Building BIM model generation method and system based on point cloud segmentation, and storage medium

Abstract

The invention discloses a building BIM model generation method and system based on point cloud segmentation and a storage medium, and relates to the technical field of building BIM models, and the method comprises the steps: obtaining point cloud positioning features through a point cloud analysis method; based on the point cloud positioning features, using a feature generation method to obtain a built model, and supplementing the point cloud positioning features to obtain perfect features; acquiring a building BIM model based on the perfect features and a positioning acquisition method; the method is used for solving the problems that in an existing building BIM model generation method based on point cloud data, when the point cloud data cannot be effectively extracted due to the fact that the building components are special, the extracted point cloud data cannot effectively generate the corresponding building components due to the fact that the extraction of the point cloud data is insufficient, so that the precision of the building components is low, and the generation efficiency is high. And modeling does not conform to reality.

Description

Technical Field

[0001] The present invention relates to the technical field of building BIM models, and specifically to a method, system and storage medium for generating a building BIM model based on point cloud segmentation. Background Art

[0002] A building BIM model is a digital tool in engineering applications, with rich building information, capable of simulating the construction of a building on a computer; the BIM model digitally represents the physical and functional characteristics of a building in the form of a 3D model, covering the whole process from design, construction to operation; point cloud data refers to a set of vectors in a three-dimensional coordinate system, where each point contains three-dimensional coordinates (X, Y, Z) and can carry additional information such as RGB, reflection intensity, and timestamp, etc.; point cloud segmentation is a key technology in computer vision and three-dimensional data processing, mainly used to divide unstructured point cloud data into subsets with semantic or instance meanings.

[0003] Existing methods for generating a building BIM model based on point cloud data usually extract the point cloud data in a certain area of the model and the point cloud data on a certain plane in the point cloud data, and obtain a geometric model based on the extracted point cloud data and get the BIM model through model analysis. Although this improved method can improve the efficiency of generating building components, when the building components are special and effective areas and planes cannot be obtained, the insufficient extraction of point cloud data will lead to the ineffective generation of corresponding building components from the extracted point cloud data, resulting in low precision of building components and problems where the modeling does not match the actual situation. For example, in the patent application with the publication number CN116108526A, a method and device for generating a building component BIM model based on point cloud data are disclosed. This solution is to select the initial end cross-section plane of the original point cloud data, use the point cloud data between the initial end cross-section planes as sub-point cloud data, and obtain multiple regional point cloud data based on the sub-point cloud data, so as to obtain a geometric model and its corresponding BIM model. Other improvements for generating a building BIM model based on point cloud data are usually improvements in the recognition of point cloud data, and still cannot solve the problem that when the building components are special and the point cloud data cannot be effectively extracted, the insufficient extraction of point cloud data will lead to the ineffective generation of corresponding building components from the extracted point cloud data, resulting in low precision of building components and problems where the modeling does not match the actual situation. In view of this, it is necessary to improve the existing method for generating a building BIM model based on point cloud data. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems in the prior art to some extent. By providing a method, system and storage medium for generating a building BIM model based on point cloud segmentation, it is used to solve the problem that in the existing method for generating a building BIM model based on point cloud data, when the building components are special and it is impossible to effectively extract the point cloud data, the insufficient extraction of the point cloud data will cause the extracted point cloud data to be unable to effectively generate the corresponding building components, resulting in low accuracy of the building components and the modeling not conforming to the actual situation.

[0005] To achieve the above object, in the first aspect, the present application provides a method for generating a building BIM model based on point cloud segmentation, including the following steps: Obtain the point cloud data of multiple buildings and multiple building BIM models generated based on the point cloud data; use the point cloud analysis method to analyze all the point cloud data and all the building BIM models, and obtain the point cloud positioning features based on the analysis results; Based on the point cloud positioning features and the feature generation method of the point cloud data, obtain the built model, compare the built model with the point cloud model corresponding to the point cloud data, and supplement the point cloud positioning features based on the comparison results to obtain the perfect features; Based on the perfect features and the positioning acquisition method, obtain the point cloud positioning points in the point cloud data corresponding to the model to be generated, and use the point cloud positioning points to generate the building BIM model corresponding to the point cloud data.

[0006] Further, obtain the point cloud data of multiple buildings and multiple building BIM models generated based on the point cloud data; using the point cloud analysis method to analyze all the point cloud data and all the building BIM models, and obtaining the point cloud positioning features includes: Obtain multiple building BIM models generated based on the point cloud data, and all are denoted as point cloud models; obtain the point cloud data corresponding to each point cloud model; the point cloud analysis method includes: For any point cloud data: denote the points corresponding to the point cloud data in the point cloud model as model points; obtain the reflection intensity and RGB values of all the model points, and denote the model points that are the vertices of any plane in the point cloud model among all the model points as plane vertices; Establish a plane rectangular coordinate system, and denote it as the preliminary screening coordinate system. Among them, the coordinate points on the X-axis of the preliminary screening coordinate system that extend to the right from the coordinate origin are filled in the coordinates of each plane vertex in turn, and the unit of the Y-axis is a percentage or a constant; When the unit of the Y-axis is a percentage, punctuation is performed within the preliminary screening coordinate system based on the reflection intensity corresponding to each planar vertex, and the curve obtained by fitting all the punctuation points is denoted as the reflection vertex curve; when the unit of the Y-axis is a constant, punctuation is performed within the preliminary screening coordinate system based on the color mean value corresponding to each planar vertex, and the curve obtained by fitting all the punctuation points is denoted as the color vertex curve, where the color mean value is the average of the three values in the RGB values of the planar vertex.

[0007] Furthermore, the point cloud analysis method further includes: Denote the closed interval formed by the minimum value and the maximum value of the ordinate in the reflection vertex curve as the reflection vertex interval; denote the interval formed by the minimum value and the maximum value of the ordinate in the color vertex curve as the color vertex interval; Place the reflection vertex curve and the color vertex curve in the same preliminary screening coordinate system. When there is an intersection point between the reflection vertex curve and the color vertex curve, execute Method V; when there is no intersection point between the reflection vertex curve and the color vertex curve, perform an equal-proportion scaling on the Y-axis corresponding to the color vertex curve until there is an intersection point between the reflection vertex curve and the color vertex curve, and then execute Method V.

[0008] Furthermore, Method V includes: Denote the current preliminary screening coordinate system as the division coordinate system; for any point A on the reflection vertex curve in the division coordinate system, denote the abscissa of point A as X1; when the point on the color vertex curve with abscissa X1 is above point A, denote point A as the reflection upper point, when the point on the color vertex curve with abscissa X1 is below point A, denote point A as the reflection lower point; when the point on the color vertex curve with abscissa X1 coincides with point A, denote point A as the reflection segmentation point; Denote the curve formed by all the reflection upper points in the reflection vertex curve as the reflection upper curve, and denote the open interval formed by the abscissas of the leftmost point and the rightmost point of each reflection upper curve as the reflection upper interval; denote the curve formed by all the reflection lower points in the reflection vertex curve as the reflection lower curve, and denote the open interval formed by the abscissas of the leftmost point and the rightmost point of each reflection lower curve as the reflection lower interval.

[0009] Furthermore, Method V also includes: For any one reflection upper interval: Denote the open interval formed by the highest point and the lowest point of the ordinate of the reflection upper curve in the reflection upper interval as the upper value interval of the reflection vertex interval, and denote the open interval formed by the highest point and the lowest point of the ordinate of the color vertex interval in the reflection upper interval as the lower value interval of the color vertex interval; For any reflection lower-level interval: Denote the open interval formed by the ordinates of the highest point and the lowest point of the reflection lower-level curve in the reflection lower-level interval as the lower value interval of the reflection vertex interval, and denote the open interval formed by the ordinates of the highest point and the lowest point of the color vertex interval in the reflection lower-level interval as the upper value interval of the color vertex interval; Denote the interval that is simultaneously recorded as the upper value interval and the lower value interval in the color vertex interval as the color mixing interval; Denote the interval that is simultaneously recorded as the upper value interval and the lower value interval in the reflection vertex interval as the reflection mixing interval.

[0010] Furthermore, the point cloud analysis method further includes: Obtain the color vertex intervals and reflection vertex intervals corresponding to all point cloud data, and after executing method V, denote the intersection of the color mixing intervals corresponding to all color vertex intervals as the color feature interval, and denote the intersection of the reflection mixing intervals corresponding to all reflection vertex intervals as the reflection feature interval; For any color mixing interval that is not recorded as the color feature interval: Use the interval sorting algorithm to obtain the interval parameters of the color mixing interval. The interval sorting algorithm is: , where F is the interval parameter, c is the number of point cloud data, G i is the interval length of the upper value interval corresponding to the color mixing interval in the X-axis of the i-th point cloud data, and H j is the interval length of the lower value interval corresponding to the color mixing interval in the X-axis of the point cloud data; When the interval parameter of the color mixing interval is greater than 0, re-denote the color mixing interval as the upper value interval; when the interval parameter of the color mixing interval is less than 0, re-denote the color mixing interval as the lower value interval; when the interval parameter of the color mixing interval is equal to 0, re-denote the color mixing interval as the color feature interval; For any reflection mixing interval that is not recorded as the reflection feature interval: Based on the way of re-denoting the color mixing interval as the upper value interval, lower value interval, and color feature interval, use the interval sorting algorithm to re-denote the reflection mixing interval as the upper value interval, lower value interval, and reflection feature interval; Denote the color vertex interval containing the upper value interval, lower value interval, and color feature interval, and the reflection vertex interval containing the upper value interval, lower value interval, and reflection feature interval as the point cloud positioning feature.

[0011] Furthermore, the feature generation method includes: Randomly obtain a point cloud model and its corresponding point cloud data; Based on the point cloud positioning feature, use the positioning acquisition method to obtain the point cloud positioning points in the point cloud data; The positioning acquisition method is as follows: obtain the reflection intensity and color mean value of all points in the point cloud data. When any point α satisfies that the reflection intensity is in the upper value interval of the reflection vertex interval and the color mean value is in the lower value interval of the color vertex interval, or the reflection intensity is in the lower value interval of the reflection vertex interval and the color mean value is in the upper value interval of the color vertex interval, or the reflection intensity is in the reflection feature interval of the reflection vertex interval and the color mean value is in the color feature interval of the color vertex interval, then mark point α as the point cloud positioning point; Obtain all the point cloud positioning points in the point cloud data, and build a building BIM model based on all the point cloud positioning points, denoted as the built model; when the built model is exactly the same as the point cloud model corresponding to the point cloud data, mark the point cloud positioning feature as the perfect feature; When the built model is not exactly the same as the point cloud model corresponding to the point cloud data, mark the points corresponding to the point cloud data in the area of the built model that is different from the point cloud model as supplementary points, and based on the analysis method of analyzing model points by point cloud analysis method, use the point cloud analysis method to analyze the supplementary points, and mark the intersection of the obtained point cloud positioning feature and the existing point cloud positioning feature as the latest point cloud positioning feature.

[0012] Furthermore, supplement the point cloud positioning feature based on the comparison result to obtain the perfect feature, including: When there is a latest point cloud positioning feature, process the latest point cloud positioning feature based on the feature analysis method until the perfect feature is obtained.

[0013] In the second aspect, the present application also provides a building BIM model generation system based on point cloud segmentation, including a point cloud feature analysis module, a point cloud feature supplement module, and a BIM model generation module; The point cloud feature analysis module is used to obtain the point cloud data of multiple buildings and multiple building BIM models generated based on the point cloud data; use the point cloud analysis method to analyze all the point cloud data and all the building BIM models, and obtain the point cloud positioning feature based on the analysis result; The point cloud feature supplement module is used to obtain the built model based on the point cloud positioning feature and the point cloud data using the feature generation method, compare the built model with the point cloud model corresponding to the point cloud data, and supplement the point cloud positioning feature based on the comparison result to obtain the perfect feature; The BIM model generation module is used to obtain the point cloud positioning points in the point cloud data corresponding to the model to be generated based on the perfect feature and the positioning acquisition method, and generate the building BIM model corresponding to the point cloud data using the point cloud positioning points.

[0014] In the third aspect, the present application provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, it runs the steps in the above method.

[0015] Advantages of the present invention: This application first obtains the point cloud data of multiple buildings and multiple building BIM models generated based on the point cloud data; uses the point cloud analysis method to analyze all the point cloud data and all the building BIM models, and obtains the point cloud positioning features based on the analysis results. The advantage of this is that by using the point cloud analysis method to obtain the point cloud positioning features, it is possible to obtain the features corresponding to the key points that make up the building BIM model in the point cloud data based on the existing generation data of the building BIM model generated from the point cloud data, that is, the cloud positioning features corresponding to the planar vertices, which helps to screen the existing point cloud data through the improved features supplemented by the cloud positioning features during subsequent analysis, ensuring that the selected point cloud data can accurately generate the required BIM model. At the same time, since there is less point cloud data to be analyzed after screening, the purpose of improving the BIM model generation efficiency can still be achieved; This application also uses the feature generation method based on the point cloud positioning features and the point cloud data usage features to obtain a building model, compares the building model with the point cloud model corresponding to the point cloud data, and supplements the point cloud positioning features based on the comparison results to obtain improved features; finally, based on the improved features and the positioning acquisition method, the point cloud positioning points in the point cloud data corresponding to the model to be generated are obtained, and the point cloud positioning points are used to generate the building BIM model corresponding to the point cloud data. The advantage of this is that supplementing the point cloud positioning features based on the feature generation method and obtaining improved features can ensure that when generating the BIM model based on the improved features and the positioning acquisition method, the obtained point cloud positioning points can generate the BIM model more accurately, thereby improving the accuracy of building components and ensuring that the modeling conforms to the actual requirements. Brief Description of the Drawings

[0016] Figure 1 is the principle block diagram of the system of the present invention; Figure 2 is the step flow chart of the method of the present invention; Figure 3 is the schematic diagram of the preliminary screening coordinate system and the division coordinate system of the present invention; Figure 4 is the structural schematic diagram of the electronic device of the present invention. Detailed Embodiments

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] Embodiment 1, please refer to Figure 1As shown in the figure, the present application provides a building BIM model generation system based on point cloud segmentation, including a point cloud feature analysis module, a point cloud feature supplement module, and a BIM model generation module; The point cloud feature analysis module is used to obtain the point cloud data of multiple buildings and multiple building BIM models generated based on the point cloud data; analyze all the point cloud data and all the building BIM models using the point cloud analysis method, and obtain the point cloud positioning features based on the analysis results; The point cloud feature analysis module includes a point cloud feature analysis unit, and the point cloud feature analysis unit is configured with a point cloud feature analysis strategy, and the point cloud feature analysis strategy includes: Obtain multiple building BIM models generated based on the point cloud data, and all are recorded as point cloud models; obtain the point cloud data corresponding to each point cloud model; The point cloud analysis method includes: for any point cloud data: mark the points corresponding to the point cloud data in the point cloud model as model points; obtain the reflection intensity and RGB values of all model points, and mark the model points that are the vertices of any plane in the point cloud model as plane vertices; In the specific implementation process, since the subsequent acquisition of the point cloud positioning features is all realized based on the data of the plane vertices, when there are special components or special cross-sections in the actually to-be-generated building model, the plane vertices can be added, deleted, or modified to ensure that the point cloud positioning features obtained through analysis can meet the component requirements of the actual building, and reduce the update times of the point cloud positioning features when obtaining perfect features, thereby improving the accuracy and generation efficiency of the model when generating the BIM model based on the point cloud data; Establish a plane rectangular coordinate system, and record it as the preliminary screening coordinate system. Among them, the coordinate points on the X-axis of the preliminary screening coordinate system from the origin to the right are filled in the coordinates of each plane vertex in turn, and the unit of the Y-axis is a percentage or a constant; When the unit of the Y-axis is a percentage, punctuate in the preliminary screening coordinate system based on the reflection intensity corresponding to each plane vertex, and record the curve obtained by fitting all the punctuations as the reflection vertex curve; when the unit of the Y-axis is a constant, punctuate in the preliminary screening coordinate system based on the color mean value corresponding to each plane vertex, and record the curve obtained by fitting all the punctuations as the color vertex curve, where the color mean value is the average of the three values in the RGB value of the plane vertex; In the specific implementation process, for example, in a data analysis, when the RGB of a plane vertex obtained is (255, 15, 0), through calculation, the color mean value of this plane vertex is 90; Record the closed interval formed by the minimum and maximum values of the ordinate in the reflection vertex curve as the reflection vertex interval; record the interval formed by the minimum and maximum values of the ordinate in the color vertex curve as the color vertex interval; In the specific implementation process, for example, during a data analysis, the initially screened coordinate system obtained is as follows Figure 3 shown. Among them, the coordinate system in PP1 is the initially screened coordinate system, the curve QQ1 is the reflection vertex curve, and the curve QQ2 is the color vertex curve. Then, through analysis, it can be obtained that [yy1, yy2] is the reflection vertex interval, and [yy3, yy4] is the color vertex interval; since there is no intersection between the reflection vertex interval and the color vertex interval in PP1, therefore, by performing an equal-proportion scaling on the Y-axis corresponding to the color vertex curve, the curve QQ2 is adjusted to QQ3 in PP2, and at the same time, the coordinate system in PP2 is the divided coordinate system; Put the reflection vertex curve and the color vertex curve into the same initially screened coordinate system. When there is an intersection between the reflection vertex curve and the color vertex curve, execute method V; when there is no intersection between the reflection vertex curve and the color vertex curve, perform an equal-proportion scaling on the Y-axis corresponding to the color vertex curve until there is an intersection between the reflection vertex curve and the color vertex curve, and then execute method V; Method V includes: Denote the current initially screened coordinate system as the divided coordinate system; for any point A on the reflection vertex curve in the divided coordinate system, denote the abscissa of point A as X1; when the point on the color vertex curve with abscissa X1 is above point A, denote point A as the reflection upper point; when the point on the color vertex curve with abscissa X1 is below point A, denote point A as the reflection lower point; when the point on the color vertex curve with abscissa X1 coincides with point A, denote point A as the reflection segmentation point; Denote the curve formed by all the reflection upper points on the reflection vertex curve as the reflection upper curve, and denote the open interval formed by the abscissas of the leftmost point and the rightmost point of each reflection upper curve as the reflection upper interval; denote the curve formed by all the reflection lower points on the reflection vertex curve as the reflection lower curve, and denote the open interval formed by the abscissas of the leftmost point and the rightmost point of each reflection lower curve as the reflection lower interval; For any one reflection upper interval: Denote the open interval formed by the ordinates of the highest point and the lowest point of the reflection upper curve in the reflection upper interval as the upper value interval of the reflection vertex interval, and denote the open interval formed by the ordinates of the highest point and the lowest point of the color vertex interval in the reflection upper interval as the lower value interval of the color vertex interval; In the specific implementation process, the color vertex interval is [50, 100], and for a certain reflection upper interval, the ordinates of the highest point and the lowest point of this color vertex interval are 55 and 52 respectively. Then, (52, 55) in the color vertex interval can be denoted as the lower value interval of the color vertex interval; since in this embodiment, all the points on the reflection vertex curve are denoted as reflection upper points, reflection lower points, or reflection segmentation points, therefore, after analysis, both the color vertex interval and the reflection vertex interval can be completely divided into upper value intervals or lower value intervals to ensure the integrity of the data analysis; For any one of the lower reflection intervals: Denote the open interval formed by the ordinates of the highest point and the lowest point of the lower reflection curve in the lower reflection interval as the lower value interval of the reflection vertex interval, and denote the open interval formed by the ordinates of the highest point and the lowest point of the color vertex interval in the lower reflection interval as the upper value interval of the color vertex interval; Denote the interval that is simultaneously recorded as the upper value interval and the lower value interval in the color vertex interval as the color mixing interval; Denote the interval that is simultaneously recorded as the upper value interval and the lower value interval in the reflection vertex interval as the reflection mixing interval; In the specific implementation process, for example, during a data processing, if there is an upper value interval (53, 55) and a lower value interval (54, 56) in the color vertex interval, then (54, 55) can be denoted as the color mixing interval, while the intervals (53, 54] and [55, 56) are still the corresponding upper value interval and lower value interval.

[0019] The point cloud analysis method further includes: obtaining the color vertex intervals and reflection vertex intervals corresponding to all point cloud data, and after executing method V, denoting the intersection of the color mixing intervals corresponding to all color vertex intervals as the color feature interval, and denoting the intersection of the reflection mixing intervals corresponding to all reflection vertex intervals as the reflection feature interval; In the specific implementation process, the color mixing intervals not denoted as the color feature interval and the reflection mixing intervals not denoted as the reflection feature interval should be reclassified to ensure the integrity of data analysis. Therefore, by obtaining interval parameters, the reclassification of the above intervals can be achieved; For any color mixing interval not denoted as the color feature interval: Use the interval sorting algorithm to obtain the interval parameters of the color mixing interval. The interval sorting algorithm is: , where F is the interval parameter, c is the number of point cloud data, G i is the interval length of the upper value interval corresponding to the color mixing interval in the X-axis of the i-th point cloud data, and H j is the interval length of the lower value interval corresponding to the color mixing interval in the X-axis of the point cloud data; In the specific implementation process, for example, during a data processing, the interval lengths of the upper value intervals corresponding to the color mixing interval in the point cloud data of a color mixing interval not denoted as the color feature interval are 2, 5, and 1 respectively, and the interval lengths of the lower value intervals corresponding to the color mixing interval in the X-axis are 1, 2, and 1.5 respectively. Then, through calculation, the interval parameter is 3.5, indicating that the interval length of the color feature interval as the upper value interval is larger. Then, the color mixing interval can be re-denoted as the upper value interval; When the interval parameter of the color mixing interval is greater than 0, the color mixing interval is re - denoted as the upper - value interval; when the interval parameter of the color mixing interval is less than 0, the color mixing interval is re - denoted as the lower - value interval; when the interval parameter of the color mixing interval is equal to 0, the color mixing interval is re - denoted as the color feature interval; For any reflection mixing interval that has not been denoted as the reflection feature interval: Based on the way of re - denoting the color mixing interval as the upper - value interval, lower - value interval, and color feature interval, use the interval sorting algorithm to re - denote the reflection mixing interval as the upper - value interval, lower - value interval, and reflection feature interval; Denote the color vertex interval containing the upper - value interval, lower - value interval, and color feature interval and the reflection vertex interval containing the upper - value interval, lower - value interval, and reflection feature interval as the point cloud positioning feature.

[0020] The point cloud feature supplement module is used to obtain a built model based on the point cloud positioning feature and the feature generation method of the point cloud data usage feature, compare the built model with the point cloud model corresponding to the point cloud data, and supplement the point cloud positioning feature based on the comparison result to obtain a perfect feature; The point cloud feature supplement module includes a point cloud feature supplement unit, and the point cloud feature supplement unit is configured with a point cloud feature supplement strategy. The point cloud feature supplement strategy includes: The feature generation method includes: randomly obtaining a point cloud model and its corresponding point cloud data; using the positioning acquisition method to obtain the point cloud positioning points in the point cloud data based on the point cloud positioning feature; The positioning acquisition method is: obtain the reflection intensity and color mean value of all points in the point cloud data. When any point α satisfies that the reflection intensity is in the upper - value interval of the reflection vertex interval and the color mean value is in the lower - value interval of the color vertex interval, the reflection intensity is in the lower - value interval of the reflection vertex interval and the color mean value is in the upper - value interval of the color vertex interval, or the reflection intensity is in the reflection feature interval of the reflection vertex interval and the color mean value is in the color feature interval of the color vertex interval, point α is denoted as the point cloud positioning point; In the specific implementation process, for example, in a data processing, if the reflection intensity of point α is in the upper - value interval of the reflection vertex interval and the color mean value of point α is in the upper - value interval of the color vertex interval, it means that point α does not meet the screening conditions of the plane vertex in the point cloud analysis method. Therefore, point α does not have a high application demand for building the BIM model compared with other points. Therefore, when generating the BIM model based on the point cloud data, it is not necessary to analyze the data of point α; Obtain all the point cloud positioning points in the point cloud data, and build a building BIM model based on all the point cloud positioning points, denoted as the built model; when the built model is exactly the same as the point cloud model corresponding to the point cloud data, denote the point cloud positioning feature as the perfect feature; When the point cloud model corresponding to the built model is not exactly the same as the point cloud model, the points corresponding to the point cloud data in the area of the built model that is different from the point cloud model are recorded as supplementary points, and based on the analysis method of analyzing model points by point cloud analysis, the supplementary points are analyzed by point cloud analysis, and the intersection of the obtained point cloud positioning features and the existing point cloud positioning features is recorded as the latest point cloud positioning features.

[0021] The point cloud feature supplement strategy also includes that when there are the latest point cloud positioning features, the latest point cloud positioning features are processed based on the feature analysis method until perfect features are obtained.

[0022] The BIM model generation module is used to obtain the point cloud positioning points in the point cloud data corresponding to the model to be generated based on the perfect features and the positioning acquisition method, and use the point cloud positioning points to generate the building BIM model corresponding to the point cloud data.

[0023] Embodiment 2, please refer to Figure 2 As shown, the present application also provides a method for generating a building BIM model based on point cloud segmentation, including the following steps: Step S1, obtain the point cloud data of multiple buildings and multiple building BIM models generated based on the point cloud data; analyze all the point cloud data and all the building BIM models by point cloud analysis method, and obtain the point cloud positioning features based on the analysis results; Step S1 includes: Step S101, obtain multiple building BIM models generated based on the point cloud data, and all are recorded as point cloud models; obtain the point cloud data corresponding to each point cloud model; Step S102, the point cloud analysis method includes: Step S1021, for any point cloud data: record the points corresponding to the point cloud data in the point cloud model as model points; obtain the reflection intensity and RGB values of all model points, and record the model points at the vertices of any plane in the point cloud model among all model points as plane vertices; Step S1022, establish a plane rectangular coordinate system, and record it as the preliminary screening coordinate system. Among them, the coordinate points on the X-axis of the preliminary screening coordinate system from the origin to the right are filled in the coordinates of each plane vertex in turn, and the unit of the Y-axis is a percentage or a constant; Step S1023, when the unit of the Y-axis is a percentage, mark points in the preliminary screening coordinate system based on the reflection intensity corresponding to each plane vertex, and record the curve obtained by fitting all the marked points as the reflection vertex curve; when the unit of the Y-axis is a constant, mark points in the preliminary screening coordinate system based on the color mean corresponding to each plane vertex, and record the curve obtained by fitting all the marked points as the color vertex curve, where the color mean is the average of the three values in the RGB values of the plane vertex; Step S1024: Denote the closed interval formed by the minimum and maximum values of the ordinates in the reflection vertex curve as the reflection vertex interval; denote the interval formed by the minimum and maximum values of the ordinates in the color vertex curve as the color vertex interval. Step S1025: Place the reflection vertex curve and the color vertex curve in the same preliminary screening coordinate system. When there is an intersection point between the reflection vertex curve and the color vertex curve, execute Method V; when there is no intersection point between the reflection vertex curve and the color vertex curve, perform an equal-proportion scaling of the Y-axis corresponding to the color vertex curve until there is an intersection point between the reflection vertex curve and the color vertex curve, and then execute Method V. Method V includes: Step VV1: Denote the current preliminary screening coordinate system as the division coordinate system; for any point A on the reflection vertex curve in the division coordinate system, denote the abscissa of point A as X1; when the point on the color vertex curve with abscissa X1 is above point A, denote point A as the reflection upper point; when the point on the color vertex curve with abscissa X1 is below point A, denote point A as the reflection lower point; when the point on the color vertex curve with abscissa X1 coincides with point A, denote point A as the reflection segmentation point. Step VV2: Denote the curve formed by all the reflection upper points on the reflection vertex curve as the reflection upper curve, and denote the open interval formed by the abscissas of the leftmost point and the rightmost point of each reflection upper curve as the reflection upper interval; denote the curve formed by all the reflection lower points on the reflection vertex curve as the reflection lower curve, and denote the open interval formed by the abscissas of the leftmost point and the rightmost point of each reflection lower curve as the reflection lower interval. Step VV3: For any one reflection upper interval: Denote the open interval formed by the maximum and minimum values of the ordinates of the reflection upper curve in the reflection upper interval as the upper value interval of the reflection vertex interval, and denote the open interval formed by the maximum and minimum values of the ordinates of the color vertex interval in the reflection upper interval as the lower value interval of the color vertex interval. Step VV4: For any one reflection lower interval: Denote the open interval formed by the maximum and minimum values of the ordinates of the reflection lower curve in the reflection lower interval as the lower value interval of the reflection vertex interval, and denote the open interval formed by the maximum and minimum values of the ordinates of the color vertex interval in the reflection lower interval as the upper value interval of the color vertex interval. Step VV5: Denote the interval in the color vertex interval that is simultaneously recorded as the upper value interval and the lower value interval as the color mixing interval; denote the interval in the reflection vertex interval that is simultaneously recorded as the upper value interval and the lower value interval as the reflection mixing interval.

[0024] The point cloud analysis method further includes: Step S1026, obtaining the color vertex intervals and reflection vertex intervals corresponding to all point cloud data, and after executing method V, denoting the intersection of the color mixing intervals corresponding to all color vertex intervals as the color feature interval, and denoting the intersection of the reflection mixing intervals corresponding to all reflection vertex intervals as the reflection feature interval; Step S1027, for any color mixing interval that has not been denoted as the color feature interval: using the interval sorting algorithm to obtain the interval parameters of the color mixing interval, and the interval sorting algorithm is: , where F is the interval parameter, c is the number of point cloud data, G i is the interval length of the upper value interval corresponding to the color mixing interval in the X-axis in the i-th point cloud data, and H j is the interval length of the lower value interval corresponding to the color mixing interval in the X-axis in the point cloud data; Step S1028, when the interval parameter of the color mixing interval is greater than 0, re-denoting the color mixing interval as the upper value interval; when the interval parameter of the color mixing interval is less than 0, re-denoting the color mixing interval as the lower value interval; when the interval parameter of the color mixing interval is equal to 0, re-denoting the color mixing interval as the color feature interval; Step S1029, for any reflection mixing interval that has not been denoted as the reflection feature interval: based on the way of re-denoting the color mixing interval as the upper value interval, lower value interval, and color feature interval, using the interval sorting algorithm to re-denote the reflection mixing interval as the upper value interval, lower value interval, and reflection feature interval; Denote the color vertex interval containing the upper value interval, lower value interval, and color feature interval, and the reflection vertex interval containing the upper value interval, lower value interval, and reflection feature interval as the point cloud positioning feature.

[0025] Step S2, based on the point cloud positioning feature and the point cloud data, use the feature generation method to obtain the construction model, compare the construction model with the point cloud model corresponding to the point cloud data, and supplement the point cloud positioning feature based on the comparison result to obtain the improved feature; Step S201, the feature generation method includes: Step S2011, randomly obtain a point cloud model and its corresponding point cloud data; use the positioning acquisition method based on the point cloud positioning feature to obtain the point cloud positioning points in the point cloud data; Step S2012, the positioning acquisition method is: obtain the reflection intensity and color mean value of all points in the point cloud data. When any point α satisfies that the reflection intensity is in the upper value interval of the reflection vertex interval and the color mean value is in the lower value interval of the color vertex interval, the reflection intensity is in the lower value interval of the reflection vertex interval and the color mean value is in the upper value interval of the color vertex interval, or the reflection intensity is in the reflection feature interval of the reflection vertex interval and the color mean value is in the color feature interval of the color vertex interval, denote point α as the point cloud positioning point; In step S2013, all the point cloud positioning points in the point cloud data are obtained, and a building BIM model is built based on all the point cloud positioning points, denoted as the built model. When the built model is exactly the same as the point cloud model corresponding to the point cloud data, the point cloud positioning feature is denoted as the perfect feature. In step S2014, when the built model is not exactly the same as the point cloud model corresponding to the point cloud data, the points corresponding to the point cloud data in the area of the built model that is different from the point cloud model are denoted as supplementary points, and based on the analysis method of analyzing model points by point cloud analysis method, the supplementary points are analyzed by the point cloud analysis method, and the intersection of the obtained point cloud positioning feature and the existing point cloud positioning feature is denoted as the latest point cloud positioning feature.

[0026] Step S2 further includes: step S202, when there is a latest point cloud positioning feature, the latest point cloud positioning feature is processed based on the feature analysis method until a perfect feature is obtained.

[0027] In step S3, the point cloud positioning points in the point cloud data corresponding to the model to be generated are obtained based on the perfect feature and the positioning acquisition method, and the building BIM model corresponding to the point cloud data is generated using the point cloud positioning points.

[0028] Example 3, please refer to Figure 4 as shown in Figure 4 illustrates a schematic structural diagram of an electronic device. The electronic device may include: a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in the method for generating a building BIM model based on point cloud segmentation are run to implement the following functions: First, obtain the point cloud data of multiple buildings and multiple building BIM models generated based on the point cloud data; use the point cloud analysis method to analyze all the point cloud data and all the building BIM models, and obtain the point cloud positioning feature based on the analysis results; then obtain the built model using the feature generation method based on the point cloud positioning feature and the point cloud data, compare the built model with the point cloud model corresponding to the point cloud data, and supplement the point cloud positioning feature based on the comparison result to obtain the perfect feature; finally, obtain the point cloud positioning points in the point cloud data corresponding to the model to be generated based on the perfect feature and the positioning acquisition method, and generate the building BIM model corresponding to the point cloud data using the point cloud positioning points.

[0029] In addition, when the logical instructions in the above-mentioned memory are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0030] Embodiment 4, this application also provides a computer-readable storage medium. This application provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, it runs the steps in the above-mentioned method for generating a building BIM model based on point cloud segmentation to achieve the following functions: First, obtain the point cloud data of multiple buildings and multiple building BIM models generated based on the point cloud data; use the point cloud analysis method to analyze all the point cloud data and all the building BIM models, and obtain the point cloud positioning features based on the analysis results; then, based on the point cloud positioning features and the point cloud data usage feature generation method, obtain a building model, and compare the building model with the point cloud model corresponding to the point cloud data, and supplement the point cloud positioning features based on the comparison results to obtain improved features; finally, based on the improved features and the positioning acquisition method, obtain the point cloud positioning points in the point cloud data corresponding to the model to be generated, and use the point cloud positioning points to generate the building BIM model corresponding to the point cloud data.

[0031] Through the description of the above embodiments, the embodiments of the present invention can be provided as a method, a system, or a computer program product. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disks, optical discs, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments.

[0032] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For another example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some communication interfaces. The indirect coupling or communication connection of systems, modules, and units can be electrical, mechanical, or other forms.

[0033] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for generating a building BIM model based on point cloud segmentation, characterized in that, The steps are as follows: Obtain the point cloud data of multiple buildings and multiple building BIM models generated based on the point cloud data; use point cloud analysis method to analyze all the point cloud data and all the building BIM models, and obtain point cloud positioning features based on the analysis results; Based on the point cloud positioning features and the point cloud data use feature generation method to obtain the building model, and compare the building model with the point cloud model corresponding to the point cloud data, and supplement the point cloud positioning features based on the comparison results to obtain the improved features; Based on the improved features and the positioning acquisition method, obtain the point cloud positioning points in the point cloud data corresponding to the model to be generated, and use the point cloud positioning points to generate the building BIM model corresponding to the point cloud data.

2. The method for generating a building BIM model based on point cloud segmentation according to claim 1, wherein, Obtain the point cloud data of multiple buildings and multiple building BIM models generated based on the point cloud data; Use point cloud analysis method to analyze all the point cloud data and all the building BIM models, and the point cloud positioning features obtained based on the analysis results include: Obtain multiple building BIM models generated based on the point cloud data, and all are denoted as point cloud models, and obtain the point cloud data corresponding to each point cloud model; The point cloud analysis method includes: for any point cloud data: denote the points corresponding to the point cloud data in the point cloud model as model points; obtain the reflection intensity and RGB values of all model points, and denote the model points that are the vertices of any plane in the point cloud model among all model points as plane vertices; Establish a plane rectangular coordinate system, and denote it as the preliminary screening coordinate system. Among them, the coordinate points on the X-axis of the preliminary screening coordinate system from the coordinate origin to the right are filled in the coordinates of each plane vertex in turn, and the unit of the Y-axis is percentage or constant; When the unit of the Y-axis is percentage, mark points in the preliminary screening coordinate system based on the reflection intensity corresponding to each plane vertex, and denote the curve obtained by fitting all the marked points as the reflection vertex curve; when the unit of the Y-axis is constant, mark points in the preliminary screening coordinate system based on the color mean value corresponding to each plane vertex, and denote the curve obtained by fitting all the marked points as the color vertex curve, where the color mean value is the average of the three values in the RGB values of the plane vertex.

3. The method for generating a building BIM model based on point cloud segmentation according to claim 2, wherein The point cloud analysis method also includes: Denote the closed interval formed by the minimum value and the maximum value of the ordinate in the reflection vertex curve as the reflection vertex interval; denote the interval formed by the minimum value and the maximum value of the ordinate in the color vertex curve as the color vertex interval; Put the reflection vertex curve and the color vertex curve into the same preliminary screening coordinate system. When there is an intersection point between the reflection vertex curve and the color vertex curve, execute method V; when there is no intersection point between the reflection vertex curve and the color vertex curve, perform an equal proportion scaling on the Y-axis corresponding to the color vertex curve until there is an intersection point between the reflection vertex curve and the color vertex curve, and then execute method V.

4. The method for generating a building BIM model based on point cloud segmentation according to claim 3, wherein Method V includes: The preliminary screening coordinate system at this time is denoted as the division coordinate system; for any point A on the reflection vertex curve in the division coordinate system, the abscissa of point A is denoted as X1; when the point with abscissa X1 on the color vertex curve is above point A, point A is denoted as the reflection upper point, and when the point with abscissa X1 on the color vertex curve is below point A, point A is denoted as the reflection lower point; when the point with abscissa X1 on the color vertex curve coincides with point A, point A is denoted as the reflection segmentation point; The curve formed by all the reflection upper points on the reflection vertex curve is denoted as the reflection upper curve, and the open interval formed by the abscissas of the leftmost point and the rightmost point of each reflection upper curve is denoted as the reflection upper interval; the curve formed by all the reflection lower points on the reflection vertex curve is denoted as the reflection lower curve, and the open interval formed by the abscissas of the leftmost point and the rightmost point of each reflection lower curve is denoted as the reflection lower interval.

5. The method for generating a building BIM model based on point cloud segmentation according to claim 4, wherein Method V further includes: For any one reflection upper interval: the open interval formed by the ordinates of the highest point and the lowest point of the reflection upper curve in the reflection upper interval is denoted as the upper value interval of the reflection vertex interval, and the open interval formed by the ordinates of the highest point and the lowest point of the color vertex interval in the reflection upper interval is denoted as the lower value interval of the color vertex interval; For any one reflection lower interval: the open interval formed by the ordinates of the highest point and the lowest point of the reflection lower curve in the reflection lower interval is denoted as the lower value interval of the reflection vertex interval, and the open interval formed by the ordinates of the highest point and the lowest point of the color vertex interval in the reflection lower interval is denoted as the upper value interval of the color vertex interval; The interval in the color vertex interval that is simultaneously denoted as the upper value interval and the lower value interval is denoted as the color mixing interval; the interval in the reflection vertex interval that is simultaneously denoted as the upper value interval and the lower value interval is denoted as the reflection mixing interval.

6. The method for generating a building BIM model based on point cloud segmentation according to claim 5, wherein, The point cloud analysis method further includes: Obtain the color vertex intervals and reflection vertex intervals corresponding to all the point cloud data, and after executing Method V, denote the intersection of the color mixing intervals corresponding to all the color vertex intervals as the color feature interval, and denote the intersection of the reflection mixing intervals corresponding to all the reflection vertex intervals as the reflection feature interval; For any color mixing interval that is not marked as a color feature interval: Use the interval sorting algorithm to obtain the interval parameters of the color mixing interval. The interval sorting algorithm is as follows: , where F is the interval parameter, c is the number of point cloud data, and G i is the interval length of the upper value interval corresponding to the color mixing interval in the i-th point cloud data on the X-axis, and H j is the interval length of the lower value interval corresponding to the color mixing interval in the point cloud data on the X-axis; When the interval parameter of the color mixing interval is greater than 0, re-denote the color mixing interval as the upper value interval; when the interval parameter of the color mixing interval is less than 0, re-denote the color mixing interval as the lower value interval; when the interval parameter of the color mixing interval is equal to 0, re-denote the color mixing interval as the color feature interval; For any reflection mixing interval that is not denoted as the reflection feature interval: based on the way of re-denoting the color mixing interval as the upper value interval, the lower value interval, and the color feature interval, use the interval sorting algorithm to re-denote the reflection mixing interval as the upper value interval, the lower value interval, and the reflection feature interval; The color vertex interval containing the upper value interval, the lower value interval, and the color feature interval and the reflection vertex interval containing the upper value interval, the lower value interval, and the reflection feature interval are denoted as the point cloud positioning feature.

7. The method for generating a building BIM model based on point cloud segmentation according to claim 6, wherein, The feature generation method includes: Randomly obtain a point cloud model and its corresponding point cloud data; use the positioning acquisition method based on the point cloud positioning features to obtain the point cloud positioning points in the point cloud data; The positioning acquisition method is as follows: obtain the reflection intensity and color mean value of all points in the point cloud data. When any point α satisfies that the reflection intensity is in the upper value interval of the reflection vertex interval and the color mean value is in the lower value interval of the color vertex interval, or the reflection intensity is in the lower value interval of the reflection vertex interval and the color mean value is in the upper value interval of the color vertex interval, or the reflection intensity is in the reflection feature interval of the reflection vertex interval and the color mean value is in the color feature interval of the color vertex interval, then record point α as the point cloud positioning point; Obtain all the point cloud positioning points in the point cloud data, and build a building BIM model based on all the point cloud positioning points, denoted as the built model; when the built model is exactly the same as the point cloud model corresponding to the point cloud data, record the point cloud positioning feature as the perfect feature; When the built model is not exactly the same as the point cloud model corresponding to the point cloud data, record the points corresponding to the point cloud data in the area of the built model that is different from the point cloud model as supplementary points, and use the point cloud analysis method to analyze the supplementary points based on the analysis method of analyzing model points by the point cloud analysis method, and record the intersection of the obtained point cloud positioning feature and the existing point cloud positioning feature as the latest point cloud positioning feature.

8. The method for generating a building BIM model based on point cloud segmentation according to claim 7, wherein, Supplement the point cloud positioning feature based on the comparison result to obtain the perfect feature, including: When there is a latest point cloud positioning feature, process the latest point cloud positioning feature based on the feature analysis method until the perfect feature is obtained.

9. A building BIM model generation system based on point cloud segmentation, which is used to implement the building BIM model generation method based on point cloud segmentation according to any one of claims 1-8, characterized in that, It includes a point cloud feature analysis module, a point cloud feature supplement module, and a BIM model generation module; The point cloud feature analysis module is used to obtain the point cloud data of multiple buildings and multiple building BIM models generated based on the point cloud data; use the point cloud analysis method to analyze all the point cloud data and all the building BIM models, and obtain the point cloud positioning feature based on the analysis result; The point cloud feature supplement module is used to obtain the built model based on the point cloud positioning feature and the point cloud data using the feature generation method, compare the built model with the point cloud model corresponding to the point cloud data, and supplement the point cloud positioning feature based on the comparison result to obtain the perfect feature; The BIM model generation module is used to obtain the point cloud positioning points in the point cloud data corresponding to the model to be generated based on the perfect feature and the positioning acquisition method, and use the point cloud positioning points to generate the building BIM model corresponding to the point cloud data.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it runs the steps in the method described in any one of claims 1-8.

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