Differentiation comparison method, device and system of subway three-dimensional model

Abstract

The invention discloses a differentiation comparison method, device and system for subway three-dimensional models, and the method comprises the steps: carrying out the coordinate reference unified processing of obtained first subway three-dimensional information through initial subway three-dimensional information, and carrying out the geometric feature extraction of the processed first three-dimensional information and initial three-dimensional information; and classifying the extracted feature data according to data types, performing difference detection on a plurality of classified feature data sets to obtain a difference detection result, performing change data calculation on the difference detection result, integrating the difference change data obtained by calculation and the difference detection result, and obtaining and displaying a difference comparison report. The feature data is classified, so that the detection efficiency and accuracy during differential comparison detection and the recognition precision of fine change of the model are improved; through a differentiation comparison method, high-precision difference detection of the subway three-dimensional model is realized, tiny geometric changes of the model are accurately identified, and the efficiency and accuracy of model design auditing are improved.

Description

Technical Field

[0001] The present invention relates to the field of subway three-dimensional design, and in particular to a method, device and system for differential comparison of subway three-dimensional models. Background Art

[0002] In modern urban construction, subway projects are the core components of urban public transportation systems. The accuracy of their design and construction directly affects the safety, stability and service life of the subway. With the increase in the scale and complexity of engineering projects, traditional two-dimensional design and comparison methods have gradually become insufficient in accuracy and efficiency. With the introduction of three-dimensional modeling technology, the visualization and precision of subway project design have become possible. However, with the widespread application of three-dimensional modeling technology, how to effectively compare design models of different periods or versions has become an important challenge in subway design and construction.

[0003] Existing technologies mostly use two-dimensional drawings or simple visual inspections to achieve model comparison, that is, engineers usually rely on manual comparison of three-dimensional models of different design versions. However, this method requires a lot of time and effort, especially in large-scale subway projects, where the model data to be compared is huge, which increases the workload and comparison difficulty. In addition, if different versions of models are compared by simple geometric overlap or difference highlighting, it is difficult to accurately identify subtle changes in fine design (such as fine-tuning of the structure, slight changes in size), which can easily lead to some key design changes being overlooked. Summary of the invention

[0004] The present invention provides a method, device and system for differential comparison of subway three-dimensional models, so as to achieve the technical effect of improving the recognition accuracy of subway three-dimensional model comparison and reducing the complexity of model information comparison.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a method for differential comparison of a subway three-dimensional model, comprising the following steps:

[0006] Acquire first three-dimensional subway information, and perform benchmark unified processing on coordinate data of the first three-dimensional subway information according to the initial three-dimensional subway information;

[0007] Extracting geometric features from the processed first subway three-dimensional information and the initial subway three-dimensional information, dividing the extracted initial feature data and the first feature data into a plurality of feature data sets according to data types, and then performing difference detection on the plurality of feature data sets respectively to obtain difference detection results;

[0008] The difference detection result is subjected to change data calculation to obtain difference change data, and the difference detection result and the difference change data are integrated to obtain a difference comparison report for display.

[0009] The differential comparison method provided by the present invention will first calibrate the coordinate data of the acquired first subway three-dimensional information, that is, the system will adjust the coordinate information in the input new version of the subway model information so that the model coordinate data of the adjusted new version and the model coordinate data of the initial version stored in the system are in the same coordinate reference, which is convenient for the subsequent system to compare the above two, and also convenient for the subsequent system to further extract feature data.

[0010] After completing the coordinate processing of the first subway 3D information, the system will extract geometric features from the processed information, that is, extract the geometric feature data of the first subway 3D model as the data basis for the subsequent system to perform model differentiation comparison, and divide the feature data extracted from the first subway 3D model and the initial subway 3D model into several feature data sets according to different data types. By classifying the extracted feature data, the system improves the detection efficiency of subsequent model differentiation comparison detection, and also improves the accuracy of comparison detection and the recognition accuracy of fine changes in the model during comparison.

[0011] After obtaining the comparative differential detection results of the first subway model and the initial subway model, the system will also calculate the difference change data between the above two models based on the detection results, that is, for the detection components contained in the detection results, that is, the adjustment components determined by the detection, the change data calculation is performed, that is, the degree of change of the geometric features of the first subway model compared with the initial subway model is calculated, and the calculated difference change data and the difference detection results are integrated into a comparison report for display. By calculating the degree of change of the geometric features of the comparative difference components between the two models, the integrity and detail of the output differential comparison detection results are improved. Through the above differential comparison method, high-precision difference detection of subway three-dimensional models is achieved, and small geometric changes of the model can be accurately identified, which improves the efficiency and accuracy of model design review.

[0012] As a preferred example, the step of performing benchmark unified processing on the coordinate data of the first three-dimensional subway information according to the initial three-dimensional subway information includes:

[0013] Parsing the first subway 3D information to obtain first 3D model data and subway version information, acquiring the initial subway 3D information according to the subway version information, and associating the initial 3D model data in the initial subway 3D information with the first 3D model data;

[0014] The coordinate data of the first three-dimensional model data are adjusted according to the coordinate reference of the initial three-dimensional model data, so that the coordinates of the adjusted first three-dimensional model data are in the same reference as the coordinates of the initial three-dimensional model data.

[0015] After receiving the first subway 3D model information input by the user, the system will parse the model information to obtain the corresponding model data and the version information corresponding to the subway model. After determining the version information corresponding to the subway model, the system can retrieve the model data of the initial version corresponding to the version from the system database according to the version information to ensure the accuracy of the coordinate reference data for adjusting the first subway 3D model data. After retrieving the subway 3D model data of the initial version corresponding to the first subway 3D model data, the retrieved model data is associated with the model data input by the user so that the system can store the first subway 3D model data later.

[0016] After associating the above two types of model data, the system can use the coordinate data of the initial three-dimensional model data as the coordinate reference data to adjust the coordinate data of the first three-dimensional model data, so that the coordinate data of the adjusted first three-dimensional model data is on the same reference as the coordinate data of the initial three-dimensional model data, which is convenient for the subsequent system to extract feature data and perform differential comparison, thereby improving the efficiency of the system in extracting feature data as well as the efficiency and accuracy of the system in performing differential comparison on the extracted feature data.

[0017] As a preferred example, the step of extracting geometric features from the processed first subway three-dimensional information and the initial subway three-dimensional information includes:

[0018] Extracting geometric features from the first subway three-dimensional information and the initial subway three-dimensional information to obtain corresponding first model data and initial model data;

[0019] Noise data removal and duplicate data filtering are performed on the first model data and the initial model data to obtain the processed first feature data and the initial feature data.

[0020] After completing the coordinate benchmark of the first subway 3D model data, the system will extract the geometric feature data of the first subway 3D model data and the initial subway 3D model data, and denoise and deduplicate the extracted geometric feature data, that is, remove noise and duplicate data from the extracted geometric feature data to improve the efficiency and accuracy of subsequent differential comparison.

[0021] As a preferred example, the extracted initial feature data and the first feature data are divided into a plurality of feature data sets according to data types, including:

[0022] Respectively extracting and integrating the initial feature data and the first feature data according to the node data type, the edge data type and the surface data type in the data types to obtain corresponding node data sets, edge data sets and surface data sets;

[0023] The node data in the node data set is quickly retrieved and the spatial distance is calculated by a spatial index algorithm, and the nodes corresponding to the spatial positions in the node data set are associated as node data pairs according to the calculated spatial distance to obtain a node feature data set;

[0024] The edge dataset and the surface dataset are paired according to the node feature dataset, and the paired edge feature dataset and surface feature dataset as well as the node feature dataset are integrated into the plurality of feature datasets.

[0025] After extracting the feature data of the two model data, the system will classify the extracted feature data according to the node data type, edge data type and face data type to obtain data sets corresponding to different data types, namely, node data sets, edge data sets and face data sets. The spatial distance calculation is performed on the data in each data set through the spatial index method, that is, the difference in the node position in the model is calculated. The corresponding node data pairs in the two models are determined based on the calculated data, that is, the nodes in the same position in the two versions of the model, and the above calculation and determination process is repeated until all the data in the node data set are matched to the corresponding data pairs, and then the data in the edge data set and the face data set are paired according to the paired node feature data set, so that all the processed feature data are constructed to obtain corresponding data pairs, so as to determine the two corresponding sets of data in the extracted feature data.

[0026] As a preferred example, performing difference detection on the plurality of feature data sets respectively to obtain difference detection results includes:

[0027] Performing noise point filtering and data clustering processing on the point cloud data in the plurality of feature data sets by using a density-based spatial clustering algorithm to obtain a plurality of feature data clusters;

[0028] According to the pairing relationship between the plurality of feature data clusters, a differential detection is performed on the first feature data in the plurality of feature data clusters, and the detection result is marked according to the difference type to obtain the difference detection result.

[0029] The point cloud data in the data set is filtered out of noise points by a density-based spatial clustering algorithm to improve the accuracy of the difference detection results. The data after noise removal is clustered according to the spatial clustering algorithm to obtain several feature data clusters, that is, data clusters of several subway model components. Since each point cloud data in the data cluster has paired point cloud data, each data cluster also has a paired data cluster. According to the pairing relationship of each data cluster, each data cluster can be subjected to differential comparison detection, that is, each model component can be subjected to differential comparison detection to obtain differential detection results, and the differential detection results are marked to determine what type of difference the differential result is.

[0030] As a preferred example, the step of marking the detection result according to the difference type to obtain the difference detection result includes:

[0031] The detection results are divided into newly added differences, deleted differences and modified differences according to the difference types, and the corresponding detection results are marked according to the determined difference types to obtain the difference detection results.

[0032] The system provided by the present invention divides the difference detection results into new differences, deleted differences and modified differences according to the difference types, that is, corresponding new components, deleted components and modified components. By automatically marking different types of differences, it realizes comprehensive identification of new, deleted and modified parts in the designed subway three-dimensional model, improves the efficiency and accuracy of discovering and identifying potential problems, and reduces the construction and rework costs of the design project.

[0033] As a preferred example, the step of calculating the change data on the difference detection result to obtain the difference change data includes:

[0034] The difference detection results are analyzed to obtain newly added differences, deleted differences and modified differences, and then the area changes, volume changes and angle changes between each pair of data clusters corresponding to the newly added differences, deleted differences and modified differences are calculated, and the calculated area change values, volume change values ​​and angle change values ​​are integrated into the difference change data.

[0035] After determining each type of difference detection result, the system will also calculate the difference change data for each type of difference detection result, that is, calculate and determine the change value between each difference component and its corresponding initial component, that is, calculate and determine the area change value, volume change value and angle change value between each difference component and the initial component, so as to determine the difference change data of each difference component, thereby improving the completeness of the output difference comparison report.

[0036] As a preferred example, after obtaining and displaying the difference comparison report, the method further includes:

[0037] The first subway three-dimensional information is stored in a preset three-dimensional information database so that the three-dimensional information database is updated, and the first subway three-dimensional information is version-marked and the comparison result remarks are made according to the difference comparison report, and a corresponding search directory is generated according to the updated three-dimensional information database and associated with the three-dimensional information database.

[0038] After obtaining and displaying the corresponding difference comparison report, the system will store the first subway 3D information in the system database, and mark the version of the 3D information and make notes on the comparison results according to the output difference comparison report, that is, the first subway 3D model information is systematically stored. By systematically managing the version of the subway 3D model data and generating difference reports, the tracking and standardized recording of the subway model design modification history are realized, which improves the systematicness and convenience of subway design model data management.

[0039] Correspondingly, an embodiment of the present invention further provides a differential comparison device for a subway three-dimensional model, the differential comparison device comprising a data acquisition module, a data comparison module and a data display module; wherein:

[0040] The data acquisition module is used to acquire the first subway three-dimensional information, and perform benchmark unified processing on the coordinate data of the first subway three-dimensional information and the initial subway three-dimensional information;

[0041] The data comparison module is used to extract geometric features from the processed first subway three-dimensional information and the initial subway three-dimensional information, divide the extracted initial feature data and the first feature data into a plurality of feature data sets according to data types, and then perform difference detection on the plurality of feature data sets respectively to obtain difference detection results;

[0042] The data display module is used to perform change data calculation on the difference detection result to obtain difference change data, and integrate the difference detection result and the difference change data to obtain a difference comparison report for display.

[0043] Correspondingly, an embodiment of the present invention also provides a differential comparison system for a subway three-dimensional model, the differential comparison system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor processes the computer program, it implements any of the above-described methods for differential comparison of subway three-dimensional models. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 : A flow chart of an embodiment of a method for differential comparison of subway three-dimensional models provided by the present invention;

[0045] Figure 2 : A structural diagram of an embodiment of a differentiated comparison device for a subway three-dimensional model provided by the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Embodiment 1

[0048] Please refer to Figure 1 , which is a flow chart of an embodiment of the method for differential comparison of subway three-dimensional models provided by the present invention, including steps 101 to 103, each of which is specifically as follows:

[0049] Step 101: Acquire first three-dimensional subway information, and perform benchmark unified processing on coordinate data of the first three-dimensional subway information according to initial three-dimensional subway information.

[0050] The differentiated comparison method provided in the embodiment of the present invention will first calibrate the coordinate data of the acquired first subway three-dimensional information, that is, the system will adjust the coordinate information in the input new version of the subway model information so that the model coordinate data of the adjusted new version and the model coordinate data of the initial version stored in the system are in the same coordinate reference, which is convenient for the subsequent system to compare the above two, and also convenient for the subsequent system to further extract feature data.

[0051] In this embodiment, the user imports the first subway three-dimensional information into the system. The imported information formats include: IFC, Rev it, Navisworks and other engineering software formats. The system provided by this embodiment also supports engineering software data in the above formats.

[0052] Specifically, the step of performing benchmark unified processing on the coordinate data of the first three-dimensional subway information according to the initial three-dimensional subway information in this embodiment includes:

[0053] Parsing the first subway 3D information to obtain first 3D model data and subway version information, acquiring the initial subway 3D information according to the subway version information, and associating the initial 3D model data in the initial subway 3D information with the first 3D model data;

[0054] The coordinate data of the first three-dimensional model data are adjusted according to the coordinate reference of the initial three-dimensional model data, so that the coordinates of the adjusted first three-dimensional model data are in the same reference as the coordinates of the initial three-dimensional model data.

[0055] After receiving the first subway 3D model information input by the user, the system will parse the model information to obtain the corresponding model data and the version information corresponding to the subway model. After determining the version information corresponding to the subway model, the system can retrieve the model data of the initial version corresponding to the version from the system database according to the version information to ensure the accuracy of the coordinate reference data for adjusting the first subway 3D model data. After retrieving the subway 3D model data of the initial version corresponding to the first subway 3D model data, the retrieved model data is associated with the model data input by the user so that the system can store the first subway 3D model data later.

[0056] After associating the above two types of model data, the system can use the coordinate data of the initial three-dimensional model data as the coordinate reference data to adjust the coordinate data of the first three-dimensional model data, so that the coordinate data of the adjusted first three-dimensional model data is on the same reference as the coordinate data of the initial three-dimensional model data, which is convenient for the subsequent system to extract feature data and perform differential comparison, thereby improving the efficiency of the system in extracting feature data as well as the efficiency and accuracy of the system in performing differential comparison on the extracted feature data.

[0057] In this embodiment, the system preferably uses an iterative closest point algorithm to achieve coordinate system alignment, that is, an ICP algorithm (Iterative Closest Point) to accurately align the model. The minimization objective function of the ICP algorithm is as follows:

[0058]

[0059] In the above function, E(R,t) represents the matching error between the two models; p i and q i are the corresponding points of the two versions of the model, R is the rotation matrix, and t is the translation vector.

[0060] Step 102: extract geometric features from the processed first subway three-dimensional information and the initial subway three-dimensional information, divide the extracted initial feature data and the first feature data into a plurality of feature data sets according to data types, and then perform difference detection on the plurality of feature data sets respectively to obtain difference detection results.

[0061] After completing the coordinate processing of the first subway 3D information, the system will extract geometric features from the processed information, that is, extract the geometric feature data of the first subway 3D model as the data basis for the subsequent system to perform model differentiation comparison, and divide the feature data extracted from the first subway 3D model and the initial subway 3D model into several feature data sets according to different data types. By classifying the extracted feature data, the system improves the detection efficiency of subsequent model differentiation comparison detection, and also improves the accuracy of comparison detection and the recognition accuracy of fine changes in the model during comparison.

[0062] Specifically, the step of extracting geometric features from the processed first subway three-dimensional information and the initial subway three-dimensional information in this embodiment includes:

[0063] Extracting geometric features from the first subway three-dimensional information and the initial subway three-dimensional information to obtain corresponding first model data and initial model data;

[0064] Noise data removal and duplicate data filtering are performed on the first model data and the initial model data to obtain the processed first feature data and the initial feature data.

[0065] After completing the coordinate benchmark of the first subway 3D model data, the system will extract the geometric feature data of the first subway 3D model data and the initial subway 3D model data, and denoise and deduplicate the extracted geometric feature data, that is, remove noise and duplicate data from the extracted geometric feature data to improve the efficiency and accuracy of subsequent differential comparison.

[0066] In this embodiment, the system preferably uses normal vector and curvature calculation to extract geometric features from the first subway 3D model data and the initial subway 3D model data. The calculation formula is:

[0067]

[0068] In the above formula, and These are two edge vectors on the component surface. The system can determine the surface features of the component, i.e., the model data, through the normal vector.

[0069] Furthermore, in this embodiment, the extracted initial feature data and the first feature data are divided into a plurality of feature data sets according to data types, including:

[0070] Respectively extracting and integrating the initial feature data and the first feature data according to the node data type, the edge data type and the surface data type in the data types to obtain corresponding node data sets, edge data sets and surface data sets;

[0071] The node data in the node data set is quickly retrieved and the spatial distance is calculated by a spatial index algorithm, and the nodes corresponding to the spatial positions in the node data set are associated as node data pairs according to the calculated spatial distance to obtain a node feature data set;

[0072] The edge dataset and the surface dataset are paired according to the node feature dataset, and the paired edge feature dataset and surface feature dataset as well as the node feature dataset are integrated into the plurality of feature datasets.

[0073] After extracting the feature data of the two model data, the system will classify the extracted feature data according to the node data type, edge data type and face data type to obtain data sets corresponding to different data types, namely, node data sets, edge data sets and face data sets. The spatial distance calculation is performed on the data in each data set through the spatial index method, that is, the difference in the node position in the model is calculated. The corresponding node data pairs in the two models are determined based on the calculated data, that is, the nodes in the same position in the two versions of the model, and the above calculation and determination process is repeated until all the data in the node data set are matched to the corresponding data pairs, and then the data in the edge data set and the face data set are paired according to the paired node feature data set, so that all the processed feature data are constructed to obtain corresponding data pairs, so as to determine the two corresponding sets of data in the extracted feature data.

[0074] In this embodiment, the system uses a KD tree index for fast retrieval. When calculating the position difference of nodes in the model, that is, the spatial distance described in this embodiment, the three-dimensional spatial distance formula used is as follows:

[0075]

[0076] Among them, Δd represents the spatial distance difference between two corresponding nodes, (x1, y1, z1) and (x2, y2, z2) are the three-dimensional coordinates of the same node in two different versions of the model.

[0077] In order to improve the pairing relationship between the two model data, the system provided in this embodiment preferably determines the corresponding nodes in the two different versions of the model by using the geometric comparison method of Hausdorff distance, which is used to measure the distance between the two point sets, i.e., the two versions of the model data point clouds described in this embodiment. The formula for calculating the similarity between the model faces using the Hausdorff distance is as follows:

[0078]

[0079] Among them, d H (A,B) represents the Hausdorff distance between set A and set B, where A and B are the geometric sets of the two versions of the model, sup represents the maximum value, and i nf represents the minimum value.

[0080] Furthermore, in this embodiment, performing difference detection on the plurality of feature data sets respectively to obtain difference detection results includes:

[0081] Performing noise point filtering and data clustering processing on the point cloud data in the plurality of feature data sets by using a density-based spatial clustering algorithm to obtain a plurality of feature data clusters;

[0082] According to the pairing relationship between the plurality of feature data clusters, a differential detection is performed on the first feature data in the plurality of feature data clusters, and the detection result is marked according to the difference type to obtain the difference detection result.

[0083] The point cloud data in the data set is filtered out of noise points by a density-based spatial clustering algorithm to improve the accuracy of the difference detection results. The data after noise removal is clustered according to the spatial clustering algorithm to obtain several feature data clusters, that is, data clusters of several subway model components. Since each point cloud data in the data cluster has paired point cloud data, each data cluster also has a paired data cluster. According to the pairing relationship of each data cluster, each data cluster can be subjected to differential comparison detection, that is, each model component can be subjected to differential comparison detection to obtain differential detection results, and the differential detection results are marked to determine what type of difference the differential result is.

[0084] In this embodiment, the system preferably uses the DBSCAN clustering algorithm, which is a density-based clustering algorithm. This embodiment uses the above clustering algorithm to filter and cluster the noise points in the point cloud. The core calculation formula of the DBSCAN algorithm is:

[0085] CorePoint={p∥N ∈ (p)|≥MinPts}

[0086] Among them, CorePoint is the definition of the core point, |N ∈ (p)| is the number of data points within the radius ∈, p is a point in the point cloud, and MinPts is the minimum number of neighborhood points set.

[0087] Furthermore, in this embodiment, the detection result is marked according to the difference type to obtain the difference detection result, including:

[0088] The detection results are divided into newly added differences, deleted differences and modified differences according to the difference types, and the corresponding detection results are marked according to the determined difference types to obtain the difference detection results.

[0089] The system provided in the embodiment of the present invention will classify the difference detection results into new differences, deleted differences and modified differences according to the difference type, that is, the corresponding new components, deleted components and modified components. By automatically marking different types of differences, it can achieve comprehensive identification of new, deleted and modified parts in the designed subway three-dimensional model, improve the efficiency and accuracy of discovering and identifying potential problems, and reduce the construction and rework costs of the design project.

[0090] In this embodiment, the system may mark different differences in different ways, such as marking newly added components with green, marking deleted components with red, and marking modified components with yellow.

[0091] Step 103: performing change data calculation on the difference detection result to obtain difference change data, and integrating the difference detection result and the difference change data to obtain a difference comparison report for display.

[0092] After obtaining the comparative differential detection results of the first subway model and the initial subway model, the system will also calculate the difference change data between the above two models based on the detection results, that is, for the detection components contained in the detection results, that is, the adjustment components determined by the detection, the change data calculation is performed, that is, the degree of change of the geometric features of the first subway model compared with the initial subway model is calculated, and the calculated difference change data and the difference detection results are integrated into a comparison report for display. By calculating the degree of change of the geometric features of the comparative difference components between the two models, the integrity and detail of the output differential comparison detection results are improved. Through the above differential comparison method, high-precision difference detection of subway three-dimensional models is achieved, and small geometric changes of the model can be accurately identified, which improves the efficiency and accuracy of model design review.

[0093] In this embodiment, when the system visually displays the user with the detected difference results, that is, the difference comparison report generated according to the difference detection results, the user can view the specific difference detection results, that is, view the detected different components, by rotating, zooming, and dragging operations. Moreover, the system provided in this embodiment also provides a local magnification viewing function, which is convenient for the user to conduct a detailed analysis of the difference details of the model.

[0094] In addition, the report generated and displayed by the system also includes: component change types, geometric position changes, area and volume change information, and is attached with a timestamp and version number, so as to facilitate the user's subsequent review records. The difference comparison report provided in this embodiment supports exports and displays in different formats, such as PDF or CSV formats.

[0095] Specifically, calculating the change data of the difference detection results in this embodiment to obtain difference change data includes:

[0096] Parsing the difference detection results to obtain new differences, deletion differences, and modification differences, and then calculating the area change, volume change, and angle change between each pair of data clusters corresponding to the new differences, the deletion differences, and the modification differences, and integrating the calculated area change values, volume change values, and angle change values into the difference change data.

[0097] After determining the difference detection results of each type, the system will also calculate the difference change data for the difference detection results of each type, that is, calculate and determine the change values between each different component and its corresponding initial component, that is, calculate and determine the area change value, volume change value, and angle change value between each different component and the initial component, so as to determine the difference change data of each different component, improving the integrity of the output difference comparison report.

[0098] In this embodiment, for the modified components detected by the system, it is necessary to further calculate the degree of change in their geometric features, including the differences in the area, volume, and inclination angle of the components. Preferably, the following formulas can be used to calculate the area and volume changes respectively:

[0099] ΔA = |A2 - A1| and ΔV = |V2 - V1|

[0100] Where, ΔA represents the area change amount of the component, A1 and A2 are the areas of the component in the two version models respectively; ΔV represents the volume change amount, and V1 and V2 are the volumes of the component in the two version models respectively. When calculating the above data, the system will also synchronously record the change in the normal vector of the component to detect the rotation and inclination of the components in the two version models.

[0101] In this embodiment, the inclination angle change calculation of the model component is preferably determined by calculating the angle between the normal vectors. The specific formula is as follows:

[0102]

[0103] in, and are the normal vectors of the component before and after modification, and Δθ represents the angular change of the component surface rotation.

[0104] At the same time, when the system provided in this embodiment displays the difference results determined by the detection, it also uses a color gradient to represent the degree of geometric difference between the two models, wherein the intensity ΔI of the difference is calculated according to the following formula:

[0105]

[0106] Among them, ΔA is the area change, ΔV is the volume change, and ΔI is used to set the depth of the visualization color.

[0107] When the system of this embodiment generates a difference comparison report, it also automatically generates an image snapshot of the difference change. The position of the snapshot is determined according to the coordinates (x d ,y d ,z d ) for positioning.

[0108] Furthermore, after obtaining the difference comparison report for display, the present embodiment further includes:

[0109] The first subway three-dimensional information is stored in a preset three-dimensional information database so that the three-dimensional information database is updated, and the first subway three-dimensional information is version-marked and the comparison result remarks are made according to the difference comparison report, and a corresponding search directory is generated according to the updated three-dimensional information database and associated with the three-dimensional information database.

[0110] After obtaining and displaying the corresponding difference comparison report, the system will store the first subway 3D information in the system database, and mark the version of the 3D information and make notes on the comparison results according to the output difference comparison report, that is, the first subway 3D model information is systematically stored. By systematically managing the version of the subway 3D model data and generating difference reports, the tracking and standardized recording of the subway model design modification history are realized, which improves the systematicness and convenience of subway design model data management.

[0111] In this embodiment, when the system performs version management on the model data, it supports the recording of changes at the component level, and the system records the geometric center (x c ,y c,z c ) is recorded by calculating the displacement Δc of the geometric center of each component. The change calculation formula is:

[0112]

[0113] Among them, (x c1 ,y c1 ,z c1 ) and (x c2 ,y c2 ,z c2 ) are the geometric center coordinates of the component in the two versions.

[0114] In addition, the system provided in this embodiment can also adjust the error threshold of the geometric comparison according to the generated difference comparison results to optimize the comparison results, and the adjusted parameters will be automatically applied to the imported model data to generate new difference analysis results. Among them, the adjustment range of the error threshold of this embodiment is preferably 0.1% to 5%. The system dynamically filters geometric differences by adjusting the error threshold δ, and the error range Δ adjusted The calculation formula is as follows:

[0115] Δ adjusted =Δ default ×(1+δ)

[0116] Among them, Δ adjusted is the adjusted error range, Δ default is the error range after adjustment, and δ is the error coefficient set by the user.

[0117] In order to specifically illustrate the differential comparison method provided by this embodiment, the embodiment of the present invention also provides the following examples:

[0118] The first example is a comparison of subway platform designs, which specifically includes the following steps:

[0119] Import of 3D model data: During the design process of the subway platform, the user generated two versions of 3D models in the initial design stage and the optimization design stage, and imported the two model data into the comparison system. The imported model format is the IFC file format; and the version information of the corresponding model is specified during the import, for example, the initial design version is V1, and the optimization design version is V2.

[0120] Coordinate system alignment and preprocessing: The system performs coordinate system alignment on the imported V1 and V2 models. In this example, the ICP algorithm is used for precise alignment. The ICP algorithm is implemented by minimizing the following error function:

[0121]

[0122] Among them, p i is a point in the V1 model, q i is the corresponding point in the V2 model, R is the rotation matrix, and t is the translation vector. After alignment, the system will also perform denoising and repeated geometric filtering on the model to further ensure the accuracy of the data.

[0123] Geometric feature extraction: Extract geometric features in each model, including information such as nodes, edges, and faces, and classify the geometric components in the platform area. Among them, the calculation of the normal vector is used to determine the directionality of the surface, and the formula is:

[0124]

[0125] Among them, and are the edge vectors on the surface of the component.

[0126] Geometric comparison initialization: Use the KD-tree index to quickly match the geometric features in the V1 and V2 models. For the position difference of each node, the following formula is used to calculate the three-dimensional space distance:

[0127]

[0128] Among them, (x1, y1, z1) and (x2, y2, z2) are the coordinates of the corresponding nodes in V1 and V2.

[0129] Difference detection: According to the matching results of geometric features, perform difference detection on the geometric components in the platform area. Among them, the newly added components in this example are marked in green, the deleted components are marked in red, and the modified components are marked in yellow. For the detected geometrically modified components, the system will also record the change in the normal vector to analyze the rotation situation. The formula is as follows:

[0130]

[0131] Among them, and are the normal vectors of this component in V1 and V2 respectively.

[0132] Geometric feature change calculation: The system calculates the area and volume changes of each component in the platform area, using the following formula:

[0133] ΔA = |A2 - A1| and ΔV = |V2 - V1|

[0134] Among them, A1 and A2 are the areas of the components in V1 and V2, and V1 and V2 are the volumes of the components. The difference visualization display and report generation use color gradients and transparencies to represent the magnitude of geometric changes. The difference report contains detailed comparison information on component types, change positions, and geometric data, and is exported in PDF format.

[0135] Through the above-mentioned differentiated comparison method, it is possible to compare the versions of the subway platform design, detect and determine the newly added escalators and modified columns in the platform, and improve the space utilization rate in the optimized design.

[0136] The second example is a comparison of subway tunnel structure design. The specific steps are as follows:

[0137] 3D model data import: When designing a subway tunnel, the user generated two versions of the 3D model in the design phase before and after construction. Therefore, the user imported the two model data into the comparison system in Revit file format. This example system uses the I CP algorithm to align the two models and minimize the error between the models by adjusting the rotation matrix R and the translation vector t.

[0138] Coordinate system alignment and preprocessing: The system will extract the geometric features of the tunnel structure, focusing on the wall thickness, curvature and track curvature changes within the tunnel. This example system uses the Hausdorff distance to evaluate the geometric similarity between models, as follows:

[0139]

[0140] Among them, A and B are the geometric sets of two models, sup and inf are the maximum and minimum operations respectively.

[0141] Difference detection and analysis: The system determines the differences between different design stages in the tunnel through geometric comparison, such as increase or decrease in tunnel thickness, changes in structural support, etc. It also uses normal vector analysis to determine changes in tunnel inclination angles, and the calculation formula is shown in the first example.

[0142] Calculation of geometric feature changes: Calculate the cross-sectional changes in the tunnel and analyze the changes in tunnel volume. The formula is as follows:

[0143] ΔV=V2-V1

[0144] Difference visualization and report generation: The DBSCAN clustering algorithm is used to filter the noise of the detected change points to ensure the accuracy of the results. This example system will also highlight the difference points, and the report generated will also include the changes in the key geometric parameters of the tunnel and the corresponding visualization graphics to facilitate subsequent construction decisions.

[0145] Through the above-mentioned differentiated comparison method, the system accurately identified the design changes in the tunnel caused by adjustments to geological conditions, effectively avoiding potential risks in construction.

[0146] The third example is a detailed comparison of subway track layout. The specific steps are as follows:

[0147] 3D model data import: In order to adjust the details of the subway track, the user imported multiple versions of 3D models, which were generated by design optimization at different stages and were in the Navisworks format.

[0148] Coordinate system alignment and preprocessing: The system uses KD tree index to coordinate align and clean up noise on the model to ensure that the geometric elements of all tracks are in the same reference coordinate system.

[0149] Geometric feature extraction: The system extracts the geometric features of the track, including the length, slope and curvature of the track. According to the curvature of the track, the curvature change is calculated using the formula:

[0150]

[0151] Where κ is the orbit curvature, is the orbital tangent vector, is the first derivative of the tangent vector.

[0152] Difference detection: The geometric features of the track are compared point by point, and the system focuses on the height difference and curvature changes of the track. For the detected track changes, the center displacement Δc is calculated. The specific formula is:

[0153]

[0154] Among them, (x c1 ,y c1 ,z c1 ) and (x c2 ,y c2 ,z c2 ) are the coordinates of the geometric center of the orbit in the two versions.

[0155] Difference visualization and report generation: The generated report contains detailed information on the geometric changes to the track layout, including length adjustments and curvature changes, and provides a visual comparison of the optimized track design.

[0156] Through the above-mentioned differentiated comparison method, an accurate comparison of the detailed adjustments in the track design was achieved, providing detailed data support for the optimization of track safety and comfort.

[0157] In order to better illustrate the working principle and step flow of the method, device and system for differential comparison of subway three-dimensional models of the present invention, it is possible but not limited to refer to the relevant records above.

[0158] Accordingly, see Figure 2 , Figure 2 This is a structural diagram of an embodiment of a differential comparison device for a subway three-dimensional model provided by the present invention. Figure 2As shown, the differential comparison device includes a data acquisition module 201, a data comparison module 202 and a data display module 203; wherein:

[0159] The data acquisition module 201 is used to acquire first three-dimensional subway information, and perform benchmark unification processing on coordinate data of the first three-dimensional subway information and initial three-dimensional subway information.

[0160] Furthermore, the data acquisition module 201 performs benchmark unified processing on the coordinate data of the first subway three-dimensional information according to the initial subway three-dimensional information, including:

[0161] Parsing the first subway 3D information to obtain first 3D model data and subway version information, acquiring the initial subway 3D information according to the subway version information, and associating the initial 3D model data in the initial subway 3D information with the first 3D model data;

[0162] The coordinate data of the first three-dimensional model data are adjusted according to the coordinate reference of the initial three-dimensional model data, so that the coordinates of the adjusted first three-dimensional model data are in the same reference as the coordinates of the initial three-dimensional model data.

[0163] The data comparison module 202 is used to extract geometric features from the processed first subway three-dimensional information and the initial subway three-dimensional information, divide the extracted initial feature data and the first feature data into several feature data sets according to the data type, and then perform difference detection on the several feature data sets respectively to obtain difference detection results.

[0164] Furthermore, the data comparison module 202 extracts geometric features from the processed first subway 3D information and the initial subway 3D information, including:

[0165] Extracting geometric features from the first subway three-dimensional information and the initial subway three-dimensional information to obtain corresponding first model data and initial model data;

[0166] Noise data removal and duplicate data filtering are performed on the first model data and the initial model data to obtain the processed first feature data and the initial feature data.

[0167] Furthermore, the data comparison module 202 divides the extracted initial feature data and the first feature data into several feature data sets according to data types, including:

[0168] Respectively extracting and integrating the initial feature data and the first feature data according to the node data type, the edge data type and the surface data type in the data types to obtain corresponding node data sets, edge data sets and surface data sets;

[0169] The node data in the node data set is quickly retrieved and the spatial distance is calculated by a spatial index algorithm, and the nodes corresponding to the spatial positions in the node data set are associated as node data pairs according to the calculated spatial distance to obtain a node feature data set;

[0170] The edge dataset and the surface dataset are paired according to the node feature dataset, and the paired edge feature dataset and surface feature dataset as well as the node feature dataset are integrated into the plurality of feature datasets.

[0171] Furthermore, the data comparison module 202 performs difference detection on the plurality of feature data sets respectively to obtain difference detection results, including:

[0172] Performing noise point filtering and data clustering processing on the point cloud data in the plurality of feature data sets by using a density-based spatial clustering algorithm to obtain a plurality of feature data clusters;

[0173] According to the pairing relationship between the plurality of feature data clusters, a differential detection is performed on the first feature data in the plurality of feature data clusters, and the detection result is marked according to the difference type to obtain the difference detection result.

[0174] Furthermore, the data comparison module 202 marks the detection results according to the difference type to obtain the difference detection results, including:

[0175] The detection results are divided into newly added differences, deleted differences and modified differences according to the difference types, and the corresponding detection results are marked according to the determined difference types to obtain the difference detection results.

[0176] The data display module 203 is used to perform change data calculation on the difference detection result to obtain difference change data, and integrate the difference detection result and the difference change data to obtain a difference comparison report for display.

[0177] Furthermore, the data display module 203 performs change data calculation on the difference detection result to obtain difference change data, including:

[0178] The difference detection results are analyzed to obtain newly added differences, deleted differences and modified differences, and then the area changes, volume changes and angle changes between each pair of data clusters corresponding to the newly added differences, deleted differences and modified differences are calculated, and the calculated area change values, volume change values ​​and angle change values ​​are integrated into the difference change data.

[0179] Furthermore, after displaying the obtained difference comparison report, the data display module 203 further includes:

[0180] The first subway three-dimensional information is stored in a preset three-dimensional information database so that the three-dimensional information database is updated, and the first subway three-dimensional information is version-marked and the comparison result remarks are made according to the difference comparison report, and a corresponding search directory is generated according to the updated three-dimensional information database and associated with the three-dimensional information database.

[0181] Correspondingly, an embodiment of the present invention also provides a differential comparison system for a subway three-dimensional model, the differential comparison system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor processes the computer program, it implements any of the above-described methods for differential comparison of subway three-dimensional models.

[0182] In summary, the embodiments of the present invention provide a method, device and system for differential comparison of subway three-dimensional models, which performs unified coordinate benchmark processing on the first subway three-dimensional information obtained through the initial subway three-dimensional information, extracts geometric features from the processed first three-dimensional information and the initial three-dimensional information, classifies the extracted feature data according to the data type, and performs difference detection on several feature data sets after classification to obtain difference detection results, calculates change data on the difference detection results, and integrates the calculated difference change data and difference detection results to obtain and display a difference comparison report. By classifying the feature data, the detection efficiency, accuracy and recognition accuracy of fine changes in the model during differential comparison detection are improved; the differential comparison method realizes high-precision difference detection of subway three-dimensional models, accurately identifies small geometric changes in the model, and improves the efficiency and accuracy of model design review.

[0183] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for differential comparison of subway three-dimensional models, characterized in that: The following steps are involved: Acquire first three-dimensional subway information, and perform benchmark unified processing on coordinate data of the first three-dimensional subway information according to the initial three-dimensional subway information; Extracting geometric features from the processed first subway three-dimensional information and the initial subway three-dimensional information, dividing the extracted initial feature data and the first feature data into a plurality of feature data sets according to data types, and then performing difference detection on the plurality of feature data sets respectively to obtain difference detection results; The difference detection result is subjected to change data calculation to obtain difference change data, and the difference detection result and the difference change data are integrated to obtain a difference comparison report for display.

2. The method for differential comparison of subway three-dimensional models according to claim 1, characterized in that: The benchmarking and unified processing of the coordinate data of the first three-dimensional subway information according to the initial three-dimensional subway information includes: Parsing the first subway 3D information to obtain first 3D model data and subway version information, acquiring the initial subway 3D information according to the subway version information, and associating the initial 3D model data in the initial subway 3D information with the first 3D model data; The coordinate data of the first three-dimensional model data are adjusted according to the coordinate reference of the initial three-dimensional model data, so that the coordinates of the adjusted first three-dimensional model data are in the same reference as the coordinates of the initial three-dimensional model data.

3. The method for differential comparison of subway three-dimensional models according to claim 1, characterized in that: The extracting geometric features of the processed first subway three-dimensional information and the initial subway three-dimensional information includes: Extracting geometric features from the first subway three-dimensional information and the initial subway three-dimensional information to obtain corresponding first model data and initial model data; Noise data removal and duplicate data filtering are performed on the first model data and the initial model data to obtain the processed first feature data and the initial feature data.

4. The method for differential comparison of subway three-dimensional models according to claim 1, characterized in that: The step of dividing the extracted initial feature data and the first feature data into a plurality of feature data sets according to data types includes: Respectively extracting and integrating the initial feature data and the first feature data according to the node data type, the edge data type and the surface data type in the data types to obtain corresponding node data sets, edge data sets and surface data sets; The node data in the node data set is quickly retrieved and the spatial distance is calculated by a spatial index algorithm, and the nodes corresponding to the spatial positions in the node data set are associated as node data pairs according to the calculated spatial distance to obtain a node feature data set; The edge dataset and the surface dataset are paired according to the node feature dataset, and the paired edge feature dataset and surface feature dataset as well as the node feature dataset are integrated into the plurality of feature datasets.

5. The method for differential comparison of subway three-dimensional models according to claim 1, characterized in that: The performing difference detection on the plurality of feature data sets respectively to obtain difference detection results includes: Performing noise point filtering and data clustering processing on the point cloud data in the plurality of feature data sets by using a density-based spatial clustering algorithm to obtain a plurality of feature data clusters; According to the pairing relationship between the plurality of feature data clusters, a differential detection is performed on the first feature data in the plurality of feature data clusters, and the detection result is marked according to the difference type to obtain the difference detection result.

6. A method for differential comparison of subway three-dimensional models as claimed in claim 5, characterized in that: The step of marking the detection result according to the difference type to obtain the difference detection result includes: The detection results are divided into newly added differences, deleted differences and modified differences according to the difference types, and the corresponding detection results are marked according to the determined difference types to obtain the difference detection results.

7. The method for differential comparison of subway three-dimensional models according to claim 1, characterized in that: The step of calculating the change data on the difference detection result to obtain the difference change data includes: The difference detection results are analyzed to obtain newly added differences, deleted differences and modified differences, and then the area changes, volume changes and angle changes between each pair of data clusters corresponding to the newly added differences, deleted differences and modified differences are calculated, and the calculated area change values, volume change values ​​and angle change values ​​are integrated into the difference change data.

8. The method for differential comparison of subway three-dimensional models according to claim 1, characterized in that: After obtaining the difference comparison report and displaying it, the method further includes: The first subway three-dimensional information is stored in a preset three-dimensional information database so that the three-dimensional information database is updated, and the first subway three-dimensional information is version-marked and the comparison result remarks are made according to the difference comparison report, and a corresponding search directory is generated according to the updated three-dimensional information database and associated with the three-dimensional information database.

9. A differential comparison device for a subway three-dimensional model, characterized in that: The differential comparison device includes a data acquisition module, a data comparison module and a data display module; wherein: The data acquisition module is used to acquire the first subway three-dimensional information, and perform benchmark unified processing on the coordinate data of the first subway three-dimensional information and the initial subway three-dimensional information; The data comparison module is used to extract geometric features from the processed first subway three-dimensional information and the initial subway three-dimensional information, divide the extracted initial feature data and the first feature data into a plurality of feature data sets according to data types, and then perform difference detection on the plurality of feature data sets respectively to obtain difference detection results; The data display module is used to perform change data calculation on the difference detection result to obtain difference change data, and integrate the difference detection result and the difference change data to obtain a difference comparison report for display.

10. A differentiated comparison system for a subway three-dimensional model, characterized in that: The differential comparison system includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor processes the computer program, it implements the differential comparison method of a subway three-dimensional model as described in any one of claims 1 to 8.

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