Fabricated prefabricated part information fusion method based on BIM

Abstract

The invention relates to the technical field of prefabricated part assembly, in particular to an assembly type prefabricated part information fusion method based on BIM, and the method comprises the steps: scanning each prefabricated part to be assembled, obtaining a point cloud model of each prefabricated part, and enabling the point cloud model to comprise a plurality of point cloud data points; obtaining surface maps of different visual angles in the point cloud model of each prefabricated part; obtaining an assembly space drawing of the prefabricated part; obtaining a plurality of splicing surface maps of the prefabricated parts; obtaining a plurality of adaptive splicing surface drawings of each splicing surface drawing of the prefabricated part; the matching priority of each splicing surface graph of the prefabricated part and each adaptive splicing surface graph is obtained; the splicing priority of the prefabricated parts is obtained; and assembling all the prefabricated parts to be assembled according to the splicing priority and the matching priority. According to the method, the efficiency of virtual pre-assembly based on the BIM is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of prefabricated component assembly, and particularly relates to a BIM-based information fusion method for prefabricated components in assembly. Background Technique

[0002] Prefabricated components have been widely used and promoted in buildings and infrastructure. Prefabricated components are pre-produced and processed in a factory, then transported to the construction site, and the components are placed at the designated position by means of hoisting. Then, the prefabricated components are connected into a whole by means of grouting sleeves, etc. Before actual assembly, virtual pre-assembly is usually carried out based on BIM (Building Information Modeling) to plan the assembly process of prefabricated components, optimize the construction plan, reduce unnecessary rework and modification, thereby saving time and cost.

[0003] When prefabricated components are assembled based on BIM technology at present, technicians manually calibrate or select the assembly surfaces of each prefabricated component model through software to determine the parts that need to be spliced. At the same time, it is also necessary to consider whether the two assembly surfaces of different component models are compatible. Since manual operation depends on the observation and judgment of technicians, and the observation accuracy of the human eye is limited and the operation speed is slow. When there are many prefabricated component models, this manual assembly process leads to slow assembly progress, which in turn affects the overall construction efficiency and reduces the accuracy and timeliness of the operation. Summary of the Invention

[0004] To solve the above problems, the present invention provides a BIM-based information fusion method for prefabricated components in assembly.

[0005] The BIM-based information fusion method for prefabricated components in assembly of the present invention adopts the following technical solutions: An embodiment of the present invention provides a BIM-based information fusion method for prefabricated components in assembly. The method includes the following steps: Scan each prefabricated component to be assembled to obtain the point cloud model of each prefabricated component. The point cloud model contains a number of point cloud data points; obtain the surface maps of different perspectives in the point cloud model of each prefabricated component; obtain the assembly space drawing of the prefabricated component. According to the curvature change of the point cloud data points corresponding to the edge lines in the surface map, obtain the complexity of the edge lines in each surface map; obtain the unevenness of the corresponding surface area of each surface map in the point cloud model; according to the complexity and unevenness, obtain the possibility that the corresponding surface area of each surface map in the point cloud model belongs to the assembly surface; according to the size of the possibility of belonging to the assembly surface, screen a number of assembly surface maps of each prefabricated component. Obtain the matching point cloud data points in any two assembled surface maps of any two prefabricated components, where the any two assembled surface maps do not belong to the same prefabricated component; obtain the matching degree of any two assembled surface maps of any two prefabricated components according to the distance distribution of the matching point cloud data points; obtain the depth error value of the corresponding surface regions of any two assembled surface maps of any two prefabricated components in the corresponding point cloud models; obtain the adaptation index of any two assembled surface maps of any two prefabricated components according to the matching degree and the depth error value; obtain several adapted assembled surface maps of each assembled surface map of each prefabricated component according to the size of the adaptation index. According to the assembly space drawing, obtain the distance between the central points of the adapted assembled surface maps of different prefabricated components; obtain the matching priority of each assembled surface map of each prefabricated component and each adapted assembled surface map according to the number of assembled surface maps of the prefabricated component to which the adapted assembled surface map belongs and the distance distribution of the central points of several adapted assembled surface maps of each assembled surface map of the prefabricated component; obtain the assembly priority of each prefabricated component; assemble all the prefabricated components to be assembled according to the size of the assembly priority and the matching priority.

[0006] Further, the specific steps of obtaining the complexity of the edge line in each surface map according to the curvature change of the point cloud data points corresponding to the edge line in the surface map are as follows: Denote any one prefabricated component as the prefabricated component to be analyzed; denote the point cloud model of the prefabricated component to be analyzed as the point cloud model to be analyzed; obtain the curvature of each point cloud data point in the point cloud model to be analyzed; denote the surface map of any one view angle in the point cloud model to be analyzed as the surface map to be analyzed. In the formula, is the average value of the curvatures corresponding to all the point cloud data points on the edge line in the surface map to be analyzed; is the range of the curvatures corresponding to all the point cloud data points on the edge line in the surface map to be analyzed; is the complexity of the edge line in the surface map to be analyzed.

[0007] Further, the specific method for obtaining the unevenness degree of the corresponding surface region of each surface map in the corresponding point cloud model is as follows: Denote the corresponding surface region of the surface map to be analyzed in the point cloud model to be analyzed as the surface region to be analyzed; denote the region formed by the outermost edge in the surface map to be analyzed as the first region of the surface map to be analyzed; denote the closed structure formed by the first region and the surface region to be analyzed as the closed structure to be analyzed; denote the region corresponding to the first region in the closed structure to be analyzed as the virtual cross-section region of the surface region to be analyzed. In the formula, is the number of point cloud data points in the surface area to be analyzed; is the perpendicular distance from the -th point cloud data point in the surface area to be analyzed to the virtual cross-section area of the surface area to be analyzed; is the average value of the perpendicular distances from all point cloud data points in the surface area to be analyzed to the virtual cross-section area of the surface area to be analyzed; is the variance of the perpendicular distances from all point cloud data points in the surface area to be analyzed to the virtual cross-section area of the surface area to be analyzed; is to take the absolute value; is the unevenness degree of the corresponding surface area of the surface map to be analyzed in the point cloud model to be analyzed.

[0008] Further, obtaining the possibility that the corresponding surface area of each surface map in the point cloud model belongs to the assembling surface according to the complexity degree and the unevenness degree includes the following specific steps: In the formula, is the complexity degree of the edge line in the surface map to be analyzed; is the sigmoid function; is the possibility that the corresponding surface area of the surface map to be analyzed in the point cloud model to be analyzed belongs to the assembling surface.

[0009] Further, the specific method for obtaining the matching point cloud data points in any two assembling surface maps of any two prefabricated components is as follows: Denote any two assembling surface maps of any two prefabricated components as the first assembling surface map and the second assembling surface map respectively; obtain the center points of the first assembling surface map and the second assembling surface map respectively; align the center point of the first assembling surface map with the center point of the second assembling surface map, and obtain the straight line passing through the -th point cloud data point on the edge line of the first assembling surface map and the aligned center point, and use the point cloud data point that intersects and is the closest to the straight line on the edge line of the second assembling surface map as the matching point cloud data point of the -th point cloud data point in the second assembling surface map; when obtaining the point cloud data point that intersects and is the closest to the straight line on the edge line of the second assembling surface map, keep the first assembling surface map fixed and rotate the second assembling surface map.

[0010] Further, obtaining the matching degree of any two assembling surface maps of any two prefabricated components according to the distance distribution of the matching point cloud data points includes the following specific steps: In the formula, is the number of point cloud data points in the first assembly surface diagram; is the distance between the \(i\)-th point cloud data point in the first assembly surface diagram and the matching point cloud data point in the second assembly surface diagram; is the average value of the distances between all point cloud data points in the first assembly surface diagram and the matching point cloud data points in the second assembly surface diagram; is to take the absolute value; is the matching degree between the first assembly surface diagram and the second assembly surface diagram.

[0011] Furthermore, the specific method for obtaining the depth error value of the corresponding surface regions of any two assembly surface diagrams of any two prefabricated components in the point cloud model is as follows: Denote the surface region corresponding to the first assembly surface diagram in the point cloud model as the first surface region; denote the surface region corresponding to the second assembly surface diagram in the point cloud model as the second surface region; according to the method for obtaining the virtual cross-section region of the surface region to be analyzed, respectively obtain the virtual cross-section region of the first surface region and the virtual cross-section region of the second surface region; In the formula, is the maximum value of the perpendicular distances from all point cloud data points in the first surface region to the virtual cross-section region of the first surface region; is the maximum value of the perpendicular distances from all point cloud data points in the second surface region to the virtual cross-section region of the second surface region; is to take the absolute value; is the depth error value of the corresponding surface regions of the first assembly surface diagram and the second assembly surface diagram in the point cloud model.

[0012] Furthermore, the specific steps for obtaining the adaptation index of any two assembly surface diagrams of any two prefabricated components according to the matching degree and the depth error value are as follows: In the formula, is the matching degree between the first assembly surface diagram and the second assembly surface diagram; is the depth error value of the corresponding surface regions of the first assembly surface diagram and the second assembly surface diagram in the point cloud model; is a preset first hyperparameter; is the sigmoid function; is the adaptation index of the first assembly surface diagram and the second assembly surface diagram.

[0013] Further, obtaining the matching priority of each assembled surface of each precast component and each adapted assembled surface according to the number of assembled surface diagrams of the precast component to which the adapted assembled surface diagram belongs and the distance distribution among several center points of adapted assembled surface diagrams of each assembled surface diagram of the precast component includes the following specific steps: Denote any adapted assembled surface diagram of the first assembled surface diagram as the to-be-analyzed adapted assembled surface diagram; In the formula, is the number of assembled surface diagrams of the precast component to which the to-be-analyzed adapted assembled surface diagram belongs; is the number of adapted assembled surface diagrams of the first assembled surface diagram; is the distance between the to-be-analyzed adapted assembled surface diagram and the th center point of the adapted assembled surface diagrams of the first assembled surface diagram; is the average value of the distances between the to-be-analyzed adapted assembled surface diagram and all center points of the adapted assembled surface diagrams of the first assembled surface diagram; is the matching priority of the first assembled surface diagram and the to-be-analyzed adapted assembled surface diagram.

[0014] Further, the specific method for obtaining the assembly priority of each precast component is as follows: Take the accumulated value of the possibility that the surface areas corresponding to all surface diagrams of each precast component in the point cloud model belong to the assembled surface as the assembly priority of the precast component.

[0015] The beneficial effects of the technical solution of the present invention are as follows: When virtually pre-assembling assembled precast components based on BIM, the present invention automatically determines the assembled surfaces of each precast component by analysis, determines whether the assembled surfaces of different precast components are adapted, determines the assembly priority of the precast components and the matching priority of two adapted assembled surfaces of different precast components, and then automatically assembles all precast components to be assembled, optimizing the efficiency of virtual pre-assembly based on BIM, improving the efficiency during overall construction, and improving the accuracy and timeliness of the operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for description in the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1The flowchart of steps of a BIM-based information fusion method for prefabricated components provided by an embodiment of the present invention; Figure 2 The schematic diagram of the scenario of arranging target balls on prefabricated components provided by an embodiment of the present invention; Figure 3 The schematic diagram of the scenario of scanning prefabricated components from multiple directions and different stations provided by an embodiment of the present invention; Figure 4 The schematic diagram of the point cloud model of prefabricated components provided by an embodiment of the present invention; Figure 5 The schematic diagram of obtaining matching point cloud data points provided by an embodiment of the present invention. Detailed implementation manners

[0018] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following combines the accompanying drawings and preferred embodiments to detail the specific implementation manners, structures, features and effects of a BIM-based information fusion method for prefabricated components according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0020] The following specifically describes the specific solution of a BIM-based information fusion method for prefabricated components provided by the present invention with reference to the accompanying drawings.

[0021] Please refer to Figure 1 , which shows the flowchart of steps of a BIM-based information fusion method for prefabricated components provided by an embodiment of the present invention. The method includes the following steps: Step S001: Scan each prefabricated component to be assembled to obtain the point cloud model of each prefabricated component, where the point cloud model contains a number of point cloud data points; obtain the surface maps of different perspectives in the point cloud model of each prefabricated component; obtain the assembly space drawings of the prefabricated components.

[0022] It should be noted that the main purpose of this embodiment is to automatically determine the assembly surface of each precast component when performing virtual pre-assembly on precast components based on BIM (Building Information Modeling), determine whether the assembly surfaces of different precast components are compatible, determine the assembly priority of precast components and the matching priority of two compatible assembly surfaces of different precast components, and then automatically assemble all precast components to be assembled, optimizing the efficiency of virtual pre-assembly based on BIM (Building Information Modeling) and reducing the consumption of manpower.

[0023] Specifically, each precast component to be assembled is scanned to obtain the point cloud model of each precast component. The point cloud model contains a number of point cloud data points, specifically as follows: Level the Trimble-TX8 type 3D laser scanner before scanning, deploy target balls on one precast component to be assembled, and scan the precast component from multiple directions and different stations according to the 3D laser scanner to obtain the point cloud model of the precast component. Similarly, all precast components to be assembled are scanned one by one to obtain the point cloud model of each precast component.

[0024] It should be noted that the 3D laser scanner will identify the positions of the target balls deployed on the precast component during the scanning process for spatial alignment and data registration. The high-contrast characteristics of the target balls can help the 3D laser scanner correctly align each scanned point cloud data in different directions and splice the point cloud data in multiple directions into a complete 3D point cloud model to form the overall model of the precast component.

[0025] It should be noted that please refer to Figure 2 , Figure 2 which is the schematic diagram of the scene where target balls are deployed on the precast component in this embodiment, Figure 2 which contains one precast component and several target balls; please refer to Figure 3 , Figure 3 which is the schematic diagram of the scene where the precast component is scanned from multiple directions and different stations in this embodiment; please refer to Figure 4 , Figure 4 which is the schematic diagram of the point cloud model of the precast component in this embodiment.

[0026] Specifically, obtain the surface maps of different perspectives in the point cloud model of each precast component. It should be noted that since the precast component is mainly a structural body, the surface maps of different perspectives obtained for the point cloud model of each precast component in this embodiment need to include all the surfaces of the precast component. The specific method of obtaining the surface maps of different perspectives is an existing method and will not be elaborated in this embodiment.

[0027] Specifically, obtain the assembly space drawings of the precast components. It should be noted that the assembly space drawings can show the positions of each precast component. Obtaining the assembly space drawings of the precast components is an existing method and will not be elaborated in this embodiment.

[0028] Thus, the point cloud models of each precast component, the surface maps of different perspectives in the point cloud models of each precast component, and the assembly space drawings of the precast components are obtained.

[0029] Step S002: According to the curvature change of the point cloud data points on the edge line in the surface map, obtain the complexity of the edge line in each surface map; obtain the unevenness of the corresponding surface area of each surface map in the point cloud model to which it belongs; according to the complexity and unevenness, obtain the possibility that the corresponding surface area of each surface map in the point cloud model belongs to the splicing surface; according to the size of the possibility of belonging to the splicing surface, screen several splicing surface maps of each precast component.

[0030] It should be noted that during the assembly process of precast components, some fitting positions are usually designed between the components, which can ensure the smooth splicing and tight combination of each component. To achieve this goal, the edges of the components are often designed into special functional forms, usually with positioning and docking functions. Specifically, in order to ensure the precise splicing of the components, the edge design often adopts a groove or protrusion structure to ensure that the two precast components can be accurately aligned and firmly combined during docking. In addition, the edges of the components may also adopt an insertion method, such as designing a jack or a pin structure, which can not only facilitate quick docking but also enhance the stability of the connection. Therefore, compared with the other edges of the components, the edges at the fitting positions usually show more complex and variable forms, which may include various twists, bends or fitting designs to meet different connection requirements and ensure the accuracy and stability of splicing.

[0031] Furthermore, it should be noted that since a precast component is a structural body and includes multiple surface maps, when the precast components are spliced, not all the surface areas corresponding to the surface maps are spliced with other precast components. Usually, only specific surface areas participate in the splicing. These surface areas have the characteristics of complex edge lines in the corresponding surface maps and unevenness of the corresponding surface areas of the surface maps in the point cloud model to which they belong. To determine which surface maps of the precast components have corresponding surface areas in the point cloud model that belong to the splicing surface, it is necessary to analyze the complexity of the edge lines in the surface maps and the unevenness of the corresponding surface areas of the surface maps in the point cloud model to which they belong to determine several splicing surface maps of the precast components.

[0032] Specifically, according to the curvature change of the point cloud data points on the edge line in the surface map, obtain the complexity of the edge line in each surface map as follows: Any prefabricated component is recorded as the prefabricated component to be analyzed; the point cloud model of the prefabricated component to be analyzed is recorded as the point cloud model to be analyzed; the curvature of each point cloud data point in the point cloud model to be analyzed is obtained; and the surface map of any perspective in the point cloud model to be analyzed is recorded as the surface map to be analyzed. It should be noted that obtaining the curvature of the point cloud data point in the point cloud model is an existing method and will not be repeated in this embodiment.

[0033] In the formula, is the average value of the curvatures corresponding to all the point cloud data points on the edge line of the surface image to be analyzed. It should be noted that obtaining the edge line in an image is an existing method and will not be described in detail in this embodiment; is the range of curvature corresponding to all point cloud data points on the edge line of the surface image to be analyzed; is the complexity of the edge lines in the surface image to be analyzed.

[0034] It should be noted that the larger the average value of the curvature corresponding to all the point cloud data points on the edge line of the surface diagram to be analyzed, the more drastic the change in the curvature of the edge line in the surface diagram to be analyzed, the more complex the shape, and the more likely it is that it is an assembly surface diagram corresponding to assembly with other prefabricated components; the larger the range of the curvature corresponding to all the point cloud data points on the edge line of the surface diagram to be analyzed, the larger the range of the curvature change, the obvious difference in the curvature of the edge line, the more irregular the overall shape of the edge line, the higher the complexity, and the more likely it is that it is an assembly surface diagram corresponding to assembly with other prefabricated components.

[0035] It should be noted that the above analysis is about the complexity of the edge lines in the surface image. Next, the unevenness of the surface area corresponding to the surface image in the corresponding point cloud model is analyzed.

[0036] Furthermore, the roughness of the corresponding surface area of ​​each surface image in the corresponding point cloud model is obtained, as follows: The surface area corresponding to the surface map to be analyzed in the point cloud model to be analyzed is recorded as the surface area to be analyzed; the area formed by the outermost edge of the surface map to be analyzed is recorded as the first area of ​​the surface map to be analyzed; the closed structure formed by the first area and the surface area to be analyzed is recorded as the closed structure to be analyzed; the area corresponding to the first area in the closed structure to be analyzed is recorded as the virtual cross-sectional area of ​​the surface area to be analyzed. It should be noted that the surface map to be analyzed is a two-dimensional plane view at a viewing angle, and the surface area to be analyzed is a partial surface area on the point cloud model, which is a three-dimensional surface, that is, there are concave and convex depth changes.

[0037] In the formula, is the number of point cloud data points in the surface area to be analyzed; is the perpendicular distance from the n-th point cloud data point in the surface area to be analyzed to the virtual cross-section area of the surface area to be analyzed; is the average value of the perpendicular distances from all point cloud data points in the surface area to be analyzed to the virtual cross-section area of the surface area to be analyzed; is the variance of the perpendicular distances from all point cloud data points in the surface area to be analyzed to the virtual cross-section area of the surface area to be analyzed; is to take the absolute value; is the unevenness degree of the corresponding surface area of the surface map to be analyzed in the point cloud model to be analyzed.

[0038] It should be noted that the larger it is, it indicates that the perpendicular distance from the point cloud data points in the surface area to be analyzed to the virtual cross-section area changes greatly compared with the average distance, and the unevenness degree of the surface area is higher. At the same time, if the variance of the perpendicular distances from all point cloud data points in the surface area to be analyzed to the virtual cross-section area is larger, it indicates that the fluctuation of the perpendicular distance changes greatly, and the unevenness degree of the surface area is also higher.

[0039] It should be noted that the complexity of the edge line in the surface map and the unevenness degree of the corresponding surface area of the surface map in the corresponding point cloud model are analyzed above. Next, several assembled surface maps of each precast component are determined through the two. The assembled surface map is the surface map for assembling with other precast components.

[0040] Specifically, according to the complexity and unevenness degree, the possibility that the corresponding surface area of each surface map in the point cloud model belongs to the assembled surface is obtained, as follows: In the formula, is the complexity of the edge line in the surface map to be analyzed; is the unevenness degree of the corresponding surface area of the surface map to be analyzed in the point cloud model to be analyzed; is the sigmoid function, which is used for normalization processing; is the possibility that the corresponding surface area of the surface map to be analyzed in the point cloud model to be analyzed belongs to the assembled surface.

[0041] It should be noted that when the complexity of the edge line in the surface map to be analyzed is higher, it indicates that it is more likely to be the assembled surface map corresponding to the assembly with other precast components. At the same time, if the unevenness degree of the corresponding surface area of the surface map to be analyzed in the point cloud model to be analyzed is larger, it indicates that there are groove or convex structures on the surface of the precast component, and it is more likely to be the assembled surface map corresponding to the assembly with other precast components.

[0042] Further, according to the likelihood of belonging to the assembly surface, several assembly surface diagrams of each precast component are screened as follows: Preset a likelihood threshold. In this embodiment, the likelihood threshold is described as 0.5. If the likelihood that the surface area corresponding to the surface diagram to be analyzed in the point cloud model to be analyzed belongs to the assembly surface is greater than the likelihood threshold, the surface diagram to be analyzed is taken as an assembly surface diagram of the precast component to be analyzed; otherwise, it is not taken as an assembly surface diagram of the precast component to be analyzed. The surface diagrams from other perspectives in the point cloud model to be analyzed are analyzed in the same way to obtain several assembly surface diagrams of the precast component to be analyzed.

[0043] Thus, several assembly surface diagrams of each precast component are screened.

[0044] Step S003: Obtain the matching point cloud data points in any two assembly surface diagrams of any two precast components, where the two assembly surface diagrams do not belong to the same precast component; obtain the matching degree of any two assembly surface diagrams of any two precast components according to the distance distribution of the matching point cloud data points; obtain the depth error value of the corresponding surface areas of any two assembly surface diagrams of any two precast components in the point cloud models to which they belong; obtain the adaptation index of any two assembly surface diagrams of any two precast components according to the matching degree and the depth error value; obtain several adapted assembly surface diagrams of each assembly surface diagram of each precast component according to the magnitude of the adaptation index.

[0045] It should be noted that the above steps determine several assembly surface diagrams of each precast component. When one precast component is assembled with another precast component, the assembly surface diagrams between them need to have a certain degree of matching. Only when the sizes, shapes, and surface precisions of the two assembly surface diagrams reach a sufficient matching level can it be ensured that they fit together accurately, avoiding gaps or misalignments. When the edge lines of the assembly surface diagram of a precast component are consistent with the edge lines of the assembly surface diagram of another precast component to be assembled, it means that they are highly matched in geometric shape. Moreover, the higher the dimensional accuracy, the higher the matching degree of the two assembly surface diagrams in each detail, and the smaller the error. Precise dimensional control ensures that there is almost no gap between their contact surfaces, thus avoiding errors or deformations caused by non-matching during assembly. When the two assembly surface diagrams are precisely adapted, the assembly process can be smoother, the joint of the components is more solid, and the stability and reliability are also improved. Therefore, it is also necessary to analyze the adaptation index of two assembly surface diagrams of different precast components.

[0046] It should be noted that the edge lines in the assembled surface diagram can well reflect the morphological characteristics of the assembled surface diagram. If the distribution of the matching point cloud data points on the edge lines of two assembled surface diagrams is closer, it indicates that the two assembled surface diagrams are adaptable. First, obtain the matching point cloud data points in any two assembled surface diagrams of precast components, and then determine the distance distribution between the matching point cloud data points to determine the matching degree of any two assembled surface diagrams of any two precast components.

[0047] Specifically, obtain the matching point cloud data points in any two assembled surface diagrams of any two precast components. The two assembled surface diagrams do not belong to the same precast component, as follows: Denote any two assembled surface diagrams of any two precast components as the first assembled surface diagram and the second assembled surface diagram respectively; obtain the center points of the first assembled surface diagram and the second assembled surface diagram respectively; align the center point of the first assembled surface diagram with the center point of the second assembled surface diagram, and obtain the straight line passing through the th point cloud data point on the edge line of the first assembled surface diagram and the aligned center point. The point cloud data point that intersects the edge line of the second assembled surface diagram and is the closest to the straight line is used as the th matching point cloud data point of the point cloud data point in the second assembled surface diagram; when obtaining the point cloud data point that intersects the edge line of the second assembled surface diagram and is the closest to the straight line, keep the first assembled surface diagram fixed and rotate the second assembled surface diagram to obtain the intersecting and closest point cloud data point. Please refer to Figure 5 , Figure 5 which is a schematic diagram for obtaining the matching point cloud data points in this embodiment, Figure 5 including the first assembled surface diagram, the second assembled surface diagram, the center point where the first assembled surface diagram and the second assembled surface diagram are aligned, the straight line passing through the th point cloud data point and the aligned center point, the th point cloud data point, and the matching point cloud data point of the th point cloud data point.

[0048] It should be noted that for the matching point cloud data points obtained in any two assembled surface diagrams of any two precast components, if the distance between the matching point cloud data points in the two assembled surface diagrams is closer and the distribution is more uniform, it indicates that the matching degree of the two assembled surface diagrams is higher. Therefore, by analyzing the distance distribution between the matching point cloud data points, the matching degree of any two assembled surface diagrams of any two precast components is determined.

[0049] Specifically, according to the distance distribution between the matching point cloud data points, obtain the matching degree of any two assembled surface diagrams of any two precast components, as follows: In the formula, is the number of point cloud data points in the first assembly surface diagram; is the distance between the average value of the distances between all the point cloud data points in the first assembly surface diagram and the matching point cloud data points in the second assembly surface diagram; is to take the absolute value; is the matching degree between the first assembly surface diagram and the second assembly surface diagram.

[0050] It should be noted that represents the degree of uniformity of the distance distribution between the matching point cloud data points in two assembly surface diagrams of two different precast components. The larger it is, the more uniform the distribution of the matching point cloud data points in the two assembly surface diagrams. At the same time, if the cumulative value of the distances between the matching point cloud data points in the two assembly surface diagrams is smaller, it means that the distance error between the matching point cloud data points is smaller, and the matching degree between the two assembly surface diagrams is higher.

[0051] It should be noted that the above analyzes the matching degree of any two assembly surface diagrams of any two precast components. Since there may be groove or protrusion structures in the corresponding surface areas of the assembly surface diagrams of the precast components in the point cloud model, in order to better judge the fitting relationship between any two assembly surface diagrams of any two precast components and reduce misfitting, it is necessary to analyze the depth error values of the corresponding surface areas of any two assembly surface diagrams of any two precast components in the point cloud model.

[0052] Specifically, the depth error values of the corresponding surface areas of any two assembly surface diagrams of any two precast components in the point cloud model are obtained as follows: The surface area corresponding to the first assembly surface diagram in the point cloud model is denoted as the first surface area; the surface area corresponding to the second assembly surface diagram in the point cloud model is denoted as the second surface area; according to the method of obtaining the virtual cross-section area of the surface area to be analyzed, the virtual cross-section area of the first surface area and the virtual cross-section area of the second surface area are obtained respectively.

[0053] In the formula, is the maximum value of the perpendicular distances from all the point cloud data points in the first surface area to the virtual cross-section area of the first surface area; is the maximum value of the perpendicular distances from all the point cloud data points in the second surface area to the virtual cross-section area of the second surface area; is to take the absolute value; is the depth error value of the corresponding surface regions of the first assembly surface map and the second assembly surface map in the corresponding point cloud model.

[0054] It should be noted that the smaller the depth error value of the corresponding surface regions of the first assembly surface map and the second assembly surface map in the corresponding point cloud model, the closer the groove or protrusion structures of the corresponding surface regions of the precast component's assembly surface map in the corresponding point cloud model are, and the more suitable the corresponding surface regions of the first assembly surface map and the second assembly surface map in the corresponding point cloud model are for assembly.

[0055] It should be noted that the matching degree of any two assembly surface maps of any two precast components and the depth error value of the corresponding surface regions of any two assembly surface maps of any two precast components in the corresponding point cloud model are analyzed above. Next, by combining the two, several matching assembly surface maps of each assembly surface map of each precast component are determined.

[0056] Specifically, according to the matching degree and the depth error value, the adaptation index of any two assembly surface maps of any two precast components is obtained, as follows: In the formula, is the matching degree of the first assembly surface map and the second assembly surface map; is the depth error value of the corresponding surface regions of the first assembly surface map and the second assembly surface map in the corresponding point cloud model; is a preset first hyperparameter, the purpose of which is to prevent the denominator from being 0. In this embodiment, is used for description; is the sigmoid function, which is used for normalization processing; is the adaptation index of the first assembly surface map and the second assembly surface map.

[0057] It should be noted that when the matching degree of the first assembly surface map and the second assembly surface map is larger, it indicates that the adaptation relationship between the first assembly surface map and the second assembly surface map is better, and the adaptation index is larger. At the same time, the smaller the depth error value of the corresponding surface regions of the first assembly surface map and the second assembly surface map in the corresponding point cloud model, the closer the groove or protrusion structures of the corresponding surface regions of the precast component's assembly surface map in the corresponding point cloud model are, the better the depth adaptation relationship is, and the larger the adaptation index of the first assembly surface map and the second assembly surface map is.

[0058] Furthermore, according to the magnitude of the adaptation index, several matching assembly surface maps of each assembly surface map of each precast component are obtained, as follows: Preset an adaptation index threshold. In this embodiment, the probability threshold is described as 0.5. If the adaptation index of the first assembled surface diagram and the second assembled surface diagram is greater than the adaptation index threshold, the second assembled surface diagram is used as an adapted assembled surface diagram of the first assembled surface diagram; otherwise, it is not used as an adapted assembled surface diagram of the first assembled surface diagram. Obtain the adaptation index between the first assembled surface diagram and other assembled surface diagrams, and make a judgment with the adaptation index threshold to obtain several adapted assembled surface diagrams of the first assembled surface diagram.

[0059] Thus, several adapted assembled surface diagrams of each assembled surface diagram of each precast component are obtained.

[0060] Step S004: According to the assembly space drawing, obtain the distance between the center points of the adapted assembled surface diagrams of different precast components; according to the number of assembled surface diagrams of the precast component to which the adapted assembled surface diagram belongs and the distance distribution of the center points of several adapted assembled surface diagrams of each assembled surface diagram of the precast component, obtain the matching priority of each assembled surface diagram of each precast component and each adapted assembled surface diagram; obtain the assembly priority of each precast component; and assemble all the precast components to be assembled according to the magnitudes of the assembly priority and the matching priority.

[0061] It should be noted that during the assembly of the precast components, there are multiple adapted assembled surface diagrams for one assembled surface diagram of a component, that is, one precast component needs to be assembled with multiple other precast components. Therefore, when obtaining several adapted assembled surface diagrams of the first assembled surface diagram, the precast components to which these adapted assembled surface diagrams belong all need to be assembled with the precast component to which the first assembled surface diagram belongs. In order to perform the assembly better, it is necessary to determine the matching priority of these adapted assembled surface diagrams.

[0062] Furthermore, it should be noted that when there are multiple adapted assembled surface diagrams for one assembled surface diagram, if a precast component to which an adapted assembled surface diagram belongs has multiple assembled surface diagrams, it indicates that it is more closely assembled and connected with other precast components, and this precast component is more important. At the same time, if the precast component to which the adapted assembled surface diagram belongs is more at the center of the precast components to which other adapted assembled surface diagrams belong, the matching priority of the precast component to which the assembled surface diagram belongs and the precast component to which the adapted assembled surface diagram belongs is higher, and it should be assembled first.

[0063] Specifically, according to the assembly space drawing, obtain the distance between the center points of the adapted assembled surface diagrams of different precast components. It should be noted that the positions of each precast component can be seen from the assembly space drawing, and thus, according to the assembly space drawing, the distance between the center points of the adapted assembled surface diagrams of different precast components can be obtained. The specific acquisition method is an existing method and will not be elaborated in this embodiment.

[0064] Further, according to the number of fitting and assembling surface diagrams of the precast members to which the fitting and assembling surface diagram belongs, and the distance distribution among the centers of several fitting and assembling surface diagrams of each fitting and assembling surface diagram of the precast member, the matching priority of each fitting and assembling surface diagram of each precast member with each fitting and assembling surface diagram is obtained, which is specifically as follows: Denote any one of the fitting and assembling surface diagrams of the first assembling surface diagram as the to-be-analyzed fitting and assembling surface diagram.

[0065] In the formula, is the number of fitting and assembling surface diagrams of the precast member to which the to-be-analyzed fitting and assembling surface diagram belongs; is the number of fitting and assembling surface diagrams of the first assembling surface diagram; is the distance between the to-be-analyzed fitting and assembling surface diagram and the center of the th fitting and assembling surface diagram center of the first assembling surface diagram; is the average value of the distances between the to-be-analyzed fitting and assembling surface diagram and the centers of all fitting and assembling surface diagrams of the first assembling surface diagram; is the matching priority of the first assembling surface diagram and the to-be-analyzed fitting and assembling surface diagram.

[0066] It should be noted that the more the number of fitting and assembling surface diagrams of the precast member to which the to-be-analyzed fitting and assembling surface diagram belongs, the closer its assembly connection with other precast members, and the more important this precast member is. The larger

[0067] is, the more the precast member to which the to-be-analyzed fitting and assembling surface diagram belongs is at the center of the precast members to which the other fitting and assembling surface diagrams of the first assembling surface diagram belong, the more critical the to-be-analyzed fitting and assembling surface diagram is, and the greater the matching priority of the first assembling surface diagram and the to-be-analyzed fitting and assembling surface diagram.

[0068] It should be noted that the above determines the matching priority of each fitting and assembling surface diagram of each precast member with each fitting and assembling surface diagram, that is, the matching priority of two fitting and assembling surface diagrams of two precast members. When assembling all the precast members to be assembled, it is also necessary to determine the assembly priority of each precast member itself, and then assemble all the precast members to be assembled according to the assembly priority of the precast member and the matching priority of the two fitting and assembling surface diagrams. Specifically, the assembly priority of each precast member is obtained, which is specifically as follows:

[0069] The cumulative value of the probabilities that all the surface diagrams of each precast member belong to the assembling surface in the corresponding surface area in the point cloud model is used as the assembly priority of this precast member. Obtain the assembly priorities of all precast components, obtain the matching priorities between all different assembly surface diagrams and the adapted assembly surface diagrams of all precast components, and assemble all precast components and all assembly surface diagrams of the precast components in descending order of the assembly priorities and the matching priorities to complete the assembly of all precast components to be assembled.

[0070] It should be noted that automatically assembling all precast components to be assembled according to the magnitudes of the assembly priorities and the matching priorities optimizes the efficiency during virtual pre-assembly based on BIM (Building Information Modeling) and reduces the consumption of human resources.

[0071] Through the above steps, a BIM-based information fusion method for assembled precast components is completed.

[0072] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. An information fusion method for prefabricated components based on BIM, characterized in that, The method includes the following steps: Obtain surface maps from different perspectives in the point cloud model of each precast component; Obtain the complexity of the edge line in each surface map according to the curvature change of the point cloud data points on the edge line in the surface map; obtain the unevenness of the corresponding surface area of each surface map in the point cloud model; according to the complexity and unevenness, obtain the possibility that the corresponding surface area of each surface map in the point cloud model belongs to the assembly surface and screen several assembly surface maps of each precast component; Obtain the matching point cloud data points in any two assembly surface maps of any two precast components; obtain the matching degree of any two assembly surface maps of any two precast components according to the distance distribution of the matching point cloud data points; obtain the depth error value of the corresponding surface areas of any two assembly surface maps of any two precast components in the point cloud model; according to the matching degree and the depth error value, obtain the adaptation index of any two assembly surface maps of any two precast components; according to the size of the adaptation index, obtain several adapted assembly surface maps for each assembly surface map of each precast component; Obtain the matching priority of each assembly surface map of each precast component and each adapted assembly surface map according to the number of assembly surface maps of the precast component to which the adapted assembly surface map belongs and the distance distribution of the centers of several adapted assembly surface maps of each assembly surface map of the precast component; obtain the assembly priority of each precast component; assemble all the precast components to be assembled according to the size of the assembly priority and the matching priority.

2. The information fusion method for prefabricated components based on BIM according to claim 1, wherein The specific steps included in obtaining the complexity of the edge line in each surface map according to the curvature change of the point cloud data points on the edge line in the surface map are as follows: Denote any one precast component as the precast component to be analyzed; denote the point cloud model of the precast component to be analyzed as the point cloud model to be analyzed; obtain the curvature of each point cloud data point in the point cloud model to be analyzed; Denote the surface map from any one perspective in the point cloud model to be analyzed as the surface map to be analyzed; Wherein, is the average value of the curvatures corresponding to all the point cloud data points on the edge line in the surface graph to be analyzed; is the range of the curvatures corresponding to all the point cloud data points on the edge line in the surface graph to be analyzed; is the complexity of the edge line in the surface graph to be analyzed.

3. The information fusion method for prefabricated components based on BIM according to claim 2, characterized in that The specific method for obtaining the unevenness of the corresponding surface area of each surface map in the point cloud model is as follows: Denote the corresponding surface area of the surface map to be analyzed in the point cloud model to be analyzed as the surface area to be analyzed; denote the area formed by the outermost edge in the surface map to be analyzed as the first area of the surface map to be analyzed; Denote the closed structure formed by the first area and the surface area to be analyzed as the closed structure to be analyzed; denote the area corresponding to the first area in the closed structure to be analyzed as the virtual cross-section area of the surface area to be analyzed; Wherein, is the number of point cloud data points in the surface area to be analyzed; is the perpendicular distance from the -th point cloud data point in the surface area to be analyzed to the virtual cross-section area of the surface area to be analyzed; is the average value of the perpendicular distances from all point cloud data points in the surface area to be analyzed to the virtual cross-section area of the surface area to be analyzed; is the variance of the perpendicular distances from all point cloud data points in the surface area to be analyzed to the virtual cross-section area of the surface area to be analyzed; is to take the absolute value; is the unevenness degree of the surface graph to be analyzed corresponding to the surface area in the point cloud model to be analyzed.

4. The information fusion method of prefabricated components based on BIM according to claim 3, characterized in that The specific steps included in obtaining the possibility that the corresponding surface area of each surface map in the point cloud model belongs to the assembly surface according to the complexity and unevenness are as follows: In the formula, represents the complexity of the edge line in the surface diagram to be analyzed; is the sigmoid function; represents the possibility that the corresponding surface area of the surface diagram to be analyzed in the point cloud model to be analyzed belongs to the assembled surface.

5. The information fusion method for prefabricated components based on BIM according to claim 3, characterized in that The specific method for obtaining the matching point cloud data points in any two assembly surface maps of any two precast components is as follows: Arbitrarily denote any two assembly surface diagrams of any two prefabricated components as the first assembly surface diagram and the second assembly surface diagram respectively; obtain the center points of the first assembly surface diagram and the second assembly surface diagram respectively; align the center point of the first assembly surface diagram with the center point of the second assembly surface diagram, and obtain a straight line passing through the th point cloud data point on the edge line of the first assembly surface diagram and the aligned center point, and use the point cloud data point that intersects the edge line of the second assembly surface diagram and is the closest to the straight line as the matching point cloud data point of the th point cloud data point on the second assembly surface diagram; when obtaining the point cloud data point that intersects the edge line of the second assembly surface diagram and is the closest to the straight line, keep the first assembly surface diagram stationary and rotate the second assembly surface diagram.

6. The information fusion method for prefabricated components based on BIM according to claim 5, wherein The specific steps included in obtaining the matching degree of any two assembly surface maps of any two precast components according to the distance distribution of the matching point cloud data points are as follows: Wherein, is the number of point cloud data points in the first assembly surface diagram; is the distance between the th point cloud data point in the first assembly surface diagram and the matching point cloud data point in the second assembly surface diagram; is the average value of the distances between all point cloud data points in the first assembly surface diagram and the matching point cloud data points in the second assembly surface diagram; is to take the absolute value; is the matching degree between the first assembly surface diagram and the second assembly surface diagram.

7. The information fusion method of prefabricated components based on BIM according to claim 5, characterized in that The specific method for obtaining the depth error value of the corresponding surface areas of any two assembly surface maps of any two prefabricated components in the point cloud model is as follows: Denote the surface area corresponding to the first assembly surface map in the point cloud model as the first surface area; denote the surface area corresponding to the second assembly surface map in the point cloud model as the second surface area; respectively obtain the virtual cross-section area of the first surface area and the virtual cross-section area of the second surface area according to the method for obtaining the virtual cross-section area of the surface area to be analyzed; In the formula, is the maximum value of the perpendicular distance from all the point cloud data points in the first surface area to the virtual cross-section area of the first surface area; is the maximum value of the perpendicular distance from all the point cloud data points in the second surface area to the virtual cross-section area of the second surface area; represents taking the absolute value; is the depth error value of the corresponding surface areas of the first assembly surface map and the second assembly surface map in the point cloud model to which they belong.

8. The information fusion method for prefabricated components based on BIM according to claim 5, characterized in that The specific steps for obtaining the adaptation index of any two assembly surface maps of any two prefabricated components according to the matching degree and the depth error value are as follows: In the formula, is the matching degree between the first assembly surface diagram and the second assembly surface diagram; is the depth error value of the corresponding surface areas of the first assembly surface diagram and the second assembly surface diagram in the point cloud model to which they belong; is a preset first hyperparameter; is the sigmoid function; is the adaptation index between the first assembly surface diagram and the second assembly surface diagram.

9. The information fusion method of prefabricated components based on BIM according to claim 5, characterized in that The specific steps for obtaining the matching priority of each assembly surface map of each prefabricated component and each adapted assembly surface map according to the number of assembly surface maps of the prefabricated component to which the adapted assembly surface map belongs and the distance distribution among the centers of several adapted assembly surface maps of each assembly surface map of the prefabricated component are as follows: Denote any one of the adapted assembly surface maps of the first assembly surface map as the to-be-analyzed adapted assembly surface map; In the formula, is the number of the fitting assembly surface diagrams of the precast component to which the fitting assembly surface diagram to be analyzed belongs; is the number of the fitting assembly surface diagrams of the first fitting assembly surface diagram; is the th distance between the center points of the fitting assembly surface diagrams of the fitting assembly surface diagram to be analyzed and the first fitting assembly surface diagram; is the average value of the distances between the center points of all the fitting assembly surface diagrams of the fitting assembly surface diagram to be analyzed and the first fitting assembly surface diagram; is the matching priority of the first fitting assembly surface diagram and the fitting assembly surface diagram to be analyzed.

10. The information fusion method for prefabricated components based on BIM according to claim 1, wherein, The specific method for obtaining the assembly priority of each prefabricated component is as follows: Take the cumulative value of the probabilities that the surface areas corresponding to all surface maps of each prefabricated component in the point cloud model belong to the assembly surface as the assembly priority of the prefabricated component.

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