BIM Technology-Based Visualization Modeling Method and System for Road Engineering

Abstract

The present invention relates to the field of geographic model technology, and more particularly to a method and system for visual modeling of road projects based on BIM technology. The present invention obtains a complete road surface range based on the splicing results of multiple measured depth images; then, combining the construction status and terrain complexity of different areas within the road surface range, the degree of modeling accuracy required for different areas is obtained, thereby adjusting the model accuracy of different areas, achieving dynamic matching of road models and construction progress, effectively retaining detailed information, and improving modeling accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of geographic models, and in particular to a road engineering visualization modeling method and system based on BIM technology. Background Art

[0002] BIM (Building Information Modeling) is a building information modeling technology that uses computer technology to integrate various information such as buildings, structures, and equipment, forming a design, construction, operation, and maintenance management method based on three-dimensional digital models. Accurate modeling of road projects helps integrate basic project data, thereby understanding the basic project situation and accurately controlling project implementation. Therefore, it is necessary to ensure the accuracy of road project modeling to accurately reflect the topography of the project location and adjust the specific model based on the specific topography and the complexity of the road construction itself.

[0003] Existing technologies for visual modeling road projects (such as municipal pavement construction) typically require acquiring point cloud data within the project area and modeling the road project based on this point cloud data. The resulting model maintains consistent modeling accuracy across all parts. However, in actual construction, different road areas have varying terrain and road shapes, and project implementation schedules vary across regions. Existing methods are unable to distinguish these differences, resulting in an inability to effectively retain detailed information and thus an inability to guarantee modeling accuracy. Furthermore, these methods can cause mismatches between the model and the project progress, impacting construction efficiency. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a road engineering visualization modeling method and system based on BIM technology.

[0005] According to a first aspect of an embodiment of the present application, a road engineering visual modeling method based on BIM technology is provided, and the technical solution adopted is as follows:

[0006] Acquire a depth image within the scope of the road project, and pre-process the depth image to obtain a point cloud;

[0007] Performing cluster analysis on the point cloud to obtain a road area;

[0008] Analyzing local structural features of the road area to obtain key points, and matching and splicing the key points to obtain a complete road surface range;

[0009] Based on the complete road surface range, analyzing the distribution characteristics of the key points to obtain similar groups and construction centers;

[0010] Analyzing the positional relationship between the similarity group and the construction center, and analyzing the structural complexity of the area where the similarity group is located, to obtain a model accuracy requirement index for the area where the similarity group is located;

[0011] An initial three-dimensional model is constructed based on the point cloud, and a modeling accuracy index of a region where the similarity group is located in the initial three-dimensional model is adjusted according to the required index to obtain an adjusted three-dimensional model.

[0012] In some embodiments of the present invention, performing cluster analysis on the point cloud to obtain a road area includes:

[0013] Clustering the point cloud to obtain clusters;

[0014] Analyzing the depth similarity and grayscale similarity of the data points in the cluster to obtain the distribution similarity coefficient of the data points in the cluster;

[0015] Setting a distribution similarity coefficient threshold, and obtaining a suspected road area based on the distribution similarity coefficient;

[0016] Analyzing the grayscale distribution difference and depth distribution difference between the data point in the suspected road area and its neighboring data points, and combining the distribution similarity coefficient to obtain the deviation coefficient of the data point in the suspected road area;

[0017] A deviation coefficient threshold is set, and a road area is obtained according to the deviation coefficient.

[0018] In some embodiments of the present invention, analyzing the local structural features of the road area to obtain key points includes:

[0019] Analyzing the curvature differences of the data points in the road area and combining the deviation coefficients of the data points to obtain the contribution coefficients of the data points to the road structure characteristics;

[0020] A contribution coefficient threshold is set, and a key point is obtained according to the contribution coefficient.

[0021] In some embodiments of the present invention, the key points are matched and spliced to obtain a complete road surface range, including:

[0022] Placing the point clouds corresponding to all the depth images into the same coordinate system;

[0023] Matching the key points in the point cloud corresponding to the depth images acquired at adjacent moments using an IPC algorithm;

[0024] The matched key points are spliced together to obtain a complete road surface range.

[0025] In some embodiments of the present invention, based on the complete road surface range, analyzing the distribution characteristics of the key points to obtain similar groups and construction centers includes:

[0026] Based on the complete road surface range, the distribution characteristics of the key points are analyzed, and the similarity index of the key points in the similar groups and the similarity index of the key points in the similar groups are obtained by combining the contribution coefficients of the key points;

[0027] Based on the similarity index of the key points, the distribution characteristics of the key points in the similarity group are analyzed to obtain the construction area and the construction center.

[0028] In some embodiments of the present invention, based on the complete road surface range, the distribution characteristics of the key points are analyzed, and the similarity index of the similarity group and the key points in the similarity group is obtained by combining the contribution coefficient of the key points, including:

[0029] Based on the complete road surface range, analyzing the distance relationship between the key point and its neighboring key points, and combining the difference in contribution coefficients between the key point and its neighboring key points, obtaining a probability index that the key point and its neighboring key points are in the same similarity group;

[0030] Traversing all neighborhood key points of the key point, obtaining the possibility indexes corresponding to all neighborhood key points, setting a possibility index threshold, and obtaining a similarity group;

[0031] An average of the likelihood indexes between all the key points in the similarity group is calculated as the similarity index of the key points in the similarity group.

[0032] In some embodiments of the present invention, based on the similarity index of the key points, the distribution characteristics of the key points in the similarity group are analyzed to obtain the construction area and the construction center, including:

[0033] Set the search radius;

[0034] Analyze the similarity index consistency of all similarity groups within the search radius of the similarity group, and combine the number of key points in the similarity group to obtain the probability coefficient that the area where the similarity group is located belongs to the construction area;

[0035] Merging the adjacent similar groups to obtain a merged group;

[0036] The average of the likelihood coefficients corresponding to all the similar groups in the merged group is used as the likelihood that the area where the merged group is located belongs to the construction area;

[0037] The location where the maximum probability that the area where the merged group is located belongs to the construction area is marked as the construction area, and the point corresponding to the mean value of the coordinates of all key points in the construction area is the construction center.

[0038] In some embodiments of the present invention, analyzing the positional relationship between the similarity group and the construction center, and analyzing the structural complexity of the area where the similarity group is located, to obtain the model accuracy requirement index of the area where the similarity group is located, includes:

[0039] Calculating the distance between the similarity group and the construction center to obtain the positional relationship of the similarity group;

[0040] Calculating the ratio of the likelihood coefficient of the construction area in the area where the similar group is located to the maximum value of the likelihood coefficients corresponding to all the similar groups to obtain the structural complexity of the area where the similar group is located;

[0041] The positional relationship and the structural complexity corresponding to the similarity groups are combined to obtain a model accuracy requirement index for the region where the similarity groups are located.

[0042] According to a second aspect of an embodiment of the present application, a road engineering visual modeling system based on BIM technology is provided, comprising: a memory and a processor, wherein:

[0043] The memory is used to store program code;

[0044] The processor is used to read the program code stored in the memory and execute the method described in the first aspect of the embodiment of the present application.

[0045] In some embodiments of the present invention, the processor includes:

[0046] The road surface range acquisition module is used to obtain a depth image within the road project range and pre-process the depth image to obtain a point cloud; then perform cluster analysis on the point cloud to obtain the road area; then analyze the local structural features of the road area to obtain key points, and match and splice the key points to obtain the complete road surface range;

[0047] A need index analysis module is used to analyze the distribution characteristics of the key points based on the complete road surface range to obtain similarity groups and construction centers; and to analyze the positional relationship between the similarity groups and the construction centers, as well as the structural complexity of the area where the similarity groups are located, to obtain a need index for model accuracy in the area where the similarity groups are located;

[0048] The three-dimensional model adjustment module is used to construct an initial three-dimensional model based on the point cloud, and adjust the modeling accuracy index of the area where the similarity group is located in the initial three-dimensional model according to the required index to obtain an adjusted three-dimensional model.

[0049] Compared with the existing technology, the road engineering visualization modeling method and system based on BIM technology provided by the present invention has the following beneficial effects:

[0050] The present invention obtains a depth image within the scope of a road project and pre-processes the depth image to obtain a point cloud; performs cluster analysis on the point cloud to obtain a road area; then analyzes the local structural features of the road area to obtain key points, and matches and splices the key points to obtain a complete road surface range; that is, the complete road surface range is obtained based on the splicing results of the depth images measured multiple times. Then, the positional relationship between the similarity group and the construction center and the structural complexity of the area where the similarity group is located are analyzed to obtain the required index of model accuracy for the area where the similarity group is located; and constructs an initial three-dimensional model based on the point cloud, and adjusts the modeling accuracy index of the area where the similarity group is located in the initial three-dimensional model according to the required index to obtain an adjusted three-dimensional model; that is, according to the construction status and terrain complexity of different areas within the road surface range, the required degree of modeling accuracy of different areas is obtained, thereby adjusting the model accuracy of different areas, achieving dynamic matching of the road model and construction progress, effectively retaining detailed information, and improving modeling accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or 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.

[0052] Figure 1 A schematic diagram of the basic process of a road engineering visual modeling method based on BIM technology provided by one embodiment of the present invention;

[0053] Figure 2 A schematic diagram of the working process of a drone-mounted depth camera provided by one embodiment of the present invention;

[0054] Figure 3 A schematic diagram of a partial road area provided by an embodiment of the present invention;

[0055] Figure 4 A schematic diagram of the basic flow of a road area acquisition method provided by one embodiment of the present invention;

[0056] Figure 5 A schematic diagram of a local point cloud of a road area provided by one embodiment of the present invention;

[0057] Figure 6 A schematic diagram of key points of a road area provided by an embodiment of the present invention;

[0058] Figure 7 A schematic diagram of a similar group provided by one embodiment of the present invention;

[0059] Figure 8 A schematic diagram of a construction area provided by one embodiment of the present invention;

[0060] Figure 9 A schematic diagram of the basic composition of a road engineering visual modeling system based on BIM technology provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0061] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the BIM-based road engineering visualization modeling method and system proposed by the present invention, including its specific implementation, structure, features, and effectiveness. In the following description, references to different "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.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. Terms such as "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a circuit structure, article, or device comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such article or device. In the absence of further limitations, the phrase "comprising a ..." to define an element does not preclude the presence of other identical elements in the article or device comprising the element.

[0063] The following describes in detail a specific solution of a road engineering visualization modeling method based on BIM technology provided by the present invention in conjunction with the accompanying drawings.

[0064] See also Figure 1 , which shows the basic process of a road engineering visual modeling method based on BIM technology provided by an embodiment of the present invention.

[0065] like Figure 1 As shown, a road engineering visualization modeling method based on BIM technology provided by one embodiment of the present invention specifically includes:

[0066] S100: Acquire a depth image within the scope of the road project, and pre-process the depth image to obtain a point cloud.

[0067] Figure 2 This is a schematic diagram of the working process of the drone-mounted depth camera. In the figure, number 3 is the drone equipped with the depth camera, number 4 is the ground where the road project is located, and number 5 is the shooting angle. Figure 2As shown, a drone-mounted time-of-flight (TOF) depth camera acquires depth images (RGBD images) of the road construction area. Specifically, after the drone is raised to a fixed altitude, it is moved at a constant speed and captures images at different locations within the construction area from the same angle. The TOF depth camera acquires a depth image once per second. The depth images captured by the TOF depth camera undergo preprocessing, including image correction and filtering for noise reduction. The depth images are then converted into a dual-channel grayscale depth map. This involves separating the depth information in the depth image into a single channel and grayscale-converting the RGB channels. This conversion is then completed. An initial point cloud of the road construction area is obtained based on the processed depth image, and a simplified point cloud is obtained through voxel downsampling. The simplified point cloud is used to analyze the model accuracy requirements for different locations within the road construction area. The model accuracy is then determined, and modeling is performed based on the determined accuracy.

[0068] S200: Perform cluster analysis on the point cloud to obtain the road area.

[0069] Since the internal structures of different objects are different and their distances from the depth camera are also different, the point cloud distributions of their corresponding areas are also different. Therefore, the point cloud distributions of the same type of objects under the same terrain are relatively similar. For example, if the road surface has a relatively regular shape (i.e., the color is relatively uniform and the depth is relatively consistent), the point cloud distributions of the corresponding areas are relatively similar. Based on the above characteristics, the point cloud data is filtered to determine the road area, such as Figure 3 shown.

[0070] See also Figure 4 , which shows the basic process of a road area acquisition method provided by an embodiment of the present invention.

[0071] like Figure 4 As shown, cluster analysis is performed on the point cloud to obtain the road area, including:

[0072] S201: Cluster the point cloud to obtain clusters.

[0073] The simplified point cloud obtained in step S100 is clustered. The neighborhood radius of the data point in the DBASCAN algorithm is set to the mean of the Euclidean distances between all adjacent data points. The minimum number of samples is 5, and multiple clusters with similar distributions are obtained.

[0074] S202: Analyze the depth similarity and grayscale similarity of the data points in the cluster to obtain the distribution similarity coefficient of the data points in the cluster.

[0075] Taking a cluster as an example, the above analysis shows that the color within the road surface is uniform, and road construction requires a smooth road surface for driving. Therefore, the data points within the road surface are distributed in similar positions in the point cloud. Therefore, this embodiment analyzes the depth similarity and grayscale similarity of the data points within the cluster to obtain the distribution similarity coefficient of the data points within the cluster. Furthermore, the calculation formula for constructing the distribution similarity coefficient is:

[0076]

[0077] Where, Indicates the The distribution similarity coefficient of each data point in a cluster; Indicates the The minimum depth of all data points in a cluster; Indicates the The mean depth of all data points in a cluster; No. The maximum gray value among all data points in a cluster; Indicates the The grayscale mean of all data points in a cluster; Indicates the The number of data points in a cluster; represents the linear normalization function.

[0078] It should be noted that, in order to ensure that the calculation results are meaningful, when performing fractional operations in the embodiment of the present invention, when encountering a situation where the denominator is 0, a parameter adjustment factor greater than 0 needs to be added to the denominator to prevent the denominator from being 0. The value of the parameter adjustment factor is set by the implementer according to the actual situation, and this application does not impose any special restrictions. That is to say, in the above formula, When it is 0, a parameter adjustment factor of 0.01 can be automatically added to the denominator to change the denominator to 0.01, thereby avoiding the calculation problem caused by the denominator being 0. This method is applicable to all subsequent formulas in this solution and will not be further limited or elaborated on.

[0079] Indicates the uniformity of grayscale within the current cluster. The larger the value, the greater the grayscale difference between the data points within the cluster, indicating that the distribution similarity coefficient of the data points within the current cluster is smaller. Indicates the uniformity of the depth within the current cluster. The larger the value, the more similar the depth of each point in the cluster, indicating that the distribution similarity coefficient of each data point in the current cluster is larger; the number of data points in the current cluster The larger it is, the greater the distribution similarity coefficient of each data point in the corresponding current cluster.

[0080] Traverse all clusters and obtain the corresponding distribution similarity coefficients.

[0081] S203: Setting a distribution similarity coefficient threshold, and obtaining a suspected road area based on the distribution similarity coefficient.

[0082] After obtaining the distribution similarity coefficients corresponding to all clusters, the areas corresponding to clusters with higher similarity are marked as suspected road areas. Therefore, by setting the distribution similarity coefficient threshold to 0.5, the areas corresponding to clusters with distribution similarity coefficients greater than 0.5 are marked as suspected road areas, and suspected road areas are obtained.

[0083] S204: Analyze the grayscale distribution difference and depth distribution difference between the data point in the suspected road area and its neighboring data points, and combine the distribution similarity coefficient to obtain the deviation coefficient of the data point in the suspected road area.

[0084] In order to obtain an accurate road area and avoid the situation where noise points exist in the suspected road area obtained in the above steps due to interference from factors such as lighting, all data points in the suspected road area are screened based on the high similarity of each data point within the road surface range. That is, if the grayscale and depth distribution differences between the data points in the neighborhood of a data point are large (the neighborhood radius is set to the mean of the Euclidean distance between all adjacent data points), and the similarity relative to all suspected road areas is poor, then the data point does not meet the road surface area characteristics and is marked as a noise point.

[0085] Based on the above analysis, this embodiment analyzes the grayscale distribution difference and depth distribution difference between the data point in the suspected road area and its neighboring data points, and combines the distribution similarity coefficient to obtain the deviation coefficient of the data point in the suspected road area. The calculation formula for constructing the deviation coefficient is:

[0086]

[0087] Where, Indicates the The first cluster in the cluster corresponding to the suspected road area Deviation coefficient of a data point from the road surface area characteristics; Indicates the The first cluster in the cluster corresponding to the suspected road area The variance of the grayscale of all data points in the neighborhood of a data point; Indicates the The first cluster in the cluster corresponding to the suspected road area The number of data points in the neighborhood of a data point; Indicates the The first cluster in the cluster corresponding to the suspected road area The depth of data points; Indicates the The distribution similarity coefficient of each data point in the cluster corresponding to the suspected road area; Indicates the mean depth of all data points in the cluster corresponding to the maximum distribution similarity coefficient; represents the linear normalization function.

[0088] Indicates the difference between the depth of each data point in the neighborhood of the current data point and the average depth of the clusters with a high degree of similarity. The larger the value, the greater the deviation of the depth of each data point in the neighborhood of the current data point from the depth of the road area data point, indicating that the deviation coefficient of the data point from the road area characteristics is greater; The larger it is, the greater the grayscale difference between the data points in the neighborhood of the current data point is, which means that the possibility that the data point is in the road area is smaller, that is, the greater the deviation coefficient of the data point from the road area characteristics.

[0089] Traverse all the data points in the clusters corresponding to the suspected road areas and analyze the deviation coefficient corresponding to each data point.

[0090] S205: Setting a deviation coefficient threshold, and obtaining a road area according to the deviation coefficient.

[0091] After obtaining the deviation coefficients corresponding to all data points in the suspected road area, the data points with large deviation coefficients are screened out. Therefore, by setting the deviation coefficient threshold to 0.5, the data points with deviation coefficients greater than 0.5 are screened out, and then the remaining data points in the clusters corresponding to the suspected road area are marked as data points of the road area, and the connected domain where they are located is extracted as the road area.

[0092] S300: Analyze local structural features of the road area to obtain key points, and match and splice the key points to obtain a complete road surface range.

[0093] Due to the large scale of the road project, a single drone scan cannot capture the entire road area. Therefore, as in step S100, depth images of different road areas must be acquired at a fixed height and stitched together to obtain complete point cloud data for the road area. Because point cloud distributions for the same area are similar across different depth images, the present invention can achieve point cloud stitching based on key point matching.

[0094] Based on the above analysis, this embodiment obtains key points by analyzing the local structural features of the road area, and matches and splices the key points to obtain a complete road surface range.

[0095] In order to preserve the detailed information of the road area as much as possible and thus improve the accuracy of point cloud splicing, it is necessary to obtain key points that can represent the structural characteristics of the road surface. Therefore, it is necessary to screen the point cloud of the road area. In areas with complex terrain (where the depth difference from the surrounding data points is large), in areas where the road type changes (such as the transition area from a straight road to a curve), the corresponding data points have a large curvature and a large difference with their adjacent data points (such as the curve). Figure 5 As shown in the figure, where label 1 is a flat terrain and straight road area, label 2 is a complex terrain and turning road area), key points are selected based on the above analysis.

[0096] Therefore, the local structural features of the road area are analyzed to obtain key points, which further includes: analyzing the curvature differences of data points in the road area, combining the deviation coefficients of the data points to obtain the contribution coefficients of the data points to the road structural features; setting the contribution coefficient threshold, and obtaining the key points based on the contribution coefficients.

[0097] Taking the point cloud corresponding to the depth image obtained by a single drone as an example, all data points in the road area are traversed and the calculation formula for the contribution coefficient is constructed as follows:

[0098]

[0099] Where, Indicates the The depth image obtained the second time corresponds to the road area of the point cloud. The contribution coefficient of each data point to the road structure characteristics; Indicates the Deviation coefficient of a data point from the road surface area characteristics; Indicates the The curvature of the data points; Indicates the The mean curvature of all data points in the neighborhood of a data point; Indicates the The mean curvature of all data points in the road area of the point cloud corresponding to the depth image obtained this time; represents the linear normalization function.

[0100] The larger the value is, the greater the curvature of the data point is relative to the other data points in the current road area, indicating that the contribution coefficient of the data point to the road structure characteristics is greater; The larger the value is, the greater the difference in curvature between the current data point and the data points in its neighborhood, indicating that the contribution coefficient of the data point to the road structure characteristics is greater; The larger the value is, the greater the difference between the data point and the overall depth of the road area is, which means that the contribution coefficient of the data point to the road structure characteristics is greater.

[0101] Traverse all data points in the road area and obtain their corresponding contribution coefficients. Set the contribution coefficient threshold to 0.5 and mark the data points with a contribution coefficient greater than 0.5 as key points, such as Figure 6 As shown (number 6 in the figure is the key point).

[0102] The key points are matched and spliced to obtain a complete road surface range, further including: placing the point clouds corresponding to all depth images in the same coordinate system; using the IPC algorithm to match the key points in the point clouds corresponding to the depth images obtained at adjacent moments; and splicing the matched key points to obtain a complete road surface range.

[0103] S400: Based on the complete road surface range, the distribution characteristics of key points are analyzed to obtain similar groups and construction centers.

[0104] To ensure that the determined model matches the actual progress of the road project, and to ensure that detailed information about the actual construction area within the pavement is relatively complete, thus providing an accurate reference for actual road project implementation, the construction area must be determined based on the differences between different locations within the pavement area. Due to the uneven surface of the construction area, the internal structure of the construction area is relatively complex and contains more detailed information. Therefore, compared to other pavement areas, the construction area has a larger number of corresponding key points, and the specific distribution of these key points varies greatly. Based on this logic, the construction area within the current pavement area is obtained.

[0105] This embodiment analyzes the distribution characteristics of key points within the complete road surface to obtain similarity groups and construction centers. This further includes: analyzing the distribution characteristics of key points within the complete road surface, combining the key points' contribution coefficients to obtain similarity groups and similarity indices for key points within similarity groups; and analyzing the distribution characteristics of key points within similarity groups based on the key points' similarity indices to obtain construction areas and construction centers.

[0106] In order to conduct a specific analysis of the local area in the road surface area, key points with similar locations and similar point cloud distribution are grouped. Specifically, based on the complete road surface range, the distribution characteristics of the key points are analyzed, and the contribution coefficients of the key points are combined to obtain the similarity index of the similar groups and the key points within the similar groups. This includes: based on the complete road surface range, the distance relationship between the key point and its adjacent key points is analyzed, and the difference in contribution coefficients between the key point and its adjacent key points is combined to obtain the possibility index of the key point and its adjacent key points being in the same similar group. The calculation formula for constructing the possibility index of the key point and its adjacent key points being in the same similar group is:

[0107]

[0108] Where, Indicates the A key point and its adjacent The probability index of the key points being the same similar group; Indicates the A key point and its adjacent The Euclidean distance between key points; Indicates the Contribution coefficient of each key point to the road structure characteristics; Indicates the Contribution coefficient of each key point to the road structure characteristics; express Type curve normalization function; represents the linear normalization function.

[0109] The larger the value is, the greater the Euclidean distance between the key point and its adjacent key points is, indicating that the probability index of the two points belonging to the same similarity group is smaller. The larger the value is, the greater the difference in contribution coefficient between the key point and its adjacent key points is, which means the greater the difference between the two points is, and the smaller the possibility index of the two points belonging to the same similarity group is.

[0110] Traverse all adjacent key points of the key point, obtain the corresponding possibility index of all adjacent key points, set the possibility index threshold to 0.5, if the possibility index of two key points is greater than 0.5, then the corresponding key points are divided into the same similarity group, repeat the above steps until no new key points are added to the similarity group, and obtain the similarity group (such as Figure 7 As shown in the figure, number 6 is a key point and number 7 is a similarity group). The average of the likelihood indexes of all key points in the similarity group is calculated as the similarity index of the key points in the similarity group.

[0111] Based on the above analysis, we know that the construction area has more detailed information, that is, the key points are more concentrated, and the specific distribution differences between key points are large. Therefore, the mean of the Euclidean distances between the centroids of each similarity group is used as the search radius. If a similarity group satisfies the conditions that there are more key points in the group, there are more similar groups within the search radius, and the similarity index differences between the key points in each group are large, then the location of the similarity group belongs to the construction area.

[0112] Therefore, in this embodiment, based on the similarity index of key points, the distribution characteristics of key points in the similarity group are analyzed to obtain the construction area and construction center. Specifically, the search radius is set, that is, the mean of the Euclidean distance between the centroids of each similarity group is used as the search radius; the similarity index consistency of all similarity groups within the search radius of the similarity group is analyzed, and the probability coefficient of the area where the similarity group is located belonging to the construction area is obtained in combination with the number of key points in the similarity group. The calculation formula for the probability coefficient of the area where the similarity group is located belonging to the construction area is constructed as follows:

[0113]

[0114] Where, Indicates the The probability coefficient of the area where the similarity group is located belongs to the construction area; Indicates the The number of all key points in a similar group; represents the mean number of key points in all similar groups, Indicates the Similarity groups within the search radius Similarity index of key points within similar groups; Indicates the The mean of the similarity indexes of the key points corresponding to all similarity groups within the search radius of the similarity group; Indicates the The number of all similar groups within the search radius of similar groups; represents the linear normalization function.

[0115] Indicates the degree of difference in the similarity indexes of all similarity groups within the search radius of the current similarity group. The larger the value, the greater the difference in the similarity indexes of key points in the similarity groups around the previous similarity group, indicating that the probability coefficient of the area where the current similarity group is located is a construction area. It represents the ratio of the number of key points in the current similarity group to the average number of key points in all similarity groups. The larger the value, the larger the number of key points in the current similarity group, indicating that the area where the current similarity group is located is more likely to be a construction area.

[0116] Traverse all similar groups and obtain the corresponding likelihood coefficients.

[0117] Since the construction areas are relatively concentrated during road construction, the adjacent similar groups are merged to obtain a merged group. Among them, the two similar groups whose centroid distance is less than the search radius are defined as adjacent similar groups. The mean of the likelihood coefficients corresponding to all similar groups in the merged group is taken as the likelihood that the area where the merged group is located belongs to the construction area, which is recorded as ; The maximum probability that the area where the merged group is located belongs to the construction area The location is marked as a construction area, such as Figure 8 The number 8 is shown in the figure. The point corresponding to the mean value of the coordinates of all key points in the construction area is the construction center.

[0118] S500: Analyze the positional relationship between the similarity group and the construction center, and analyze the structural complexity of the area where the similarity group is located, to obtain the model accuracy requirement index of the area where the similarity group is located.

[0119] The level of refinement in the model construction must match the actual construction progress. That is, the closer the area is to the construction center, the higher the need for modeling accuracy. In order to provide a more accurate reference for the specific construction process, more detailed information must be retained in areas with more complex terrain and road types. In other words, the corresponding areas require a higher degree of modeling accuracy.

[0120] Based on the above analysis, this embodiment obtains the index of need for model accuracy in the area where the similar group is located by analyzing the positional relationship between the similar group and the construction center, and analyzing the structural complexity of the area where the similar group is located. Further including: calculating the distance between the similar group and the construction center to obtain the positional relationship of the similar group; calculating the ratio of the probability coefficient of the construction area in the area where the similar group is located to the maximum probability coefficient corresponding to all similar groups to obtain the structural complexity of the area where the similar group is located; combining the positional relationship and structural complexity corresponding to the similar group to obtain the index of need for model accuracy in the area where the similar group is located. The calculation formula for constructing the index of need for model accuracy in the area where the similar group is located is:

[0121]

[0122] Where, Indicates the The index of the need for model accuracy in the region where the similarity group is located; Indicates the The Euclidean distance between the center point of a similar group and the construction center, where the center point of a similar group is the point corresponding to the mean value of the coordinates of all key points in the similar group; Indicates the The probability coefficient of the area where the similarity group is located belongs to the construction area; Indicates the maximum value of the probability coefficient that the area where all similar groups are located belongs to the construction area; express Normalization function, the range after normalization is [-1,1].

[0123] The larger it is, the more complex the structure of the area where the current similarity group is located is, which means that the area where the current similarity group is located has a greater need index for model accuracy. The smaller it is, the closer the area where the current similarity group is located is to the construction center, which means that the area where the current similarity group is located has a greater need index for model accuracy.

[0124] Traverse the areas where all similar groups are located and obtain their corresponding need indexes .

[0125] S600: constructing an initial three-dimensional model based on the point cloud, and adjusting the modeling accuracy index of the area where the similar group is located in the initial three-dimensional model according to the required index to obtain an adjusted three-dimensional model.

[0126] The initial 3D model is constructed based on the point cloud, and the modeling accuracy index of the area where the similar group is located in the initial 3D model is adjusted exponentially as needed to obtain the adjusted 3D model. Specifically:

[0127] An initial 3D model is established based on the point cloud of the road surface area, and its modeling accuracy is adjusted according to the required modeling accuracy index determined in different areas. Take the area where a single similarity group is located as an example:

[0128]

[0129] Where, Indicates the Modeling accuracy index of the area where the similarity group is located; Indicates the The initial modeling accuracy of the area where the similarity groups are located; Indicates the The index of the need for model accuracy in the area where the similarity group is located.

[0130] Traverse all similar groups and obtain the corresponding modeling accuracy indicators The modeling accuracy index is input into the 3D modeling software to adjust the modeling accuracy of the corresponding area in the initial 3D model to obtain the adjusted 3D model.

[0131] The determined adjusted three-dimensional model is outputted via an output device.

[0132] Based on the same inventive concept as the above method, this embodiment also provides a road engineering visualization modeling system based on BIM technology.

[0133] See also Figure 9 , which shows the basic composition of a road engineering visualization modeling system based on BIM technology provided by an embodiment of the present invention.

[0134] like Figure 9 As shown, a road engineering visual modeling system based on BIM technology includes: a memory 10 and a processor 20, wherein:

[0135] Memory 10, for storing program code;

[0136] The processor 20 is used to read the program code stored in the memory 10, and execute it to obtain a depth image within the scope of the road project, and pre-process the depth image to obtain a point cloud; perform cluster analysis on the point cloud to obtain a road area; analyze the local structural characteristics of the road area to obtain key points, and match and splice the key points to obtain a complete road surface range; based on the complete road surface range, analyze the distribution characteristics of the key points to obtain similar groups and construction centers; analyze the positional relationship between the similar groups and the construction centers, and analyze the structural complexity of the areas where the similar groups are located to obtain a model accuracy requirement index for the areas where the similar groups are located; construct an initial three-dimensional model based on the point cloud, and adjust the modeling accuracy index of the areas where the similar groups are located in the initial three-dimensional model according to the required index to obtain an adjusted three-dimensional model.

[0137] The processor 20 includes a road surface range acquisition module 21, a demand index analysis module 22, and a three-dimensional model adjustment module 23.

[0138] The road surface range acquisition module 21 is used to obtain a depth image within the road project range and pre-process the depth image to obtain a point cloud; then perform cluster analysis on the point cloud to obtain the road area; then analyze the local structural characteristics of the road area to obtain key points, and match and splice the key points to obtain the complete road surface range;

[0139] The need index analysis module 22 is used to analyze the distribution characteristics of key points based on the complete road surface range to obtain similar groups and construction centers; and analyze the positional relationship between the similar groups and the construction centers, as well as the structural complexity of the areas where the similar groups are located, to obtain the need index for model accuracy in the areas where the similar groups are located;

[0140] The three-dimensional model adjustment module 23 is used to construct an initial three-dimensional model based on the point cloud, and adjust the modeling accuracy index of the area where the similar group is located in the initial three-dimensional model according to the required index to obtain an adjusted three-dimensional model.

[0141] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0142] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A road engineering visual modeling method based on BIM technology, characterized in that: The method comprises: Acquire a depth image within the scope of the road project, and pre-process the depth image to obtain a point cloud; Performing cluster analysis on the point cloud to obtain a road area; Analyzing local structural features of the road area to obtain key points, and matching and splicing the key points to obtain a complete road surface range; Based on the complete road surface range, analyzing the distribution characteristics of the key points to obtain similar groups and construction centers; Analyzing the positional relationship between the similarity group and the construction center, and analyzing the structural complexity of the area where the similarity group is located, to obtain a model accuracy requirement index for the area where the similarity group is located; constructing an initial three-dimensional model based on the point cloud, and adjusting a modeling accuracy index of a region where the similarity group is located in the initial three-dimensional model according to the required index to obtain an adjusted three-dimensional model; Analyze the local structural features of the road area to obtain key points, including: Analyzing the curvature differences of the data points in the road area and combining the deviation coefficients of the data points to obtain the contribution coefficients of the data points to the road structure characteristics; Setting a contribution coefficient threshold, and obtaining a key point based on the contribution coefficient; Based on the complete road surface range, the distribution characteristics of the key points are analyzed to obtain similar groups and construction centers, including: Based on the complete road surface range, the distribution characteristics of the key points are analyzed, and the similarity index of the key points in the similar groups and the similarity index of the key points in the similar groups are obtained by combining the contribution coefficients of the key points; Analyzing the distribution characteristics of the key points in the similarity group based on the similarity index of the key points to obtain the construction area and construction center; Based on the similarity index of the key points, the distribution characteristics of the key points in the similarity group are analyzed to obtain the construction area and construction center, including: Set the search radius; Analyze the similarity index consistency of all similarity groups within the search radius of the similarity group, and combine the number of key points in the similarity group to obtain the probability coefficient that the area where the similarity group is located belongs to the construction area; Merging the adjacent similar groups to obtain a merged group; The average of the likelihood coefficients corresponding to all the similar groups in the merged group is used as the likelihood that the area where the merged group is located belongs to the construction area; The location with the maximum probability that the area where the merged group is located belongs to the construction area is marked as the construction area, and the point corresponding to the mean value of the coordinates of all key points in the construction area is the construction center; Analyze the positional relationship between the similarity group and the construction center, and analyze the structural complexity of the area where the similarity group is located, and obtain the required index of model accuracy for the area where the similarity group is located, including: Calculating the distance between the similarity group and the construction center to obtain the positional relationship of the similarity group; Calculating the ratio of the likelihood coefficient of the construction area in the area where the similar group is located to the maximum value of the likelihood coefficients corresponding to all the similar groups to obtain the structural complexity of the area where the similar group is located; The positional relationship and the structural complexity corresponding to the similarity groups are combined to obtain a model accuracy requirement index for the region where the similarity groups are located.

2. The road engineering visual modeling method based on BIM technology according to claim 1 is characterized in that: Perform cluster analysis on the point cloud to obtain the road area, including: Clustering the point cloud to obtain clusters; Analyzing the depth similarity and grayscale similarity of the data points in the cluster to obtain the distribution similarity coefficient of the data points in the cluster; Setting a distribution similarity coefficient threshold, and obtaining a suspected road area based on the distribution similarity coefficient; Analyzing the grayscale distribution difference and depth distribution difference between the data point in the suspected road area and its neighboring data points, and combining the distribution similarity coefficient to obtain the deviation coefficient of the data point in the suspected road area; A deviation coefficient threshold is set, and a road area is obtained according to the deviation coefficient.

3. The road engineering visual modeling method based on BIM technology according to claim 1 is characterized in that: Match and splice the key points to obtain the complete road surface range, including: Placing the point clouds corresponding to all the depth images into the same coordinate system; Matching the key points in the point cloud corresponding to the depth images acquired at adjacent moments using an IPC algorithm; The matched key points are spliced together to obtain a complete road surface range.

4. The road engineering visual modeling method based on BIM technology according to claim 1 is characterized in that: Based on the complete road surface range, the distribution characteristics of the key points are analyzed, and combined with the contribution coefficients of the key points, similarity indexes of similar groups and key points within similar groups are obtained, including: Based on the complete road surface range, analyzing the distance relationship between the key point and its neighboring key points, and combining the difference in contribution coefficients between the key point and its neighboring key points, obtaining a probability index that the key point and its neighboring key points are in the same similarity group; Traversing all neighborhood key points of the key point, obtaining the possibility indexes corresponding to all neighborhood key points, setting a possibility index threshold, and obtaining a similarity group; An average of the likelihood indexes between all the key points in the similarity group is calculated as the similarity index of the key points in the similarity group.

5. A road engineering visual modeling system based on BIM technology, characterized by: The system comprises: a memory and a processor, wherein: The memory is used to store program code; The processor is configured to read the program code stored in the memory and execute the method according to any one of claims 1 to 4.

6. The road engineering visual modeling system based on BIM technology according to claim 5 is characterized in that: The processor includes: The road surface range acquisition module is used to obtain a depth image within the road project range and pre-process the depth image to obtain a point cloud; then perform cluster analysis on the point cloud to obtain the road area; then analyze the local structural features of the road area to obtain key points, and match and splice the key points to obtain the complete road surface range; A need index analysis module is used to analyze the distribution characteristics of the key points based on the complete road surface range to obtain similarity groups and construction centers; and to analyze the positional relationship between the similarity groups and the construction centers, as well as the structural complexity of the area where the similarity groups are located, to obtain a need index for model accuracy in the area where the similarity groups are located; a three-dimensional model adjustment module, configured to construct an initial three-dimensional model based on the point cloud, and adjust a modeling accuracy index of a region where the similarity group is located in the initial three-dimensional model according to the required index to obtain an adjusted three-dimensional model; Analyze the local structural features of the road area to obtain key points, including: Analyzing the curvature differences of the data points in the road area and combining the deviation coefficients of the data points to obtain the contribution coefficients of the data points to the road structure characteristics; Setting a contribution coefficient threshold, and obtaining a key point based on the contribution coefficient; Based on the complete road surface range, the distribution characteristics of the key points are analyzed to obtain similar groups and construction centers, including: Based on the complete road surface range, the distribution characteristics of the key points are analyzed, and the similarity index of the key points in the similar groups and the similarity index of the key points in the similar groups are obtained by combining the contribution coefficients of the key points; Analyzing the distribution characteristics of the key points in the similarity group based on the similarity index of the key points to obtain the construction area and construction center; Based on the similarity index of the key points, the distribution characteristics of the key points in the similarity group are analyzed to obtain the construction area and construction center, including: Set the search radius; Analyze the similarity index consistency of all similarity groups within the search radius of the similarity group, and combine the number of key points in the similarity group to obtain the probability coefficient that the area where the similarity group is located belongs to the construction area; Merging the adjacent similar groups to obtain a merged group; The average of the likelihood coefficients corresponding to all the similar groups in the merged group is used as the likelihood that the area where the merged group is located belongs to the construction area; The location with the maximum probability that the area where the merged group is located belongs to the construction area is marked as the construction area, and the point corresponding to the mean value of the coordinates of all key points in the construction area is the construction center; Analyze the positional relationship between the similarity group and the construction center, and analyze the structural complexity of the area where the similarity group is located, and obtain the required index of model accuracy for the area where the similarity group is located, including: Calculating the distance between the similarity group and the construction center to obtain the positional relationship of the similarity group; Calculating the ratio of the likelihood coefficient of the construction area in the area where the similar group is located to the maximum value of the likelihood coefficients corresponding to all the similar groups to obtain the structural complexity of the area where the similar group is located; The positional relationship and the structural complexity corresponding to the similarity groups are combined to obtain a model accuracy requirement index for the region where the similarity groups are located.

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