A BIM-based prefabricated component information fusion method

Abstract

The present invention relates to the technical field of prefabricated component assembly, and more specifically to a BIM-based prefabricated component information fusion method, comprising: scanning each prefabricated component to be assembled to obtain a point cloud model of each prefabricated component, wherein the point cloud model includes a plurality of point cloud data points; obtaining surface images of each prefabricated component from different perspectives in the point cloud model; obtaining an assembly space drawing of the prefabricated component; obtaining a plurality of assembly surface images of the prefabricated component; obtaining a plurality of adapted assembly surface images of each assembly surface image of the prefabricated component; obtaining a matching priority between each assembly surface image of the prefabricated component and each adapted assembly surface image; obtaining an assembly priority of the prefabricated component; and assembling all prefabricated components to be assembled according to the assembly priority and the matching priority. The present invention optimizes the efficiency of virtual pre-assembly based on BIM.

Description

Technical Field

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

[0002] Prefabricated components have been widely used and promoted in construction and infrastructure. Prefabricated components are pre-produced and processed in factories, then transported to the construction site, and placed in designated locations by hoisting. They are then connected into a whole through grouting sleeves and other methods. Before actual assembly, virtual pre-assembly is usually performed based on BIM (Building Information Modeling) to plan the assembly process of prefabricated components, optimize construction plans, reduce unnecessary rework and modifications, and thus save time and costs.

[0003] Currently, when assembling prefabricated components based on BIM technology, 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, they also need to consider whether the two assembly surfaces of different component models are compatible. Since manual operation relies on the observation and judgment of technicians, and the human eye has limited observation accuracy and slow operation speed, 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] In order to solve the above problems, the present invention provides a BIM-based prefabricated component information fusion method.

[0005] The present invention provides a BIM-based prefabricated component information fusion method using the following technical solutions:

[0006] An embodiment of the present invention provides a method for integrating prefabricated components based on BIM, the method comprising the following steps:

[0007] Scan each prefabricated component to be assembled to obtain a point cloud model of each prefabricated component, wherein the point cloud model includes a plurality of point cloud data points; obtain surface images of each prefabricated component from different perspectives in the point cloud model; and obtain an assembly space drawing of the prefabricated component;

[0008] Based on the curvature changes corresponding to the point cloud data points on the edge lines of the surface images, the complexity of the edge lines in each surface image is obtained; the unevenness of the surface area corresponding to each surface image in the corresponding point cloud model is obtained; based on the complexity and unevenness, the probability that the surface area corresponding to each surface image in the point cloud model belongs to the assembly surface is obtained; based on the probability of belonging to the assembly surface, several assembly surface images of each prefabricated component are screened;

[0009] Obtaining matching point cloud data points in any two assembly surface maps of any two prefabricated components, where the two assembly surface maps do not belong to the same prefabricated component; obtaining a matching degree of any two assembly surface maps of any two prefabricated components based on a distance distribution between the matching point cloud data points; obtaining a depth error value of a corresponding surface area of ​​any two assembly surface maps of any two prefabricated components in their corresponding point cloud model; obtaining a matching index of any two assembly surface maps of any two prefabricated components based on the matching degree and the depth error value; and obtaining a plurality of matching assembly surface maps of each assembly surface map of each prefabricated component based on the size of the matching index;

[0010] According to the assembly space drawing, the distances between the center points of the adaptive assembly surface diagrams of different prefabricated components are obtained; according to the number of assembly surface diagrams of the prefabricated component to which the adaptive assembly surface diagram belongs and the distribution of the distances between the center points of several adaptive assembly surface diagrams of each assembly surface diagram of the prefabricated component, the matching priority between each assembly surface diagram of each prefabricated component and each adaptive assembly surface diagram is obtained; the assembly priority of each prefabricated component is obtained; and all prefabricated components to be assembled are assembled according to the size of the assembly priority and the matching priority.

[0011] Furthermore, the complexity of the edge line in each surface image is obtained according to the curvature change corresponding to the point cloud data points on the edge line in the surface image, including the following specific steps:

[0012] Record any prefabricated component as the prefabricated component to be analyzed; record 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; record the surface map of any viewpoint in the point cloud model to be analyzed as the surface map to be analyzed;

[0013]

[0014] Where, is the average value of the curvature corresponding to all point cloud data points on the edge line of the surface image to be analyzed; 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.

[0015] Furthermore, the specific method for obtaining the unevenness of the surface area corresponding to each surface image in the corresponding point cloud model is as follows:

[0016] The surface area corresponding to the surface image 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 image to be analyzed is recorded as the first area of ​​the surface image 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;

[0017]

[0018] Where, is the number of point cloud data points in the surface area to be analyzed; The surface area to be analyzed The perpendicular distance from each point cloud data point 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; To take the absolute value; It is the roughness degree of the surface area corresponding to the surface image to be analyzed in the point cloud model to be analyzed.

[0019] Furthermore, the method of obtaining the possibility that the corresponding surface area of ​​each surface image in the point cloud model belongs to the assembly surface according to the complexity and unevenness includes the following specific steps:

[0020]

[0021] Where, is the complexity of the edge lines in the surface image to be analyzed; is the sigmoid function; It is the possibility that the corresponding surface area of ​​the surface image to be analyzed in the point cloud model to be analyzed belongs to the assembly surface.

[0022] Furthermore, the specific method for obtaining matching point cloud data points in any two assembled surface images of any two prefabricated components is as follows:

[0023] Record any two assembly surface drawings of any two prefabricated components as the first assembly surface drawing and the second assembly surface drawing respectively; obtain the center points of the first assembly surface drawing and the second assembly surface drawing respectively; align the center points of the first assembly surface drawing and the second assembly surface drawing, and obtain the first assembly surface drawing on the edge line of the first assembly surface drawing. The point cloud data point and the straight line aligned with the center point are used as the point cloud data point that intersects the edge line of the second assembled surface map and is closest to the straight line. the matching point cloud data point of each point cloud data point in the second assembled surface image; when obtaining the point cloud data point where the straight line intersects the edge line of the second assembled surface image and is closest to the point cloud data point, the first assembled surface image is fixed and the second assembled surface image is rotated.

[0024] Furthermore, the matching degree of any two assembly surface images of any two prefabricated components is obtained based on the distance distribution between the matched point cloud data points, and the specific steps include the following:

[0025]

[0026] Where, is the number of point cloud data points in the first assembled surface image; The first assembled surface is The distance between the point cloud data point and the matching point cloud data point in the second assembled surface map; is the average of the distances between all point cloud data points in the first assembled surface image and the matching point cloud data points in the second assembled surface image; To take the absolute value; is the matching degree between the first assembled surface graph and the second assembled surface graph.

[0027] Furthermore, the specific method for obtaining the depth error value of the corresponding surface area of ​​any two assembled surface images of any two prefabricated components in the corresponding point cloud model is as follows:

[0028] The surface area corresponding to the first assembled surface image in the corresponding point cloud model is recorded as the first surface area; the surface area corresponding to the second assembled surface image in the corresponding point cloud model is recorded as the second surface area; according to the method for obtaining the virtual cross-sectional area of ​​the surface area to be analyzed, the virtual cross-sectional area of ​​the first surface area and the virtual cross-sectional area of ​​the second surface area are respectively obtained;

[0029]

[0030] Where, 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 area to the virtual cross-sectional area of ​​the second surface area; To take the absolute value; is the depth error value of the corresponding surface area of ​​the first assembled surface image and the second assembled surface image in the corresponding point cloud model.

[0031] Furthermore, the adaptation index of any two assembled surface images of any two prefabricated components is obtained according to the matching degree and the depth error value, and the specific steps include the following:

[0032]

[0033] Where, is the matching degree between the first assembled surface graph and the second assembled surface graph; is the depth error value of the corresponding surface area of ​​the first assembled surface image and the second assembled surface image in the corresponding point cloud model; is the first preset hyperparameter; is the sigmoid function; is the adaptation index between the first assembled surface graph and the second assembled surface graph.

[0034] Furthermore, the method of obtaining a matching priority between each assembly surface diagram of each prefabricated component and each adaptive assembly surface diagram according to the number of assembly surface diagrams of the prefabricated component to which the adaptive assembly surface diagram belongs and the distance distribution between the center points of several adaptive assembly surface diagrams of each assembly surface diagram of the prefabricated component includes the following specific steps:

[0035] Any matching assembly surface graph of the first assembly surface graph is recorded as the matching assembly surface graph to be analyzed;

[0036]

[0037] Where, The number of assembly surface diagrams of the prefabricated component to which the assembly surface diagram to be analyzed belongs; is the number of adapted assembled surface graphs of the first assembled surface graph; The first assembly surface graph and the first assembly surface graph to be analyzed are The distance between the center points of the adapted assembled surface graphs; is the average value of the distances between the center points of all the adapted assembled surface graphs of the to-be-analyzed adapted assembled surface graph and the first assembled surface graph; It is the matching priority between the first assembled surface graph and the adapted assembled surface graph to be analyzed.

[0038] Furthermore, the specific method for obtaining the assembly priority of each prefabricated component is as follows:

[0039] The cumulative value of the possibility that the corresponding surface area of ​​all surface images of each prefabricated component in the point cloud model belongs to the assembly surface is used as the assembly priority of the prefabricated component.

[0040] The beneficial effect of the technical solution of the present invention is that when the present invention performs virtual pre-assembly of prefabricated components based on BIM, the assembly surface of each prefabricated component is automatically determined through analysis, and whether the assembly surfaces of different prefabricated components are compatible, the assembly priority of the prefabricated components and the matching priority of two compatible assembly surfaces of different prefabricated components are determined, and then all prefabricated components to be assembled are automatically assembled, thereby optimizing the efficiency of virtual pre-assembly based on BIM, improving the efficiency of overall construction, and improving the accuracy and timeliness of operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 A flowchart of a BIM-based prefabricated component information fusion method according to an embodiment of the present invention;

[0043] Figure 2 A schematic diagram of a scene of placing target balls on a prefabricated component provided by one embodiment of the present invention;

[0044] Figure 3 A schematic diagram of a scenario for performing multi-directional scanning of prefabricated components at different locations according to an embodiment of the present invention;

[0045] Figure 4 A schematic diagram of a point cloud model of a prefabricated component provided by one embodiment of the present invention;

[0046] Figure 5 A schematic diagram of obtaining matching point cloud data points provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0047] To further illustrate the technical means and effectiveness of the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of a BIM-based prefabricated component information fusion method proposed by the present invention. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0048] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0049] The following describes in detail a BIM-based prefabricated component information fusion method provided by the present invention with reference to the accompanying drawings.

[0050] See also Figure 1 , which shows a flowchart of a method for fusion of prefabricated components information based on BIM according to an embodiment of the present invention, the method comprising the following steps:

[0051] Step S001: Scan each prefabricated component to be assembled to obtain a point cloud model of each prefabricated component, wherein the point cloud model includes a plurality of point cloud data points; obtain surface images of the point cloud model of each prefabricated component from different perspectives; and obtain an assembly space drawing of the prefabricated component.

[0052] It should be noted that the main purpose of this embodiment is to automatically determine the assembly surface of each prefabricated component when virtually pre-assembling prefabricated components based on BIM (Building Information Model), and determine whether the assembly surfaces of different prefabricated components are compatible, determine the assembly priority of the prefabricated components and the matching priority of two compatible assembly surfaces of different prefabricated components, and then automatically assemble all prefabricated components to be assembled, thereby optimizing the efficiency of virtual pre-assembly based on BIM (Building Information Model) and reducing manpower consumption.

[0053] Specifically, each prefabricated component to be assembled is scanned to obtain a point cloud model of each prefabricated component. The point cloud model includes a number of point cloud data points, as follows:

[0054] The Trimble-TX8 3D laser scanner was leveled before scanning, and a target sphere was placed on a prefabricated component to be assembled. The prefabricated component was scanned from multiple locations using the 3D laser scanner to obtain a point cloud model of the prefabricated component. Similarly, all prefabricated components to be assembled were scanned one by one to obtain a point cloud model of each prefabricated component.

[0055] It should be noted that the 3D laser scanner will identify the position of the target balls placed on the prefabricated components during the scanning process, and perform spatial alignment and data registration. The high contrast characteristics of the target balls can help the 3D laser scanner correctly align each scanning point cloud data in different orientations, and splice the point cloud data in multiple orientations into a complete 3D point cloud model, forming an overall model of the prefabricated component.

[0056] Please note that Figure 2 , Figure 2This is a schematic diagram of the scene of placing target balls on prefabricated components in this embodiment. Figure 2 Contains a prefabricated component and several target balls; see Figure 3 , Figure 3 This is a schematic diagram of the scene of multi-directional scanning of prefabricated components at different sites in this embodiment; please refer to Figure 4 , Figure 4 Schematic diagram of the point cloud model of the prefabricated component in this embodiment.

[0057] Specifically, surface maps of different perspectives are obtained in the point cloud model of each prefabricated component. It should be noted that since prefabricated components are mainly structures, the surface maps of different perspectives of the point cloud model of each prefabricated component obtained in this embodiment need to include all surfaces of the prefabricated components. The specific method of obtaining surface maps from different perspectives is an existing method and will not be repeated in this embodiment.

[0058] Specifically, the assembly space drawings of the prefabricated components are obtained; it should be noted that the assembly space drawings can show the position of each prefabricated component. Obtaining the assembly space drawings of the prefabricated components is an existing method and will not be repeated in this embodiment.

[0059] At this point, the point cloud model of each prefabricated component, the surface diagrams of each prefabricated component at different perspectives in the point cloud model, and the assembly space drawings of the prefabricated components are obtained.

[0060] Step S002: Obtain the complexity of the edge lines in each surface image based on the curvature changes corresponding to the point cloud data points on the edge lines in the surface image; obtain the degree of unevenness of the surface area corresponding to each surface image in the point cloud model; obtain the possibility that the surface area corresponding to each surface image in the point cloud model belongs to the assembly surface based on the complexity and unevenness; and screen several assembly surface images of each prefabricated component based on the possibility of belonging to the assembly surface.

[0061] It should be noted that during the assembly process of prefabricated components, some fitting positions are usually designed between the components. These positions can ensure the smooth splicing and tight combination of each component. In order to achieve this goal, the edges of the components are often designed to have special functions, usually with the functions of positioning and docking. Specifically, in order to ensure that the components can be accurately spliced, the edge design often adopts a groove or protrusion structure to ensure that the two prefabricated components can be accurately aligned and firmly combined when docking. In addition, the edges of the components may also adopt a plug-in method, such as designing a socket or pin structure, which not only facilitates quick docking, but also enhances the stability of the connection. Therefore, compared with other edges of the components, the edges at the fitting positions usually present more complex and varied shapes, which may include various twists, bends or interlocking designs to adapt to different connection requirements and ensure the accuracy and stability of the splicing.

[0062] It should be further explained that since the prefabricated component is a structure, it includes multiple surface maps. When the prefabricated components are spliced, not all surface areas corresponding to all surface maps are spliced ​​with other prefabricated components. Usually, only a few specific surface areas are involved in the splicing. These surface areas have complex edge lines in the corresponding surface maps, and the surface maps correspond to uneven surface areas in the corresponding point cloud models. In order to determine which surface maps of the prefabricated component correspond to surface areas in the point cloud model that belong to the assembly surfaces, it is necessary to analyze the complexity of the edge lines in the surface maps and the unevenness of the surface areas corresponding to the surface maps in the point cloud model to determine several assembly surface maps of the prefabricated component.

[0063] Specifically, the complexity of the edge line in each surface image is obtained according to the curvature change corresponding to the point cloud data points on the edge line of the surface image, as follows:

[0064] Denote any 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; and denote the surface map of any viewpoint in the point cloud model to be analyzed as the surface map to be analyzed. It should be noted that obtaining the curvature of the point cloud data points in the point cloud model is an existing method and will not be further described in this embodiment.

[0065]

[0066] Where, is the average value of the curvatures corresponding to all 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. 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.

[0067] What needs to be explained is 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.

[0068] 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 will be analyzed.

[0069] Furthermore, the roughness of the corresponding surface area of ​​each surface image in the corresponding point cloud model is obtained as follows:

[0070] The surface area corresponding to the surface map to be analyzed in the point cloud model to be analyzed is denoted as the surface area to be analyzed; the area formed by the outermost edge of the surface map to be analyzed is denoted 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 denoted as the closed structure to be analyzed; and the area corresponding to the first area in the closed structure to be analyzed is denoted 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 image from a single perspective, while the surface area to be analyzed is a partial surface area on the point cloud model, which is a three-dimensional surface, i.e., it has concave and convex depth variations.

[0071]

[0072] Where, is the number of point cloud data points in the surface area to be analyzed; The surface area to be analyzed The perpendicular distance from each point cloud data point 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; To take the absolute value; It is the roughness degree of the surface area corresponding to the surface image to be analyzed in the point cloud model to be analyzed.

[0073] What needs to be explained is that The larger the value is, the greater the variation of the vertical distance from the point cloud data points in the surface area to be analyzed to the virtual cross-section area compared with the average distance, and the higher the unevenness of the surface area. At the same time, if the variance of the vertical distance from all point cloud data points in the surface area to be analyzed to the virtual cross-section area is, the greater the fluctuation of the vertical distance is, and the higher the unevenness of the surface area is.

[0074] It should be noted that the above analysis includes the complexity of the edge lines in the surface map and the unevenness of the corresponding surface area in the point cloud model to which the surface map belongs. Next, the two are used to determine several assembly surface maps of each prefabricated component. The assembly surface map is the surface map for assembly with other prefabricated components.

[0075] Specifically, based on the complexity and unevenness, the possibility of each surface image corresponding to the surface area in the point cloud model belonging to the assembly surface is obtained as follows:

[0076]

[0077] Where, is the complexity of the edge lines in the surface image to be analyzed; The roughness of the surface area corresponding to the surface image to be analyzed in the point cloud model to be analyzed; is the sigmoid function, used for normalization; It is the possibility that the corresponding surface area of ​​the surface image to be analyzed in the point cloud model to be analyzed belongs to the assembly surface.

[0078] It should be noted that the higher the complexity of the edge lines in the surface map to be analyzed, the more likely it is an assembly surface map corresponding to the assembly with other prefabricated components. At the same time, if the surface map to be analyzed has a greater degree of unevenness in the corresponding surface area in the point cloud model to be analyzed, it means that there are grooves or protrusions on the surface of the prefabricated component, and the more likely it is an assembly surface map corresponding to the assembly with other prefabricated components.

[0079] Furthermore, according to the probability of belonging to the assembly surface, several assembly surface diagrams of each prefabricated component are screened, as follows:

[0080] A possibility threshold is preset. In this embodiment, the possibility threshold is 0.5. If 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 assembly surface is greater than the possibility threshold, the surface map to be analyzed is used as an assembly surface map of the prefabricated component to be analyzed. Otherwise, it is not used as an assembly surface map of the prefabricated component to be analyzed. The same analysis is performed on the surface maps of other perspectives in the point cloud model to be analyzed to obtain several assembly surface maps of the prefabricated component to be analyzed.

[0081] At this point, several assembly surface diagrams of each prefabricated component are screened.

[0082] Step S003: obtain matching point cloud data points in any two assembly surface maps of any two prefabricated components, where the any two assembly surface maps do not belong to the same prefabricated component; obtain the degree of matching of any two assembly surface maps of any two prefabricated components based on the distance distribution between the matching point cloud data points; obtain the depth error value of the corresponding surface area of ​​any two assembly surface maps of any two prefabricated components in the corresponding point cloud model; obtain the adaptation index of any two assembly surface maps of any two prefabricated components based on the matching degree and the depth error value; and obtain a number of adapted assembly surface maps of each assembly surface map of each prefabricated component based on the size of the adaptation index.

[0083] It should be noted that the above steps determine several assembly surface drawings of each prefabricated component. When a prefabricated component is assembled with another prefabricated component, the assembly surface drawings between them need to have a certain degree of matching. Only when the size, shape and surface accuracy of the two assembly surface drawings reach a sufficient matching level can they be ensured to fit together accurately to avoid gaps or misalignment. When the edge line of the assembly surface drawing of a prefabricated component is consistent with the edge line shape of the assembly surface drawing of another prefabricated component to be assembled, it means that the two are highly matched in geometry, and the higher the dimensional accuracy, the higher the degree of matching of the two assembly surface drawings in each detail, and the smaller the error. Precise dimensional control ensures that there is almost no gap between the contact surfaces of the two, thereby avoiding errors or deformation caused by mismatch during assembly. When the two assembly surface drawings are precisely matched, the assembly process can be smoother, the joint of the components is more firm, and the stability and reliability are also improved. Therefore, it is also necessary to analyze the adaptation index of the two assembly surface drawings of different prefabricated components.

[0084] It should be noted that the edge lines in the assembly surface map can well reflect the morphological characteristics of the assembly surface map. If the distribution of the matching point cloud data points on the edge lines of the two assembly surface maps is closer, it means that the two assembly surface maps are adapted. First, the matching point cloud data points in any two assembly surface maps are obtained, and then the distance distribution between the matching point cloud data points is determined to determine the matching degree of any two assembly surface maps of any two prefabricated components.

[0085] Specifically, matching point cloud data points in any two assembled surface images of any two prefabricated components are obtained, and the any two assembled surface images do not belong to the same prefabricated component, as follows:

[0086] Record any two assembly surface drawings of any two prefabricated components as the first assembly surface drawing and the second assembly surface drawing respectively; obtain the center points of the first assembly surface drawing and the second assembly surface drawing respectively; align the center points of the first assembly surface drawing and the second assembly surface drawing, and obtain the first assembly surface drawing on the edge line of the first assembly surface drawing. The point cloud data point and the straight line aligned with the center point are used as the point cloud data point that intersects the edge line of the second assembled surface map and is closest to the straight line. The matching point cloud data points of the point cloud data points in the second assembled surface map; when obtaining the point cloud data point where the straight line intersects and is closest to the edge line of the second assembled surface map, it is necessary to fix the first assembled surface map and rotate the second assembled surface map to obtain the intersecting and closest point cloud data points. Figure 5 , Figure 5 This is a schematic diagram of obtaining matching point cloud data points in this embodiment. Figure 5The 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 The point cloud data point and the straight line of the alignment center point, Point cloud data points, matching point cloud data points.

[0087] It should be noted that, for the above-mentioned matching point cloud data points in any two assembly surface maps of any two prefabricated components, if the distance between the matching point cloud data points in the two assembly surface maps is closer and the distribution is more uniform, it means that the matching degree of the two assembly surface maps is higher. Therefore, by analyzing the distance distribution between the matching point cloud data points, the matching degree of any two assembly surface maps of any two prefabricated components is determined.

[0088] Specifically, according to the distance distribution between the matched point cloud data points, the matching degree of any two assembly surface images of any two prefabricated components is obtained, as follows:

[0089]

[0090] Where, is the number of point cloud data points in the first assembled surface image; The first assembled surface is The distance between the point cloud data point and the matching point cloud data point in the second assembled surface map; is the average of the distances between all point cloud data points in the first assembled surface image and the matching point cloud data points in the second assembled surface image; To take the absolute value; is the matching degree between the first assembled surface graph and the second assembled surface graph.

[0091] What needs to be explained is that Indicates the uniformity of the distance distribution between the matching point cloud data points in two assembly surface images of two different prefabricated components. The larger the value is, the more evenly the distribution of the matching point cloud data points in the two assembled surface images is. At the same time, if the cumulative distance between the matching point cloud data points in the two assembled surface images is smaller, it means that the distance error between the matching point cloud data points is smaller, and the matching degree of the two assembled surface images is higher.

[0092] It should be noted that the above analysis is about the matching degree of any two assembled surface images of any two prefabricated components. Since the surface area corresponding to the assembled surface image of the prefabricated components in the corresponding point cloud model may have grooves or protrusions, in order to better judge the adaptation relationship between any two assembled surface images of any two prefabricated components and reduce mismatching, it is necessary to analyze the depth error value of the surface area corresponding to any two assembled surface images of any two prefabricated components in the corresponding point cloud model.

[0093] Specifically, the depth error values ​​of the corresponding surface areas of any two assembled surface images of any two prefabricated components in their corresponding point cloud models are obtained as follows:

[0094] The surface area corresponding to the first assembled surface image in the corresponding point cloud model is recorded as the first surface area; the surface area corresponding to the second assembled surface image in the corresponding point cloud model is recorded as the second surface area; according to the method of obtaining the virtual cross-sectional area of ​​the surface area to be analyzed, the virtual cross-sectional area of ​​the first surface area and the virtual cross-sectional area of ​​the second surface area are respectively obtained.

[0095]

[0096] Where, 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 area to the virtual cross-sectional area of ​​the second surface area; To take the absolute value; is the depth error value of the corresponding surface area of ​​the first assembled surface image and the second assembled surface image in the corresponding point cloud model.

[0097] It should be noted that the smaller the depth error value of the corresponding surface area of ​​the first assembly surface map and the second assembly surface map in the corresponding point cloud model, the closer the groove or convex structure of the surface area corresponding to the assembly surface map of the prefabricated component in the corresponding point cloud model is, and the more suitable the surface area corresponding to the first assembly surface map and the second assembly surface map in the corresponding point cloud model are for assembly.

[0098] It should be noted that the above analysis includes the matching degree of any two assembly surface maps of any two prefabricated components and the depth error value of the corresponding surface area of ​​any two assembly surface maps of any two prefabricated components in the corresponding point cloud model. Next, the two are combined to determine several adapted assembly surface maps of each assembly surface map of each prefabricated component.

[0099] Specifically, according to the matching degree and the depth error value, the adaptation index of any two assembled surface images of any two prefabricated components is obtained as follows:

[0100]

[0101] Where, is the matching degree between the first assembled surface graph and the second assembled surface graph; is the depth error value of the corresponding surface area of ​​the first assembled surface image and the second assembled surface image in the corresponding point cloud model; is the first preset hyperparameter, the purpose of which is to prevent the denominator from being 0. to give a narrative; is the sigmoid function, used for normalization; is the adaptation index between the first assembled surface graph and the second assembled surface graph.

[0102] What needs to be explained is that the greater the degree of matching between the first assembly surface map and the second assembly surface map, the better the adaptation relationship between the first assembly surface map and the second assembly surface map, and the larger the adaptation index. At the same time, the smaller the depth error value of the corresponding surface area of ​​the first assembly surface map and the second assembly surface map in the corresponding point cloud model, the smaller the groove or protrusion structure of the corresponding surface area of ​​the assembly surface map of the prefabricated component in the corresponding point cloud model, the better the depth adaptation relationship, and the larger the adaptation index of the first assembly surface map and the second assembly surface map.

[0103] Furthermore, according to the size of the adaptation index, several adapted assembly surface diagrams of each assembly surface diagram of each prefabricated component are obtained, as follows:

[0104] A fitting index threshold is preset. This embodiment is described with a possibility threshold of 0.5. If the fitting index of the first assembly surface diagram and the second assembly surface diagram is greater than the fitting index threshold, the second assembly surface diagram is used as an fitting assembly surface diagram of the first assembly surface diagram. Otherwise, it is not used as an fitting assembly surface diagram of the first assembly surface diagram. The fitting index of the first assembly surface diagram and other assembly surface diagrams is obtained, and judged with the fitting index threshold to obtain several fitting assembly surface diagrams of the first assembly surface diagram.

[0105] At this point, several adapted assembly surface graphs of each assembly surface graph of each prefabricated component are obtained.

[0106] Step S004: According to the assembly space drawing, obtain the distance between the center points of the adaptive assembly surface diagrams of different prefabricated components; according to the number of assembly surface diagrams of the prefabricated component to which the adaptive assembly surface diagram belongs and the distribution of the distances between the center points of several adaptive assembly surface diagrams of each assembly surface diagram of the prefabricated component, obtain the matching priority of each assembly surface diagram of each prefabricated component and each adaptive assembly surface diagram; obtain the assembly priority of each prefabricated component; and assemble all prefabricated components to be assembled according to the size of the assembly priority and the matching priority.

[0107] It should be noted that during the assembly of prefabricated components, a component assembly surface diagram may have multiple matching assembly surface diagrams, meaning that a prefabricated component needs to be assembled with multiple other prefabricated components. Therefore, the matching priorities of the matching assembly surface diagrams for the aforementioned first assembly surface diagram must be determined for optimal assembly.

[0108] It should be further explained that when an assembly surface diagram has multiple adaptive assembly surface diagrams, if the prefabricated component to which a certain adaptive assembly surface diagram belongs has multiple assembly surface diagrams, it means that the tighter the assembly connection with other prefabricated components is, the more important the prefabricated component is. At the same time, if the prefabricated component to which the adaptive assembly surface diagram belongs is closer to the center of the prefabricated components to which other adaptive assembly surface diagrams belong, the higher the matching priority of the prefabricated component to which the assembly surface diagram belongs and the prefabricated component to which the adaptive assembly surface diagram belongs, the more it should be assembled first.

[0109] Specifically, the distances between the center points of the adaptive assembly surface diagrams of different prefabricated components are obtained based on the assembly space drawings. As explained above, the assembly space drawings allow the position of each prefabricated component to be displayed, and thus the distances between the center points of the adaptive assembly surface diagrams of different prefabricated components can be obtained based on the assembly space drawings. This specific method for obtaining the distances is an existing method and will not be further described in this embodiment.

[0110] Furthermore, according to the number of assembly surface diagrams of the prefabricated component to which the adaptive assembly surface diagram belongs and the distribution of the distances between the center points of several adaptive assembly surface diagrams of each assembly surface diagram of the prefabricated component, the matching priority of each assembly surface diagram of each prefabricated component and each adaptive assembly surface diagram is obtained, as follows:

[0111] Any adapted assembly surface graph of the first assembly surface graph is recorded as the adapted assembly surface graph to be analyzed.

[0112]

[0113] Where, The number of assembly surface diagrams of the prefabricated component to which the assembly surface diagram to be analyzed belongs; is the number of adapted assembled surface graphs of the first assembled surface graph; The first assembly surface graph and the first assembly surface graph to be analyzed are The distance between the center points of the adapted assembled surface graphs; is the average value of the distances between the center points of all the adapted assembled surface graphs of the to-be-analyzed adapted assembled surface graph and the first assembled surface graph; It is the matching priority between the first assembled surface graph and the adapted assembled surface graph to be analyzed.

[0114] It should be noted that the more assembly surface diagrams a prefabricated component to which the adapted assembly surface diagram to be analyzed has, the tighter its assembly connection with other prefabricated components is, and the more important the prefabricated component is. The larger it is, the prefabricated component to which the adaptive assembly surface diagram to be analyzed belongs is in the center of the prefabricated components belonging to other adaptive assembly surface diagrams of the first assembly surface diagram. The more critical the adaptive assembly surface diagram to be analyzed is, the greater the matching priority between the first assembly surface diagram and the adaptive assembly surface diagram to be analyzed.

[0115] It should be noted that the above determines the matching priority of each assembly surface diagram of each prefabricated component and each adapted assembly surface diagram, that is, the matching priority of the two assembly surface diagrams of two prefabricated components. When assembling all the prefabricated components to be assembled, it is also necessary to determine the assembly priority of each prefabricated component itself, and then assemble all the prefabricated components to be assembled according to the assembly priority of the prefabricated components and the matching priority of the two assembly surface diagrams.

[0116] Specifically, the assembly priority of each prefabricated component is obtained as follows:

[0117] The cumulative value of the possibility that the corresponding surface area of ​​all surface images of each prefabricated component in the point cloud model belongs to the assembly surface is used as the assembly priority of the prefabricated component.

[0118] Furthermore, all prefabricated components to be assembled are assembled according to the assembly priority and the matching priority, as follows:

[0119] Obtain the assembly priority of all prefabricated components, obtain the matching priority of all different assembly surface drawings of all prefabricated components and the adapted assembly surface drawings, assemble all prefabricated components and all assembly surface drawings of prefabricated components in descending order of assembly priority and matching priority, and complete the assembly of all prefabricated components to be assembled.

[0120] It should be noted that all prefabricated components to be assembled are automatically assembled through the size of assembly priority and matching priority, which optimizes the efficiency of virtual pre-assembly based on BIM (Building Information Modeling) and reduces manpower consumption.

[0121] Through the above steps, a BIM-based prefabricated component information fusion method is completed.

[0122] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A BIM-based prefabricated component information fusion method, characterized in that: The method comprises the following steps: Obtain surface images of each prefabricated component from different perspectives in the point cloud model; Based on the curvature changes corresponding to the point cloud data points on the edge lines of the surface images, the complexity of the edge lines in each surface image is obtained; the unevenness of the surface area corresponding to each surface image in the point cloud model is obtained; based on the complexity and unevenness, the possibility that the surface area corresponding to each surface image in the point cloud model belongs to the assembly surface is determined, and several assembly surface images of each prefabricated component are selected; Obtain matching point cloud data points in any two assembly surface maps of any two prefabricated components; obtain the matching degree of any two assembly surface maps of any two prefabricated components based on the distance distribution between the matching point cloud data points; obtain the depth error value of the corresponding surface area of ​​any two assembly surface maps of any two prefabricated components in the corresponding point cloud model; obtain the adaptation index of any two assembly surface maps of any two prefabricated components based on the matching degree and the depth error value; and obtain a number of adapted assembly surface maps for each assembly surface map of each prefabricated component based on the size of the adaptation index; According to the number of assembly surface diagrams of the prefabricated component to which the adaptive assembly surface diagram belongs and the distribution of the distances between the center points of several adaptive assembly surface diagrams of each assembly surface diagram of the prefabricated component, a matching priority between each assembly surface diagram of each prefabricated component and each adaptive assembly surface diagram is obtained; the assembly priority of each prefabricated component is obtained; and all prefabricated components to be assembled are assembled according to the assembly priority and the matching priority. The complexity of the edge line in each surface image is obtained according to the curvature change corresponding to the point cloud data points on the edge line in the surface image, and the specific steps include the following: Record any prefabricated component as the prefabricated component to be analyzed; record 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; record the surface map of any viewpoint in the point cloud model to be analyzed as the surface map to be analyzed; Where, is the average value of the curvature corresponding to all point cloud data points on the edge line of the surface image to be analyzed; 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; The specific method for obtaining the unevenness of the surface area corresponding to each surface image in the corresponding point cloud model is as follows: The surface area corresponding to the surface image 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 image to be analyzed is recorded as the first area of ​​the surface image 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; Where, is the number of point cloud data points in the surface area to be analyzed; The surface area to be analyzed The perpendicular distance from each point cloud data point 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; To take the absolute value; It is the roughness degree of the surface area corresponding to the surface image to be analyzed in the point cloud model to be analyzed.

2. The BIM-based prefabricated component information fusion method according to claim 1, characterized in that: The method of obtaining the possibility that the corresponding surface area of ​​each surface image in the point cloud model belongs to the assembly surface according to the complexity and unevenness includes the following specific steps: Where, is the complexity of the edge lines in the surface image to be analyzed; is the sigmoid function; It is the possibility that the corresponding surface area of ​​the surface image to be analyzed in the point cloud model to be analyzed belongs to the assembly surface.

3. The BIM-based prefabricated component information fusion method according to claim 1, characterized in that: The specific method for obtaining the matching point cloud data points in any two assembled surface images of any two prefabricated components is as follows: Record any two assembly surface drawings of any two prefabricated components as the first assembly surface drawing and the second assembly surface drawing respectively; obtain the center points of the first assembly surface drawing and the second assembly surface drawing respectively; align the center points of the first assembly surface drawing and the second assembly surface drawing, and obtain the first assembly surface drawing on the edge line of the first assembly surface drawing. The point cloud data point and the straight line aligned with the center point are used as the point cloud data point that intersects the edge line of the second assembled surface map and is closest to the straight line. the matching point cloud data point of each point cloud data point in the second assembled surface image; when obtaining the point cloud data point where the straight line intersects the edge line of the second assembled surface image and is closest to the point cloud data point, the first assembled surface image is fixed and the second assembled surface image is rotated.

4. The BIM-based prefabricated component information fusion method according to claim 3, characterized in that: The method of obtaining the matching degree of any two assembled surface images of any two prefabricated components based on the distance distribution between the matched point cloud data points includes the following specific steps: Where, is the number of point cloud data points in the first assembled surface image; The first assembled surface is The distance between the point cloud data point and the matching point cloud data point in the second assembled surface map; is the average of the distances between all point cloud data points in the first assembled surface image and the matching point cloud data points in the second assembled surface image; To take the absolute value; is the matching degree between the first assembled surface graph and the second assembled surface graph.

5. The BIM-based prefabricated component information fusion method according to claim 3, characterized in that: The specific method for obtaining the depth error value of the corresponding surface area of ​​any two assembled surface images of any two prefabricated components in the corresponding point cloud model is as follows: The surface area corresponding to the first assembled surface image in the corresponding point cloud model is recorded as the first surface area; the surface area corresponding to the second assembled surface image in the corresponding point cloud model is recorded as the second surface area; according to the method for obtaining the virtual cross-sectional area of ​​the surface area to be analyzed, the virtual cross-sectional area of ​​the first surface area and the virtual cross-sectional area of ​​the second surface area are respectively obtained; Where, 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 area to the virtual cross-sectional area of ​​the second surface area; To take the absolute value; is the depth error value of the corresponding surface area of ​​the first assembled surface image and the second assembled surface image in the corresponding point cloud model.

6. The BIM-based prefabricated component information fusion method according to claim 3, characterized in that: The specific steps of obtaining the adaptation index of any two assembled surface images of any two prefabricated components based on the matching degree and the depth error value are as follows: Where, is the matching degree between the first assembled surface graph and the second assembled surface graph; is the depth error value of the corresponding surface area of ​​the first assembled surface image and the second assembled surface image in the corresponding point cloud model; is the first preset hyperparameter; is the sigmoid function; is the adaptation index between the first assembled surface graph and the second assembled surface graph.

7. The BIM-based prefabricated component information fusion method according to claim 3, characterized in that: The method of obtaining the matching priority between each assembly surface diagram of each prefabricated component and each adaptive assembly surface diagram according to the number of assembly surface diagrams of the prefabricated component to which the adaptive assembly surface diagram belongs and the distance distribution between the center points of several adaptive assembly surface diagrams of each assembly surface diagram of the prefabricated component includes the following specific steps: Any matching assembly surface graph of the first assembly surface graph is recorded as the matching assembly surface graph to be analyzed; Where, The number of assembly surface diagrams of the prefabricated component to which the assembly surface diagram to be analyzed belongs; is the number of adapted assembled surface graphs of the first assembled surface graph; The first assembly surface graph and the first assembly surface graph to be analyzed are The distance between the center points of the adapted assembled surface graphs; is the average value of the distances between the center points of all the adapted assembled surface graphs of the to-be-analyzed adapted assembled surface graph and the first assembled surface graph; It is the matching priority between the first assembled surface graph and the adapted assembled surface graph to be analyzed.

8. The BIM-based prefabricated component information fusion method according to claim 1, characterized in that: The specific method for obtaining the assembly priority of each prefabricated component is as follows: The cumulative value of the possibility that the corresponding surface area of ​​all surface images of each prefabricated component in the point cloud model belongs to the assembly surface is used as the assembly priority of the prefabricated component.

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