A method for recognizing and extracting building curtain wall structure data based on laser point cloud

Abstract

The application discloses a kind of based on laser point cloud's building curtain wall structure data identification and extraction method, its method steps are: data measurement, establish construction survey control network;Laying site, obtain the scanning data of target curtain wall;Data processing, generate point cloud model, respectively extract to point cloud data, obtain the characteristic parameter of curtain wall board and board joint;Data analysis, generate curtain wall board fitting curve;The direction of using curtain wall board edge fitting curve is obtained, the intersection is obtained, and the board plane equation is generated.The application has beneficial effects: can quickly generate the size data of curtain wall board, when composite existing building curtain wall size, efficiently batch generation processing work material single, which greatly reduces the repeated labor of first-line project engineering surveying and mapping personnel, reduces the error of project engineering surveying and mapping personnel, and then can improve data measurement efficiency and reduce measurement data processing and application cost, for fast intelligent building project engineering surveying and mapping can play a good supporting role.

Description

Technical Field

[0001] This invention relates to the field of construction surveying and mapping engineering technology, and in particular to a method for identifying and extracting building curtain wall structure data based on laser point clouds. Background Technology

[0002] To achieve specific design concepts and unique shapes, modern large-scale public buildings require curtain wall engineering with irregular and curved structures. However, the design and construction of complex curtain wall systems often face shortcomings such as limited data collection, high environmental requirements, long measurement cycles, and difficulty in meeting accuracy requirements using traditional measurement methods.

[0003] With the rapid development of building information technology (BIM), 3D laser scanning technology can automatically perform high-precision stereoscopic scanning of completed buildings to obtain point cloud data models of a series of spatial coordinate points on their surface. Compared with using a total station for single-point data measurement, 3D laser scanning technology for building engineering surveying has advantages such as large single-data acquisition volume, rapid speed, non-contact, high precision, and no site limitations. It can better solve the problems existing in traditional measurement methods. In the process of installing and renovating existing buildings and curtain walls, 3D laser scanning point cloud technology can be further used to solve problems such as inconsistencies between the positioning on the drawings and the actual site.

[0004] To address the aforementioned issues, a method for identifying and extracting building curtain wall structural data based on laser point clouds is proposed. This method significantly reduces repetitive work for frontline project surveyors, minimizes their errors, and ultimately improves data measurement efficiency while reducing the cost of data processing and application. It can provide strong support for rapid and intelligent building project surveying. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method for identifying and extracting building curtain wall structure data based on laser point cloud, so as to solve the above problems.

[0006] A method for identifying and extracting building curtain wall structure data based on laser point clouds, the method steps are as follows:

[0007] S1. Data measurement: Establish a construction measurement control network; Deploy stations to acquire scanning data of the target curtain wall;

[0008] S2. Data processing: perform splicing and noise reduction to generate a point cloud model. Extract the point cloud data separately to obtain the feature parameters of the curtain wall panels and panel seams.

[0009] S3, data analysis, calculate the point cloud data of the plate joint area, generate the curtain wall plate fitting curve through point cloud data analysis; the intersection is obtained by using the curtain wall plate edge fitting curve trend, and the plate plane equation is generated; compare the total station measurement data, if the comparison is accurate, go to the next step, if there is error, re-analyze and calculate the point cloud data;

[0010] S4, batch generate curtain wall plate material list, submit data.

[0011] Further steps S1: S11, according to the site reconnaissance, establish a high-precision construction measurement control network to ensure the accuracy of the measurement starting data;

[0012] S12, use measurement equipment to arrange measurement stations, set scanning parameters, and set target balls in the repeated area according to the site measurement target situation, complete the entire curtain wall to obtain point cloud data.

[0013] Further steps S2:

[0014] S21, sample, splice, delete and denoise the point cloud raw data scanned by the sub-station, obtain the complete point cloud data of the curtain wall through data simplification and redundancy processing, and further generate a point cloud model using the point cloud data;

[0015] S22, according to the denoising and splicing processed point cloud data, extract the curtain wall plate joint and the curtain wall structure characteristics of the curtain wall plate respectively, and obtain the feature parameters of the curtain wall plate and the curtain wall plate joint.

[0016] Further steps S3:

[0017] S31, import the curtain wall point cloud data extracted according to the curtain wall structure characteristics into Matlab, calculate the point cloud data, analyze the data in the intersection line area of different feature parameters of the curtain wall point cloud data, and obtain the curtain wall plate edge curve by using the least square method fitting;

[0018] S32, use the curtain wall plate edge fitting curve trend to obtain the intersection to generate the curtain wall plate plane equation;

[0019] S33, select a representative curtain wall area, compare the curtain wall edge length obtained by the curtain wall plate plane equation with the curtain wall plate edge length measured by the total station, and determine whether the measurement error is within the required range.

[0020] Further steps S4: batch export the curtain wall plate fitting data that meets the error requirement, generate a curtain wall plate material list, and submit the material list data to the curtain wall processing unit for curtain wall processing.

[0021] In the step S12, in order to ensure the measurement accuracy of the gap between the curtain wall plates, a low-speed mode scanning is used in the outdoor large-area scanning measurement, and the overlapping area of the scanning area is more than 30%, and three or more measurement target balls are arranged in the repeated area.

[0022] In the step S12, the measurement device comprises a three-dimensional laser scanner, a target and a support.

[0023] In the step S22, the feature parameter extraction step comprises:

[0024] (1) a threshold of the reflection intensity is set, the plate joint points are filtered out through the reflection intensity, the 1% quantile is obtained for the non-transparent curtain wall eave aluminum plate, and the other types of curtain wall forms can be adjusted according to the structure parameters;

[0025] (2) the image filtered out through the reflection intensity still has many noise points, and the RGB value is converted into a gray value after the points are preliminarily distinguished through the color;

[0026] (3) a concentrated area of the RGB value conversion feature parameter value is selected, the feature parameter value range is selected,

[0027] and the feature parameter extraction of the different structures of the curtain wall plates can be completed.

[0028] In the step S31, the curtain wall plate edge curve fitting is that after the point cloud data is imported into Matlab, the least square method is used for data reading, replacement and fitting calculation, and then the curtain wall plate curve equation is obtained.

[0029] In the step S32, the fitting of the curtain wall plate plane equation is that the plane projection method is used to generate the intersection points of the curtain wall plate curves,

[0030] the curtain wall plate plane equation is further generated by using the intersection points of the curtain wall plate curves, and the steps are as follows:

[0031] (21) if the two foot points are on the two straight line segments, the Lamta is brought in, two

[0032] foot point coordinates can be obtained, and the length thereof is calculated;

[0033] (22) if the foot point does not fall on the line segment, the next step is judged, and the lengths of the four coordinate points of the two

[0034] line segments to the other line segment are calculated;

[0035] (23) if the foot point is not on the line segment, the lengths of the line segments to the two end points are directly judged,

[0036] and the minimum is taken.

[0037] Compared with the prior art, the present application has the following beneficial effects:

[0038] 1. Compared with using a total station for single-point data measurement, using this invention for surveying building curtain wall projects has advantages such as large data acquisition volume per session, rapid speed, non-contact operation, high precision, and no site limitations, which can better solve the problems existing in traditional measurement methods.

[0039] 2. In the process of installing and renovating existing buildings and curtain walls, this invention can further utilize three-dimensional laser scanning point cloud technology to solve problems such as inconsistencies between the positioning in drawings and the actual site conditions. It can quickly generate the size data of the curtain wall panels and efficiently generate processing material lists in batches when the dimensions of the existing building curtain wall are combined.

[0040] 3. This invention will greatly reduce the repetitive work of front-line project surveyors, reduce their errors, and thus improve data measurement efficiency and reduce the cost of data processing and application. It can play a very good supporting role in the surveying of rapid and intelligent building projects. Attached Figure Description

[0041] Figure 1 This is a flowchart of the method for identifying and extracting building curtain wall structural data based on laser point clouds according to the present invention.

[0042] Figure 2 This is a diagram illustrating the feature parameter extraction of building curtain wall structure data based on laser point clouds, as presented in this invention.

[0043] Figure 3 A diagram illustrating the identification and extraction method of this invention;

[0044] Figure 4 Two figures illustrate the identification and extraction method of the present invention. Detailed Implementation

[0045] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0046] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0047] like Figure 1 and combined Figures 2 to 4 As shown, a method for identifying and extracting building curtain wall structure data based on laser point clouds is described, and the method steps are as follows:

[0048] S1. Data measurement: Establish a construction measurement control network; Deploy stations to acquire scanning data of the target curtain wall;

[0049] S2. Data processing: perform splicing and noise reduction to generate a point cloud model. Extract the point cloud data separately to obtain the feature parameters of the curtain wall panels and panel seams.

[0050] S3, data analysis, calculate the point cloud data of the plate joint area, generate the curtain wall plate fitting curve through point cloud data analysis, use the curtain wall plate edge fitting curve trend to obtain the intersection, generate the plate plane equation, compare the total station measurement data, if the comparison is accurate, then enter the next step, if there is an error in the comparison, then re-analyze and calculate the point cloud data;

[0051] S4, batch generate curtain wall plate material list, submit data.

[0052] Compared with the prior art, the present application has the following advantages:

[0053] 1. Compared with using a total station to measure single-point data, using the present application to measure and draw a building curtain wall engineering has the advantages of large amount of data collected at a time, rapid speed, non-contact, high precision and no site restrictions, and can better solve the problems existing in traditional measurement methods.

[0054] 2. The present application can further utilize three-dimensional laser scanning point cloud technology to solve problems such as inconsistencies between drawing positioning and actual site conditions during the installation and modification of existing buildings and curtain walls, and can quickly generate size data of curtain wall plates, efficiently and batch generate processing material lists when combining the size of existing building curtain walls.

[0055] 3. The present application greatly reduces the repetitive labor of first-line project engineering survey personnel, reduces the errors of project engineering survey personnel, and thus can improve data measurement efficiency and reduce measurement data processing and application costs, and can play a good supporting role for fast and intelligent building project engineering survey.

[0056] Further steps S1 are as follows:

[0057] S12, using measurement equipment according to the site measurement target situation, arranging measurement stations, setting scanning parameters, and setting target balls in the repeated area to complete the point cloud data acquisition of the entire curtain wall.

[0058] Further steps S2 are as follows:

[0059] S21, sample, splice, delete and denoise the point cloud raw data scanned by the sub-station, obtain complete point cloud data of the curtain wall through data simplification and redundancy processing, and further generate a point cloud model using the point cloud data;

[0060] S22, according to the denoised and spliced point cloud data, extract the curtain wall plate joint and the curtain wall structure characteristics of the curtain wall plate respectively, and obtain the characteristic parameters of the curtain wall plate and the curtain wall plate joint.

[0061] Wherein step S3 further comprises:

[0062] S31, the curtain wall point cloud data extracted according to the curtain wall structure characteristics is imported into Matlab, the point cloud data is calculated, the data in the intersection line region of different characteristic parameters of curtain wall point cloud data is analyzed, and the least square method is used to fit the curtain wall plate edge curve;

[0063] S32, the intersection point is obtained by using the curtain wall plate edge fitting curve trend to generate the curtain wall plate plane equation;

[0064] S33, select the representative curtain wall area, compare the curtain wall edge length obtained by the curtain wall plate plane equation with the curtain wall plate edge length measured by the total station instrument, and judge whether the measurement error is within the required range.

[0065] Wherein step S4 further comprises batch exporting the curtain wall plate fitting data meeting the error requirement, generating a curtain wall plate supply list, and submitting the material list to the curtain wall processing unit for curtain wall processing.

[0066] Wherein step S12 in outdoor large area scanning measurement, in order to ensure the measurement accuracy of the gap between the curtain wall plates, low speed mode scanning is used, the overlapping area of the scanning area is more than 30%, and more than three measuring target balls are set in the repeated area.

[0067] Wherein the measuring equipment in step S12 comprises a three-dimensional laser scanner, a target and a support.

[0068] The feature parameter extraction step in step S22 is:

[0069] (1) set the threshold of reflection intensity, filter out the plate joint points through the reflection intensity, and obtain the 1% quantile for the non-transparent curtain wall eaves aluminum plate, and other types of curtain wall forms can be adjusted according to the structure parameters;

[0070] The core code is as follows:

[0071] #set the threshold of reflection intensity, filter out the plate joint points through the reflection intensity, and obtain the 1% quantile

[0072] threshold=df['Intensity'].quantile(0.01)

[0073] df_bf=df[df['Intensity']<=threshold]#plate joint point df_bf['color']=df.apply(lambda x:[x['Red'] / 255,x['Green'] / 255,x['Blue'] / 255],axis=1)

[0074] (2) The image filtered by reflection intensity still has a lot of noise. It needs to be further distinguished by the color of the points and then the RGB values ​​are converted to grayscale values. The code is as follows:

[0075] # Convert RGB to grayscale values; grayscale conversion is used here.

[0076] df_bf['gray']=df_bf.apply(lambda x:0.299*x['Red']+0.578*x['Green']+0.114*x['Blue'],axis=1)

[0077] sns.kdeplot(df_bf['gray'])

[0078] (3) Select the concentrated region of RGB value conversion feature parameter values, and select the range of feature parameter values.

[0079] This allows for the extraction of feature parameters for different structures of the curtain wall panel; where # selects the concentrated area of ​​grayscale, here the selected range is (80, 120), df_bf2 =

[0080] df_bf[(df_bf['gray']>80)&(df_bf['gray']<120)].

[0081] The curtain wall panel edge curve fitting in step S31 involves importing point cloud data into Matlab, then using the least squares method to read, permutate, and fit the data to obtain the curtain wall panel curve equation. The code is as follows:

[0082] data = load('C:\Users\ZETTAKIT\Desktop\New Folder')

[0083] (4)\l1.txt'); %12 rows and 3 columns of data, x, y, z

[0084] xData = data(:,1); % Read n rows of data, one column of data, and transpose them.

[0085] yData = data(:,2)';

[0086] zData = data(:,3)';

[0087] L = length(xData); % Get the array length

[0088] ZM = [zData; ones(1, L)]; % This corresponds to 12 columns of data.

[0089] MM = ZM * ZM';

[0090] XM = xData * ZM'; %1*2

[0091] YM = yData * ZM'; %1*2

[0092] A = (XM / MM); %1 row * 2 col, faster than XM*inv(MM)

[0093] B = (YM / MM)';

[0094] z1 = 20:30;

[0095] x1 = A(1)*z1 + A(2); % space line sub-equation 1: x = a*z + b

[0096] y1 = B(1)*z1 + B(2); % space line sub-equation 2: y = c*z + d

[0097] plot3(x1,y1,z1,'r',xData,yData,zData,'o').

[0098] The fitting of curtain wall plate plane equation in step S32 is generated by the plane projection method,

[0099] Its code

[0100] s1 = p2-p1; % direction vector

[0101] s2 = q2-q1;

[0102] res1 = ((s1*s2')*((p1-q1)*s2')-(s2*s2')*((p1-q1)*s1')) / ((s1*s1')*(s2*s2')-(s1*s2')*(s1*s2')); %lamta1

[0103] res2 = -((s1*s2')*((p1-q1)*s1')-(s1*s1')*((p1-q1)*s2')) / ((s1*s1')*(s2*s2')-(s1*s2')*(s1*s2')); %lamta2

[0104] Further generate curtain wall plate plane equation by using curtain wall plate curve intersection point, the steps are:

[0105] (21) If both feet are on two straight line segments, then Lamta is brought in, two

[0106] foot coordinates can be obtained, and the length can be calculated;

[0107] if(res1<=1&&res1>=0&&res2<=1&&res2>=0)

[0108] tmp1 = p1 + res1 * s1;

[0109] tmp2 = q1 + res2 * s2;

[0110] tmp = tmp1 - tmp2;

[0111] d = sqrt(tmp * tmp');

[0112] else

[0113] res3=(q1-p1)*s1' / (s1*s1')

[0114] (22) If the foot of the perpendicular does not fall on the line segment, the next step is required: calculate the two...

[0115] The distance from the four coordinate points of a line segment to another line segment;

[0116] if (res3>=0 && res3<=1)

[0117] tmp = q1 - (p1 + res3 * s1);

[0118] d1 = sqrt(tmp * tmp');

[0119] else

[0120] d1=sqrt(min((q1-p1)*(q1-p1)',(q1-p2)*(q1-p2)'));

[0121] end res4=(q2-p1)*s1' / (s1*s1')

[0122] (23) If the foot of the perpendicular is not on the line segment, simply determine the length of the line segment reaching the two endpoints.

[0123] Take the minimum.

[0124] if (res4>=0 && res4<=1)

[0125] tmp = q2 - (p1 + res4 * s1);

[0126] d2 = sqrt(tmp * tmp');

[0127] else

[0128] d2=sqrt(min((q2-p1)*(q2-p1)',(q2-p2)*(q2-p2)'));

[0129] end

[0130] res5 = (p1 - q1) * s2' / (s2 * s2'); % Distance from p1 to line segment q1q2

[0131] if (res5>=0 && res5<=1)

[0132] tmp = p1 - (q1 + res5 * s2);

[0133] d3 = sqrt(tmp * tmp');

[0134] else

[0135] d3=sqrt(min((p1-q1)*(p1-q1)',(p1-q2)*(p1-q2)'));

[0136] end

[0137] res6 = (p2 - q1) * s2' / (s2 * s2'); % Distance from p2 to line segment q1q2

[0138] if (res6>=0 &&res6<=1)

[0139] tmp = p2 - (q1 + res6 * s2);

[0140] d4 = sqrt(tmp * tmp');

[0141] else

[0142] d4=sqrt(min((p2-q1)*(p2-q1)',(p2-q2)*(p2-q2)'));

[0143] end

[0144] d = min(min(d1,d2),min(d3,d4)); % Take the shortest of the four. end

[0145] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for identifying and extracting building curtain wall structural data based on laser point clouds, characterized in that: The method and steps are as follows: S1. Data measurement: Establish a construction measurement control network; Deploy stations to acquire scanning data of the target curtain wall; S2. Data processing: perform splicing and noise reduction to generate a point cloud model. Extract the point cloud data separately to obtain the feature parameters of the curtain wall panel and panel seams. S3. Data analysis: Calculate the point cloud data of the panel joint area, generate the curtain wall panel fitting curve through point cloud data analysis; use the direction of the fitting curve of the curtain wall panel edge to find the intersection point and generate the panel plane equation; compare with the total station measurement data. If the comparison is accurate, proceed to the next step. If there is an error in the comparison, re-analyze and calculate the point cloud data. S4. Batch generate curtain wall panel material lists and submit the data; Step S2 further includes: S21. The original point cloud data scanned by the substation is sampled, spliced, deleted and denoised. After data simplification and redundancy processing, the complete point cloud data of the curtain wall is obtained. The point cloud data is then used to generate a point cloud model. S22. Based on the point cloud data after noise reduction and splicing, extract the curtain wall panel seams and curtain wall structural features of the curtain wall panels respectively to obtain the feature parameters of the curtain wall panels and curtain wall panel seams. Step S3 further includes: S31. Import the curtain wall point cloud data extracted based on the curtain wall structural features into Matlab, calculate the point cloud data, analyze the data in the area of ​​the intersection line of different feature parameters of the curtain wall point cloud data, and use the least squares method to fit the curtain wall panel edge curve. S32. Use the curve fitting direction of the curtain wall panel edge to obtain the intersection point and generate the plane equation of the curtain wall panel; S33. Select a representative curtain wall area, compare the curtain wall side length obtained by the curtain wall panel plane equation with the curtain wall panel side length measured by the total station, and determine whether the measurement error is within the required range. The feature parameter extraction steps in step S22 are as follows: (1) Set the threshold of reflection intensity and filter out the joint points of the panel by reflection intensity. For non-transparent curtain wall eaves aluminum panels, the threshold is 1 percentile. Other types of curtain wall forms can be adjusted according to structural parameters. (2) The image filtered by reflection intensity still has a lot of noise. It is necessary to further distinguish the points by color and then convert the RGB values ​​to grayscale values. (3) Select the concentrated area of ​​RGB value conversion feature parameter value, and select the range of feature parameter values ​​to complete the feature parameter extraction of different structures of the curtain wall panel; The curtain wall panel edge curve fitting in step S31 involves importing point cloud data into Matlab, and then using the least squares method to read, permutate, and fit the data to obtain the curtain wall panel curve equation. In step S32, the fitting of the curtain wall panel plane equation involves generating the intersection points of the curtain wall panel curves using the plane projection method, and then further generating the curtain wall panel plane equation using these intersection points. The steps are as follows: (21) If both feet of the perpendicular lie on two line segments, then by substituting Lamta, we can obtain the coordinates of the two feet of the perpendicular and then calculate their lengths. (22) If the foot of the perpendicular does not fall on the line segment, the next step is to make a judgment and calculate the length from the four coordinate points of the two line segments to the other line segment. (23) If the foot of the perpendicular is not on the line segment, you can directly determine the length of the line segment that reaches the two endpoints and take the minimum.

2. The method for identifying and extracting building curtain wall structure data based on laser point clouds as described in claim 1, characterized in that: Step S1 further includes: S11. Based on the site survey, establish a high-precision construction survey control network to ensure the accuracy of the measurement starting data; S12. Using measuring equipment, according to the on-site measurement target conditions, arrange measuring stations, set scanning parameters, and set target balls in the repeated areas to complete the mapping of the entire curtain wall and obtain point cloud data.

3. The method for identifying and extracting building curtain wall structure data based on laser point clouds as described in claim 1, characterized in that: Step S4 further involves batch exporting the fitting data of the curtain wall panels that meet the error requirements, generating a curtain wall panel supply list, and submitting the material list to the curtain wall processing unit for curtain wall processing.

4. The method for identifying and extracting building curtain wall structure data based on laser point clouds as described in claim 2, characterized in that: In step S12, during the large-area outdoor scanning measurement, a low-speed scanning mode was used to ensure the measurement accuracy of the gaps between the curtain wall panels. The overlapping area of ​​the scanning area was more than 30%, and more than three measurement target balls were set in the overlapping area.

5. The method for identifying and extracting building curtain wall structure data based on laser point clouds as described in claim 2, characterized in that: The measuring equipment in step S12 includes a 3D laser scanner, a target, and a support.

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