A three-dimensional visualization operation control method and system for intelligent roadbed construction

Abstract

The present invention relates to the technical field of three-dimensional visualization, and discloses a method and system for controlling three-dimensional visualization operations for intelligent roadbed construction. The method comprises: collecting multi-source roadbed data, performing position correction on the multi-source roadbed data using an improved SLAM algorithm; performing multi-source fusion on the position-corrected multi-source roadbed data to obtain roadbed fusion data; constructing a three-dimensional model of the roadbed construction based on the roadbed fusion data; and performing lightweight processing and dynamic operation rendering on the three-dimensional model of the roadbed construction using a quadratic error measure grid simplification algorithm. The present invention calculates the multi-dimensional feature entropy of different roadbed areas, selects roadbed areas with complex terrain for multiple resampling, improves the accuracy of three-dimensional modeling of complex roadbed areas, merges triangle face vertices in the three-dimensional model of the roadbed construction based on their error matrix, realizes lightweight processing of the three-dimensional model of the roadbed construction, and performs real-time three-dimensional visualization of the roadbed construction operation process.

Description

Technical Field

[0001] The present invention relates to the field of three-dimensional visualization, and in particular to a three-dimensional visualization operation control method and system for intelligent roadbed construction. Background Art

[0002] With rapid socioeconomic development and increasing transportation demand, highway construction has become a crucial component of national infrastructure. Roadbeds are the foundation of highway construction, and their quality and stability directly impact their service life and safety. Traditional roadbed construction, due to the complex construction environment, numerous operational steps, and challenging quality control, often hinders precise management and is prone to various construction issues, such as substandard quality and long construction periods. Therefore, improving the quality, efficiency, and precision of roadbed construction management has become a major challenge in current road construction. With the advancement of information technology and intelligent technologies, 3D visualization and operation control methods are increasingly being applied to engineering construction, particularly in intelligent roadbed construction. 3D visualization technology digitally integrates, displays, and analyzes various aspects of roadbed construction, operational processes, and data, making monitoring and management of the construction process more intuitive and transparent. Furthermore, the rise of intelligent construction enables the construction process to leverage automation and information technology to perceive and control the construction environment and progress in real time, improving construction accuracy and safety. However, because roadbed projects involve large-scale terrain and structural models, complex geological scenarios often present significant model distortion and loading delays. Summary of the Invention

[0003] In view of this, the present invention proposes a path intelligent construction three-dimensional visualization operation control method and system, which realizes real-time visualization of the three-dimensional model by optimizing the three-dimensional model rendering process and accuracy, thereby achieving the purpose of precise quality management and real-time monitoring.

[0004] To achieve the above-mentioned object, the present invention provides a three-dimensional visualization operation control method for intelligent roadbed construction, comprising the following steps:

[0005] S1: Collect multi-source roadbed data, use the improved SLAM algorithm to perform position correction on the multi-source roadbed data, and obtain the multi-source roadbed data after position correction;

[0006] S2: Perform multi-source fusion on the multi-source roadbed data after position correction to obtain roadbed fusion data;

[0007] S3: Construct a 3D model of roadbed construction based on roadbed fusion data;

[0008] S4: Use the quadratic error measure grid simplification algorithm to lightweight the roadbed construction 3D model to obtain a lightweight roadbed construction 3D model;

[0009] S5: Dynamically render the 3D model of lightweight roadbed construction and visualize the roadbed construction process.

[0010] As a further improvement method of the present invention:

[0011] Optionally, the three-dimensional point cloud data includes three-dimensional position coordinates and color vectors;

[0012] The radar echo data includes three-dimensional position coordinates, signal amplitude, phase information, interference phase and coherence;

[0013] The satellite positioning data includes three-dimensional position coordinates and movement speed;

[0014] The representation of the multi-source roadbed data is:

[0015] ;

[0016] ;

[0017] in:

[0018] G represents multi-source roadbed data, Represents a 3D point cloud data set, represents the radar echo data set, represents the satellite positioning data set, and N represents the number of data collections;

[0019] Represents a 3D point cloud data set The nth group of 3D point cloud data in Represents a radar echo data set The nth group of radar echo data in Represents a collection of satellite positioning data The nth group of satellite positioning data in ;

[0020] An improved SLAM algorithm is used to construct a multi-source factor error function of the multi-source roadbed data. The multi-source factor error function takes a global position correction parameter as a variable and the degree of matching of the three-dimensional position coordinates of the multi-source roadbed data after global position correction as a function value. The improved SLAM algorithm is used to perform position matching on the multi-source roadbed data, and the three-dimensional position coordinates in the three-dimensional point cloud data, radar echo data and satellite positioning data are used as nodes, and the multi-source factor error functions between the nodes are used as edges to construct a factor graph.

[0021] Optionally, the expression of the multi-source factor error function is:

[0022] ;

[0023] ;

[0024] ;

[0025] ;

[0026] ;

[0027] ;

[0028] in:

[0029] Indicates the The quality weight of the error factor, represents the global position correction parameter to be solved, It represents the position correction parameters of 3D point cloud data, radar echo data and satellite positioning data in turn. Indicates the The first to third factor errors are the factor errors of three-dimensional point cloud data, radar echo data and satellite positioning data, respectively. The first to third factor error functions are the factor error functions of three-dimensional point cloud data, radar echo data and satellite positioning data, respectively. express The standard deviation of the N three-dimensional position coordinates in ;

[0030] represents the L1 norm, Represents 3D point cloud data , radar echo data and satellite positioning data The three-dimensional position coordinates in ;

[0031] Representing a collection Zhongyu The nearest three-dimensional position coordinates;

[0032] Representing a collection Zhongyu The nearest three-dimensional position coordinates;

[0033] Representing a collection Zhongyu The nearest 3D position coordinates.

[0034] Optionally, with the goal of minimizing the multi-source factor error function, solving the multi-source factor error function to obtain global position correction parameters includes:

[0035] Initializing and generating global position correction parameters, and using the generated global position correction parameters as input values of the multi-source factor error function;

[0036] Calculate the first-order gradient of the multi-source factor error function as the Jacobian matrix of the generated global position correction parameter, and calculate the second-order inverse of the Jacobian matrix as the Hessian matrix of the generated global position correction parameter;

[0037] Iterating the generated global position correction parameters based on the Jacobian matrix and the Hessian matrix, and re-iterating the Jacobian matrix and the Hessian matrix of the global position correction parameters;

[0038] After each iteration, the rate of change of the global position correction parameter is calculated. If the rate of change is less than a preset threshold, the iteration is terminated and the current iteration result is used as the solved global position correction parameter; the three-dimensional position coordinates in the multi-source roadbed data are corrected using the global position correction parameter to obtain the multi-source roadbed data after position correction.

[0039] Optionally, performing multi-source fusion on the multi-source roadbed data after position correction includes:

[0040] Calculating the three-dimensional position coordinate distances among the three-dimensional point cloud data, radar echo data, and satellite positioning data in the multi-source roadbed data after position correction, and fusing the data from different sources whose three-dimensional position coordinate distances are less than a preset distance threshold to obtain the multi-source fused roadbed data at the same three-dimensional position coordinates;

[0041] The multidimensional feature entropy of different roadbed areas is calculated based on multi-source fused roadbed data. The larger the multidimensional feature entropy, the more complex the surface terrain. Multiple multi-source roadbed data collections are performed in roadbed areas where the multidimensional feature entropy is higher than the preset entropy threshold. Based on the global position correction parameters, the three-dimensional position coordinates in the newly collected multi-source roadbed data are corrected and integrated into the multi-source fused roadbed data to form roadbed fusion data.

[0042] Optionally, the roadbed construction three-dimensional model includes a filtering module and a three-dimensional surface reconstruction module, the filtering module is used to extract multi-source information of each group of multi-source fused roadbed data in the roadbed fusion data, perform multi-scale filtering on the multi-source fused roadbed data to obtain filtered roadbed fusion data, and the three-dimensional surface reconstruction module is used to extract three-dimensional position coordinates and multi-source information from the filtered roadbed fusion data, perform three-dimensional network reconstruction using a Poisson reconstruction method, and obtain the roadbed construction three-dimensional model;

[0043] The multi-source information in the multi-source fusion roadbed data is the signal amplitude, phase information, interference phase, coherence and motion speed in the multi-source fusion roadbed data;

[0044] The multi-scale filtering processing method is as follows: based on multi-source information, filter scale information of the multi-source fused roadbed data is calculated; if the filter scale information is higher than a preset filter threshold, it indicates that the multi-source fused roadbed data is noise data; the noise data in the roadbed fused data is filtered and cleaned to obtain filtered roadbed fused data;

[0045] The Poisson reconstruction method is: using principal component analysis to calculate the normal vector of each group of multi-source fused roadbed data in the filtered roadbed fusion data, wherein the higher the coherence, the higher the weight of the multi-source fused roadbed data in the normal vector calculation process;

[0046] Constructing an octree to store the multi-source fused roadbed data in the filtered roadbed fusion data, and performing spatial hierarchical division on the filtered roadbed fusion data;

[0047] Poisson equations at different spatial levels are constructed and solved to obtain smooth implicit surface equations for different roadbed areas. Isosurfaces are extracted to generate a closed triangular mesh composed of triangular faces. Small isolated triangular faces are identified and deleted to obtain a three-dimensional model of roadbed construction.

[0048] Optionally, a quadratic error measure grid simplification algorithm is used to perform lightweight processing on the roadbed construction three-dimensional model, including:

[0049] Calculating the normal vector of each triangular face in the three-dimensional model of the roadbed construction, and calculating the sum of the differences between the normal vector of the triangular face and the adjacent normal vectors as the curvature of the triangular face, where the adjacent normal vectors of the triangular face are the normal vectors of the triangular faces connected to the triangular face;

[0050] The error weight of the triangle is calculated based on the curvature, and the plane weighted error matrix of the triangle is calculated based on the error weight and the normal vector. The plane weighted error matrix is used to measure the distance from the triangle vertex to the plane. The calculation formula of the error weight is:

[0051] ;

[0052] in:

[0053] represents the error weight corresponding to the curvature C, Indicates the preset error weight threshold;

[0054] It represents the mean curvature of the adjacent triangles corresponding to the curvature C, and F represents the normal vector of the triangle corresponding to the curvature C. represents the gravity vector, ;

[0055] The accumulated weighted error matrices of all adjacent planes of the triangle vertex are calculated as the error matrix of the triangle vertex;

[0056] Select triangle vertices and shared edge vertices in descending order of the error matrix L2 norm to form triangle vertex pairs. Calculate the new triangle vertex with the minimum error matrix after merging the triangle vertex pairs. Delete the merged triangle vertex and update the adjacent triangle to point to the new triangle vertex.

[0057] The vertices are repeatedly merged, the error matrix is updated, and the number of triangles is gradually reduced until the set total number of triangles is reached. The merged 3D model is smoothed using Laplacian and used as the 3D model for lightweight roadbed construction.

[0058] Optionally, the color vector in the roadbed fusion data is extracted, and the color vector is mapped to the corresponding position of the lightweight roadbed construction three-dimensional model to obtain the color-rendered lightweight roadbed construction three-dimensional model, and multi-source roadbed data is collected in real time during the roadbed construction operation to dynamically update the lightweight roadbed construction three-dimensional model.

[0059] In order to solve the above problems, the present invention provides a three-dimensional visualization operation control system for intelligent roadbed construction, which includes a server and a data acquisition device. The server includes a data processing module and a three-dimensional visualization module:

[0060] The data processing module is used to perform position correction on the multi-source roadbed data using the improved SLAM algorithm, and perform multi-source fusion on the multi-source roadbed data after position correction to obtain roadbed fusion data;

[0061] The three-dimensional visualization module is used to construct a three-dimensional model of the roadbed construction based on the roadbed fusion data, use a quadratic error measure grid simplification algorithm to perform lightweight processing on the three-dimensional model of the roadbed construction to obtain a lightweight three-dimensional model of the roadbed construction, perform dynamic operation rendering on the lightweight three-dimensional model of the roadbed construction, and perform visual operation rendering on the roadbed construction process;

[0062] The data acquisition device is used to collect multi-source roadbed data.

[0063] In order to solve the above problem, the present invention further provides an electronic device, comprising:

[0064] a memory storing at least one instruction;

[0065] Communication interfaces to enable electronic equipment to communicate; and

[0066] The processor executes the instructions stored in the memory to implement the above-mentioned three-dimensional visualization operation control method for intelligent roadbed construction.

[0067] In order to solve the above problems, the present invention also provides a computer-readable storage medium, which stores at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned three-dimensional visualization operation control method for intelligent roadbed construction.

[0068] Compared with the existing technology, the present invention proposes a three-dimensional visualization operation control method for intelligent roadbed construction, which has the following advantages:

[0069] First, this scheme proposes a global position correction method, which uses a position matching algorithm to perform position correction on multi-source roadbed data. By taking the data acquisition channel as a factor, the position deviation between different factors is identified, and a multi-source factor error function is constructed. The multi-source factor error function is solved to obtain the correction result of global position matching. In the solution process, a quality weight representing the quality of the factor is introduced. The higher the quality weight, the higher the position accuracy of the collected data. Finally, high-precision, aligned multi-source fused roadbed data is generated. According to the multi-dimensional feature entropy of different roadbed areas, the roadbed areas with complex terrain are screened for multiple resampling to improve the accuracy of three-dimensional modeling of complex roadbed areas.

[0070] At the same time, this scheme proposes a three-dimensional modeling method, which calculates the filtering scale information of the multi-source fused roadbed data based on multi-source information. If the filtering scale information is higher than the preset filtering threshold, it means that the multi-source fused roadbed data is noise data. An octree is constructed to store the multi-source fused roadbed data in the filtered roadbed fusion data. The filtered roadbed fusion data is spatially hierarchical, and Poisson equations of different spatial levels are constructed. The Poisson equations are solved to obtain smooth implicit surface equations of different roadbed areas, extract isosurfaces, and generate closed triangular meshes. The curvature is used to identify key areas such as slopes and faults, where high-curvature triangular faces are key areas such as slopes and faults, and low-curvature triangular faces are flat areas. The curvature is normalized by introducing the local curvature mean to ensure that the curvature distribution is consistent on different triangular faces. The triangular face vertices in the low-curvature area are merged based on the error matrix to reduce the complexity of the triangular mesh, and the merging of triangular vertices in the high-curvature area is restricted to ensure the integrity of features such as slopes and faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic flow chart of a three-dimensional visualization operation control method for intelligent roadbed construction provided by one embodiment of the present invention;

[0072] Figure 2This is a functional module diagram of a three-dimensional visualization operation control system for intelligent roadbed construction provided by one embodiment of the present invention;

[0073] Figure 2 In: 100 roadbed intelligent construction 3D visualization operation control system, 101 data processing module, 102 3D visualization module, 103 data acquisition device;

[0074] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0075] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0076] The embodiments of the present application provide a method for controlling three-dimensional visualization operations for intelligent roadbed construction. The execution subject of the method includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiments of the present application. In other words, the method for controlling three-dimensional visualization operations for intelligent roadbed construction can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0077] Reference Figure 1 , embodiment 1 of the present invention is:

[0078] S1: Collect multi-source roadbed data, use the improved SLAM algorithm to perform position correction on the multi-source roadbed data, and obtain the multi-source roadbed data after position correction.

[0079] emitting a laser radar signal to the roadbed area using a laser radar, constructing three-dimensional point cloud data of the roadbed area based on the laser radar echo signal, collecting radar echo data of the roadbed area using ground-based equipment, transmitting coherent radar signals to the roadbed area, extracting phase information and coherence from the echo signals to form the radar echo data, acquiring satellite positioning data of the roadbed area using a global navigation satellite system, and forming multi-source roadbed data from the three-dimensional point cloud data of the roadbed area, the radar echo data, and the satellite positioning data;

[0080] The three-dimensional point cloud data includes three-dimensional position coordinates and color vectors;

[0081] The radar echo data includes three-dimensional position coordinates, signal amplitude, phase information, interference phase and coherence;

[0082] The satellite positioning data includes three-dimensional position coordinates and movement speed;

[0083] The representation of the multi-source roadbed data is:

[0084] ;

[0085] ;

[0086] in:

[0087] G represents multi-source roadbed data, Represents a 3D point cloud data set, represents the radar echo data set, represents the satellite positioning data set, and N represents the number of data collections;

[0088] Represents a 3D point cloud data set The nth group of 3D point cloud data in Represents a radar echo data set The nth group of radar echo data in Represents a collection of satellite positioning data The nth group of satellite positioning data in ;

[0089] As an embodiment of the present invention, the ground-based equipment is a ground-based interferometric synthetic aperture radar (GB-InSAR), which uses coherent radar signals to perform high-precision measurements of surface deformation, foundation settlement, landslide monitoring, etc.

[0090] Specifically, the ground-based InSAR transmits two radar signals continuously to the same three-dimensional position coordinates in the roadbed area as coherent radar signals, extracts the phase information difference and phase information similarity of the two radar signals as the interference phase and coherence corresponding to the three-dimensional position coordinates, and extracts the signal negative value and phase information of the second radar signal to form radar echo data;

[0091] An improved SLAM algorithm is used to construct a multi-source factor error function for the multi-source roadbed data. The multi-source factor error function uses a global position correction parameter as a variable and a function value of the degree of matching of the three-dimensional position coordinates of the multi-source roadbed data after global position correction. The improved SLAM algorithm is used to perform position matching on the multi-source roadbed data, and a factor graph is constructed using the three-dimensional position coordinates of the three-dimensional point cloud data, radar echo data, and satellite positioning data as nodes and the multi-source factor error functions between the nodes as edges.

[0092] The expression of the multi-source factor error function is:

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] ;

[0098] ;

[0099] in:

[0100] Indicates the The quality weight of the error factor, represents the global position correction parameter to be solved, It represents the position correction parameters of 3D point cloud data, radar echo data and satellite positioning data in turn. Indicates the The first to third factor errors are the factor errors of three-dimensional point cloud data, radar echo data and satellite positioning data, respectively. The first to third factor error functions are the factor error functions of three-dimensional point cloud data, radar echo data and satellite positioning data, respectively. express The standard deviation of the N three-dimensional position coordinates in ;

[0101] represents the L1 norm, Represents 3D point cloud data , radar echo data and satellite positioning data The three-dimensional position coordinates in ;

[0102] Representing a collection Zhongyu The nearest three-dimensional position coordinates;

[0103] Representing a collection Zhongyu The nearest three-dimensional position coordinates;

[0104] Representing a collection Zhongyu The nearest 3D position coordinates.

[0105] With the goal of minimizing the multi-source factor error function, the multi-source factor error function is solved to obtain global position correction parameters, including:

[0106] Initializing and generating global position correction parameters, and using the generated global position correction parameters as input values of the multi-source factor error function;

[0107] Calculate the first-order gradient of the multi-source factor error function as the Jacobian matrix of the generated global position correction parameter, and calculate the second-order inverse of the Jacobian matrix as the Hessian matrix of the generated global position correction parameter;

[0108] Iterating the generated global position correction parameters based on the Jacobian matrix and the Hessian matrix, and re-iterating the Jacobian matrix and the Hessian matrix of the global position correction parameters;

[0109] As an embodiment of the present invention, the iterative formula of the global position correction parameter is:

[0110] ;

[0111] in:

[0112] represents the tth iteration result of the global position correction parameter, express The Jacobian matrix of express The Hessian matrix of Indicates the preset maximum correction parameter iteration result;

[0113] After each iteration, the rate of change of the global position correction parameter is calculated. If the rate of change is less than a preset threshold, the iteration is terminated and the current iteration result is used as the global position correction parameter obtained by the solution. Specifically, the global position correction parameter The rate of change is: ;

[0114] The three-dimensional position coordinates in the multi-source roadbed data are corrected using the global position correction parameters to obtain multi-source roadbed data after position correction.

[0115] As a preferred embodiment of the present invention, the three-dimensional position coordinates The correction formula is:

[0116] ;

[0117] ;

[0118] ;

[0119] in:

[0120] These are the position correction parameters of the solved three-dimensional point cloud data, radar echo data, and satellite positioning data;

[0121] The three-dimensional position coordinates are The correction result of .

[0122] S2: Perform multi-source fusion on the multi-source roadbed data after position correction to obtain roadbed fusion data.

[0123] Performing multi-source fusion on the multi-source roadbed data after position correction, including:

[0124] Calculating the three-dimensional position coordinate distances among the three-dimensional point cloud data, radar echo data, and satellite positioning data in the multi-source roadbed data after position correction, and fusing the data from different sources whose three-dimensional position coordinate distances are less than a preset distance threshold to obtain the multi-source fused roadbed data at the same three-dimensional position coordinates;

[0125] Based on the multi-source fused roadbed data, the multi-dimensional feature entropy of different roadbed areas is calculated. The larger the multi-dimensional feature entropy, the more complex the surface terrain. Multiple multi-source roadbed data collection is performed in the roadbed area where the multi-dimensional feature entropy is higher than the preset entropy threshold. Based on the global position correction parameters, the three-dimensional position coordinates in the newly collected multi-source roadbed data are corrected and integrated into the multi-source fused roadbed data to form the roadbed fusion data.

[0126] The calculation process of the multidimensional feature entropy is as follows:

[0127] Obtain the multi-source fusion roadbed data set of different roadbed areas and calculate the multi-dimensional feature entropy of the roadbed area, where the multi-source fusion roadbed data set of the r-th roadbed area is , , R represents the total number of roadbed areas, Indicates the e-th group of multi-source fused roadbed data whose three-dimensional position coordinates are located in the r-th roadbed area, Indicates the number of multi-source fused roadbed data whose three-dimensional position coordinates are located in the r-th roadbed area;

[0128] ;

[0129] in:

[0130] In turn, it represents multi-source fusion roadbed data The three-dimensional position coordinates, color vector, signal amplitude, phase information, interference phase, coherence and motion speed;

[0131] The multidimensional characteristic entropy of the r-th roadbed area is:

[0132] ;

[0133] ;

[0134] ;

[0135] ;

[0136] in:

[0137] represents the multidimensional feature entropy of the r-th roadbed area, represents the point cloud entropy of the r-th roadbed area, represents the echo entropy of the r-th roadbed area, represents the velocity entropy of the r-th roadbed area, Indicates the preset point cloud entropy extreme value, echo entropy extreme value and velocity entropy extreme value in turn;

[0138] represents the coherence mean in the r-th roadbed area, represents the mean speed in the r-th roadbed area, represents the standard deviation of speed in the rth roadbed area;

[0139] Specifically, the roadbed fusion data is represented as follows:

[0140] ;

[0141] in:

[0142] represents the roadbed fusion data, represents the mth group of multi-source fused roadbed data, M represents the number of groups of multi-source fused roadbed data in the roadbed fusion data g, In turn, The three-dimensional position coordinates, color vector, signal amplitude, phase information, interference phase, coherence and motion speed in is the fusion result of the mth group of data from different sources whose three-dimensional position coordinate distance is less than the preset distance threshold. The 3D position coordinates in are the average of the fused 3D point cloud data, radar echo data, and satellite positioning data.

[0143] S3: Construct a 3D model of roadbed construction based on roadbed fusion data.

[0144] The three-dimensional roadbed construction model includes a filtering module and a three-dimensional surface reconstruction module. The filtering module is used to extract multi-source information of each group of multi-source fused roadbed data in the roadbed fusion data, perform multi-scale filtering on the multi-source fused roadbed data to obtain filtered roadbed fusion data, and the three-dimensional surface reconstruction module is used to extract three-dimensional position coordinates and multi-source information from the filtered roadbed fusion data, perform three-dimensional network reconstruction using a Poisson reconstruction method, and obtain the three-dimensional roadbed construction model.

[0145] The multi-source information in the multi-source fusion roadbed data is the signal amplitude, phase information, interference phase, coherence and motion speed in the multi-source fusion roadbed data;

[0146] The multi-scale filtering processing method is as follows: based on multi-source information, filter scale information of multi-source fused roadbed data is calculated. If the filter scale information is higher than a preset filter threshold, it indicates that the multi-source fused roadbed data is noise data; the noise data in the roadbed fusion data is filtered and cleaned to obtain filtered roadbed fusion data; specifically, the multi-source fused roadbed data The filtering scale information is :

[0147] ;

[0148] in:

[0149] The preset signal amplitude extreme value, phase information extreme value, interference phase extreme value, coherence extreme value and motion speed extreme value;

[0150] The Poisson reconstruction method is: using principal component analysis to calculate the normal vector of each group of multi-source fused roadbed data in the filtered roadbed fusion data, wherein the higher the coherence, the higher the weight of the multi-source fused roadbed data in the normal vector calculation process;

[0151] Constructing an octree to store the multi-source fused roadbed data in the filtered roadbed fusion data, and performing spatial hierarchical division on the filtered roadbed fusion data; specifically, this solution uses adaptive resolution for spatial hierarchical division, using a finer resolution for a multi-source fused roadbed data set with a strong signal amplitude and high coherence, and using a lower resolution for other multi-source fused roadbed data;

[0152] Poisson equations are constructed at different spatial levels and solved to obtain smooth implicit surface equations for different roadbed areas. Isosurfaces are extracted to generate a closed triangular mesh composed of triangular facets. Small, isolated triangular facets are identified and deleted to obtain a three-dimensional model of the roadbed. As one embodiment of the present invention, more details are retained in high-coherence spatial levels to obtain a smooth implicit surface equation with a higher degree of complexity. In low-coherence spatial levels, the smooth implicit surface equation is appropriately smoothed to reduce the impact of noise on the three-dimensional modeling process.

[0153] S4: The quadratic error measure grid simplification algorithm is used to lightweight the roadbed construction three-dimensional model to obtain a lightweight roadbed construction three-dimensional model.

[0154] The three-dimensional model of the roadbed construction is lightweighted using a quadratic error measure mesh simplification algorithm, including:

[0155] Calculating the normal vector of each triangular face in the three-dimensional model of the roadbed construction, and calculating the sum of the differences between the normal vector of the triangular face and the adjacent normal vectors as the curvature of the triangular face, where the adjacent normal vectors of the triangular face are the normal vectors of the triangular faces connected to the triangular face;

[0156] The error weight of the triangle is calculated based on the curvature, and the plane weighted error matrix of the triangle is calculated based on the error weight and the normal vector. The plane weighted error matrix is used to measure the distance from the triangle vertex to the plane. The calculation formula of the error weight is:

[0157] ;

[0158] in:

[0159] represents the error weight corresponding to the curvature C, Indicates the preset error weight threshold;

[0160] It represents the mean curvature of the adjacent triangles corresponding to the curvature C, and F represents the normal vector of the triangle corresponding to the curvature C. represents the gravity vector, ;

[0161] The accumulated weighted error matrices of all adjacent planes of the triangle vertex are calculated as the error matrix of the triangle vertex;

[0162] Select triangle vertices and shared edge vertices in descending order of the error matrix L2 norm to form triangle vertex pairs. Calculate the new triangle vertex with the minimum error matrix after merging the triangle vertex pairs. Delete the merged triangle vertex and update the adjacent triangle to point to the new triangle vertex.

[0163] Vertices are repeatedly merged, the error matrix is updated, and the number of triangles is gradually reduced until the set total number of triangles is reached. The merged 3D model is then smoothed using Laplacian and used as the 3D model for lightweight roadbed construction. As a preferred embodiment of the present invention, curvature is used to identify key areas such as slopes and faults. High-curvature triangles are identified as these key areas, while low-curvature triangles are flat areas. The curvature is normalized by introducing a local mean curvature to ensure a consistent curvature distribution across different triangles. The vertices of triangles in low-curvature areas are merged based on the error matrix to reduce the complexity of the triangular mesh. The merging of triangle vertices in high-curvature areas is limited to ensure the integrity of features such as slopes and faults.

[0164] S5: Dynamically render the 3D model of lightweight roadbed construction and visualize the roadbed construction process.

[0165] The color vectors in the roadbed fusion data are extracted and mapped to the corresponding positions of the lightweight roadbed construction three-dimensional model to obtain the color-rendered lightweight roadbed construction three-dimensional model. In addition, multi-source roadbed data are collected in real time during the roadbed construction operation to dynamically update the lightweight roadbed construction three-dimensional model.

[0166] Example 2:

[0167] like Figure 2 , which is a functional module diagram of a three-dimensional visualization operation control system 100 for intelligent roadbed construction provided by an embodiment of the present invention, which can implement the three-dimensional visualization operation control method for intelligent roadbed construction in Example 1.

[0168] Based on the functions to be implemented, the intelligent roadbed construction 3D visualization operation control system 100 may include a data processing module 101, a 3D visualization module 102, and a data acquisition device 103. The modules described in the present invention, also known as units, are a series of computer program segments that can be executed by an electronic device processor and perform a fixed function.

[0169] The data processing module 101 is used to perform position correction on the multi-source roadbed data using the improved SLAM algorithm, and perform multi-source fusion on the multi-source roadbed data after position correction to obtain roadbed fusion data;

[0170] The three-dimensional visualization module 102 is used to construct a three-dimensional model of the roadbed construction based on the roadbed fusion data, use a quadratic error measure grid simplification algorithm to perform lightweight processing on the three-dimensional model of the roadbed construction to obtain a lightweight three-dimensional model of the roadbed construction, perform dynamic operation rendering on the lightweight three-dimensional model of the roadbed construction, and perform visual operation rendering on the roadbed construction process;

[0171] The data acquisition device 103 is used to collect multi-source roadbed data.

[0172] In detail, the modules in the roadbed intelligent construction three-dimensional visualization operation control system 100 in the embodiment of the present invention are used in the same manner as above. Figure 1 The technical means are the same as the three-dimensional visualization operation control method for intelligent roadbed construction described in , and can produce the same technical effects, so they will not be repeated here.

[0173] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0174] It should be noted that the serial numbers of the above-mentioned embodiments of the present invention are for descriptive purposes only and do not represent the advantages or disadvantages of the embodiments. In addition, the terms "including", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "including a ..." does not exclude the presence of other identical elements in the process, device, article or method comprising the element.

[0175] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0176] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A three-dimensional visualization operation control method for intelligent roadbed construction, characterized in that: The method comprises: S1: Collect multi-source roadbed data, use the improved SLAM algorithm to perform position correction on the multi-source roadbed data, and obtain the multi-source roadbed data after position correction; The multi-source roadbed data includes three-dimensional point cloud data of the roadbed area, radar echo data and satellite positioning data; S2: Perform multi-source fusion on the multi-source roadbed data after position correction to obtain roadbed fusion data; S3: Construct a 3D model of roadbed construction based on roadbed fusion data; The three-dimensional roadbed construction model includes a filtering module and a three-dimensional surface reconstruction module. The filtering module is used to extract multi-source information of each group of multi-source fused roadbed data in the roadbed fusion data, perform multi-scale filtering on the multi-source fused roadbed data to obtain filtered roadbed fusion data, and the three-dimensional surface reconstruction module is used to extract three-dimensional position coordinates and multi-source information from the filtered roadbed fusion data, perform three-dimensional network reconstruction using a Poisson reconstruction method, and obtain the three-dimensional roadbed construction model. The multi-source information in the multi-source fusion roadbed data is the signal amplitude, phase information, interference phase, coherence and motion speed in the multi-source fusion roadbed data; The multi-scale filtering processing method is as follows: based on multi-source information, filter scale information of the multi-source fused roadbed data is calculated; if the filter scale information is higher than a preset filter threshold, it indicates that the multi-source fused roadbed data is noise data; the noise data in the roadbed fused data is filtered and cleaned to obtain filtered roadbed fused data; The Poisson reconstruction method is: using principal component analysis to calculate the normal vector of each group of multi-source fused roadbed data in the filtered roadbed fusion data, wherein the higher the coherence, the higher the weight of the multi-source fused roadbed data in the normal vector calculation process; Constructing an octree to store the multi-source fused roadbed data in the filtered roadbed fusion data, and performing spatial hierarchical division on the filtered roadbed fusion data; Construct Poisson equations at different spatial levels, solve the Poisson equations, obtain smooth implicit surface equations for different roadbed areas, extract isosurfaces, generate closed triangular meshes composed of triangular faces, identify and delete small isolated triangular faces, and obtain a three-dimensional model of the roadbed construction; S4: Use the quadratic error measure grid simplification algorithm to lightweight the roadbed construction 3D model to obtain a lightweight roadbed construction 3D model; The three-dimensional model of the roadbed construction is lightweighted using a quadratic error measure mesh simplification algorithm, including: Calculating the normal vector of each triangular face in the three-dimensional model of the roadbed construction, and calculating the sum of the differences between the normal vector of the triangular face and the adjacent normal vectors as the curvature of the triangular face; The error weight of the triangle is calculated based on the curvature, and the plane weighted error matrix of the triangle is calculated based on the error weight and the normal vector. The plane weighted error matrix is used to measure the distance from the triangle vertex to the plane. The calculation formula of the error weight is: ； in: represents the error weight corresponding to the curvature C, Indicates the preset error weight threshold; It represents the mean curvature of the adjacent triangles corresponding to the curvature C, and F represents the normal vector of the triangle corresponding to the curvature C. represents the gravity vector, ; The accumulated weighted error matrices of all adjacent planes of the triangle vertex are calculated as the error matrix of the triangle vertex; Select triangle vertices and shared edge vertices in descending order of the error matrix L2 norm to form triangle vertex pairs. Calculate the new triangle vertex with the minimum error matrix after merging the triangle vertex pairs. Delete the merged triangle vertex and update the adjacent triangle to point to the new triangle vertex. Repeatedly merge vertices, update the error matrix, and gradually reduce the number of triangles until the set total number of triangles is reached. Use Laplacian smoothing to process the merged 3D model and use it as the 3D model for lightweight roadbed construction. S5: Dynamically render the 3D model of lightweight roadbed construction and visualize the roadbed construction process.

2. A three-dimensional visualization operation control method for intelligent roadbed construction according to claim 1, characterized in that: The three-dimensional point cloud data includes three-dimensional position coordinates and color vectors; The radar echo data includes three-dimensional position coordinates, signal amplitude, phase information, interference phase and coherence; The satellite positioning data includes three-dimensional position coordinates and movement speed; The representation of the multi-source roadbed data is: ； ； in: G represents multi-source roadbed data, Represents a 3D point cloud data set, represents the radar echo data set, represents the satellite positioning data set, and N represents the number of data collections; Represents a 3D point cloud data set The nth group of 3D point cloud data in Represents a radar echo data set The nth group of radar echo data in Represents a collection of satellite positioning data The nth group of satellite positioning data in ; An improved SLAM algorithm is used to construct a multi-source factor error function of the multi-source roadbed data. The multi-source factor error function takes a global position correction parameter as a variable and takes the degree of matching of the three-dimensional position coordinates of the multi-source roadbed data after global position correction as a function value.

3. A three-dimensional visualization control method for intelligent roadbed construction according to claim 2, characterized in that: The expression of the multi-source factor error function is: ； ； ； ； ； ； in: Indicates the The quality weight of the error factor, represents the global position correction parameter to be solved, It represents the position correction parameters of 3D point cloud data, radar echo data and satellite positioning data in turn. Indicates the The first to third factor errors are the factor errors of three-dimensional point cloud data, radar echo data and satellite positioning data, respectively. The first to third factor error functions are the factor error functions of three-dimensional point cloud data, radar echo data and satellite positioning data, respectively. express The standard deviation of the N three-dimensional position coordinates in ; represents the L1 norm, Represents 3D point cloud data , radar echo data and satellite positioning data The three-dimensional position coordinates in ; Representing a collection Zhongyu The nearest three-dimensional position coordinates; Representing a collection Zhongyu The nearest three-dimensional position coordinates; Representing a collection Zhongyu The nearest 3D position coordinates.

4. A three-dimensional visualization control method for intelligent roadbed construction according to claim 3, characterized in that: With the goal of minimizing the multi-source factor error function, the multi-source factor error function is solved to obtain global position correction parameters, including: Initializing and generating global position correction parameters, and using the generated global position correction parameters as input values of the multi-source factor error function; Calculate the first-order gradient of the multi-source factor error function as the Jacobian matrix of the generated global position correction parameter, and calculate the second-order derivative of the Jacobian matrix as the Hessian matrix of the generated global position correction parameter; Iterating the generated global position correction parameters based on the Jacobian matrix and the Hessian matrix, and re-iterating the Jacobian matrix and the Hessian matrix of the global position correction parameters; After each iteration, the rate of change of the global position correction parameter is calculated. If the rate of change is less than a preset threshold, the iteration is terminated and the current iteration result is used as the solved global position correction parameter; the three-dimensional position coordinates in the multi-source roadbed data are corrected using the global position correction parameter to obtain the multi-source roadbed data after position correction.

5. A three-dimensional visualization operation control method for intelligent roadbed construction according to claim 4, characterized in that: Performing multi-source fusion on the multi-source roadbed data after position correction, including: Calculating the three-dimensional position coordinate distances among the three-dimensional point cloud data, radar echo data, and satellite positioning data in the multi-source roadbed data after position correction, and fusing the data from different sources whose three-dimensional position coordinate distances are less than a preset distance threshold to obtain the multi-source fused roadbed data at the same three-dimensional position coordinates; The multidimensional feature entropy of different roadbed areas is calculated based on multi-source fused roadbed data. The larger the multidimensional feature entropy, the more complex the surface terrain. Multiple multi-source roadbed data collections are performed in roadbed areas where the multidimensional feature entropy is higher than the preset entropy threshold. Based on the global position correction parameters, the three-dimensional position coordinates in the newly collected multi-source roadbed data are corrected and integrated into the multi-source fused roadbed data to form roadbed fusion data.

6. A three-dimensional visualization control method for intelligent roadbed construction according to claim 1, characterized in that: The color vectors in the roadbed fusion data are extracted and mapped to the corresponding positions of the lightweight roadbed construction three-dimensional model to obtain the color-rendered lightweight roadbed construction three-dimensional model. In addition, multi-source roadbed data are collected in real time during the roadbed construction operation to dynamically update the lightweight roadbed construction three-dimensional model.

7. A three-dimensional visualization operation control system for intelligent roadbed construction, characterized in that: The roadbed intelligent construction 3D visualization operation control system includes a server and a data acquisition device, and the server includes a data processing module and a 3D visualization module: The data processing module is used to perform position correction on the multi-source roadbed data using the improved SLAM algorithm, and perform multi-source fusion on the multi-source roadbed data after position correction to obtain roadbed fusion data; The three-dimensional visualization module is used to construct a three-dimensional model of the roadbed construction based on the roadbed fusion data, use a quadratic error measure grid simplification algorithm to perform lightweight processing on the three-dimensional model of the roadbed construction to obtain a lightweight three-dimensional model of the roadbed construction, perform dynamic operation rendering on the lightweight three-dimensional model of the roadbed construction, and perform visual operation rendering on the roadbed construction process; The data acquisition device is used to collect multi-source roadbed data; To realize a three-dimensional visualization operation control method for intelligent roadbed construction as described in any one of claims 1-6.

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