A method for reverse identification and modeling of indoor point cloud wall components for stress analysis of existing masonry structures

Abstract

This invention relates to the field of existing building inspection and assessment and 3D point cloud reverse engineering technology, and particularly to a method for reverse identification and modeling of indoor point cloud wall components for stress analysis of existing masonry structures. The method includes: obtaining unified coordinates of the point cloud to obtain the point cloud of the single-layer structural foundation and determining the structural calculation floor height; projecting onto a 2D plane for orthogonal tracing to generate a set of outer wall boundary segments; establishing a grid sliding scan of sparse wall core strip areas, and combining the floor height with prior screening to eliminate pseudo-components and obtain candidate load-bearing inner walls; merging collinear inner and outer wall segments and connecting them to their endpoints to obtain a closed load-bearing wall network; idealizing the stretched components and rounding the cross-sectional thickness to the standard modulus, extracting the wall centerline as the load transfer axis; mapping to generate an intermediate structural data model and exporting it as the underlying interface model of the structural stress calculation software. This invention solves the problem of missing inner walls due to the lack of points inside the moving scan entity, and establishes a link between point clouds and mechanical software.

Description

Technical Field

[0001] This invention relates to the field of existing building inspection and identification and 3D point cloud reverse engineering technology, and in particular to a method for reverse identification and modeling of indoor point cloud wall components for stress analysis of existing masonry structures. Background Technology

[0002] In the inspection, assessment, and seismic strengthening design of existing masonry structures, accurately obtaining the spatial location, standard section values, and structural calculation story height of load-bearing walls is fundamental for subsequent combined internal force calculations in professional structural software. Reverse engineering techniques, which involve using mobile laser scanning equipment to collect indoor 3D point clouds on-site and then constructing a stress analysis model, have been widely applied.

[0003] However, most existing methods for reverse identification of building point clouds assume that solid walls are distributed in a high-density continuous strip in the point cloud space, and extract component features based on a surface high-density echo fitting mechanism. In practical engineering, because mobile laser scanning equipment is placed inside a closed room to collect data as people move around, the laser beam can only capture the surface echo of the wall facing inwards. The core area of ​​the brickwork of solid load-bearing interior walls has a natural physical echo blind zone. When existing continuous strip fitting algorithms are used to reconstruct discrete point clouds without points inside such entities, the algorithm may classify the surface of the left and right walls of a single room as two independent, non-load-bearing, thin walls, or directly trigger bottom-level filtering interception due to the low number of point clouds in the core area of ​​the wall. This results in serious omissions of existing masonry load-bearing interior wall components and incomplete geometric sections of the reverse-derived structural stress calculation model. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides a reverse identification and modeling method for indoor point cloud wall components for stress analysis of existing masonry structures. It aims to improve the problem that traditional building point cloud reconstruction mostly uses dense strip fitting, which results in the omission of load-bearing walls and incomplete mechanical models due to the lack of points inside the entity when the moving laser scans.

[0005] This invention provides the following technical solution: a method for reverse identification and modeling of indoor point cloud wall components for stress analysis of existing masonry structures, comprising the following steps: S1. Obtain the original indoor 3D point cloud and unify the spatial coordinates to obtain the single-layer structure foundation point cloud. Based on the statistical vertical projection distribution characteristics of the single-layer structure foundation point cloud, determine the calculated floor height of the structure. S2. Project the single-layer structure base point cloud onto a horizontal two-dimensional plane, extract the outer contour features and perform orthogonal tracing to generate a set of outer wall boundary wall segments; S3. Establish a statistical grid for the projection area of ​​the single-layer structural foundation point cloud in the horizontal two-dimensional plane and slide and scan along the main axis to search for strip-shaped areas with point cloud support on both sides and a sparse point cloud wall core in the middle to obtain candidate inner walls. Combine the calculated floor height of the structure to apply a combination of prior screening conditions of cross-sectional width modulus, vertical top extension continuity, and continuous sparseness of the entire floor height of the inner wall candidates to eliminate pseudo-components and obtain candidate load-bearing inner walls. S4. Perform collinearity merging of the candidate load-bearing inner walls and the set of boundary wall segments of the outer walls and connect them with the endpoint nodes to obtain a closed load-bearing wall network. S5. Idealize and stretch all wall segments in the closed load-bearing wall network into story-height structural wall components and round the cross-sectional thickness to the standard module. Extract the wall centerline of the story-height structural wall component. The wall centerline is used as the load transfer axis. S6. Map the floor height structural wall components to the wall centerline to generate an intermediate structural data model, and export it as the underlying interface model of the structural stress calculation software.

[0006] By adopting the above technical solution, sparse wall cores in the point cloud are searched and combined prior screening is applied, thereby constructing a closed load-bearing wall network and centerline in reverse. This improves the problem that traditional building point cloud reconstruction mostly uses dense strip fitting, which results in the omission of load-bearing walls and incomplete mechanical models due to the lack of points inside the entity when the moving laser scans.

[0007] The present invention has the following beneficial effects: 1. In this invention, by searching for sparse wall cores in the point cloud and applying combined prior screening, a closed load-bearing wall network and centerline are constructed in reverse. This improves the problem that traditional building point cloud reconstruction mostly uses dense strip fitting, which results in the omission of load-bearing walls and incomplete mechanical models due to the lack of points inside the entity when the moving laser scans.

[0008] 2. In this invention, by applying the cross-sectional modulus, top height and layered continuous sparse combination prior screening to the candidate interior walls, non-load-bearing objects attached to the walls are eliminated, thereby improving the problem that traditional indoor point cloud recognition mostly uses two-dimensional projection blank gap judgment, which causes furniture misidentification because the outline of the wardrobe attached to the wall is similar to the wall.

[0009] 3. In this invention, by extracting the endpoints of the outer envelope boundary and performing segmented polyline tracing under orthogonal constraints, a set of outer wall boundary segments is generated, thereby improving the problem that traditional building outline extraction mostly uses a single overall rectangular frame fitting, which causes the misintroduction of outdoor suspended area due to the existence of concave and convex modules in the existing planar layout.

[0010] 4. In this invention, the center line of the component is extracted by stretching and rounding and mapped to the intermediate data model of the structure, and then the underlying interface of the mechanical software is directly exported. This improves the problem that traditional point cloud bridging mostly uses pure visual patch modeling. Since the reconstructed patch lacks mechanical stiffness semantics, it causes the problem of manual redrawing of the line. Attached Figure Description

[0011] Figure 1 This is a flowchart of a method for reverse identification and modeling of indoor point cloud wall components for stress analysis of existing masonry structures, as proposed in this invention. Figure 2 This invention presents a flowchart of the single-story structure foundation point cloud acquisition and calculation of the floor height determination method for reverse identification and modeling of indoor point cloud wall components for stress analysis of existing masonry structures. Figure 3 This invention presents a flowchart of the process for generating an external wall boundary segment set of a method for reverse identification and modeling of indoor point cloud wall components for stress analysis of existing masonry structures. Figure 4 This is a flowchart of the prior screening process for load-bearing interior wall candidate combinations in a reverse identification and modeling method for indoor point cloud wall components for stress analysis of existing masonry structures proposed in this invention. Figure 5 This is a flowchart illustrating the underlying interface model conversion and direct export to industrial software for an indoor point cloud wall component reverse identification and modeling method for stress analysis of existing masonry structures proposed in this invention. Detailed Implementation

[0012] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] Example 1: In the first embodiment of the present invention, the present invention provides a method for reverse identification and modeling of indoor point cloud wall components for stress analysis of existing masonry structures, such as... Figures 1-2 As shown, it includes the following steps: S1. Obtain the original indoor 3D point cloud and unify the spatial coordinates to obtain the single-layer structure foundation point cloud. Based on the statistical vertical projection distribution characteristics of the single-layer structure foundation point cloud, determine the calculated floor height of the structure. Furthermore, in S1, the original indoor 3D point cloud is acquired and its spatial coordinates are unified to obtain the basic point cloud of the single-layer structure, including: Receive the original indoor 3D point cloud collected by the mobile laser scanning device and perform voxel mesh downsampling processing, and call the statistical filter to perform point cloud noise removal processing; Calculate the normal vector features of the denoised point cloud surface, and call the principal component analysis operator to solve the Manhattan orthogonal principal axis direction corresponding to the original indoor 3D point cloud; Rigid body rotation normalization alignment is performed on the point cloud spatial coordinate system according to the principal axis direction, and single-layer slicing is performed according to the set three-dimensional limits to obtain a single-layer structural basic point cloud.

[0014] In S1, the structural calculation layer height is determined based on the statistical vertical projection distribution characteristics of the single-layer structure's basic point cloud, including: Statistical analysis of the elevation frequency histogram distribution of the base point cloud of a single-layer structure along the vertical coordinate axis; The high-frequency distribution intervals at the bottom and top levels are identified from the elevation frequency histogram distribution. The random sampling consistent fitting operator is called on the discrete point cloud falling into the bottom high frequency distribution interval and the top high frequency distribution interval respectively to obtain the bottom fitting horizontal feature surface and the top fitting horizontal feature surface. The vertical coordinates of the bottom fitted horizontal feature surface are established as the floor fitted elevation, and the vertical coordinates of the top fitted horizontal feature surface are established as the floor top slab fitted elevation. Calculate the absolute height difference between the fitted elevation of the top slab and the fitted elevation of the floor, and establish the absolute height difference as the structural calculation floor height.

[0015] Specifically, the data input and output process follows a strict data direction and distribution path at the system's underlying level: the input end receives raw indoor 3D point clouds collected on-site by a mobile laser scanning device. After processing by the preprocessing module, the output terminal outputs two independent types of physical data: the first type is an orthogonally normalized single-layer basic point cloud. The second type is the calculation of floor height for structures with length scalar forms. In the data transmission chain, a single-layer basic point cloud structure is used. The subsequent S2 step of directional injection serves as a pure spatial carrier for tracing the zigzag lines of the exterior walls; structural calculations determine the floor height. The subsequent S3 and S5 steps of directional injection serve as physical comparison thresholds for determining the continuity of the load-bearing inner wall at the top and as the dimensional modulus benchmark for the idealized vertical tension of the component.

[0016] To address the high-density redundant echoes and suspended particle noise generated by mobile LiDAR scanning in enclosed indoor spaces as people move with their gait, a statistical filter is used to remove point cloud noise. During the processing, the statistical filter constructs a spatially scattered neighborhood average Euclidean distance operator at the underlying level. ; ; in Represents the three-dimensional position vector of the current discrete point to be inspected in the spatial coordinate system. Its physical dimension is length, and its unit is meter. Represents the first discrete point to be detected within the spatial critical neighborhood. The position vectors of adjacent points are in meters; This represents the upper limit of the number of adjacent points involved in the arithmetic distance calculation, and the value is set to a positive integer in the range of constant 20 to constant 50; This represents the arithmetic mean Euclidean distance between the current discrete point to be examined and its neighboring points, in meters; This represents the arithmetic mean of the neighborhood distances of all scattered points in the global space of the input point cloud, in meters. The global standard deviation of the spatial neighborhood distance of the input point cloud, in meters; This represents the sensitivity ratio constant, with a value set from constant 1 to constant 3. This represents the critical distance threshold for determining whether a scatter point belongs to spatially isolated noise, and the unit is meters.

[0017] Modern mathematical solutions yielded At this time, the underlying layer performs a forced stripping action of the currently inspected discrete points. Noise removal ensures that the point cloud entering the subsequent extraction process is closely attached to the surface of the solid wall, isolating free suspended dust echoes from interfering with the density of subsequent candidate operators for the inner wall.

[0018] To address the characteristic that the random starting point of the on-site mobile scanning trajectory results in the original point cloud being at an arbitrary tilted world coordinate angle, principal component analysis (PCA) is used to solve for the principal axes of the Manhattan orthogonal space, and a rotation operator is used to perform rigid body rotation in coordinate space. During the solution process, the PCA operator constructs a third-order discrete point-centered displacement vector at the underlying level. With covariance matrix : ; ; in Represents the total number of feature points in the slice space that participate in the principal axis operation, with the dimension being count; Representing the The coordinate position vectors of the feature points in the unnormalized space, in meters; The spatial centroid coordinate vector of the set of scattered points participating in the operation, in meters; Representing the The three-dimensional centered displacement vectors of each feature point relative to the spatial centroid, in meters; It represents a third-order orthogonal covariance matrix, and the matrix eigenvectors refer to the orientation of the load-bearing walls in the masonry structure.

[0019] The bottom-level rotation operator rotates the spatial coordinate system based on the calculated eigenvectors, ensuring that all load-bearing interior walls in the single-layer structural foundation point cloud are strictly parallel to the horizontal or vertical coordinate axes. This rigid body rotation in coordinate space corrects the tilted world coordinate angle of the original point cloud, providing an orthogonal reference for subsequent steps such as dividing orthogonal statistical grids and using sliding window detection units to accurately capture the geometric shape of densely supported side walls and sparsely sparse wall cores in the central part of the point cloud.

[0020] To address the need for accurate vertical member height control in the stress analysis of existing masonry structures, and considering the maximum elevation uncertainty caused by drainage and electromechanical pipes or localized plaster ceiling beams hanging under the roof slab of existing buildings, a joint solution using elevation frequency histograms and a random sampling consistent plane fitting operator is employed to calculate the structural story height. The joint solution follows spatial algebraic equations during execution. ; ; in , , Represents the horizontal, vertical, and absolute vertical components of the scattered points in the orthogonal reference space, in meters; , , The spatial normal vector components representing the fitted feature plane satisfy the identity. Because the fitted target is strictly locked to a horizontal feature plane, and The value of is always equal to the constant 0. The value of is always equal to the constant 1; The vertical geometric intercept of the fitted feature plane to the spatial origin is expressed in meters. This represents the absolute elevation of the top-level fitted horizontal feature surface on the vertical coordinate axis, in meters. This represents the absolute elevation of the bottom-level fitted horizontal feature surface on the vertical coordinate axis, in meters. This represents the final calculated floor height of the structure, in meters.

[0021] The elevation calculation established the vertical mechanical boundary of the masonry weight transmission system. The calculated structural floor height values ​​provide a continuous reference scale for subsequent steps to separate low-profile interior furniture partitions based on height conditions, enabling the automatic injection of precise floor height parameters into the mechanical software.

[0022] like Figure 3As shown, S2, the single-layer structure foundation point cloud is projected onto a horizontal two-dimensional plane, the outer contour features are extracted and orthogonal tracking is performed to generate a set of outer wall boundary wall segments; Furthermore, in S2, the point cloud of the single-layer structure foundation is projected onto a horizontal two-dimensional plane, the outer contour features are extracted and orthogonally traced to generate a set of outer wall boundary segments, including: The single-layer basic point cloud is projected and mapped onto a horizontal two-dimensional plane along the vertical coordinate axis, and the set of endpoints of the outer envelope boundary of the point cloud in the horizontal two-dimensional plane is extracted. Under the prior constraints of the Manhattan orthogonal space, polyline tracing is performed sequentially on the set of endpoints of the outer envelope boundary. The envelope polyline generated by the tracking is discretized into multiple horizontally oriented straight line segments parallel to the horizontal principal axis and multiple vertically oriented straight line segments parallel to the vertical principal axis. Encapsulate the horizontally oriented straight line segments and the vertically oriented straight line segments that are perpendicularly connected end to end into an orthogonal closed geometric frame to generate a set of outer wall boundary wall segments.

[0023] Specifically, the data input and output process, and the underlying computing carrier of the system follow a strict receiving and feeding-back path: the input object is the single-layer basic point cloud structure output after denoising and normalization in step S1. After two-dimensional mapping, pathfinding, and orthogonal connection operations, the output terminal outputs a set of outer wall boundary segments with geometric vector shapes. In the end-to-end data topology, the set of outer wall boundary segments... The directional feed is sent to step S3, serving as the absolute spatial boundary for subsequent sliding window detection units scanning load-bearing interior walls. The bottom-level detection engine only aggregates at the boundary wall segments of the exterior walls. The internal wall operator search is initiated within the enclosed planar area, directly physically isolating the point cloud data of non-structural suspended areas such as outdoor balconies and extended canopies.

[0024] Since indoor mobile lidar can only capture the physical observation characteristics of the exterior wall facing only one side of the interior, a dimensionality reduction projection operator is used to extract the set of scattered points of the outer envelope boundary. The dimensionality reduction projection operator can flatten the three-dimensional elevation coordinate components to zero, realizing the transformation of single-sided observation data into planar geometric boundaries and constructing planar contour scattered points.

[0025] When the underlying layer performs two-dimensional mapping to extract endpoints, a dimensionality reduction projection operator is constructed: ; in The first point cloud representing a single-layer structure A three-dimensional position vector of a discrete point in a spatial coordinate system, with the dimension of length and the unit of meters; This represents a two-dimensional orthogonal mapping matrix, which has an algebraic form of a two-row, three-column constant matrix. The constants in the first row are constant 1, constant 0, and constant 0, respectively, and the constants in the second row are constant 0, constant 1, and constant 0, respectively. This represents a 2x1 vector of planar point coordinates generated by the dimensionality reduction mapping, in meters.

[0026] For existing masonry buildings with concave or convex orthogonal wall layouts, orthogonal polyline tracing is performed on the set of endpoints of the outer envelope boundary under the prior constraints of Manhattan orthogonal space. The specific driving and conflict backoff process of orthogonal polyline tracing is as follows: the extreme points of the x and y coordinates in the set of endpoints of the outer envelope boundary are used as the starting tracking anchor points. A clockwise tracking direction is set, and candidate endpoints with the closest Euclidean distance are searched sequentially in the orthogonal neighborhood of the current anchor point and connected point by point. When there are more than two candidate endpoints in the search neighborhood and the absolute value of the difference in Euclidean distance is less than 0.05 meters, the candidate endpoint with the angle closest to 90 degrees orthogonal to the previously generated line segment is selected for connection. When a deadlock occurs due to line segment intersection in the tracking path, the system triggers a backoff mechanism, automatically cancels the most recently generated line segment and skips the endpoint that caused the deadlock conflict, and re-searches for subsequent candidate endpoints in the orthogonal neighborhood until the connection closes and returns to the starting tracking anchor point. Orthogonal pursuit discretizes the outer envelope polygonal line into multiple horizontally oriented line segments parallel to the transverse principal axis and multiple vertically oriented line segments parallel to the longitudinal principal axis, with the equations satisfying: ; ; in and These represent the horizontal and vertical coordinate variables in a two-dimensional orthogonal coordinate system, both in meters. The ordinate intercept constant of the horizontal line segment established by the polyline tracking fit is represented in meters. and The values ​​representing the starting and ending points of the horizontal line segment's actual horizontal extension are in meters. The x-intercept constant of the vertical line segment established by the polyline tracking fit is represented in meters. and The values ​​represent the starting and ending points of the actual longitudinal extension of the vertical line segment, both in meters.

[0027] Orthogonal segmented tracing provides an adjustable polygonal topology for the exterior wall profile, ensuring that the reverse-guided envelope profile conforms to the concave-convex modulus of the actual facade of the existing masonry building, and avoiding the misintroduction of outdoor suspended area caused by a single standard rectangular range.

[0028] To address the gaps at the ends of straight segments caused by spatial object occlusion, a wall grid node closure check is performed, and the intersection points of adjacent line segments are calculated and encapsulated into an orthogonal closed geometric frame.

[0029] The equation for calculating the area of ​​the closed envelope satisfies: ; in The total number of corner points defined by the encapsulation of an orthogonal closed geometric framework is expressed in terms of the number of points. The index of the intersection point in the current algebraic traversal is dimensionless. and Representing the The x and y coordinates of each geometric corner point in a horizontal two-dimensional plane are given in meters. and Representing the adjacent The coordinates of each corner point are calculated when the index reaches the last corner point. hour, The numerical value corresponds to the x-coordinate of the starting point. , The numerical value corresponds to the ordinate of the starting point. All units are meters; It represents the enclosed area of ​​the first-floor structure established by the orthogonal closed geometric frame, in square meters.

[0030] like Figure 4 As shown in Figure S3, a statistical grid is established for the projection area of ​​the single-layer structural foundation point cloud in the horizontal two-dimensional plane. The grid is slidably scanned along the main axis to search for strip-shaped areas with point cloud support on both sides and a sparse point cloud wall core in the middle to obtain candidate inner walls. Combined with the structural calculation floor height, a combination of prior screening conditions of section width modulus, vertical top extension continuity, and continuous sparseness of the entire floor height is applied to the candidate inner walls to eliminate pseudo-components and obtain candidate load-bearing inner walls. Furthermore, in S3, searching for strip-shaped regions with point cloud support on both sides and a sparse point cloud core in the middle yields candidate interior walls, including: A two-dimensional statistical grid with a fixed step size is established in a horizontal two-dimensional plane, and the two-dimensional statistical grid is slid-scanned along mutually perpendicular horizontal and vertical principal axes. Within any strip-shaped area to be inspected covered by the sliding window, a sliding window detection unit is constructed, consisting of a left support area, a right support area, and a core area sandwiched between the left and right support areas; The number of first point clouds falling into the left support area, the number of second point clouds falling into the right support area, and the number of third point clouds falling into the core area are counted separately. Calculate the sparsity ratio coefficient of the number of third point clouds relative to the sum of the number of first and second point clouds, and calculate the symmetry balance coefficient of the comparison between the number of first and second point clouds. When the sparsity ratio coefficient is lower than the preset sparsity threshold and the symmetry balance coefficient is higher than the preset balance threshold, the strip-shaped inspection area is established as a candidate for interior wall.

[0031] In S3, a combined prior screening process is applied to the candidate interior walls, incorporating structural calculations of the floor height, conditions of cross-sectional width modulus, vertical extension to the top, and continuous sparseness across the entire floor height. This process eliminates spurious components, resulting in the following candidate load-bearing interior walls: Calculate the actual span corresponding to the central core area of ​​the candidate interior walls, compare the actual span with the set standard modular set, and eliminate the candidate interior walls whose actual span exceeds the range of the standard modular set; the standard modular set is limited to the discrete load-bearing wall thickness specifications corresponding to the preset building code; The actual observed highest elevation of the support areas on both sides of the candidate inner wall is statistically analyzed in the direction of the vertical coordinate axis. The actual observed highest elevation is compared with the effective critical height value obtained by subtracting the preset top tolerance from the structural calculated floor height. Candidate inner walls whose actual observed highest elevation is lower than the effective critical height value are eliminated. The space corresponding to the candidate interior walls is uniformly divided into multiple height layers along the vertical coordinate axis along the structural calculation floor height range. The point cloud occupancy rate of the core area inside each height layer is counted layer by layer. Candidate interior walls that meet the following conditions are eliminated: there are multiple consecutive height layers, and the point cloud occupancy rate of the core area inside each of the multiple consecutive height layers exceeds the preset solidification threshold, thus obtaining the candidate load-bearing interior walls.

[0032] Specifically, in the data input and output process, step S3 is at the core decoupling stage of the algorithm's closed loop. The underlying flow object receives the single-layer basic point cloud structure output after the normalization process in step S1. The calculated floor height of the structure is determined by the solution. And the set of outer wall boundary segments generated after orthogonal encapsulation in step S2. After operator decoupling detection and filtering using a combination of three prior locks, the output terminal outputs a set of candidate load-bearing interior walls with geometrically aligned centers and zero residual non-structural pseudo-objects. In subsequent data links, the candidate set of load-bearing interior walls... The directional feed is connected to the endpoint nodes of the outer wall boundary wall segment set in step S4 to construct the first-floor closed load-bearing wall network.

[0033] When a mobile laser scanning device is placed inside a room to collect data, only the surface layers of the load-bearing interior walls on both sides are captured by the echo, while the core area of ​​the solid brickwork remains a laser blind zone. When the underlying detection engine establishes a statistical grid within the space enclosed by the set of exterior wall segments and slides along the main axis to scan, it constructs a decoupling operator: ; ; in This represents the number of the first point cloud that falls into the left support area, and its dimension is the number of discrete scattered points. This represents the number of second point clouds that fall into the right support area, and its dimension is the number of counts. This represents the number of third point clouds that fall into the core area, measured in count. The sparse ratio coefficient established by the solution is dimensionless. The symmetry equilibrium coefficients established by the solution are dimensionless.

[0034] Underlying joint verification double threshold judgment formula: ; in This represents the preset sparsity threshold constant, with a value set between 0.2 and 0.4. This represents the preset balance threshold constant, with a value set between 0.7 and 0.9.

[0035] When the logical condition is true, the system confirms that the strip-shaped area to be inspected belongs to the candidate inner wall. The statistical decoupling feature transforms the phenomenon that there are no physical points inside the entity into direct evidence for measuring the core location of the component, thus establishing the core spatial coordinates of the load-bearing inner wall.

[0036] For existing masonry residential buildings that simultaneously contain load-bearing brick walls and 12cm thick non-load-bearing lightweight partition walls, a mechanism is employed to intercept non-structural component data through cross-sectional span calculation and comparison with a standard masonry module set. The span comparison module calculates the actual span. With discrete modulus set When, follow the algebraic expression: ; ; in The value of the innermost x-coordinate of the point cloud boundary of the right support area along the direction perpendicular to the wall, in meters; The innermost x-coordinate value of the point cloud boundary of the left support region, in meters; The actual span of the candidate core area for the interior wall, in meters; This represents the set of standard masonry modules, where discrete values ​​correspond to the standard load-bearing wall thickness specifications defined in the pre-defined building codes. The underlying operation operator verifies the modulus condition of the independent variable: ; in This represents the maximum allowable modulus matching tolerance, set to 0.03 meters. When the algebraic inequality is true, the system determines that the actual span has successfully matched to the standard modulus set, thus eliminating the possibility of fitting continuous arbitrary values.

[0037] To address the physical issues of missing echoes at the top of walls caused by the common hanging of fire-fighting pipelines under the roof slabs of existing residential buildings and partial plasterboard ceilings, a vertical ceiling shortage tolerance threshold is established. The continuity comparison module calculates the actual highest observed elevation. With effective critical height value When, follow the relation: ; ; in The calculated floor height of the structure established in step S1 is represented by meters. This represents the preset top connection tolerance constant, with a value set between 0.10 meters and 0.50 meters; This represents the effective critical height value established by the solution, in meters. The highest elevation value actually captured along the vertical coordinate axis by the point cloud of the candidate support areas on both sides of the inner wall, in meters.

[0038] When the contemporary numerical inequality is true, candidate components are allowed at the bottom level. The tolerance setting establishes an effective continuous reference for the continuous height of load-bearing components, preserving the actual load-bearing walls with laser breakpoints caused by partial obstruction at the top.

[0039] For the planar projection characteristics of large pseudo-components such as full-height solid wood storage wardrobes or bookshelves placed against the wall on the first floor of the interior, the vertical space is uniformly sliced ​​and the occupancy rate of each floor is verified.

[0040] The sliced ​​statistical unit calculates the floor height range along the structure, dividing the space into equal intervals along the vertical coordinate axis. Each sub-level, solving for any number of sub-levels. Height ratio : ; in This represents the total number of height layers in a vertically uniformly divided segment, and its value is set to a constant from 8 to 12. This represents the index of the current traversal level. The representative fell into the first Point cloud frequency of the core area within each height layer; and Representing the Scattered point counts within the support areas on the left and right sides of the height level; Representing the Point cloud occupancy rate of the core region within the height layer, dimensionless.

[0041] Low-level interceptor execution verification formula: Make and ; This represents a preset solidification threshold constant, with a value set between 0.35 and 0.55. When the check is true, the system confirms that the location belongs to non-load-bearing furniture with a continuously filled surface, triggering an interception and removal action. The layered slicing feature is based on the physical mechanism difference between the surface filling echo of the furniture and the core of the masonry brick block without any points throughout the entire layer, stripping away the data of unstructured objects and ensuring the purity of the subsequent extraction of the wall centerline.

[0042] like Figure 5 As shown in Figure S4, the set of candidate load-bearing inner walls and boundary wall segments of the outer walls is processed by merging collinear segments and connecting them with the endpoint nodes to obtain a closed load-bearing wall network. Furthermore, in S4, the set of candidate load-bearing interior walls and boundary wall segments is processed by merging collinear segments and connecting them with endpoint nodes to obtain a closed load-bearing wall network including: Each load-bearing inner wall candidate and the set of outer wall boundary segments are subjected to collinear clustering according to the main axis direction and spatial interval. The spatial determination criterion for collinear clustering is as follows: calculate the absolute value of the inner product of the unit direction vectors of the center lines of any first wall segment and the second wall segment. When the absolute value of the inner product is greater than the constant 0.98, and the absolute value of the vertical spatial geometric offset distance between the two center lines along the mutually perpendicular horizontal normal directions is less than 0.15 meters, it is determined that the two wall segments belong to the same main axis direction and collinear spatial interval, and they are included in the same collinear cluster segment selection group for overlapping length calculation.

[0043] Compare the length of the overlapping area of ​​the collinear segments, and perform a process to connect and merge adjacent short wall segments whose overlapping area length exceeds the set collinearity threshold; Perform the wall network node closure check action to limit the outer wall boundary wall segment set to close end to form a closed outer contour, and limit the end nodes of each load-bearing inner wall candidate to extend and anchor to the surface intersection of the outer wall boundary wall segment set or adjacent load-bearing inner wall candidates to obtain a closed load-bearing wall network.

[0044] Specifically, in the data input and output process, step S4 is the geometric bridging stage of the transformation from three-dimensional discrete segmentation to the underlying mechanical stiffness topology. The preceding step for the transfer object is: the input end loads the candidate set of load-bearing interior walls established by combining prior conditions in step S3. And step S2 tracks the set of outer wall boundary segments generated by discretization. After collinear clustering of line segments, algebraic merging of overlapping intervals, and topological extension of endpoint intersections, the output terminal outputs a closed load-bearing wall network with uninterrupted connectivity and closed planar load transmission paths. In subsequent data links, the load-bearing wall network is closed. The directional injection step S5 serves as a planar spatial contour carrier for the vertical equivalent stretching of the component and the normalization and rounding of the section modulus.

[0045] The physical characteristics of axial fracture and splitting of the same continuous load-bearing interior wall in the point cloud caused by people walking or temporary indoor decorations are analyzed by indoor mobile laser scanning. The continuity of axial force transmission of the component is reconstructed by collinear clustering of line segments and overlapping region merging operators.

[0046] The collinear clustering unit calculates the overlap interval length of the first and second candidate short segments defined by collinearity. : ; in and The coordinates of the beginning and end points of the projection of the first candidate short segment along the principal axis are represented by the physical dimension of length and the unit of meters. and The first and last coordinates of the projection of the second candidate short segment along the same principal axis are represented in meters. The scalar represents the length of overlap of the collinear segments established by the solution, in meters.

[0047] Collinearity determination inequality for bottom-level joint comparison: ; in This represents the preset collinearity threshold constant, with a value set from -0.50 meters to +0.50 meters; calculated using modern numerical methods. When the value is negative and the absolute value is less than 0.50 meters, it indicates that there is a slight physical obstruction gap between the first candidate short segment and the second candidate short segment in the collinear principal axis direction.

[0048] When the collinearity inequality holds, the system performs connectivity merging, outputting the endpoint coordinate range of the overall selected segment after connectivity merging. The connection and merging action automatically resumes the selected broken lines caused by open room doorways or temporary obstacles, establishing the physical integrity of the same load-bearing component on the mechanical calculation load-guiding axis.

[0049] When assembling the structural stiffness matrix at the bottom layer of the masonry structure stress analysis software, the boundary condition requirement of zero tolerance closure of the intersection points of the wall network nodes is met. The endpoint node extension anchoring operator is used to eliminate the geometric fracture gaps at the endpoints left by the physical radar blind zone in the indoor corner.

[0050] Extend the anchoring module to the endpoint position vector of the candidate load-bearing interior wall Normalized direction unit vector along the endpoint position vector Initiate ray tracing and jointly calculate the extension parameters of the intersection point between the ray and the straight line containing the wall segment of the intersecting target. Intersection point spatial coordinate vector : ; ; in and Represents the endpoint position vector The values ​​of the x-coordinate and y-coordinate in an orthogonal two-dimensional plane are both in meters; and Represents the normalization towards a unit vector The horizontal and vertical orthogonal components are constrained by the Manhattan orthogonal space condition. and The numerical combination is always equal to 1 and 0 or always equal to 0 and 1, and is dimensionless; , , The coefficients of the standard general form equation of the plane line containing the intersecting wall segments represent the wall segments belonging to the set of outer wall boundary segments. Alternatively, adjacent load-bearing interior walls can be used as candidates, all in meters; Represents the scalar length of the ray extension obtained from the solution, in meters; This represents the coordinate vector of the intersection points of the two-dimensional surface nodes established by the solution, in meters.

[0051] The underlying execution node adsorption verification logic is as follows: ; in This represents the maximum permissible extension threshold, set to a value between 0.10 meters and 0.80 meters. When the relationship holds, the system forcibly updates and replaces the original endpoint coordinates of the candidate load-bearing interior walls with the node intersection coordinate vector. .

[0052] The closed-loop verification action establishes a closed mechanical network where the orthogonal enclosure of the exterior walls intersects with the endpoints of the interior walls. The endpoint extension and adsorption forcefully stitches together the blank corner areas not scanned by the mobile radar equipment at the inner corners of the room, ensuring the absolute closure of the transmission path when the finite element software divides the load transmission elements of the floor surface.

[0053] like Figure 5 As shown in Figure S5, all wall segments in the closed load-bearing wall grid are idealized and stretched into floor-height structural wall components, and the cross-sectional thickness is rounded to the standard module. The wall centerline of the floor-height structural wall component is extracted and used as the load transfer axis. Furthermore, in S5, all wall segments in the closed load-bearing wall grid are idealized and stretched into story-height structural wall components, and the cross-sectional thickness is rounded to the standard module. The centerline of the story-height structural wall component is extracted, including: The vertical extension height of each wall segment in the closed load-bearing wall grid is stretched and mapped from bottom to top from the bottom fitted plane to the structural calculation floor height. Calculate the actual transverse span of each wall segment, compare it with the standard modular specification library, and convert the actual transverse span into the standard value in the specification discrete library as close as possible. Extract the outline centerline corresponding to each wall segment after standard numerical conversion, and establish the outline centerline as the wall centerline.

[0054] Specifically, in the data input and output process, step S5 is the regularization stage of converting point cloud spatial segments into structural engineering mechanical component attributes. The underlying transfer object receives the preceding steps: the input end loads the closed load-bearing wall mesh output after the endpoint connection processing in step S4. The structural floor height determined by the elevation calculation in step S1. Elevation fitted to the floor level After vertical equivalent stretching, transverse section quantization, and contour alignment refinement calculations, the output terminal outputs a set of structural wall components with standard stiffness semantic specifications for each story height. ; Assembly of structural wall components with floor height Internal decoupling and encapsulation have standard specifications and values. Centerline of the wall In subsequent data links, the set of structural wall components with varying floor heights... The directional injection step S6 serves as the carrier of the basic mechanical components for compiling the intermediate data model of the structure.

[0055] The measured point cloud data was obstructed by ground debris and affected by breaks in the suspended pipelines on the roof slab, resulting in jagged, uneven upper and lower boundaries for the discrete wall sections along the vertical direction. When the general-purpose structural mechanics finite element analysis software meshes the shell elements and assembles the overall stiffness matrix, the load-bearing components must be continuously fitted to the characteristic surfaces of the floor slab to transmit vertical gravity loads. When the stretched mapping elements are vertically regularized, the spatial elevation interval constraint equations are established: ; in It represents any actual elevation component of the structural wall member along the vertical coordinate axis after stretching and regularization. Its physical dimension is length and its unit is meter. The floor level fitting elevation constant established in step S1 is represented by the unit in meters. The structural calculation floor height constant, represented by step S1 elevation calculation input, is in meters.

[0056] The underlying computational operator performs spatial coordinate substitution, aligning the bottom elevation of the point cloud of any wall segment in the closed load-bearing wall network to the fitted floor elevation. Align its top elevation to The stretching mapping establishes the vertical continuous force transmission path of masonry components within the structural calculation story height, providing upper and lower geometrically closed boundaries for finite element software to generate continuous seismic wall element meshes.

[0057] The existing masonry load-bearing walls are generally covered with a layer of plaster mortar. The actual span captured by the mobile lidar scan is the total thickness of the building decoration, including the plaster layers on both sides. Structural stress calculations rigorously determine the compressive and shear bearing capacity based on the mechanical stiffness of the core section of the solid masonry bricks. By using a proximity quantization operator to remove the dimensional overflow of the plaster layer from the measured span, the true mechanical moment of inertia of the core load-bearing section of the masonry is established. When performing section specification quantization using the proximity transformation unit, an Euclidean distance quantization operator is constructed: ; in The actual span of the inner wall's central core area, represented by the decoupling solution output of operator S3, is in meters. Represents any candidate standard masonry specification variable that is traversed and iterated within the standard modular specification library, in meters; This represents the set of standard masonry modules; the set of standard masonry modules is limited to a library of standard load-bearing brick wall thicknesses within the range of 0.18 meters to 0.36 meters. The representative quantitative calculation establishes standard numerical values, with the unit being meters. The underlying computational units calculate the absolute value of the algebraic difference item by item. And the minimum term is locked. Quantization is converted into an inverse model, which assigns mechanical section stiffness properties that conform to the design specifications for masonry structures.

[0058] The underlying analytical engine of the general-purpose structural mechanics software uses one-dimensional load-conducting segments or two-dimensional mid-surface shells to represent solid wall components. The load transmission axis represents the mechanical neutral axis of the component resisting horizontal seismic shear force and in-plane bending moment. The original point cloud is distributed as a three-dimensional scattered cluster in space, possessing spatial geometric volume but lacking the semantic meaning of axial topological connections. When solving the contour centerline coordinates of the contour centering element, it follows the orthogonal bisecting algebraic equation: ; ; in and The x-coordinates of the left and right outer contours of the target wall segment in the horizontal two-dimensional projection plane are represented by the values ​​in meters. and The vertical coordinates represent the outer contours of the left and right sides of the target wall segment, both in meters. and The x-coordinate and y-coordinate values ​​of the wall centerline established by the solution are both in meters.

[0059] The bottom-level operator continuously solves the midpoint coordinate sequence along the longitudinal main extension axis of the component and fits and connects it; further, for the endpoints of the center lines of multi-story high structural wall components that intersect in a T-shape, right angle, or cross shape at the spatial intersection, the system solves the common geometric intersection point by simultaneously establishing the plane algebraic equations of the lines containing the intersecting center lines: ; ; in , , and , , These represent the constant coefficients of the plane equations for the two intersecting centerlines. After solving for the unique intersection point coordinates, the system forcibly snaps and aligns the endpoint coordinates of all wall centerlines connected in the intersection area to the same intersection point coordinates, establishing the geometric consistency of the nodes of the structural force transmission axis. The refinement process reduces and normalizes the point cloud patch cluster with three-dimensional volume into a single-line load conduction topology interface at the bottom layer of the structural analysis solver.

[0060] like Figure 5 As shown in Figure S6, the structural wall components of the story height are mapped to the wall centerline to generate an intermediate structural data model, which is then exported as the underlying interface model of the structural stress calculation software. Furthermore, in S6, mapping the floor height structural wall components to the wall centerline to generate the intermediate structural data model includes: Obtain the set of three-dimensional coordinates of the endpoints of each wall centerline in the orthogonal reference space; Associate the set of three-dimensional coordinates of the endpoints corresponding to the center lines of each wall, the matched standard section values, and the component stress attribute labels; The topology encapsulation operator is invoked to package the associated endpoint 3D coordinate set, standard section values, and component stress attribute labels into a structural intermediate data model.

[0061] In S6, the exported underlying interface model for structural stress calculation software includes: The endpoint 3D coordinate set and standard section value in the intermediate data model of the structure are analyzed, and the data interface conversion module is called to directionally bridge the endpoint 3D coordinate set and standard section value into the underlying input model of the general structural mechanics stress analysis software. Based on the three-dimensional coordinate set of the endpoints and the stress attribute labels of the component, a finite element mesh file is generated for elastoplastic stress calculation using general finite element analysis software; Based on the node connection relationships within the intermediate structural data model, a simulation calculation script is generated to drive the dynamic analysis solver to perform internal force combination calculations, thus enabling the direct export of the underlying interface model of the structural stress calculation software.

[0062] Specifically, in the data input and output process, step S6 serves as the final stage connecting the reverse engineering of the measured point cloud to the structural mechanics verification base. The underlying data flow follows a directional compilation and bridging path: the input end receives the set of floor height structural wall components output after quantization and regularization in the preceding step S5. ; Assembly of structural wall components with floor height Internally decoupled and encapsulated, each wall segment contains corresponding standard values. Centerline of the wall After endpoint coordinate extraction, topology association packaging, and software computing interface compilation and transfer, the output end outputs three types of industrial software mechanics input low-level files: the first type is the low-level input model of general structural mechanics force analysis software, the second type is the general finite element analysis mesh file, and the third type is the incremental dynamic elastoplastic simulation calculation script. In the full-link business closed loop, the low-level interface file output in step S6 directly terminates the point cloud geometry processing process and is loaded by the downstream structural engineering professional calculation software in the background to initiate the existing masonry residential building seismic bearing capacity verification and combination calculation.

[0063] Pure spatial point cloud patches only record the geometric coordinates of entities, lacking the component topological relationships and material physics role definitions required for the underlying stiffness matrix to be established by the finite element analysis solver. Topology encapsulation operators are used to construct the intermediate data model of the structure. At that time, it follows the set expression equation: ; in The standard values ​​corresponding to each component are in meters. This represents the force attribute label variable; the backend's judgment logic for the force attribute label is: when the standard value... If the component reaches the set load-bearing thickness threshold (0.24 meters), and all endpoints of the component's centerline are continuously anchored to the outer wall boundary frame of the closed load-bearing wall network or adjacent load-bearing wall nodes, and the component's height-to-thickness ratio is less than the upper limit of 24 for load-bearing walls specified in the code, based on the structural calculation floor height determined in step S1, the system automatically assigns a load-bearing seismic wall role code. If the component meets the thickness threshold but its two ends are not connected to the load-bearing wall network or its height-to-thickness ratio exceeds 24, it is identified as a non-load-bearing lightweight thick-walled partition and assigned a non-stressed infill wall role code. This feature eliminates the semantic gap between the reconstructed surface and the calculation input.

[0064] When assembling the stiffness matrix of the shear wall element at the bottom layer of the structural stress calculation software, the input interface does not recognize scattered patch data; it only recognizes the equivalent mechanical inertia parameters determined by the geometric length of the axis and the thickness specified in the code. After the data interface conversion module oriented and parses the intermediate structural data model, the bottom layer automatically calculates the inertia parameters of the member sections and writes them into the software data stream. ; ; ; in The in-plane section moment of inertia representing the resistance of a structural member to horizontal seismic forces, expressed in meters to the fourth power. The horizontal span of the member is obtained from the directional analytical solution obtained by the data interface conversion module. Equivalent horizontal cross-sectional area and the moment of inertia of the cross section in the plane The data is then packaged and compiled into a standard converted text file that can be recognized by the underlying layer of general structural mechanics stress analysis software. The format of the converted text file is limited to either Extensible Markup Language (XML) or YDB (Yellow Database) format, enabling direct loading of the input model into the underlying layer of the computational structure. The data interface bridging feature replaces the inefficient interaction of traditional structural engineers manually measuring wall lengths and manually inputting section parameters on the base map of the computational software, establishing a direct link between the geometric point cloud and the underlying input interface of the professional masonry computational software.

[0065] When developing simulation calculation scripts for the driving dynamic analysis solver based on the node connection relationships within the intermediate structural data model, the scripts are written in Python. The scripts automatically integrate constitutive constants of the masonry material's elastic and shear moduli, structural damping ratio parameters, and preset input time-history recording functions of the base ground motion horizontal acceleration. The script engine drives the background solver to load the mesh and automatically solve the nonlinear dynamic equilibrium equations. ; in The independent variable representing the earthquake's shaking time is in seconds. Represents the overall nodes of the structure in time The corresponding displacement vector, in meters; The vector represents the time-history velocity independent variable of the node, in meters per second; The vector of independent variables representing the absolute acceleration of the node time history, in meters per second squared. It represents the overall lumped mass matrix of the structure, which is generated by assembling the sum of the products of the geometric volume of the components and the density of the masonry material. The physical dimension is mechanical mass, and the unit is kilogram. Represents the overall Rayleigh damping matrix of the structure, with units of Newton-seconds per meter; Represents the nonlinear elastoplastic internal force resistance vector of the structural nodes, in Newtons; The vector representing the influence of the horizontal direction of the earthquake on the independent variable is dimensionless. This represents the time history recording function of the base ground motion horizontal acceleration, expressed in meters per second squared. The simulation script's direct export feature allows engineers to initiate time-history seismic analysis by running the script in the software background with a single click, supporting the direct flow from measured modeling data to the simulation force matrix.

[0066] Example 2: In the design and reinforcement of existing masonry residential buildings in urban renewal old communities, engineers use mobile laser scanning equipment to collect point clouds indoors and need to directionally bridge them to the underlying interface model of the structural stress calculation software. Faced with the on-site environment where the first floor has a full-height wardrobe against the wall, pipelines hanging from the ceiling, and the walls are plastered with decorative mortar, the following underlying technical problems exist: the mobile scan only captures the surface layer of the wall, and the core of the solid brick is an echo blind zone. Conventional visual operators cannot search for strip-shaped areas with point cloud support on both sides and a sparse point cloud core in the middle, which can easily lead to the omission of load-bearing interior walls; existing reconstruction technology lacks a priori screening mechanism that combines the applied span of the structural calculation floor height with the standard masonry module set, the vertical extension continuity, and the continuous sparseness of the entire floor height, which can easily misjudge the furniture as a load-bearing structural wall; the point cloud patches lack the semantics of the wall centerline and cross-sectional stiffness, which makes it impossible for the measured discrete data to be directly mapped to the intermediate structural data model through the data interface conversion module to initiate elastoplastic combination verification. To address the aforementioned problems, this invention provides a method for reverse identification and modeling of indoor point cloud wall components for stress analysis of existing masonry structures, the structure of which is as follows: Figure 1 As shown. The specific implementation process of this method is as follows: The orthogonal spatial carrier and vertical load boundaries are established through coordinate unification and elevation frequency histograms. Two-dimensional orthogonal tracing isolates point cloud interference in outdoor suspended areas and generates a set of exterior wall boundary segments. Addressing the physical mechanism of empty echoes within the moving scanned entity, the sparse band of the wall core is decoupled, and a combination of width modulus, top tolerance, and vertical layer density is applied for prior screening. This directly removes pseudo-components attached to the wall to obtain candidate load-bearing interior walls. Collinear clustering and endpoint adsorption stitching are used to scan fracture gaps, establishing the force transmission path of the closed load-bearing wall network. Geometric patches are equivalently stretched and rounded, and the wall centerline is extracted to assign mechanical stiffness properties to the components. These features are encapsulated into an intermediate structural data model and orientedly bridged into the software's underlying files, establishing a direct data link from the discrete point cloud to the structural mechanics verification base.

[0067] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for reverse identification and modeling of indoor point cloud wall components for stress analysis of existing masonry structures, characterized in that, Includes the following steps: S1. Obtain the original indoor 3D point cloud and unify the spatial coordinates to obtain the single-layer structure foundation point cloud. Based on the statistical vertical projection distribution characteristics of the single-layer structure foundation point cloud, determine the calculated floor height of the structure. S2. Project the single-layer structure base point cloud onto a horizontal two-dimensional plane, extract the outer contour features and perform orthogonal tracing to generate a set of outer wall boundary wall segments; S3. Establish a statistical grid for the projection area of ​​the single-layer structural foundation point cloud in the horizontal two-dimensional plane and slide and scan along the main axis to search for strip-shaped areas with point cloud support on both sides and a sparse point cloud wall core in the middle to obtain candidate inner walls. Combine the calculated floor height of the structure to apply a combination of prior screening conditions of cross-sectional width modulus, vertical top extension continuity, and continuous sparseness of the entire floor height of the inner wall candidates to eliminate pseudo-components and obtain candidate load-bearing inner walls. S4. Perform collinearity merging of the candidate load-bearing inner walls and the set of boundary wall segments of the outer walls and connect them with the endpoint nodes to obtain a closed load-bearing wall network. S5. Idealize and stretch all wall segments in the closed load-bearing wall network into story-height structural wall components and round the cross-sectional thickness to the standard module. Extract the wall centerline of the story-height structural wall component. The wall centerline is used as the load transfer axis. S6. Map the floor height structural wall components to the wall centerline to generate an intermediate structural data model, and export it as the underlying interface model of the structural stress calculation software.

2. The method for reverse identification and modeling of indoor point cloud wall components for stress analysis of existing masonry structures according to claim 1, characterized in that, In S1, obtaining the original indoor 3D point cloud and unifying the spatial coordinates to obtain the basic point cloud of the single-layer structure includes: Receive the original indoor 3D point cloud collected by the mobile laser scanning device and perform voxel mesh downsampling processing, and call the statistical filter to perform point cloud noise removal processing; Calculate the normal vector features of the denoised point cloud surface, and call the principal component analysis operator to solve the Manhattan orthogonal principal axis direction corresponding to the original indoor 3D point cloud; Rigid body rotation normalization alignment is performed on the point cloud spatial coordinate system according to the principal axis direction, and single-layer slicing is performed according to the set three-dimensional limits to obtain the single-layer structural basic point cloud.

3. The method for reverse identification and modeling of indoor point cloud wall components for stress analysis of existing masonry structures according to claim 1, characterized in that, In S1, determining the calculated layer height of the structure based on the statistical vertical projection distribution characteristics of the single-layer structure's basic point cloud includes: Statistical analysis of the elevation frequency histogram distribution of the single-layer structure base point cloud along the vertical coordinate axis direction; The high-frequency distribution intervals at the bottom and top levels are identified from the elevation frequency histogram distribution. The random sampling consistent fitting operator is called on the discrete point cloud falling into the bottom high frequency distribution interval and the top high frequency distribution interval respectively to obtain the bottom fitting horizontal feature surface and the top fitting horizontal feature surface. The vertical coordinates of the bottom fitted horizontal feature surface are established as the floor fitted elevation, and the vertical coordinates of the top fitted horizontal feature surface are established as the floor top slab fitted elevation. Calculate the absolute height difference between the fitted elevation of the floor slab and the fitted elevation of the floor, and establish the absolute height difference as the calculated floor height of the structure.

4. The method for reverse identification and modeling of indoor point cloud wall components for stress analysis of existing masonry structures according to claim 1, characterized in that, In S2, the step of projecting the single-layer structural foundation point cloud onto a horizontal two-dimensional plane, extracting the outer contour features and performing orthogonal tracing to generate a set of outer wall boundary segments includes: The single-layer structure basic point cloud is projected and mapped onto a horizontal two-dimensional plane along the vertical coordinate axis, and the set of endpoints of the outer envelope boundary of the point cloud in the horizontal two-dimensional plane is extracted. Under the prior constraints of the Manhattan orthogonal space, polyline tracing is performed sequentially on the set of endpoints of the outer envelope boundary. The envelope polyline generated by the tracking is discretized into multiple horizontally oriented straight line segments parallel to the horizontal principal axis and multiple vertically oriented straight line segments parallel to the vertical principal axis. The horizontal and vertical straight line segments that are perpendicularly connected end to end are encapsulated into an orthogonal closed geometric frame to generate the set of outer wall boundary wall segments.

5. The method for reverse identification and modeling of indoor point cloud wall components for stress analysis of existing masonry structures according to claim 1, characterized in that, In S3, the strip-shaped region with point cloud support on both sides and a sparse point cloud core in the middle is used to obtain candidate inner walls, including: A two-dimensional statistical grid with a fixed step size is established in a horizontal two-dimensional plane, and the two-dimensional statistical grid is slid-scanned along mutually perpendicular horizontal and vertical principal axes. Within any strip-shaped area to be inspected covered by the sliding window, a sliding window detection unit is constructed, consisting of a left support area, a right support area, and a core area sandwiched between the left and right support areas; The number of first point clouds falling into the left support area, the number of second point clouds falling into the right support area, and the number of third point clouds falling into the core area are counted separately. Calculate the sparsity ratio coefficient of the number of third point clouds relative to the sum of the number of first and second point clouds, and calculate the symmetry balance coefficient of the comparison between the number of first and second point clouds. When the sparsity ratio coefficient is lower than the preset sparsity threshold and the symmetry balance coefficient is higher than the preset balance threshold, the strip-shaped inspection area is established as the candidate inner wall.

6. The method for reverse identification and modeling of indoor point cloud wall components for stress analysis of existing masonry structures according to claim 1, characterized in that, In S3, the combined prior screening of the cross-sectional width modulus condition, vertical top extension continuity condition, and layered full-height continuous sparse condition applied to the candidate interior walls in conjunction with the structurally calculated floor height eliminates spurious components, resulting in the following candidate load-bearing interior walls: Calculate the actual span corresponding to the central core area of ​​the candidate interior walls, compare the actual span with the set standard modular set, and eliminate candidate interior walls whose actual span exceeds the range of the standard modular set; the standard modular set is limited to the discrete load-bearing wall thickness specifications corresponding to the preset building code; The actual observed highest elevation of the support areas on both sides of the candidate inner wall is statistically analyzed in the direction of the vertical coordinate axis. The actual observed highest elevation is compared with the effective critical height value obtained by subtracting the preset top tolerance from the calculated floor height of the structure. Candidate inner walls whose actual observed highest elevation is lower than the effective critical height value are eliminated. The space corresponding to the candidate inner wall is uniformly divided into multiple height layers along the vertical coordinate axis along the calculated layer height range of the structure. The point cloud occupancy rate of the core area inside each height layer is counted layer by layer. The candidate inner wall that meets the following conditions is eliminated: there are multiple consecutive height layers, and the point cloud occupancy rate of the core area inside each of the multiple consecutive height layers exceeds the preset solidification threshold, thus obtaining the candidate load-bearing inner wall.

7. The method for reverse identification and modeling of indoor point cloud wall components for stress analysis of existing masonry structures according to claim 1, characterized in that, In S4, the process of merging collinear segments of the candidate load-bearing inner walls and the set of boundary wall segments of the outer walls and connecting them with the endpoint nodes to obtain a closed load-bearing wall network includes: Each of the load-bearing interior wall candidates and the set of exterior wall boundary segments are subjected to collinear clustering according to the main axis direction and spatial interval; Compare the length of the overlapping area of ​​the collinear segments, and perform a process to connect and merge adjacent short wall segments whose overlapping area length exceeds the set collinearity threshold; Perform the wall network node closure check action to limit the outer wall boundary wall segment set to close end to form a closed outer contour, and limit the end nodes of each load-bearing inner wall candidate to extend and anchor to the surface intersection of the outer wall boundary wall segment set or adjacent load-bearing inner wall candidates to obtain the closed load-bearing wall network.

8. The method for reverse identification and modeling of indoor point cloud wall components for stress analysis of existing masonry structures according to claim 1, characterized in that, In S5, the step of idealizing and stretching all wall segments in the closed load-bearing wall network into a story-height structural wall component and rounding the cross-sectional thickness to the standard modulus, and extracting the wall centerline of the story-height structural wall component includes: The vertical extension height of each wall segment in the closed load-bearing wall network is stretched and mapped from bottom to top from the bottom fitting plane to the calculated floor height of the structure; Calculate the actual transverse span of each wall segment, compare it with the standard modular specification library, and convert the actual transverse span into the standard value in the specification discrete library as close as possible. Extract the outline centerline corresponding to each wall segment after standard numerical conversion, and establish the outline centerline as the centerline of the wall.

9. The method for reverse identification and modeling of indoor point cloud wall components for stress analysis of existing masonry structures according to claim 1, characterized in that, In S6, the step of mapping the floor height structural wall components to the wall centerline to generate an intermediate structural data model includes: Obtain the set of three-dimensional coordinates of the endpoints of the centerlines of each wall in the orthogonal reference space; Associate the three-dimensional coordinate set of the endpoints corresponding to the center lines of each wall, the matched standard section values, and the component stress attribute labels; The topology encapsulation operator is invoked to package the associated endpoint 3D coordinate set, standard section values, and component stress attribute labels into the intermediate data model of the structure.

10. The method for reverse identification and modeling of indoor point cloud wall components for stress analysis of existing masonry structures according to claim 1, characterized in that, In S6, the exported low-level interface model for structural stress calculation software includes: The endpoint 3D coordinate set and standard section value in the intermediate data model of the structure are analyzed, and the data interface conversion module is called to directionally bridge the endpoint 3D coordinate set and standard section value into the underlying input model of the general structural mechanics stress analysis software. Based on the three-dimensional coordinate set of the endpoints and the stress attribute labels of the component, a finite element mesh file is generated for elastoplastic stress calculation using general finite element analysis software; Based on the node connection relationships within the intermediate data model of the structure, a simulation calculation script is generated to drive the dynamic analysis solver to perform internal force combination calculations, thus completing the direct export of the underlying interface model of the structural stress calculation software.

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