Wall surface flatness quantification system and method based on contour curvature characteristics

Abstract

The invention discloses a wall surface flatness quantification system and method based on contour curvature characteristics, and belongs to the technical field of wall surface flatness quantification, and the method specifically comprises the steps: scanning a to-be-measured wall surface, obtaining a wall surface point cloud data set, carrying out the preprocessing of the obtained wall surface point cloud data set, and obtaining the wall surface flatness based on the preprocessed wall surface point cloud data set. The method comprises the following steps: generating a curved surface fitting model of a wall surface, extracting the curvature of a wall surface contour, quantifying based on curvature deviation of wall surface flatness, defining a local flatness index of the wall surface, generating a flatness distribution map of the wall surface based on the local flatness index of the wall surface, and outputting a flatness report. The flatness of each detection point is quantified, the overall flatness index of the wall surface is automatically calculated, the method can be used for accurate quality evaluation, and errors caused by manual intervention are greatly reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of wall surface flatness quantification, and in particular to a wall surface flatness quantification system and method based on profile curvature characteristics. Background Art

[0002] In the process of building construction and interior decoration, the flatness of the wall is an important indicator for evaluating the construction quality. Traditional wall flatness detection methods are mostly based on tools such as rulers, feeler gauges, laser levels or vertical lines. These tools are mainly used to detect flatness deviations in larger areas and rely on manual operation, so they are limited by operation accuracy, detection range and human error. Common traditional methods include: 1) Ruler and feeler gauge measurement: During construction, operators often use long rulers or feeler gauges to measure the flatness of the wall. There are fewer measurement points and the readings need to be recorded manually. This method can usually only detect larger concave and convex areas, and cannot capture the tiny uneven details of the wall. It is also easily affected by human operation deviations. 2) Laser level: The laser level measures the deviation of the wall through a horizontal laser line. It has a higher detection efficiency for larger wall areas, but it is difficult to capture small fluctuations on the wall surface. 3) Photogrammetry and image processing: Photogrammetry technology can generate wall surface contours through images, but due to limited accuracy, subtle unevenness is difficult to be accurately presented.

[0003] The main shortcomings of traditional methods are low accuracy, inability to capture tiny unevenness, and cumbersome and time-consuming detection process. Modern construction requires more sophisticated detection methods to ensure the high quality of decoration projects. The flatness quantification method based on the curvature of the wall profile can accurately extract the subtle uneven features of the wall surface and generate a quantitative flatness evaluation model through data processing. This method is more suitable for high-precision detection, and can realize automated processing in a large area, reducing manual errors. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention proposes a wall flatness quantification system and method based on contour curvature characteristics, by analyzing the wall contour curvature, constructing a flatness evaluation model, and obtaining flatness information in a quantitative manner.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The wall surface flatness quantification method based on the contour curvature feature includes:

[0007] Scan the wall to be measured to obtain a wall point cloud data set;

[0008] Preprocess the acquired wall point cloud data set;

[0009] Generate a surface fitting model of the wall based on the preprocessed wall point cloud dataset;

[0010] Extract the curvature of the wall profile, quantify the curvature deviation of the wall flatness, and define the local flatness index of the wall;

[0011] Based on the local flatness index of the wall, a flatness distribution map of the wall is generated and a flatness report is output.

[0012] Specifically, the generating of the surface fitting model of the wall based on the preprocessed wall point cloud data set includes:

[0013] Set the preprocessed wall point cloud dataset as P, P={p s (x s ,y s ,z s )|s=1,2,...,N}, where p s (x s ,y s ,z s ) represents the spatial coordinates of the sth sampling point on the wall, and N represents the total number of sampling points;

[0014] Based on the preprocessed wall point cloud dataset P, the preliminary surface of the wall is generated. The specific formula is:

[0015] ,

[0016] in, represents the preliminary surface of the wall, u and v represent parameters in the parameter domain, Pk i,j Represents the control point in the control point matrix, control point Pk i,j Denoted as Pk i,j (x i,y ,y i,j ,z i,j ), represents the weight of the control point, N i,p (u) represents the B-spline basis function in the u direction, N j,q (v) represents the B-spline basis function in the v direction, m and n represent the number of control points, and p and q represent the order of the basis function in the u and v directions, respectively;

[0017] The point cloud data of the wall is optimized and the chord length distance between adjacent sampling points is calculated. The specific formula is:

[0018] ,

[0019] Among them, d s represents the chord length distance between the sth adjacent sampling points, (x s+1 ,y s+1,z s+1 ) represents the sampling point p s+1 The spatial coordinates of

[0020] Calculate the parameterized value of the cumulative chord length of the sampling point. The specific formula is:

[0021] ,

[0022] Among them, t s Represents the parameterized value of the cumulative chord length at the sampling point, d k Represents the chord length distance between the kth adjacent sampling points.

[0023] Specifically, the generating of the surface fitting model of the wall based on the preprocessed wall point cloud data set also includes:

[0024] Select control point Pk according to the complexity of the wall i,j The number of and adjust the weight To minimize the initial surface fitting error of the wall, the specific formula of the fitting error is:

[0025] ,

[0026] Where E represents the initial surface fitting error of the wall, Indicates that according to the parameters The calculated coordinates of the fitting surface points, Represents the sampling point P s and fitting surface points The Euclidean distance of

[0027] The accuracy of the preliminary surface fitting of the wall is scored, and whether refitting is needed is determined based on the accuracy score of the preliminary surface fitting of the wall. The specific formula for the fitting accuracy score is:

[0028] ,

[0029] Where Q represents the accuracy score of the preliminary surface fitting of the wall, represents the average error based on all sampling points, represents the standard deviation of the error, D represents the total dispersion of the preliminary surface of the wall, , and represents the weight coefficient;

[0030] Set the accuracy threshold Yz of the preliminary surface fitting of the wall nh , when Q ≥ Yz nh When Q≤Yz nhWhen the wall surface is judged to have a low accuracy, the control points are increased and the weights are adjusted, and the fitting is repeated until Q ≥ Yz nh , and get the final wall surface .

[0031] Specifically, the curvature of the wall contour is extracted, and the curvature deviation of the wall flatness is quantified to define the local flatness index of the wall, including:

[0032] Curved surface on the wall In , two sampling points are randomly selected as the sampling points for curvature calculation, and the principal curvature of the selected sampling points in the wall surface is calculated. The specific formula is:

[0033] ,

[0034] ,

[0035] in, and Represents the principal curvature of the two sampling points selected from the wall surface, S u , S v Surface representing the wall The first-order partial derivative of S uu , S uv and S vv Surface representing the wall The second-order partial derivative of ;

[0036] The sum of the absolute values ​​of the principal curvatures is used to represent the sum of the curvatures of two sampling points on the wall surface. The specific formula is:

[0037] ,in, Represents the sum of the curvatures of the two sampling points in the wall surface, It represents the absolute value function;

[0038] The sum of the curvatures of two sampling points in the wall-based surface , evaluate the local flatness of the wall and calculate the local flatness index of the wall. The specific formula is:

[0039] ,

[0040] Among them, Pzd represents the local flatness index of the wall, l mean Represents the average curvature of all sampling points on the wall.

[0041] Specifically, the method generates a flatness distribution map of the wall based on the local flatness index of the wall, and outputs a flatness report, including:

[0042] Based on the local flatness index Pzd of the wall, the wall flatness is intuitively presented using color gradient;

[0043] The generated wall flatness distribution map is presented in the form of a 3D model, combined with the actual 3D contour of the wall, and the 3D contour of the wall is enhanced and displayed;

[0044] Generates detailed flatness value reports.

[0045] Specifically, the preprocessing of the acquired wall point cloud data set includes:

[0046] Denoising and data registration;

[0047] The denoising process uses Gaussian filtering or bilateral filtering to perform denoising on the acquired wall point cloud data set to remove measurement errors;

[0048] The data registration adopts multi-view scanning and uses the ICP algorithm to perform data registration.

[0049] A wall surface flatness quantification system based on contour curvature features, used to implement the wall surface flatness quantification method based on contour curvature features, comprising: a data acquisition module, a data preprocessing module, a surface fitting module, an index definition module and a result output module;

[0050] The data acquisition module is used to scan the wall surface to be measured and obtain a wall surface point cloud data set;

[0051] The data preprocessing module is used to preprocess the acquired wall point cloud data set;

[0052] The surface fitting module is used to generate a surface fitting model of the wall based on the preprocessed wall point cloud data set;

[0053] The index definition module is used to extract the curvature of the wall profile, quantify the curvature deviation of the wall flatness, and define the flatness index of the local wall surface;

[0054] The result output module is used to generate a flatness distribution map of the wall surface based on the local flatness index of the wall surface, and output a flatness report.

[0055] Specifically, the surface fitting module includes: a preliminary fitting unit, a fitting error calculation unit, a fitting accuracy scoring unit and a refitting unit;

[0056] The preliminary fitting unit is used to generate a preliminary curved surface of the wall based on the preprocessed wall point cloud data set;

[0057] The fitting error calculation unit is used to optimize the point cloud data of the wall surface and calculate the error of the wall surface fitting;

[0058] The fitting accuracy scoring unit is used to score the optimized fitting accuracy;

[0059] The refitting unit is used to set a precision threshold for the preliminary surface fitting of the wall surface. When the fitting precision does not meet the standard, control points are added and weights are adjusted to perform the fitting again.

[0060] Specifically, the index definition module includes: a principal curvature calculation unit, a curvature representation unit and a flatness index definition unit;

[0061] The principal curvature calculation unit is used to calculate the principal curvature at two sampling points selected from the curved surface of the wall;

[0062] The curvature representation unit is used to represent the sum of curvatures of two sampling points in the curved surface of the wall by using the sum of the absolute values ​​of the principal curvatures;

[0063] The flatness index definition unit is used to evaluate the local flatness of the wall based on the sum of curvatures of two sampling points in the curved surface of the wall, and calculate the local flatness index of the wall.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] 1. The present invention proposes a wall flatness quantification method based on contour curvature characteristics, which can be applied to walls of various complex structures, whether it is a flat wall, an arc wall, a three-dimensional concave-convex wall, or an arc, a curved surface, or an inclined surface, etc., solving the problem that traditional detection tools are not suitable for complex surfaces.

[0066] 2. The present invention proposes a wall flatness quantification method based on contour curvature features. By extracting the curvature features of the wall contour, the flatness of each detection point is quantified and the overall flatness index of the wall is automatically calculated. It can be used for accurate quality assessment and greatly reduces the error caused by manual intervention.

[0067] 3. The present invention proposes a wall flatness quantification method based on contour curvature characteristics. The generated three-dimensional flatness distribution map adopts color mapping, which is convenient for users to intuitively judge the flatness of different areas of the wall. It also provides detailed numerical and curvature fluctuation analysis, accurately marks areas with higher unevenness, and points out the specific location and deviation size. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A flow chart of a wall surface flatness quantification method based on contour curvature features provided by the present invention;

[0069] Figure 2 This is a diagram of the system architecture for quantifying wall flatness based on contour curvature features provided by the present invention. DETAILED DESCRIPTION

[0070] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, in the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate the orientation or position relationship based on the orientation or position relationship shown in the accompanying drawings, which is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "No. 1", "No. 2" and "No. 3" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. The present invention is further explained below in conjunction with specific embodiments.

[0071] Example 1

[0072] See also Figure 1 , an embodiment provided by the present invention: a wall surface flatness quantification method based on profile curvature features, comprising the following specific steps:

[0073] Step S1: Scan the wall to be measured using a high-precision 3D scanner or a laser rangefinder to obtain a wall point cloud data set;

[0074] Step S2: preprocessing the acquired wall point cloud data set;

[0075] The preprocessing includes: denoising and data registration;

[0076] Denoising: Use Gaussian filtering or bilateral filtering to reduce the noise of the acquired wall point cloud data set to remove measurement errors;

[0077] Data registration: Multi-view scanning is adopted, and the ICP (iterative closest point) algorithm is used for data registration to ensure the overall continuity of data points.

[0078] Step S3: generating a surface fitting model of the wall based on the preprocessed wall point cloud data set;

[0079] The specific steps of step S3 are:

[0080] Step S301: Set the pre-processed wall point cloud dataset as P, P={p s (x s ,y s ,z s )|s=1,2,...,N}, where p s (x s ,y s ,z s ) represents the spatial coordinates of the sth sampling point on the wall, and N represents the total number of sampling points;

[0081] Step S302: Generate a preliminary curved surface of the wall based on the preprocessed wall point cloud dataset P. The specific formula is:

[0082] ,

[0083] in, represents the preliminary surface of the wall, u and v represent parameters in the parameter domain, usually with values ​​of 0-1, Pk i,j Represents the control point in the control point matrix, control point Pk i,j Denoted as Pk i,j (x i,y ,y i,j ,z i,j ), which determines the shape of the initial curved surface of the wall. represents the weight of the control point, allowing greater influence on some points to better fit the data, N i,p (u) represents the B-spline basis function in the u direction, N j,q (v) represents the B-spline basis function in the v direction, m and n represent the number of control points, and p and q represent the order of the basis function in the u and v directions, respectively;

[0084] N i,p (u) and N j,q (v) are all B-spline basis functions, and the prior art provides B-spline basis functions;

[0085] Step S303: Optimize the point cloud data of the wall and calculate the chord length distance between adjacent sampling points. The specific formula is:

[0086] ,

[0087] Among them, d s represents the chord length distance between the sth adjacent sampling points, (x s+1 ,y s+1 ,z s+1 ) represents the sampling point p s+1 The spatial coordinates of

[0088] Step S304: Calculate the cumulative chord length parameterized value of the sampling point. The specific formula is:

[0089] ,

[0090] Among them, t s Represents the parameterized value of the cumulative chord length at the sampling point, d k Represents the chord length distance between the kth adjacent sampling points;

[0091] The cumulative chord length parameterization value of the sampling point is calculated by calculating the value from the first point to ps The ratio of the cumulative distance to the total distance maps each sampling point to the parameter interval [0,1] to ensure a smooth transition between different distances, which helps to maintain the uniformity of the surface;

[0092] Step S305: Select control point Pk according to the complexity of the wall i,j The number of and adjust the weight To minimize the initial surface fitting error of the wall, the specific formula of the fitting error is:

[0093] ,

[0094] Where E represents the initial surface fitting error of the wall, Indicates that according to the parameters The calculated coordinates of the fitting surface point, that is, the coordinates of the sampling point P on the surface S s The corresponding points, Represents the sampling point P s and fitting surface points The Euclidean distance of

[0095] In this embodiment, E also represents the total deviation from all sampling points to the fitting surface. This value is used to evaluate the quality of the fitting surface. The smaller the value, the better the fitting effect.

[0096] The initial number of control points is (m+1)×(n+1). A flat wall can use fewer control points, while a complex structure requires more control points to ensure fitting accuracy.

[0097] Step S306: Score the accuracy of the preliminary surface fitting of the wall, and determine whether refitting is required based on the accuracy score of the preliminary surface fitting of the wall. The specific formula for the fitting accuracy score is:

[0098] ,

[0099] Where Q represents the accuracy score of the preliminary surface fitting of the wall, represents the average error based on all sampling points, represents the standard deviation of the error, D represents the total dispersion of the preliminary surface of the wall, , and represents the weight coefficient, which is adjusted according to the measurement requirements to balance the overall error, discreteness and local smoothness of the fitting accuracy;

[0100] In this embodiment, based on the average error of all sampling points The formula is: , E sIt represents the preliminary surface fitting error of the wall based on the sth sampling point. The average error reflects the overall deviation of the fitting surface relative to the sampling point cloud data, which is used to measure the overall situation of the fitting accuracy. The error standard deviation The formula is: The error standard deviation indicates the degree of dispersion of the fitting error. High dispersion indicates that the quality of some areas is uneven, indicating that the control points or weights need to be optimized.

[0101] Surface discreteness is used to measure the smoothness and consistency of the surface. The discreteness calculation introduces quadratic difference error analysis. By calculating the error changes of adjacent sampling points, the smoothness of the surface is evaluated. The specific formula for the preliminary total surface discreteness D of the wall is: , M represents the number of all adjacent sampling point pairs, sl represents the neighboring points of sampling point s, Indicates that sampling points s and sl belong to the neighborhood, that is, sampling points s and sl are adjacent;

[0102] Step S307: Setting the accuracy threshold Yz of the preliminary surface fitting of the wall nh , when Q ≥ Yz nh When Q≤Yz nh When the wall surface is judged to have a low accuracy, the control points are increased and the weights are adjusted, and the fitting is repeated until Q ≥ Yz nh , and get the final wall surface .

[0103] Step S4: extracting the curvature of the wall profile, quantifying the curvature deviation of the wall flatness, and defining the flatness index of the local wall surface;

[0104] The specific steps of step S4 are:

[0105] Step S401: On the curved surface of the wall In , two sampling points are randomly selected as the sampling points for curvature calculation, and the principal curvature of the selected sampling points in the wall surface is calculated. The specific formula is:

[0106] ,

[0107] ,

[0108] in, and Represents the principal curvature of the two sampling points selected from the wall surface, S u , S v Surface representing the wall The first-order partial derivative of S uu , S uv and S vvSurface representing the wall The second-order partial derivative of ;

[0109] In this embodiment, S u is the first-order partial derivative with respect to u, which is the surface of the wall The rate of change in the u direction, S v is the first-order partial derivative with respect to v, which is the surface of the wall The rate of change in the v direction, S uu is the second-order partial derivative with respect to u, which is the surface of the wall The curvature of the surface in the u direction, S vv is the second-order partial derivative with respect to v, which is the surface of the wall The curvature of the surface in the v direction, S uv is the second-order partial derivative with respect to u and v, and is the surface of the wall Mutual influence in the u and v directions;

[0110] Step S402: The sum of the absolute values ​​of the principal curvatures is used to represent the sum of the curvatures of two sampling points on the wall surface. The specific formula is:

[0111] ,in, Represents the sum of the curvatures of the two sampling points in the wall surface, It represents the absolute value function;

[0112] Step S403: The sum of the curvatures of two sampling points in the wall surface , evaluate the local flatness of the wall and calculate the local flatness index of the wall. The specific formula is:

[0113] ,

[0114] Among them, Pzd represents the local flatness index of the wall, l mean Represents the average curvature of all sampling points on the wall.

[0115] In this embodiment, the flatness index formula reflects the relative flatness of the entire wall surface by calculating the average of the deviation values ​​between the sum of curvatures of each sampling point and the average sum of curvatures. The smaller the index Pzd is, the flatter the wall surface is.

[0116] Step S5: Based on the local flatness index of the wall, a flatness distribution map of the wall is generated, and a flatness report is output.

[0117] The specific steps of step S5 are:

[0118] Step S501: Based on the local flatness index Pzd of the wall, the wall flatness is visually presented using a color gradient;

[0119] Specifically, it includes: 1) Color mapping: converting the wall flatness information into color distribution so that the flatness of different areas of the wall can be observed at a glance. Specifically, areas with higher flatness deviation (i.e., higher unevenness) are represented by red, with obvious surface undulations, while areas with lower flatness deviation (i.e., better flatness) are represented by green, with a smoother surface;

[0120] 2) Gradient transition: The color changes from green to yellow, orange to red, indicating a gradual change from a flat area to a larger uneven area. Through the detailed distinction of gradient colors, users can judge the subtle changes in the local wall surface and intuitively understand the distribution status of the wall surface flatness;

[0121] Step S502: The generated wall flatness distribution map is presented in the form of a three-dimensional model, combined with the three-dimensional contour of the actual wall, and the three-dimensional contour of the wall is enhanced and displayed;

[0122] Specifically include: 1) View rotation: users can rotate and scale the model at will to observe the flatness of the wall from different angles, thereby conducting a comprehensive inspection of the wall;

[0123] 2) Depth and curvature feature highlighting: According to the flatness deviation of different areas, dynamic highlighting effects are set to add depth or raised visual cues to the red area to more intuitively present the location and shape of the problem area;

[0124] Step S503: Generate a detailed flatness value report.

[0125] Example 2

[0126] See also Figure 2 , another embodiment provided by the present invention: a wall surface flatness quantification system based on contour curvature features, comprising: a data acquisition module, a data preprocessing module, a surface fitting module, an index definition module and a result output module;

[0127] The data acquisition module is used to scan the wall surface to be measured and obtain a wall surface point cloud data set;

[0128] The data preprocessing module is used to preprocess the acquired wall point cloud data set;

[0129] The surface fitting module is used to generate a surface fitting model of the wall based on the preprocessed wall point cloud data set;

[0130] The index definition module is used to extract the curvature of the wall profile, quantify the curvature deviation of the wall flatness, and define the flatness index of the local wall surface;

[0131] The result output module is used to generate a flatness distribution map of the wall surface based on the local flatness index of the wall surface, and output a flatness report.

[0132] The surface fitting module includes: a preliminary fitting unit, a fitting error calculation unit, a fitting accuracy scoring unit and a refitting unit;

[0133] The preliminary fitting unit is used to generate a preliminary curved surface of the wall based on the preprocessed wall point cloud data set;

[0134] The fitting error calculation unit is used to optimize the point cloud data of the wall surface and calculate the error of the wall surface fitting;

[0135] The fitting accuracy scoring unit is used to score the optimized fitting accuracy;

[0136] The refitting unit is used to set a precision threshold for the preliminary surface fitting of the wall surface. When the fitting precision does not meet the standard, control points are added and weights are adjusted to perform the fitting again.

[0137] The index definition module includes: a principal curvature calculation unit, a curvature representation unit, and a flatness index definition unit;

[0138] The principal curvature calculation unit is used to calculate the principal curvature at two sampling points selected from the curved surface of the wall;

[0139] The curvature representation unit is used to represent the sum of curvatures of two sampling points in the curved surface of the wall by using the sum of the absolute values ​​of the principal curvatures;

[0140] The flatness index definition unit is used to evaluate the local flatness of the wall based on the sum of curvatures of two sampling points in the curved surface of the wall, and calculate the local flatness index of the wall.

[0141] In addition, the parts of the above-mentioned technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0142] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A wall surface flatness quantification method based on contour curvature features, characterized in that: include: Scan the wall to be measured to obtain a wall point cloud data set; Preprocess the acquired wall point cloud data set; Generate a surface fitting model of the wall based on the preprocessed wall point cloud dataset; Extract the curvature of the wall profile, quantify the curvature deviation of the wall flatness, and define the local flatness index of the wall; Based on the local flatness index of the wall, a flatness distribution map of the wall is generated and a flatness report is output.

2. The wall surface flatness quantification method based on contour curvature features according to claim 1, characterized in that: The method of generating a surface fitting model of the wall based on the preprocessed wall point cloud data set includes: Set the preprocessed wall point cloud dataset as P, P={p s (x s ,y s ,z s )|s=1,2,...,N}, where p s (x s ,y s ,z s ) represents the spatial coordinates of the sth sampling point on the wall, and N represents the total number of sampling points; Based on the preprocessed wall point cloud dataset P, the preliminary surface of the wall is generated. The specific formula is: ， in, represents the preliminary surface of the wall, u and v represent parameters in the parameter domain, Pk i,j Represents the control point in the control point matrix, control point Pk i,j Denoted as Pk i,j (x i,y ,y i,j ,z i,j ), represents the weight of the control point, N i,p (u) represents the B-spline basis function in the u direction, N j,q (v) represents the B-spline basis function in the v direction, m and n represent the number of control points, and p and q represent the order of the basis function in the u and v directions, respectively; The point cloud data of the wall is optimized and the chord length distance between adjacent sampling points is calculated. The specific formula is: ， Among them, d s represents the chord length distance between the sth adjacent sampling points, (x s+1 ,y s+1 ,z s+1 ) represents the sampling point p s+1 The spatial coordinates of Calculate the parameterized value of the cumulative chord length of the sampling point. The specific formula is: ， Among them, t s Represents the parameterized value of the cumulative chord length at the sampling point, d k Represents the chord length distance between the kth adjacent sampling points.

3. The wall surface flatness quantification method based on contour curvature features according to claim 2, characterized in that: The step of generating a surface fitting model of the wall based on the preprocessed wall point cloud data set also includes: Select control point Pk according to the complexity of the wall i,j The number of and adjust the weight To minimize the initial surface fitting error of the wall, the specific formula of the fitting error is: ， Where E represents the initial surface fitting error of the wall, Indicates that according to the parameters The calculated coordinates of the fitting surface points, Represents the sampling point P s and fitting surface points The Euclidean distance of The accuracy of the preliminary surface fitting of the wall is scored, and whether refitting is needed is determined based on the accuracy score of the preliminary surface fitting of the wall. The specific formula for the fitting accuracy score is: ， Where Q represents the accuracy score of the preliminary surface fitting of the wall, represents the average error based on all sampling points, represents the standard deviation of the error, D represents the total dispersion of the preliminary surface of the wall, , and represents the weight coefficient; Set the accuracy threshold Yz of the preliminary surface fitting of the wall nh , when Q ≥ Yz nh When Q≤Yz nh When the wall surface is judged to have a low accuracy, the control points are increased and the weights are adjusted, and the fitting is repeated until Q ≥ Yz nh , and get the final wall surface .

4. The wall surface flatness quantification method based on contour curvature features according to claim 1, characterized in that: The curvature of the wall contour is extracted, and the curvature deviation of the wall flatness is quantified to define the flatness index of the local wall surface, including: Curved surface on the wall In , two sampling points are randomly selected as the sampling points for curvature calculation, and the principal curvature of the selected sampling points in the wall surface is calculated. The specific formula is: ， ， in, and Represents the principal curvature of the two sampling points selected from the wall surface, S u , S v Surface representing the wall The first-order partial derivative of S uu , S uv and S vv Surface representing the wall The second-order partial derivative of ; The sum of the absolute values ​​of the principal curvatures is used to represent the sum of the curvatures of two sampling points on the wall surface. The specific formula is: ,in, Represents the sum of the curvatures of the two sampling points in the wall surface, It represents the absolute value function; The sum of the curvatures of two sampling points in the wall-based surface , evaluate the local flatness of the wall and calculate the local flatness index of the wall. The specific formula is: ， Among them, Pzd represents the local flatness index of the wall, l mean Represents the average curvature of all sampling points on the wall.

5. The wall surface flatness quantification method based on contour curvature features according to claim 1, characterized in that: The method generates a flatness distribution map of the wall based on the local flatness index of the wall, and outputs a flatness report, including: Based on the local flatness index Pzd of the wall, the wall flatness is intuitively presented using color gradient; The generated wall flatness distribution map is presented in the form of a 3D model, combined with the actual 3D contour of the wall, and the 3D contour of the wall is enhanced and displayed; Generates detailed flatness value reports.

6. The wall surface flatness quantification method based on profile curvature feature according to claim 1, characterized in that: The preprocessing of the obtained wall point cloud data set includes: Denoising and data registration; The denoising process uses Gaussian filtering or bilateral filtering to perform denoising on the acquired wall point cloud data set to remove measurement errors; The data registration adopts multi-view scanning and uses the ICP algorithm to perform data registration.

7. A wall surface flatness quantification system based on contour curvature features, used to implement the wall surface flatness quantification method based on contour curvature features as claimed in any one of claims 1 to 6, characterized in that: include: Data acquisition module, data preprocessing module, surface fitting module, indicator definition module and result output module; The data acquisition module is used to scan the wall surface to be measured and obtain a wall surface point cloud data set; The data preprocessing module is used to preprocess the acquired wall point cloud data set; The surface fitting module is used to generate a surface fitting model of the wall based on the preprocessed wall point cloud data set; The index definition module is used to extract the curvature of the wall profile, quantify the curvature deviation of the wall flatness, and define the flatness index of the local wall surface; The result output module is used to generate a flatness distribution map of the wall surface based on the local flatness index of the wall surface, and output a flatness report.

8. The wall surface flatness quantification system based on profile curvature characteristics according to claim 7, characterized in that: The surface fitting module includes: a preliminary fitting unit, a fitting error calculation unit, a fitting accuracy scoring unit and a refitting unit; The preliminary fitting unit is used to generate a preliminary curved surface of the wall based on the preprocessed wall point cloud data set; The fitting error calculation unit is used to optimize the point cloud data of the wall surface and calculate the error of the wall surface fitting; The fitting accuracy scoring unit is used to score the optimized fitting accuracy; The refitting unit is used to set a precision threshold for the preliminary surface fitting of the wall surface. When the fitting precision does not meet the standard, control points are added and weights are adjusted to perform the fitting again.

9. The wall surface flatness quantification system based on profile curvature characteristics according to claim 8, characterized in that: The index definition module includes: a principal curvature calculation unit, a curvature representation unit, and a flatness index definition unit; The principal curvature calculation unit is used to calculate the principal curvature at two sampling points selected from the curved surface of the wall; The curvature representation unit is used to represent the sum of curvatures of two sampling points in the curved surface of the wall by using the sum of the absolute values ​​of the principal curvatures; The flatness index definition unit is used to evaluate the local flatness of the wall based on the sum of curvatures of two sampling points in the curved surface of the wall, and calculate the local flatness index of the wall.

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