other three-dimensional elements, providing a basis for subsequent review reports.

[0113] S9, calculate and output the collision detection results, identify the component ID, position coordinates, intrusion depth value of the component that intrudes into the road buffer zone in the building BIM model, and output a three-dimensional sketch map marking the collision area.

[0114] It can be understood that S8 generates collision detection results after spatial collision detection between the local road three-dimensional buffer model and the building BIM model. This step analyzes the collision detection results, accurately identifies and quantitatively analyzes the collision conditions. Determine the specific components (such as cantilever balconies, rain shelters, etc.) in the BIM model that intrude into the road buffer zone and their corresponding triangle face set, and establish an accurate mapping relationship between the components and the intrusion area.

[0115] The automatically generated structured conflict result data includes: the unique identifier of the collision component (corresponding to the IFC GUID in the BIM model), the three-dimensional spatial coordinate range of the collision area (represented by the minimum bounding box), the maximum depth value of the illegal component intrusion into the buffer zone, and the intrusion volume and other key parameters.

[0116] An automatic view angle optimization algorithm is used to calculate the best observation angle based on the spatial characteristics of the collision area, generate a three-dimensional visualization picture showing the collision area, and ensure that the key illegal features are clearly visible.

[0117] All result data is organized according to planning review specifications to provide accurate basis for subsequent rectification suggestions, and supports cross-verification with two-dimensional review results to ensure consistency of two-dimensional and three-dimensional analysis results.

[0118] The review results (including road width, minimum retreat distance, theoretical distance, collision information) are transmitted to the front-end Web visualization engine (such as Cesium, Three.js). The front-end interface realizes two- and three-dimensional linkage: highlight the illegal buildings and associated roads on the two-dimensional map; in the three-dimensional scene, render the building BIM model, and render the local road buffer model and the intruding building components that have collided in a semi-transparent red highlight, providing an intuitive and immersive review experience, and supporting click query detailed information.

[0119] After S10, S10 is also included, which generates a comprehensive report according to the collision detection results.

[0120] It can be understood that this step is the technical endpoint and value output link of the whole two-three-dimensional collaborative review process. It innovatively constructs a semantic conversion model from geometric data to specification expression, and realizes the seamless connection of review results and planning management requirements. Through multi-dimensional data fusion and intelligent mapping algorithm, the complex three-dimensional geometric collision results are converted into structured and quantitative review reports in accordance with the requirements of the "Construction Project Design Scheme Review Guidelines", completely solving the problem of disconnection between review results and specification clauses in the prior art.

[0121] In the data integration stage, firstly, the consistency verification of two-three-dimensional results is performed: the two-dimensional shortest distance calculated by S5 and the three-dimensional intrusion depth determined by S9 are cross-verified by a geometric projection algorithm, and the calculation formula is two-dimensional plane distance=three-dimensional intrusion depth x cos θ (where θ is the included angle between the component normal and the vertical direction). The distance is the insufficient distance value, and the building continues to retreat in the direction away from the road by the distance value. Subsequently, a complete data correlation chain is constructed to accurately map the geometric data such as building component ID, spatial position, intrusion depth, and management data such as building attribute, road type, and specification requirement, forming a complete review evidence chain.

[0122] In the semantic conversion link, a rule-based reasoning engine is used to realize the conversion of geometric data to specification expression. The engine includes three key components: first, the retreat distance converter accurately maps the three-dimensional intrusion depth to the plane insufficient retreat distance value; second, the specification clause matcher automatically matches the corresponding regulatory provisions from the built-in specification knowledge base based on multi-dimensional parameters such as road type, building height, and regional characteristics; finally, the rectification instruction generator calculates the accurate rectification distance according to the intrusion depth and generates a quantitative expression conforming to the requirements of administrative documents. For example, the system not only identifies "the F5 cantilever plate in the southeast corner intrudes into the road red line buffer zone", but also accurately calculates the rectification distance of "0.52 meters of translation to the inside of the land", which is obtained by geometric correction of the difference between the theoretical minimum retreat distance and the actual retreat distance, ensuring the scientificity and operability of the rectification suggestion.

[0123] In the report structured design aspect, the system uses a modular report generation framework to output standardized documents conforming to the requirements of planning administration. The report includes five core parts:

[0124] (1) The review summary section provides an overall compliance conclusion and an overview of key indicators;

[0125] (2) The detailed data section lists the actual measured retreat distance, the theoretical required distance, and the difference value of each review point in table form, and marks the data source (two-dimensional calculation or three-dimensional verification);

[0126] (3) The violation list part describes each violation in detail, including component IFC GUID, spatial coordinates, intrusion depth, violation type (such as three-dimensional intrusion or insufficient plane);

[0127] (4) The violation clause part not only lists the relevant regulation clause number, but also provides the clause original text and specific application instructions;

[0128] (5) The drawing attachment part automatically generates a two-dimensional plane schematic diagram, a three-dimensional collision view and a data detail table of the violation position, supports multi-angle viewing and interactive query;

[0129] (6) The report content and the BIM model are bidirectionally associated, and the reviewer can highlight the corresponding area in the three-dimensional model by clicking any violation item in the report, which greatly improves the review efficiency.

[0130] The application provides a building setback distance review method based on CIM, and has the following beneficial effects:

[0131] (1) The shortest distance point of the midpoint of the boundary line is accurately positioned by decomposing the plot boundary and calculating the unit normal vector method and the high-density interpolation point method, and the accuracy requirement of the Construction Engineering Design Scheme Review Guide is met.

[0132] (2) Through the two-three-dimensional collaborative mechanism, the waste of computing resources caused by three-dimensional detection of the whole model is avoided, and the review efficiency is improved.

[0133] (3) Through the semantic conversion model, the review result is directly connected with the planning management requirement, and manual secondary analysis is avoided. It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0134] Those skilled in the art should understand that the embodiments of the application can be provided as a method, a system or a computer program product. Therefore, the application can be in the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0135] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0136] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0137] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0138] While the preferred embodiments of the application have been described, additional variations and modifications can be made to the embodiments described and shown, and it is therefore intended that the application cover any and all variations of the preferred embodiments which fall within the scope of the present application. Accordingly, the appended claims are intended to cover all such modifications and variations as falling within the scope of the application.

[0139] It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover the modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

Claims

1. A CIM-based building setback distance review method, characterized by, The method comprises the following steps: S1, obtaining building base data and road red line data; S2, decomposing the sub-plot boundary to which each building belongs to obtain a plurality of line segments of the sub-plot boundary, and calculating a unit directional vector and a normal vector of each line segment; S3, emitting a query ray in the direction of the normal vector of each line segment of the corresponding sub-plot boundary with each building as the center, and establishing an association relationship between the building and the road through spatial intersection analysis; S4, obtaining the height of each building, and obtaining the theoretical minimum retreat distance of each building relative to the road according to the height of each building and the associated road red line attribute information; S5, respectively generating two point sets from the building base contour line and the road red line by using an adaptive high-density interpolation method, performing nearest neighbor search on the two point sets through spatial index acceleration, calculating the shortest Euclidean distance between all point pairs in the two point sets, and locating the shortest distance point pair; S6, judging whether the shortest distance between each building and the road is within the warning distance range, if yes, ending; if not, generating an examination report and executing S9; S7, determining a road red line linear range in the road direction based on the distance nearest point of each building to the road and the corresponding side line length, stretching a local road three-dimensional buffer model with the same height as the building and the same depth as the theoretical minimum retreat distance based on the road red line linear range; S8, detecting whether the building components invade the road buffer zone by using a fast collision detection algorithm based on a bounding box on the generated local road three-dimensional buffer model and the building BIM model; S9, calculating and outputting the collision detection result, identifying the component ID, position coordinates and invasion depth value of the components in the building BIM model that invade the road buffer zone, and outputting a three-dimensional schematic diagram for identifying the collision area; The S1, obtaining building base data and road red line data, comprises: Obtaining the projection plane contour of each building base and all road red line data from a CIM platform, wherein the projection plane contour is a polygon, and the road red line data is linear feature data; The S3, emitting a query ray in the direction of the normal vector of each line segment of the corresponding sub-plot boundary with each building as the center, and establishing an association relationship between the building and the road through spatial intersection analysis, comprises: Emitting a query ray in the direction of the normal vector of each line segment of the corresponding sub-plot boundary with each building as the center, calculating the nearest intersection point of each query ray and the road red line by using the ray intersection function of the spatial database, establishing the spatial association relationship between each building and the road red line according to the intersection point, and recording the retreat direction and target road of each building; In the S5, performing nearest neighbor search on the two point sets through spatial index acceleration, calculating the shortest Euclidean distance between all point pairs in the two point sets, and locating the shortest distance point pair, comprising: Based on the building base contour line point set and the road red line point set, R-trees are respectively constructed, wherein the index items of the R-trees comprise point coordinates (x, y), the original line segment ID in which the point is located, and the parameter position s of the original line segment. Search the shortest distance from each building contour point to the road red line through the R-tree corresponding to the two point sets; Determine the shortest distance from the building to the road red line according to the shortest distance from all building contour points to the road red line, and record the building contour nearest point and the road red line nearest point corresponding to the shortest distance to form the shortest distance point pair.

2. The building setback review method according to claim 1, wherein, S2, decompose the sub-plot boundary to which each building belongs to obtain a plurality of line segments of the sub-plot boundary, calculate the unit directional vector and normal vector of each line segment, and previously include establishing the relationship between each building base and the sub-plot boundary to verify the geometric validity of each building base and the sub-plot boundary: Traverse all building base polygons, and verify whether each building base polygon is contained by the sub-plot boundary to which it belongs through spatial relationship judgment, if yes, execute S3; if not, rectify the building beyond the sub-plot boundary.

3. The building setback review method according to claim 1, wherein, S2, decompose the sub-plot boundary to which each building belongs to obtain a plurality of line segments of the sub-plot boundary, calculate the unit directional vector and normal vector of each line segment, including: Obtain the polygon data of the sub-plot boundary; Extracting a sequence of vertex coordinates of a polygon data of a land parcel boundary wherein ; The line segment unit set is constructed in vertex order: where each line segment is defined by a start point and an end point , from 0 to , when 1, the end point is ; Assign a unique identifier SID = [plot ID]_[segment serial number] to each line segment; For a line segment with start coordinates and end coordinates , the directional vector of the line segment is calculated, and the length of the line segment is calculated. Computing the unit normal vector of a line segment : ; Verifying whether the unit normal vector of a line segment is correct: ​ From the midpoint of the line segment , the direction of the normal vector is taken as the distance , and the point is checked to see if it is outside the building base polygon. If so, the unit normal vector of the line segment is correct, and if not, the unit normal vector of the line segment is incorrect, and the unit normal vector of the line segment is taken as .

4. The building setback review method according to claim 1, wherein, S3 previously includes: Identify the road line segments at each corner of the sub-plot boundary, calculate the included angle of the directional vectors of the two road line segments at the corner, if the included angle of the directional vectors falls within the angle threshold range, the two road line segments belong to the normal corner of the road, filter out the two road line segments, and do not participate in S3.

5. The building setback review method according to claim 1, wherein, S5, for each building base contour line and road red line, respectively generate two point sets by using the adaptive high-density interpolation method, including: Defining a point density parameter , and obtaining building footprints and associated road right-of-ways; Respectively obtain the straight line segments and curve segments in the building base contour line and the road red line; For the straight line segment, generate a point set by using the adaptive high-density interpolation method, including: The straight line segment length L is calculated to determine the number of insertion points , is rounded down; Based on the number of interpolation points N, equally interpolate the points on the straight line segment, and calculate the interpolation point positions to . ; Generate a point set based on the two end points and the interpolation points of the straight line segment; For the curve segment, generate a point set by using the adaptive high-density interpolation method, including: Computing the curvature of a curve segment where t is a parameter variable;​ Dynamically adjusting the density of the interpolation points, calculating the interpolation step length wherein a is a curvature influence coefficient; Start from t=0, insert points according to a dynamic step length until t≥1; Generate a point set based on the two end points and the interpolation points of the curve segment.

6. The building setback review method according to claim 1, wherein, S6 previously includes: According to the shortest distance point pair, emit an extension ray from the building center to the road red line nearest point in the shortest distance point pair, cross the corresponding road, and form two intersection points with the road, and the distance between the two intersection points is the actual width of the road.

7. The building setback review method according to claim 1, wherein, S7, determine the road red line linear range in the road direction based on the distance nearest point of each building to the road and the corresponding side line length, and stretch a local road three-dimensional buffer model with the same height as the building and the same depth as the theoretical minimum retreat distance in the building direction based on the road red line linear range, including: Obtain the building contour nearest point in the shortest distance point pair and the building side line where the building contour nearest point is located; Calculate the building side line length, extend the building side line length by half on both sides of the building contour nearest point to form a building side influence range; Map the building side influence range to the road red line space through the normal vector projection technology to determine the road red line linear range; With the determined linear range of the road red line as a reference, the building direction is parameterized and stretched, the depth of the stretching is the theoretical minimum setback distance, and the height of the stretching is the building height in the building BIM model, to generate a local road three-dimensional buffer model.

8. The building setback review method according to claim 1, wherein, In the S8, the generated local road three-dimensional buffer model and the building BIM model are detected by using a quick collision detection algorithm based on a bounding box, to determine whether the building components invade the road buffer area, including: The local road three-dimensional buffer model and the building BIM model are analyzed by using a collision detection algorithm for spatial intersection, wherein the collision detection algorithm includes a coarse screening stage and a precise measurement stage. The coarse screening stage includes: The space is divided by using a dynamic octree, an axial bounding box is calculated for each component in the building BIM model and the local road three-dimensional buffer model, and by using a bounding box overlap test, objects that are obviously not overlapped in space are excluded, and the remaining components in the building BIM model are taken as potential collision objects for precise measurement; The precise measurement stage includes: The potential collision objects are subjected to geometric intersection judgment, the depth and volume of each building component invading the road are calculated, the collision component ID, position coordinates and invasion depth are recorded, and by using a geometric projection algorithm, the three-dimensional invasion depth is converted into a two-dimensional plane setback distance deficiency value.

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