A system and method for quantitatively evaluating deformation of existing spatial steel structures

Abstract

The present invention relates to a system and method for quantitatively evaluating deformation of an existing spatial steel structure. The system includes a point cloud preprocessing system, a cross-section feature extraction system, a node positioning system and a deformation evaluation system. The point cloud preprocessing system includes a point cloud acquisition module and a point cloud segmentation module; the cross-section feature extraction system includes a feature direction positioning module, an orthographic parallel projection module and a cross-section centroid extraction module; the node positioning system includes a rod feature straight line extraction module and a node three-dimensional center extraction module; the deformation evaluation system includes a local deformation evaluation system and an overall deformation evaluation system. The method firstly collects point cloud data and separates the nodes and rods of the spatial steel structure point cloud model; then, automatically extracts the cross-section size and cross-section centroid of the rod; then, finds the node center coordinates; finally, according to the cross-section size of the rod and the center coordinates of the node, calculates the size error of each rod section and the initial design section and the position error of the center coordinates of each node of the structure.

Description

Technical Field

[0001] The invention belongs to the technical field of building construction, and in particular relates to a system and method for quantitatively evaluating deformation of an existing spatial steel structure. Background Art

[0002] Steel structures may deform during long-term use due to loads, environmental erosion, fatigue or other factors. If this deformation is not detected and handled in time, it may affect the integrity and safety of the structure, or even cause structural failure. In order to ensure the health and safety of steel structures, regular deformation detection is essential. The purpose of deformation detection is to identify and quantify the geometric changes of structural components, especially nodes and bars. These changes may indicate that the structure is experiencing some adverse effects and requires maintenance or reinforcement measures. Traditional deformation detection methods often rely on manual measurement, which is not only time-consuming and labor-intensive, but also limited in accuracy by the skills and experience of the surveyors. In addition, traditional manual measurement methods do not have the ability to evaluate deformation as a whole, and usually can only provide local deformation information, which may lead to insufficient evaluation of the overall performance of the structure.

[0003] Therefore, how to provide a system and method for quantitatively evaluating the deformation of existing spatial steel structures is a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0004] In response to the problems of traditional manual detection of steel structure deformation, the present invention proposes a system and method for quantitatively evaluating the deformation of existing spatial steel structures, which automatically extracts the cross-sectional feature information and node coordinates of the rods, realizes the quantitative evaluation of the rod dimensional deformation and the overall displacement of the spatial steel structure, solves the shortcomings of traditional manual operation, and improves the efficiency, timeliness and accuracy of steel structure deformation detection and evaluation.

[0005] In order to solve the above technical problems, the present invention includes the following technical solutions:

[0006] A deformation quantitative assessment system for existing spatial steel structures, comprising:

[0007] A point cloud preprocessing system, the point cloud preprocessing system includes a point cloud acquisition module and a point cloud segmentation module, the point cloud acquisition module is used to use a 3D scanner to collect point cloud data of an existing structure, perform data cleaning on the point cloud data, and transmit the cleaned data to the point cloud segmentation module; the point cloud segmentation module is used to use a segmentation algorithm to segment the structure point cloud model into two types: a node point cloud model and a rod point cloud model, and transmit the segmented rod point cloud and node point cloud to a cross-section feature extraction system and a node positioning system respectively;

[0008] A cross-sectional feature extraction system, the cross-sectional feature extraction system comprising a feature direction positioning module, an orthographic parallel projection module and a cross-sectional centroid extraction module, the feature direction positioning module adopts the best square approximation method to fit the three-dimensional straight line formula to obtain the feature straight line direction vector of each rod; the orthographic parallel projection module is used to perform orthographic parallel projection according to the plane with the feature straight line direction of the rod as the normal vector; the cross-sectional centroid extraction module detects the cross-sectional straight line based on the density detection straight line algorithm;

[0009] A node positioning system, the node positioning system includes a rod feature line extraction module and a node three-dimensional center extraction module; the feature line passes through the rod, the rod feature line extraction module determines the feature line of the rod from the cross-section centroid according to spatial analytic geometry; the node three-dimensional center extraction module solves the least squares solution of the feature line according to the feature lines of each rod at the node;

[0010] A deformation assessment system, the deformation assessment system comprising a local deformation assessment system and an overall deformation assessment system, the local deformation assessment system being used to calculate the cross-sectional size error of each rod, and the overall deformation assessment system being used to calculate the position error of each node according to the three-dimensional center of the node;

[0011] The density detection straight line algorithm includes:

[0012] Step S1, discretization interval: divide the two-dimensional point set into two directions, the x direction and the y direction, and divide the x interval range and the y interval range by a certain threshold length; wherein the threshold length needs to be determined according to the characteristics of the point set. If the point set is densely distributed, the threshold length may need to be set smaller in order to more accurately capture the distribution of points; if the point set is sparsely distributed, the threshold length can be set larger to reduce the number of intervals and simplify the calculation.

[0013] Step S2, counting: dividing the two-dimensional point set into two independent directions, x and y, and counting them in the divided length intervals, that is, calculating the number of points in each interval;

[0014] Step S3, according to the histogram of the number of points in each interval, select the first 10% intervals with the most points. These intervals contain straight lines that reflect the geometric characteristics of the cross-section. Take the mean of the points in the interval to obtain the straight lines that reflect the geometric characteristics. According to the cross-section straight line information, the cross-section centroid is obtained by integral calculation.

[0015] Furthermore, the method for fitting a three-dimensional straight line by the feature direction positioning module is as follows: Assume that there is a data set {(x1,y1,z1), (x2,y2,z2), ..., (x n ,y n ,z n)}, we want to find a three-dimensional straight line equation in the form of z=ax+by+c, so that the sum of squared errors between the fitted line and the data points is minimized;

[0016] Step S1: define the residual as the difference between the fitted line and each data point, that is: e i =z i -(ax+by+c), the goal is to minimize the sum of squares of the residuals: S=Σ(e i 2 )=Σ(z i -(ax+by+c)) 2 ; In order to find the best slope a, b and intercept c, it is necessary to minimize S by taking the derivative of S with respect to a, b and c respectively and setting the derivative equal to zero, that is

[0017] For the derivative of a: ∂S / ∂a= -2Σ(x i (z i -(ax+by+c)))=0;

[0018] For the derivative of b: ∂S / ∂b = -2Σ(y i (z i -(ax+by+c)))=0;

[0019] For the derivative of c: ∂S / ∂c = -2Σ(z i -(ax+by+c))=0

[0020] By solving the above three equations, we can obtain the optimal slope a, b and intercept c values, so that the sum of square errors between the fitted line and the data points is minimized; the above three formulas are expanded and calculated to obtain:

[0021] Σx i (z i -(ax+by+c))=0;

[0022] Σy i (z i -(ax+by+c))=0;

[0023] Σz i -aΣx i -bΣy i -nc=0;

[0024] Where n is the number of data points,

[0025] By sorting out and solving these three equations, we get the optimal expressions for slope a, b, and intercept c:

[0026] a=(nΣx i y i-Σx i Σy i ) / (nΣx 2 -(Σx) 2 );

[0027] b=(nΣx i y i -Σx i Σz i ) / (nΣy 2 -(Σy) 2 );

[0028] c=(Σz i -aΣx i -bΣy i ) / n;

[0029] By calculation, the best fit between the fitting line and the data points is obtained, and the direction vector of the fitting line is taken, that is, the characteristic line direction vector of each rod is obtained.

[0030] Furthermore, the orthographic parallel projection module comprises:

[0031] First, a set of coordinate system transformation relations is obtained to realize coordinate transformation, with the mean point of each three-dimensional axis of the rod is the coordinate origin of the transformation, and the direction vector of the bar feature line is The plane with the normal vector is used as the projection plane. At the same time, the direction of this normal vector is also the z-axis direction of the transformed coordinate system. Then the equation of the projection plane in the original coordinate system is: ;

[0032] Next, select two points on the plane with the same x direction. , , and then substitute it into the plane equation to obtain the 3D coordinates , ,by To transform the x direction of the coordinate system, take the outer product of x and z , that is, the orthographic parallel projection transformation matrix of the transformed coordinate system is obtained, and the XOY plane coordinate point after the orthographic parallel coordinate transformation is taken to obtain the orthographic parallel projection of the cross section.

[0033] Further, the rod feature straight line extraction module is used in the rod feature straight line extraction method, and the rod feature straight line extraction method includes: the rod feature straight line extraction module is used to obtain the cross-sectional centroid, and the cross-sectional centroid reflects the projection centroid of the rod on the XOY plane after the coordinate system is transformed, and the depth information in the Z direction is lost. However, since the Z axis is the normal vector of XOY, it can be assumed that the z coordinate of the spatial centroid of the rod is 0, and the centroid coordinate in the relative coordinate system is , the normal vector is the direction of the Z axis of the transformed coordinate system (the direction of the straight feature line), which determines the characteristic line of the rod (the characteristic line passes through the rod).

[0034] Furthermore, the least squares solution for solving the characteristic straight line includes:

[0035] Step S1, determine the parametric equations of the two straight lines, assuming that the parametric equation of the first straight line is:

[0036] ;

[0037] The parametric equation of the second straight line is:

[0038] ;

[0039] in, and is a known point on the line, and is the direction vector of the line, t and s are parameterized variables;

[0040] Step S2: Substitute the parametric equation of the first straight line into the parametric equation of the second straight line to obtain a system of equations about t and s:

[0041] ;

[0042] Arrange the system of equations to obtain a linear system of equations:

[0043] ;

[0044] Step S3, use the least squares method to solve the linear equations and convert the equations into a matrix form: ,in,

[0045] ;

[0046] The least squares solution can be calculated by the following formula:

[0047] ;

[0048] in, represents the transpose of A, express The inverse matrix of the solution X is used to calculate the t and s values, which are substituted into the parametric equation of the first or second straight line to obtain the coordinates of the least squares intersection point.

[0049] Step S4, combine the characteristic straight lines of each rod in pairs to find the least square solution, and then sum and average all the solutions to obtain the key points of the nodes.

[0050] The present invention also provides a method for quantitatively evaluating deformation of an existing spatial steel structure, comprising:

[0051] Step S1, using the existing spatial steel structure deformation quantitative assessment system;

[0052] Step S2, collecting point cloud data, and segmenting the nodes and rods of the spatial steel structure point cloud model;

[0053] Step S3, automatically extracting the cross-sectional dimensions and cross-sectional centroid of the rod;

[0054] Step S4, according to the extracted characteristic straight lines of the rods, find the mean coordinates of the intersections of the characteristic straight lines of the two rods connected to the node as the center of the node, and obtain the coordinates of the node center;

[0055] Step S5: Calculate the size error between each bar section and the initial design section and the position error of the center coordinates of each node of the structure according to the bar section size and the node center coordinates, so as to evaluate the local deformation and overall deformation of the structure.

[0056] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0057] The present invention provides an existing spatial steel structure deformation quantitative assessment system and method based on artificial intelligence, the system includes a point cloud preprocessing system, a cross-section feature extraction system, a node positioning system and a deformation assessment system. The point cloud preprocessing system includes a point cloud acquisition module and a point cloud segmentation module; the cross-section feature extraction system includes a feature direction positioning module, an orthographic parallel projection module and a cross-section centroid extraction module; the node positioning system includes a rod feature straight line extraction module and a node three-dimensional center extraction module; the deformation assessment system includes a local deformation assessment system and an overall deformation assessment system. The existing spatial steel structure deformation quantitative assessment method firstly collects point cloud data to separate the nodes and rods of the spatial steel structure point cloud model; then, automatically extracts the cross-sectional dimensions and cross-sectional centroid of the rod; then, according to the extracted characteristic straight lines of the rods, finds the mean coordinates of the intersections of the two rod feature straight lines connected to the node as the center of the node; finally, according to the cross-sectional dimensions of the rods and the center coordinates of the nodes, calculates the size error of each rod section and the initial design section and the position error of the center coordinates of each node of the structure, which is used to assess the local deformation and overall deformation of the structure.

[0058] Compared with the traditional existing spatial steel structure deformation detection and assessment work, the beneficial effects of the present invention are mainly as follows:

[0059] (1) Automated detection: Automatically extract the cross-sectional feature information and node coordinates of the bar, reducing the reliance on manual operation and improving the objectivity and consistency of detection.

[0060] (2) Efficiency enhancement: Automated data collection and processing significantly reduces detection time, making the detection process more efficient.

[0061] (3) Comprehensive evaluation: It can not only evaluate local deformation, but also perform displacement analysis of the entire structure, providing a more comprehensive structural performance evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A schematic diagram of a system for quantitatively evaluating deformation of an existing spatial steel structure according to an embodiment of the present invention;

[0063] Figure 2 It is a schematic diagram of the evaluation of the rod size deformation in the existing spatial steel structure deformation quantitative evaluation system in one embodiment of the present invention;

[0064] Figure 3 It is a schematic diagram of node displacement evaluation in an existing spatial steel structure deformation quantitative evaluation system in one embodiment of the present invention. DETAILED DESCRIPTION

[0065] The following is a further detailed description of a system and method for quantitatively evaluating deformation of an existing space steel structure provided by the present invention in conjunction with the accompanying drawings and specific embodiments. The advantages and features of the present invention will become more apparent from the following description.

[0066] Embodiment 1

[0067] Combine the following Figures 1 to 3 , a detailed description of the existing spatial steel structure deformation quantitative assessment system of the present invention is given.

[0068] A deformation quantitative assessment system for existing spatial steel structures, including a point cloud preprocessing system, a cross-section feature extraction system, a node positioning system, and a deformation assessment system;

[0069] The point cloud preprocessing system includes a point cloud acquisition module and a point cloud segmentation module. The point cloud acquisition module is used to use a 3D scanner to acquire point cloud data of an existing structure, clean the point cloud data, and transmit the cleaned data to the point cloud segmentation module; the point cloud segmentation module is used to use a segmentation algorithm to segment the structural point cloud model into two types: a node point cloud model and a rod point cloud model, and transmit the segmented rod point cloud and node point cloud to the section feature extraction system and the node positioning system respectively;

[0070] The cross-section feature extraction system includes a feature direction positioning module, an orthographic parallel projection module and a cross-section centroid extraction module. The feature direction positioning module uses the best square approximation method to fit the three-dimensional straight line formula to obtain the feature straight line direction vector of each rod; the orthographic parallel projection module is used to perform orthographic parallel projection on the plane with the feature straight line direction of the rod as the normal vector; the cross-section centroid extraction module detects the cross-section straight line based on the density detection straight line algorithm;

[0071] The node positioning system includes a rod feature line extraction module and a node three-dimensional center extraction module; the feature line passes through the rod, and the rod feature line extraction module determines the feature line of the rod from the cross-section centroid according to spatial analytical geometry; the node three-dimensional center extraction module solves the least square solution of the feature line according to the feature lines of each rod at the node;

[0072] The deformation evaluation system includes a local deformation evaluation system and an overall deformation evaluation system. The local deformation evaluation system is used to calculate the cross-sectional size error of each rod, and the overall deformation evaluation system is used to calculate the position error of each node according to the three-dimensional center of the node.

[0073] In particular, refer to Figure 2 The local deformation evaluation system is used to calculate the cross-sectional size error of each member, that is, the difference between the actual size and the designed size. The calculation formula can be expressed as: The actual starting and ending coordinates of the rod are (X 实际起点 , Y 实际起点 , Z 实际起点 ), (X 实际终点 , Y 实际终点 , Z 实际终点 ); The coordinates of the starting point and end point of the designed member are (X 设计起点 , Y 设计起点 , Z 设计起点 ), (X 设计终点 , Y 设计终点 , Z 设计终点 ). After determining the allowable error value according to the actual project requirements, if the bar size error is ≤ the allowable error value, it is considered that the bar section size deformation meets the requirements; otherwise, it does not meet the requirements.

[0074] In particular, refer to Figure 3 The overall deformation evaluation system is used to calculate the position error of each node based on the three-dimensional center of the node, that is, the deviation between the actual measured position and the designed position. Among them, the design position coordinates are (X 设计 , Y 设计 , Z 设计 ), while the actual measured position coordinates are (X 实际 , Y 实际 , Z 实际 ), calculated by the Euclidean distance in three-dimensional space: According to the actual project requirements, after determining the allowable value of the overall deformation error of the structure, if the position error of each node is ≤ the allowable error value, it is considered that the displacement of the structural node meets the requirements; otherwise, it does not meet the requirements.

[0075] In this embodiment, more preferably, the method for fitting a three-dimensional straight line by the feature direction positioning module is as follows: Assume that there is a data set {(x1, y1, z1), (x2, y2, z2), ..., (x n ,y n ,z n )}, we want to find a three-dimensional straight line equation in the form of z=ax+by+c, so that the sum of squared errors between the fitted line and the data points is minimized;

[0076] Step S1: define the residual as the difference between the fitted line and each data point, that is: e i =z i -(ax+by+c), the goal is to minimize the sum of squares of the residuals: S=Σ(e i 2 )=Σ(z i -(ax+by+c)) 2 ; In order to find the best slope a, b and intercept c, it is necessary to minimize S by taking the derivative of S with respect to a, b and c respectively and setting the derivative equal to zero, that is

[0077] For the derivative of a: ∂S / ∂a= -2Σ(x i (z i -(ax+by+c)))=0;

[0078] For the derivative of b: ∂S / ∂b = -2Σ(y i (z i -(ax+by+c)))=0;

[0079] For the derivative of c: ∂S / ∂c = -2Σ(z i -(ax+by+c))=0

[0080] By solving the above three equations, we can obtain the optimal slope a, b and intercept c values, so that the sum of square errors between the fitted line and the data points is minimized; the above three formulas are expanded and calculated to obtain:

[0081] Σx i (z i -(ax+by+c))=0;

[0082] Σy i (z i -(ax+by+c))=0;

[0083] Σz i -aΣx i -bΣy i -nc=0;

[0084] Where n is the number of data points,

[0085] By sorting out and solving these three equations, we get the optimal expressions for slope a, b, and intercept c:

[0086] a=(nΣx i y i -Σx i Σy i ) / (nΣx 2 -(Σx) 2 );

[0087] b=(nΣx i y i -Σx i Σz i ) / (nΣy 2 -(Σy) 2 );

[0088] c=(Σz i -aΣx i -bΣy i ) / n;

[0089] By calculation, the best fit between the fitting line and the data points is obtained, and the direction vector of the fitting line is taken, that is, the characteristic line direction vector of each rod is obtained.

[0090] In this embodiment, more preferably, the orthographic parallel projection module includes:

[0091] First, a set of coordinate system transformation relations is obtained to realize coordinate transformation, with the mean point of each three-dimensional axis of the rod is the coordinate origin of the transformation, and the direction vector of the bar feature line is The plane with the normal vector is used as the projection plane. At the same time, the direction of this normal vector is also the z-axis direction of the transformed coordinate system. Then the equation of the projection plane in the original coordinate system is: ;

[0092] Next, select two points on the plane with the same x direction. , , and then substitute it into the plane equation to obtain the 3D coordinates , ,by To transform the x direction of the coordinate system, take the outer product of x and z , that is, the orthographic parallel projection transformation matrix of the transformed coordinate system is obtained, and the XOY plane coordinate point after the orthographic parallel coordinate transformation is taken to obtain the orthographic parallel projection of the cross section.

[0093] In this embodiment, more preferably, the density detection straight line algorithm includes:

[0094] Step S1, discretization interval: divide the two-dimensional point set into two directions, the x direction and the y direction, and divide the x interval range and the y interval range by a certain threshold length; wherein the threshold length needs to be determined according to the characteristics of the point set. If the point set is densely distributed, the threshold length may need to be set smaller in order to more accurately capture the distribution of points; if the point set is sparsely distributed, the threshold length can be set larger to reduce the number of intervals and simplify the calculation.

[0095] Step S2, counting: dividing the two-dimensional point set into two independent directions, x and y, and counting them in the divided length intervals, that is, calculating the number of points in each interval;

[0096] Step S3, according to the histogram of the number of points in each interval, select the first 10% intervals with the most points. These intervals contain straight lines that reflect the geometric characteristics of the cross-section. Take the mean of the points in the interval to obtain the straight lines that reflect the geometric characteristics. According to the cross-section straight line information, the cross-section centroid is obtained by integral calculation.

[0097] Furthermore, the least squares solution for solving the characteristic straight line includes:

[0098] Step S1, determine the parametric equations of the two straight lines, assuming that the parametric equation of the first straight line is:

[0099] ;

[0100] The parametric equation of the second straight line is:

[0101] ;

[0102] in, and is a known point on the line, and is the direction vector of the line, t and s are parameterized variables;

[0103] Step S2: Substitute the parametric equation of the first straight line into the parametric equation of the second straight line to obtain a system of equations about t and s:

[0104] ;

[0105] Arrange the system of equations to obtain a linear system of equations:

[0106] ;

[0107] Step S3, use the least squares method to solve the linear equations and convert the equations into a matrix form: ,in,

[0108] ;

[0109] The least squares solution can be calculated by the following formula:

[0110] ;

[0111] in, represents the transpose of A, express The inverse matrix of the solution X is used to calculate the t and s values, which are substituted into the parametric equation of the first or second straight line to obtain the coordinates of the least squares intersection point.

[0112] Step S4, combine the characteristic straight lines of each rod in pairs to find the least square solution, and then sum and average all the solutions to obtain the key points of the nodes.

[0113] Please continue to refer to Figures 1 to 3 This embodiment also provides a method for quantitatively evaluating deformation of an existing spatial steel structure, including:

[0114] Step S1, using the existing spatial steel structure deformation quantitative assessment system;

[0115] Step S2, collecting point cloud data, and segmenting the nodes and rods of the spatial steel structure point cloud model;

[0116] Step S3, automatically extracting the cross-sectional dimensions and cross-sectional centroid of the rod;

[0117] Step S4, according to the extracted characteristic straight lines of the rods, find the mean coordinates of the intersections of the characteristic straight lines of the two rods connected to the node as the center of the node, and obtain the coordinates of the node center;

[0118] Step S5: Calculate the size error between each bar section and the initial design section and the position error of the center coordinates of each node of the structure according to the bar section size and the node center coordinates, so as to evaluate the local deformation and overall deformation of the structure.

[0119] Specifically, the present invention provides an existing spatial steel structure deformation quantitative assessment system and method based on artificial intelligence, the system includes a point cloud preprocessing system, a cross-section feature extraction system, a node positioning system and a deformation assessment system. The point cloud preprocessing system includes a point cloud acquisition module and a point cloud segmentation module; the cross-section feature extraction system includes a feature direction positioning module, an orthographic parallel projection module and a cross-section centroid extraction module; the node positioning system includes a rod feature straight line extraction module and a node three-dimensional center extraction module; the deformation assessment system includes a local deformation assessment system and an overall deformation assessment system. The existing spatial steel structure deformation quantitative assessment method firstly collects point cloud data to separate the nodes and rods of the spatial steel structure point cloud model; then, automatically extracts the cross-sectional dimensions and cross-sectional centroid of the rod; then, according to the extracted characteristic straight lines of the rods, finds the mean coordinates of the intersections of the two characteristic straight lines of the rods connected to the node as the center of the node; finally, according to the cross-sectional dimensions of the rods and the center coordinates of the nodes, calculates the size error of each rod section and the initial design section and the position error of the center coordinates of each node of the structure, which is used to assess the local deformation and overall deformation of the structure.

[0120] The above examples are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. The above embodiments only express several embodiments of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A quantitative assessment system for deformation of existing spatial steel structures, characterized in that: include: A point cloud preprocessing system, the point cloud preprocessing system includes a point cloud acquisition module and a point cloud segmentation module, the point cloud acquisition module is used to use a 3D scanner to collect point cloud data of an existing structure, clean the point cloud data, and transmit the cleaned data to the point cloud segmentation module; the point cloud segmentation module is used to use a segmentation algorithm to segment the structure point cloud model into two types: a node point cloud model and a rod point cloud model, and transmit the segmented rod point cloud and node point cloud to a section feature extraction system and a node positioning system respectively; A cross-sectional feature extraction system, the cross-sectional feature extraction system comprising a feature direction positioning module, an orthographic parallel projection module and a cross-sectional centroid extraction module, the feature direction positioning module adopts the best square approximation method to fit the three-dimensional straight line formula to obtain the feature straight line direction vector of each rod; the orthographic parallel projection module is used to perform orthographic parallel projection according to the plane with the feature straight line direction of the rod as the normal vector; the cross-sectional centroid extraction module detects the cross-sectional straight line based on the density detection straight line algorithm; A node positioning system, the node positioning system includes a rod feature line extraction module and a node three-dimensional center extraction module; the feature line passes through the rod, the rod feature line extraction module determines the feature line of the rod from the cross-section centroid according to spatial analytic geometry; the node three-dimensional center extraction module solves the least squares solution of the feature line according to the feature lines of each rod at the node; A deformation assessment system, the deformation assessment system comprising a local deformation assessment system and an overall deformation assessment system, the local deformation assessment system being used to calculate the cross-sectional size error of each rod, and the overall deformation assessment system being used to calculate the position error of each node according to the three-dimensional center of the node; The density detection straight line algorithm includes: Step S1, discretization interval: divide the two-dimensional point set into two directions, the x direction and the y direction, and divide the x interval range and the y interval range by the threshold length; Step S2, counting: dividing the two-dimensional point set into two independent directions, x and y, and counting them in the divided length intervals, that is, calculating the number of points in each interval; Step S3, according to the histogram of the number of points in each interval, select the first 10% intervals with the most points. These intervals contain straight lines that reflect the geometric characteristics of the cross-section. Take the mean of the points in the interval to obtain the straight lines that reflect the geometric characteristics. According to the cross-section straight line information, the cross-section centroid is obtained by integral calculation.

2. The existing spatial steel structure deformation quantitative assessment system according to claim 1 is characterized in that: The method for fitting a three-dimensional straight line in the feature direction positioning module: Assume that there is a data set {(x1,y1,z1), (x2,y2,z2), ..., (x n ,y n ,z n )}, we want to find a three-dimensional straight line equation in the form of z=ax+by+c, so that the sum of squared errors between the fitted line and the data points is minimized; Step S1: define the residual as the difference between the fitted line and each data point, that is: e i =z i -(ax+by+c), the goal is to minimize the sum of squares of the residuals: S=Σ(e i 2 )=Σ(z i -(ax+by+c)) 2 ; In order to find the best slope a, b and intercept c, it is necessary to minimize S by taking the derivative of S with respect to a, b and c respectively and setting the derivative equal to zero, that is For the derivative of a: ∂S / ∂a= -2Σ(x i (z i -(ax+by+c)))=0; For the derivative of b: ∂S / ∂b = -2Σ(y i (z i -(ax+by+c)))=0; For the derivative of c: ∂S / ∂c = -2Σ(z i -(ax+by+c))=0 By solving the above three equations, we can obtain the optimal slope a, b and intercept c values, so that the sum of square errors between the fitted line and the data points is minimized; the above three equations are expanded and calculated to obtain: Σx i (z i -(ax+by+c))=0; Σy i (z i -(ax+by+c))=0; Σz i -aΣx i -bΣy i -nc=0; Where n is the number of data points, By sorting out and solving these three equations, we get the best expressions for slope a, b, and intercept c: a=(nΣx i y i -Σx i Σy i ) / (nΣx 2 -(Σx) 2 ); b=(nΣx i y i -Σx i Σz i ) / (nΣy 2 -(Σy) 2 ); c=(Σz i -aΣx i -bΣy i ) / n; By calculation, the best fit between the fitting line and the data points is obtained, and the direction vector of the fitting line is taken, that is, the characteristic line direction vector of each rod is obtained.

3. The existing spatial steel structure deformation quantitative assessment system according to claim 1 is characterized in that: The orthographic parallel projection module comprises: First, a set of coordinate system transformation relations is obtained to realize coordinate transformation, with the mean point of each three-dimensional axis of the rod is the coordinate origin of the transformation, and the direction vector of the bar feature line is The plane with the normal vector is used as the projection plane. At the same time, the direction of this normal vector is also the z-axis direction of the transformed coordinate system. Then the equation of the projection plane in the original coordinate system is: ; Next, select two points on the plane with the same x direction. , , and then substitute it into the plane equation to obtain the 3D coordinates , ,by To transform the x direction of the coordinate system, take the outer product of x and z , that is, the orthographic parallel projection transformation matrix of the transformed coordinate system is obtained, and the XOY plane coordinate point after the orthographic parallel coordinate transformation is taken to obtain the orthographic parallel projection of the cross section.

4. The existing spatial steel structure deformation quantitative assessment system according to claim 1 is characterized in that: The rod feature straight line extraction module is used in a rod feature straight line extraction method, and the rod feature straight line extraction method includes: The bar feature line extraction module is used to obtain the cross-sectional centroid. The cross-sectional centroid reflects the centroid of the bar projected on the XOY plane after the coordinate system is transformed. The depth information in the Z direction is lost. Since the Z axis is the normal vector of XOY, assuming that the z coordinate of the centroid of the bar space is 0, the centroid coordinates in the relative coordinate system are , the normal vector is the direction of the Z axis of the transformed coordinate system, that is, the direction of the straight characteristic line, thereby determining the characteristic line of the rod.

5. The existing spatial steel structure deformation quantitative assessment system according to claim 1 is characterized in that: The least square solution for solving the characteristic straight line includes: Step S1, determine the parametric equations of the two straight lines, assuming that the parametric equation of the first straight line is: ； The parametric equation of the second straight line is: ； in, and is a known point on the line, and is the direction vector of the line, t and s are parameterized variables; Step S2: Substitute the parametric equation of the first straight line into the parametric equation of the second straight line to obtain a system of equations about t and s: ; Arrange the system of equations to obtain a linear system of equations: ; Step S3, use the least squares method to solve the linear equations and convert the equations into a matrix form: ,in, ; The least squares solution is calculated by the following formula: ; in, represents the transpose of A, express The inverse matrix of the solution X is used to calculate the t and s values, which are substituted into the parametric equation of the first or second straight line to obtain the coordinates of the least squares intersection point. Step S4, combine the characteristic straight lines of each rod in pairs to find the least square solution, and then sum and average all the solutions to obtain the key points of the nodes.

6. A method for quantitatively evaluating deformation of existing spatial steel structures, characterized in that: include: Step S1, using the existing spatial steel structure deformation quantitative assessment system as described in any one of claims 1 to 5; Step S2, collecting point cloud data, and segmenting the nodes and rods of the spatial steel structure point cloud model; Step S3, automatically extracting the cross-sectional dimensions and cross-sectional centroid of the rod; Step S4, according to the extracted characteristic straight lines of the rods, find the mean coordinates of the intersections of the characteristic straight lines of the two rods connected to the node as the center of the node, and obtain the coordinates of the node center; Step S5: Calculate the size error between each bar section and the initial design section and the position error of the center coordinates of each node of the structure according to the bar section size and the node center coordinates, so as to evaluate the local deformation and overall deformation of the structure.

Images