Modeling method, device and equipment of railway tunnel, storage medium and program product

Abstract

This application relates to a modeling method, apparatus, equipment, storage medium, and program product for railway tunnels, and pertains to the field of 3D modeling technology. The method includes: acquiring inertial navigation data collected by an inertial navigation trolley during its movement along a target railway tunnel, and processing this data using a preset correction algorithm to obtain corresponding target tunnel data; acquiring a unit tunnel 3D model constructed based on the design drawings of the target railway tunnel; calling a preset modeling script to determine the Z-axis deviation angle difference between the target tunnel data and the unit tunnel 3D model, and the Y-axis rotation angle corresponding to the roll data included in the target tunnel data; determining the 3D rotation angle of the tunnel model based on the Z-axis deviation angle difference and the Y-axis rotation angle, and constructing a complete 3D tunnel model of the target railway tunnel based on the 3D rotation angle of the tunnel model; this improves the efficiency and accuracy of the tunnel 3D model construction.

Description

Technical Field

[0001] This application relates to the field of 3D modeling technology, and in particular to a modeling method, apparatus, equipment, storage medium, and program product for railway tunnels. Background Technology

[0002] With the increasingly widespread application of digital twin technology in railway operation and maintenance, the demand for integrated "real-world-virtual" management of tunnels is also constantly increasing. Railway tunnels are usually constructed along the central axis using a combination of shotcrete and cast-in-place concrete or precast supports; when modeling, a three-dimensional model of the lining structure can be built at a length of 1 meter, and these models are easy to obtain and reuse; however, the actual central axis position of the tunnel is difficult to accurately grasp over a long period of time.

[0003] In related technologies, the coordinates of the tunnel centerline rely on construction drawings or meter-by-meter measurement with a total station. On the one hand, the design position in the construction drawings will deviate from the actual structure over time; on the other hand, meter-by-meter measurement with a total station is labor-intensive and time-consuming, making it difficult to meet the needs of rapid re-measurement and real-time monitoring. Summary of the Invention

[0004] Therefore, it is necessary to provide a railway tunnel modeling method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can improve the accuracy of railway tunnel modeling in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a modeling method for railway tunnels, including:

[0006] The inertial navigation data collected by the inertial navigation system on board the inertial navigation trolley during its journey along the target railway tunnel is obtained, and the inertial navigation data is processed based on a preset correction algorithm to obtain the corresponding target tunnel data.

[0007] Obtain a three-dimensional model of the unit tunnel constructed based on the design drawings of the target railway tunnel;

[0008] A preset modeling script is invoked to determine the Z-axis deviation angle difference between the target tunnel data and the unit tunnel 3D model, and the Y-axis rotation angle corresponding to the roll data included in the target tunnel data, based on the target tunnel data and the unit tunnel 3D model.

[0009] Based on the Z-axis deviation angle difference value and the Y-axis rotation angle, the three-dimensional rotation angle of the tunnel model is determined, and a complete three-dimensional tunnel model of the target railway tunnel is constructed based on the three-dimensional rotation angle of the tunnel model.

[0010] In one embodiment, determining the Z-axis deflection angle difference between the target tunnel data and the unit tunnel 3D model based on the target tunnel data and the unit tunnel 3D model includes:

[0011] The target tunnel data includes yaw angle information and the Z-axis rotation angle of the unit tunnel 3D model. Based on the yaw angle information and the Z-axis rotation angle, the Z-axis yaw angle difference value corresponding to the two is determined.

[0012] In one embodiment, the method further includes:

[0013] The design drawing data of the target railway tunnel obtained includes the tunnel design parameters of the target railway tunnel, such as the tunnel cross-sectional dimensions, lining thickness, and reinforcement spacing.

[0014] A three-dimensional model of a unit tunnel with a preset unit length is created based on the tunnel design parameters.

[0015] In one embodiment, the step of acquiring inertial navigation data collected by the onboard inertial navigation system during the trolley's journey along the target railway tunnel, and processing the inertial navigation data based on a preset correction algorithm to obtain the corresponding target tunnel data, includes:

[0016] Acquire inertial navigation data, including at least attitude data and odometer increment data, based on the inertial navigation system on board, during the inertial navigation vehicle's journey along the target railway tunnel;

[0017] The inertial navigation data is processed based on a preset correction algorithm to obtain target tunnel data, including the three-dimensional coordinates of the centerline of the target railway tunnel and the corresponding roll data and yaw angle information.

[0018] In one embodiment, the step of processing the inertial navigation data based on a preset correction algorithm to obtain the corresponding target tunnel data includes:

[0019] Based on a preset correction algorithm, the inertial navigation data is processed using a preset sampling interval as a correction window to obtain the corresponding target tunnel data; the overlap rate of adjacent correction windows is b, 30%≤b≤60%; the preset sampling interval is determined based on the length of the unit tunnel three-dimensional model in the tunnel extension direction.

[0020] In one embodiment, after processing the inertial navigation data based on a preset correction algorithm to obtain the corresponding target tunnel data, the method further includes:

[0021] Obtain the correction residuals of the inertial navigation data and the target tunnel data associated with the correction window;

[0022] If the correction residual exceeds a preset residual threshold, the correction window is expanded, and the inertial navigation data is processed again based on the preset correction algorithm to obtain the corresponding target tunnel data.

[0023] Secondly, this application also provides a modeling apparatus for railway tunnels, the apparatus comprising:

[0024] The data acquisition module is used to acquire inertial navigation data collected by the onboard inertial navigation system during the inertial navigation trolley's journey along the target railway tunnel, and to process the inertial navigation data based on a preset correction algorithm to obtain the corresponding target tunnel data.

[0025] The data acquisition module is also used to acquire a three-dimensional model of the unit tunnel constructed based on the design drawing data of the target railway tunnel;

[0026] The data processing module is used to call a preset modeling script to determine the Z-axis deviation angle difference between the target tunnel data and the unit tunnel 3D model, and the Y-axis rotation angle corresponding to the roll data included in the target tunnel data, based on the target tunnel data and the unit tunnel 3D model.

[0027] The tunnel modeling module is used to determine the three-dimensional rotation angle of the tunnel model based on the Z-axis deflection angle difference value and the Y-axis rotation angle, and to construct a complete three-dimensional tunnel model of the target railway tunnel based on the three-dimensional rotation angle of the tunnel model.

[0028] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the first aspect.

[0029] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the first aspect.

[0030] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in the first aspect.

[0031] The railway tunnel modeling method, apparatus, computer equipment, computer-readable storage medium, and computer program product provided in this application involve obtaining inertial navigation data collected by an inertial navigation trolley during its journey along a target railway tunnel, based on the inertial navigation system it carries, and processing the inertial navigation data using a preset correction algorithm to obtain corresponding target tunnel data; obtaining a unit tunnel 3D model constructed based on the design drawings of the target railway tunnel; calling a preset modeling script to determine the Z-axis deviation angle difference between the target tunnel data and the unit tunnel 3D model, as well as the Y-axis rotation angle corresponding to the roll data included in the target tunnel data; determining the 3D rotation angle of the tunnel model based on the Z-axis deviation angle difference and the Y-axis rotation angle, and constructing a complete 3D model of the target railway tunnel based on the 3D rotation angle of the tunnel model; this method can improve the accuracy of the constructed complete 3D tunnel model and is beneficial to improving the efficiency of constructing a complete 3D tunnel model. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart illustrating a modeling method for a railway tunnel in one embodiment;

[0034] Figure 2 This is a flowchart illustrating a method for modeling railway tunnels in another embodiment;

[0035] Figure 3 This is a schematic diagram of a one-meter tunnel structure model in one embodiment;

[0036] Figure 4 This is a schematic diagram of a one-meter tunnel structure model in another embodiment;

[0037] Figure 5 This is a schematic diagram of a top view of a tunnel modeled along its central axis in one embodiment;

[0038] Figure 6 This is a schematic diagram of the front view of a tunnel modeled along its central axis in one embodiment;

[0039] Figure 7 This is a schematic diagram of the relevant data for angular velocity in one embodiment;

[0040] Figure 8This is a schematic diagram of acceleration data in one embodiment;

[0041] Figure 9 This is a rendering of an image pasted inside a tunnel in Unreal Engine, as shown in one embodiment.

[0042] Figure 10 This is a schematic diagram of a complete tunnel in one embodiment;

[0043] Figure 11 This is a structural block diagram of a modeling device for a railway tunnel in one embodiment;

[0044] Figure 12 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0046] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0047] To address the technical problems mentioned in the background section regarding railway tunnel modeling, this application provides a railway tunnel modeling method. This method utilizes an Inertial Navigation System (INS) traveling along the track to collect high-frequency acceleration, angular velocity, and odometer data. By integrating Kalman filtering techniques from gyroscopes, accelerometers, and odometers to constrain roll and pitch angles, the method significantly improves attitude calculation accuracy, enabling automated recording of the spatial position and deflection angle of the target railway tunnel at 1-meter intervals. After acquiring high-precision centerline and attitude information, a standard lining structure 3D model (unit tunnel 3D model) is pre-constructed in 3ds Max (Three-Dimensional Studio Max, a 3D modeling and animation software). The coordinates and orientation provided by the INS are mapped onto the model using MaxScript scripts (pre-set modeling scripts, which can be a dedicated scripting language built into 3ds Max), completing automated and continuous modeling of the tunnel structure. This provides a precise and reliable spatial reference for subsequent safety assessments, maintenance decisions, and emergency responses for the target railway tunnel.

[0048] In one exemplary embodiment, such as Figure 1 As shown, this application provides a method for modeling railway tunnels. Taking the application of this method to a terminal as an example, the terminal can be, but is not limited to, various personal computers, laptops, smartphones, and tablets. The method for modeling railway tunnels includes the following steps 101-104. Wherein:

[0049] Step 101: Acquire inertial navigation data collected by the inertial navigation system on board during the inertial navigation trolley's journey along the target railway tunnel, and process the inertial navigation data based on a preset correction algorithm to obtain the corresponding target tunnel data.

[0050] The target railway tunnel is the railway tunnel that needs to be modeled in three dimensions.

[0051] The inertial navigation data can be relevant parameter data generated during the parameter detection of the target railway tunnel using a tunnel data measurement instrument. The tunnel data measurement instrument can be, for example, an inertial navigation vehicle (INS), which is a small mobile platform equipped with an inertial navigation system (INS). It can calculate its own position, velocity, and attitude (i.e., orientation in three-dimensional space) in real time without relying on external signals (such as GPS, Wi-Fi, visual markers, etc.), but rather through internal inertial sensors (mainly accelerometers and gyroscopes). For example, the inertial navigation data is collected by the INS based on its inertial navigation system during its movement along the extension direction of the target railway tunnel.

[0052] The preset correction algorithm can be a pre-set data processing method, such as a position and attitude data correction and calculation algorithm, used to convert inertial navigation data into target tunnel data.

[0053] For example, when the terminal obtains inertial navigation data obtained by parameter detection of the target railway tunnel through the inertial navigation trolley, it can call a pre-set preset correction algorithm to convert the inertial navigation data into target tunnel data, so as to process the inertial navigation data to obtain the corresponding target tunnel data.

[0054] One exemplary embodiment is that the inertial navigation trolley can be selectively controlled to collect data about the target railway tunnel every meter.

[0055] Step 102: Obtain a 3D model of the unit tunnel constructed based on the design drawing data of the target railway tunnel.

[0056] This includes the design drawings of the target railway tunnel, which are the relevant construction data of the target railway tunnel used by the design and construction parties during its construction. The design drawings may include detailed parameters such as tunnel cross-sectional dimensions, lining thickness, and reinforcement spacing.

[0057] Among them, the unit tunnel 3D model refers to the tunnel 3D model of a unit length along the extension direction of the tunnel, such as a basic tunnel unit with a length of 1 meter (a tunnel 3D model with a length of 1 meter).

[0058] For example, in order to model a complete 3D model of the target railway tunnel, the terminal can further acquire design drawing data of the target railway tunnel to construct a 3D model, thus obtaining a constructed unit tunnel 3D model.

[0059] Step 103: Call the preset modeling script to determine the Z-axis deviation angle difference between the target tunnel data and the unit tunnel 3D model, and the Y-axis rotation angle corresponding to the roll data included in the target tunnel data, based on the target tunnel data and the unit tunnel 3D model.

[0060] The preset modeling script can be pre-set to instruct the terminal on how to construct a complete 3D model of the target railway tunnel based on the target tunnel data and the unit tunnel 3D model, so as to realize the automated construction of the complete 3D model of the tunnel.

[0061] The “Z-axis” and “Y-axis” correspond to the “Z-axis” and “Y-axis” in the three-dimensional coordinate system (XYZ coordinate system).

[0062] For example, after obtaining the target tunnel data and the unit tunnel 3D model, the terminal can further call a pre-set preset modeling script to process the target tunnel data and the unit tunnel 3D model. Based on the target tunnel data and the unit tunnel 3D model, the terminal first converts the Z-axis deviation angle difference value corresponding to the target tunnel data and the unit tunnel 3D model; at the same time, the roll data in the target tunnel data is converted into the corresponding Y-axis rotation angle.

[0063] Step 104: Based on the Z-axis deflection angle difference value and the Y-axis rotation angle, determine the three-dimensional rotation angle of the tunnel model, and construct a complete three-dimensional tunnel model of the target railway tunnel based on the three-dimensional rotation angle of the tunnel model.

[0064] The “Z-axis” and “Y-axis” correspond to the “Z-axis” and “Y-axis” in the three-dimensional coordinate system (XYZ coordinate system).

[0065] For example, based on the instructions of the preset modeling script, the terminal obtains the Z-axis deviation angle difference value and Y-axis rotation angle based on the target tunnel data and the unit tunnel 3D model. Then, it further determines the 3D rotation angle of the tunnel model that is suitable for the target railway tunnel by using the Z-axis deviation angle difference value and the Y-axis rotation angle. Then, it further combines the 3D rotation angle of the tunnel model with the unit tunnel 3D model to realize the construction of a complete 3D tunnel model for the target railway tunnel.

[0066] The railway tunnel modeling method provided in this application involves acquiring inertial navigation data collected by the onboard inertial navigation system during the movement of an inertial navigation trolley along the target railway tunnel, and processing the inertial navigation data using a preset correction algorithm to obtain the corresponding target tunnel data; acquiring a unit tunnel 3D model constructed based on the design drawings of the target railway tunnel; calling a preset modeling script to determine the Z-axis deviation angle difference between the target tunnel data and the unit tunnel 3D model, as well as the Y-axis rotation angle corresponding to the roll data included in the target tunnel data; determining the 3D rotation angle of the tunnel model based on the Z-axis deviation angle difference and the Y-axis rotation angle, and constructing a complete 3D model of the target railway tunnel based on the 3D rotation angle of the tunnel model; this method can improve the accuracy of the constructed complete 3D tunnel model and is beneficial to improving the efficiency of constructing a complete 3D tunnel model.

[0067] The railway tunnel modeling method provided in steps 101-104 of this application is intended to accelerate the construction of a digital twin scenario for railway tunnels. It is equivalent to proposing a three-dimensional automatic modeling method for railway tunnels that integrates inertial navigation positioning (inertial navigation trolley), which uses high-precision inertial navigation data as a basis and automatically generates a three-dimensional tunnel model using three-dimensional graphics software scripts.

[0068] Can be combined Figure 1 Reference Figure 2 The railway tunnel modeling method provided in this application aims to collect data every meter using an inertial navigation trolley, then perform position and attitude data correction calculations, and simultaneously generate a 3ds Max one-meter tunnel structure model. After obtaining the relevant calculation results and the one-meter tunnel structure model, the method automatically models the position and attitude data corrected from the basic model using Max Script. Then, by automatically pasting images of the tunnel inner wall, the complete tunnel model is exported, thereby realizing the construction of a complete 3D tunnel model of the target railway tunnel.

[0069] In other words, the specific content of this application involves position and attitude data correction and calculation, generation of a 3d Max one-meter tunnel structure model, and automated modeling of position and attitude data corrected and calculated by Max Script based on the basic model, as described in detail below.

[0070] Among them, the position and attitude data correction calculation:

[0071] This application allows the control of the inertial navigation vehicle to obtain a set of attitudes (roll) based on 1-meter sampling, i.e., in the kilometer direction. , looking up ,yaw The incremental data from the gyroscope and accelerometer are combined using Kalman filtering to obtain... and High-precision initial values ​​are obtained, and drift of both axes is suppressed; yaw There is a cumulative error. The distance-driven yaw per meter model is as follows:

[0072] , ;

[0073] in For the first Yaw measurement value in meters per hour. For the initial yaw, for Total offset at meters. For the first During the journey, due to instantaneous errors caused by instruments or external factors, it is necessary to introduce a random yaw error component. Given an offset rate per meter, using a known constant sampling interval of 1 meter, the offset rate is... This indicates "accumulated error per meter," which better reflects the error characteristics of odometers.

[0074] If you use the roll angle directly Pitch angle Yaw angle Assembling rotations can sometimes lead to gimbal lock, which is inconvenient for smooth interpolation during filtering or optimization. To avoid gimbal lock issues in Euler angle representations during large attitude changes or near ±90° pitch angles, this application chooses to use quaternions for a unified attitude representation. Let the first... The roll angle, pitch angle, and yaw angle at the meter are respectively , , Using the ZYX rotation sequence (yaw first, then pitch, then roll), Euler angles are converted into quaternions. The standard formula derived is as follows:

[0075] ;

[0076] ;

[0077] ;

[0078] ;

[0079] Quaternions Construct rotation matrix Its expression is:

[0080] ;

[0081] By rotation matrix For each pair of tunnel control points The formula for the rotation matrix is: ;in, Indicates the first The three-dimensional coordinate vector of the tunnel control point at a distance of meters in the scanner's local coordinate system. For the first quaternion at the meter position The generated rotation matrix is ​​used to describe the orientation relationship between the scanner coordinate system and the engineering coordinate system. This rotation matrix... By rotating the local coordinate point, the measured coordinates of the control point after attitude correction are obtained. This enables consistent representation of control point coordinates across different attitude sampling positions.

[0082] Inertial navigation and odometry systems introduce errors during integration, such as proportional drift, unknown initial coordinates, and differing orientation references. If the raw integration result is used directly, the scanner center trajectory will deviate from the actual control point distribution, leading to inaccurate subsequent tunnel geometry reconstruction. Therefore, an affine transformation is necessary. ;in, and The expanded form is:

[0083] ;

[0084] ;

[0085] in, This indicates that after affine transformation, the first... The estimated true position of the center of the time scanner in a unified engineering coordinate system / control coordinate system. Used to correct the overall "scale error". Used to correct deviations from the overall orientation or attitude reference. Indicates the first The spatial coordinates of the scanner center, obtained by integrating the inertial navigation system (IMU) and odometry at any given moment, It is used to correct translation deviations of the starting point or global far point.

[0086] To ensure the continuity and local accuracy of the affine correction parameters across the entire tunnel length, a method combining a "meter-level sliding window" with robust estimation is used to adjust the scale. Global rotation matrix With translation vector The specific steps for piecewise solution and smoothing are as follows:

[0087] (1) Sliding window division:

[0088] According to fixed length Project the continuous integration position with the corresponding control point. Divided into several overlapping windows: each window contains Group sampling data; 50% overlap between adjacent windows;

[0089] Both the window length and overlap ratio are measured in meters, which is highly consistent with the inertial navigation system's characteristic of "sampling once per meter";

[0090] For example, based on this, it can be understood that in the railway tunnel modeling method provided in this application, the step of processing inertial navigation data based on the preset correction algorithm described above to obtain the corresponding target tunnel data can be optionally executed as follows: based on the preset correction algorithm, using preset sampling interval data as the correction window, processing inertial navigation data to obtain the corresponding target tunnel data; the overlap rate of adjacent correction windows is b, 30%≤b≤60%, and b can be selected to be 50%; the preset sampling interval data is determined based on the length of the unit tunnel three-dimensional model in the tunnel extension direction, and the preset sampling interval data is, for example, 1 meter;

[0091] (2) Outlier removal (RANSAC, RANdom Sampling Consensus):

[0092] All within each sliding window Yes, firstly, the RANSAC algorithm is used for random sampling consistency detection to eliminate outlier matches caused by measurement noise or control point positioning errors;

[0093] (3) Guarantee of parameter smoothing and continuity:

[0094] The parameter sequence calculated from adjacent sliding windows First-order low-pass filtering or weighted average algorithm is used for smoothing to eliminate abrupt changes caused by differences in window data.

[0095] (4) Dynamic monitoring and self-adaptation:

[0096] After each solution, the root mean square of correction (RMS) of the current window is calculated. If the residual exceeds a preset threshold, the sliding window length can be automatically increased to incorporate more control point data, or a manual review prompt can be triggered to ensure the correction effect.

[0097] The dynamic monitoring and adaptation here correspond to the following steps that can be selectively executed by the railway tunnel modeling method provided in this application after processing the inertial navigation data based on the preset correction algorithm to obtain the corresponding target tunnel data: obtaining the correction residuals of the inertial navigation data and the target tunnel data associated with the correction window; comparing the obtained correction residuals with the preset residual threshold; and expanding the correction window (i.e., expanding the sliding window length) when the correction residuals exceed the preset residual threshold, and re-executing the step of processing the inertial navigation data based on the preset correction algorithm to obtain the corresponding target tunnel data.

[0098] In summary, the railway tunnel modeling method provided in this application may include the following steps: acquiring inertial navigation data collected by the inertial navigation system on board during the movement of the inertial navigation trolley along the target railway tunnel, and processing the inertial navigation data based on a preset correction algorithm to obtain the corresponding target tunnel data, including: acquiring inertial navigation data, including at least attitude data and odometer increment data, collected by the inertial navigation system on board during the movement of the inertial navigation trolley along the target railway tunnel; and processing the inertial navigation data based on a preset correction algorithm to obtain target tunnel data including the three-dimensional coordinates of the central axis of the target railway tunnel and the corresponding roll data and yaw angle information.

[0099] Regarding the generation of a one-meter tunnel structure model in 3ds Max:

[0100] In the 3ds Max modeling environment, the first step is to create a basic tunnel unit (a 3D model of a single tunnel) with a length of 1 meter based on the detailed parameters shown in the design drawings, such as the tunnel cross-sectional dimensions, lining thickness, and reinforcement spacing. The specific workflow includes:

[0101] (1) Import reference drawings: Load CAD (Computer-Aided Design) drawings or plan and section views as backgrounds or textures into the viewport and accurately align them with the reference scale;

[0102] (2) Draw the cross-sectional outline: Use the spline tool in the top view and sectional view to draw the structural outlines of the tunnel initial support, secondary lining, arch, filling layer, ballast layer and other structures according to the design annotation (one-to-one scale);

[0103] (3) Generate solid model: Extrude the cross-sectional curve to extend it by 1 meter to form a preliminary tunnel lining solid, which is to obtain the three-dimensional model of the unit tunnel.

[0104] For example, the style of a one-meter tunnel structure model built in 3ds Max can be found in the attached image. Figure 3 and attached Figure 4 .

[0105] That is, the modeling method for railway tunnels provided in this application may include the following steps: obtaining the tunnel design parameters of the target railway tunnel, including the tunnel cross-sectional dimensions, lining thickness, and reinforcement spacing, from the design drawing data of the target railway tunnel; and creating a three-dimensional model of a unit tunnel with a preset unit length based on the tunnel design parameters.

[0106] Specifically, regarding Max Script's automated modeling based on the base model, calibration solution's position and orientation data:

[0107] MaxScript is a built-in scripting language in Autodesk 3ds Max (corresponding to the preset modeling scripts), specifically designed for automating modeling, animation, and rendering workflows. This application uses the MaxScript scripting language to combine the calculated 3D coordinates of the tunnel's centerline and the corresponding roll and yaw angle information with a pre-made one-meter standard tunnel unit model to automatically generate a complete tunnel structure model.

[0108] Since the tangent direction of the tunnel's central axis is perpendicular to the normal direction of the lining model, coordinate data obtained at 1-meter intervals are obtained. The deflection angle of the central axis can be calculated based on the position coordinates. Therefore, the rotation angle of the standard model along the Z-axis can be obtained. The yaw angle given by the inertial navigation system With the above formula There may be cumulative error or zero-point deviation, which can be differentiated by the following formula: This corresponds to the Z-axis deviation angle difference value obtained above; the rotation angle of the model on the Y-axis is determined based on the roll data. For a schematic diagram of the top view angle calculation along the central axis, please refer to the attached diagram. Figure 5 and attached Figure 6 ,in, Figure 5 This is a schematic diagram from a top view of a tunnel modeled along its central axis in one embodiment. Figure 6 This is a schematic diagram of the front view of a tunnel modeled along its central axis in one embodiment.

[0109] Based on this, the step of "determining the Z-axis deviation angle difference between the target tunnel data and the unit tunnel three-dimensional model" mentioned above in this application can be specifically executed as follows: obtaining the yaw angle information included in the target tunnel data and the Z-axis rotation angle of the unit tunnel three-dimensional model, and determining the Z-axis deviation angle difference between the two based on the yaw angle information and the Z-axis rotation angle.

[0110] Based on the aforementioned calculation results, tunnel unit models can be generated and placed in batches every 1 meter in 3ds Max according to the following process: every meter, the model is translated and copied to... The rotation angle of the tunnel model every meter is set to By placing the above translation and rotation operations into a script loop and executing them sequentially on all sampling points i=1,2,…,N, a coherent and precisely aligned 3D tunnel model can be quickly constructed.

[0111] In summary, this application aims to address the challenges of obtaining the centerline position, low modeling efficiency, and insufficient spatial accuracy in the digital modeling of railway tunnels. To overcome the technical bottlenecks of traditional methods relying on construction drawings or meter-by-meter measurements with total stations, which suffer from large accuracy deviations, low operational efficiency, and difficulty in meeting the demands of rapid modeling, this application proposes a tunnel structure modeling method based on the automated linkage between an inertial navigation system (INS) and a 3D modeling script.

[0112] This method deploys INS devices on the track to collect high-frequency inertial data in real time, and combines this with a Kalman filter fusion algorithm to achieve high-precision attitude calculation, obtaining continuous centerline coordinates and spatial attitude information. Subsequently, the pre-set tunnel lining module is automatically invoked in the modeling software, and the spatial pose data is mapped to standard components via scripts, achieving automatic and continuous splicing modeling of the lining structure. This method not only improves the efficiency and accuracy of tunnel modeling but also provides a unified spatial benchmark for the digital twin representation of tunnels, providing reliable support for subsequent structural deformation analysis, defect comparison, operation and maintenance management, and emergency response.

[0113] This application significantly improves the accuracy of centerline calculation by introducing an inertial navigation system (INS) to acquire high-frequency spatial pose information of the tunnel track, combined with a fusion filtering algorithm. This overcomes the problems of large errors and low efficiency associated with traditional methods that rely on drawings or manual point-by-point measurements. Compared to traditional modeling methods, this approach has the following significant advantages:

[0114] (1) Efficient modeling: Based on the standardized lining model and MaxScript automatic calling mechanism, continuous modeling with a 1-meter granularity can be achieved, which greatly improves the efficiency of tunnel modeling and significantly reduces manual intervention;

[0115] (2) Spatial precision: The attitude calculation algorithm integrates acceleration, gyroscope and odometer data to ensure that the accuracy of the central axis coordinates and deflection angle meets the millimeter-level modeling requirements, providing an accurate benchmark for subsequent disease comparison and deformation analysis;

[0116] (3) High reusability: The modular structure model facilitates rapid deployment and adjustment in different tunnel scenarios, and has good versatility and scalability;

[0117] (4) Supporting the twin platform: It can be seamlessly integrated with the digital twin platform, serving as the basis for the construction of three-dimensional scenes, and helping railway tunnels to achieve structural-level visual management and intelligent operation and maintenance.

[0118] Regarding the railway tunnel modeling method provided in this application, an exemplary embodiment is also provided, including the following inertial navigation data position and attitude data correction calculation, and related tunnel modeling processes, wherein:

[0119] Regarding the calculation of position and attitude corrections for inertial navigation data:

[0120] To obtain the spatial trajectory and attitude information of the tunnel's central axis, this embodiment employs a mobile platform equipped with an inertial navigation system (INS) (hereinafter referred to as the "INS vehicle") to travel along the tunnel track. During this travel, the vehicle's three-axis angular velocities (i.e., rotational rates around the X, Y, and Z axes) and three-axis accelerations (i.e., linear accelerations along the X, Y, and Z axes) are collected in real time. Taking a section of tunnel data as an example, the angular velocity values ​​output by the INS correspond to the vehicle's attitude change rates in the roll, pitch, and yaw directions, respectively, while the acceleration values ​​reflect its instantaneous motion trends in various directions in space. These raw data serve as the basis for subsequent attitude calculations and trajectory fitting. Some illustrative data are shown below. Figure 7 and Figure 8 As shown, Figure 7 The raw data related to angular velocity (i.e., the rate of rotation about the X, Y, and Z axes) are shown. Figure 8 The raw data related to acceleration (i.e., linear acceleration along the X, Y, and Z axes) are shown. Figure 7The blue line corresponds to the X-axis (Roll axis), representing the roll (tilt) angular velocity around the vehicle's front-to-back direction, such as the rate of change when the vehicle tilts left and right; the orange line corresponds to the Y-axis (Pitch axis), representing the pitch angular velocity around the vehicle's left and right direction, such as the rate of change when the front of the vehicle swings up and down; the red line corresponds to the Z-axis (Yaw axis), representing the yaw angular velocity around the vehicle's vertical direction, such as the rotational rate when the vehicle is turning. Figure 7 The horizontal axis can be in units of time (hours / h), and the vertical axis can be in units of angular velocity (rad / s). 2 ). Figure 8 The blue lines in the diagram correspond to the Z-axis (vertical direction), representing the vehicle's acceleration in the vertical direction, mainly reflecting the gravitational component and vertical vibration (such as bumps caused by uneven tracks); the orange and red lines represent the X-axis and Y-axis (horizontal direction), representing the forward and lateral directions, reflecting the longitudinal acceleration along the direction of motion (X) and the lateral acceleration in the direction of motion (Y). Figure 8 The horizontal axis can be in units of time (hours / h), and the vertical axis can be in units of linear acceleration (m / s²). 2 ).

[0121] Using the collected mileage and radius of curvature data as input, the proposed centerline calculation algorithm is applied. Combined with a segment-by-segment integration method along the tangent direction of the centerline in small segments, the automatic calculation and coordinate reconstruction of the tunnel's spatial path are completed. This algorithm effectively handles practical conditions such as abrupt curvature changes and data discontinuities, ensuring the continuity and accuracy of the calculation results. Table 1 below shows the two-dimensional plane coordinates (X, Y) of a portion of the centerline after calculation, reflecting the spatial orientation characteristics of the tunnel path and providing a reliable data foundation for subsequent 3D modeling and operation and maintenance analysis. Mileage represents mileage, and radius represents radius.

[0122] Table 1

[0123]

[0124] Regarding tunnel modeling:

[0125] The continuous images of the tunnel interior wall acquired by the 3D laser scanning system are divided into segments at 1-meter intervals along the tunnel's central axis, resulting in a series of independent images corresponding to each meter segment. After segmentation, each image precisely corresponds to a continuous 1-meter interval within the tunnel, meaning each image includes a texture map corresponding to the 1-meter interval along the tunnel's extension direction, specifically a texture map corresponding to the 1-meter interval along the tunnel's annular direction.

[0126] The tunnel interior image, already segmented into meter-long sections, was individually converted into texture maps, resulting in multiple maps for each 1-meter segment along the tunnel's extension direction, corresponding to the tunnel's circular path. These maps were then applied sequentially to their respective tunnel mileage sections. A diagram illustrating the effect of the textured tunnel interior in Unreal Engine can be found in the attached image. Figure 9 , Figure 9 The image shows the tunnel interior wall formed by pasting multiple 1-meter-long interval textures side by side.

[0127] Next, the complete tunnel is exported and displayed in Unreal Engine, with the display effect as follows: Figure 10 As shown.

[0128] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0129] Based on the same inventive concept, this application also provides a railway tunnel modeling apparatus for implementing the railway tunnel modeling method described above. The solution provided by this apparatus is similar to the solution described in the above method; therefore, the specific limitations of one or more railway tunnel modeling apparatus embodiments provided below can be found in the limitations of the railway tunnel modeling method described above, and will not be repeated here.

[0130] In one exemplary embodiment, such as Figure 11 As shown, a railway tunnel modeling device 400 is provided, including: a data acquisition module 41, a data processing module 42, and a tunnel modeling module 43, wherein:

[0131] The data acquisition module 41 is used to acquire inertial navigation data collected by the onboard inertial navigation system during the inertial navigation trolley's journey along the target railway tunnel, and to process the inertial navigation data based on a preset correction algorithm to obtain the corresponding target tunnel data.

[0132] The data acquisition module 41 is also used to acquire a three-dimensional model of the unit tunnel constructed based on the design drawing data of the target railway tunnel;

[0133] Data processing module 42 is used to call a preset modeling script to determine the Z-axis deviation angle difference between the target tunnel data and the unit tunnel 3D model, as well as the Y-axis rotation angle corresponding to the roll data included in the target tunnel data, based on the target tunnel data and the unit tunnel 3D model.

[0134] The tunnel modeling module 43 is used to determine the three-dimensional rotation angle of the tunnel model based on the Z-axis deflection angle difference value and the Y-axis rotation angle, and to construct a complete three-dimensional tunnel model of the target railway tunnel based on the three-dimensional rotation angle of the tunnel model.

[0135] In an exemplary embodiment, the data processing module 42 is used to determine the Z-axis deflection angle difference value between the target tunnel data and the unit tunnel 3D model based on the target tunnel data and the unit tunnel 3D model, specifically for:

[0136] The target tunnel data includes yaw angle information and the Z-axis rotation angle of the unit tunnel 3D model. Based on the yaw angle information and the Z-axis rotation angle, the Z-axis yaw angle difference value corresponding to the two is determined.

[0137] In an exemplary embodiment, the railway tunnel modeling device 400 further includes an initial modeling module for acquiring tunnel design parameters of the target railway tunnel, including tunnel cross-sectional dimensions, lining thickness, and reinforcement spacing, from the design drawing data of the target railway tunnel; and creating a three-dimensional model of a unit tunnel with a preset unit length based on the tunnel design parameters.

[0138] In an exemplary embodiment, the data acquisition module 41 is used to acquire inertial navigation data collected by the onboard inertial navigation system during the inertial navigation trolley's journey along the target railway tunnel, and to process the inertial navigation data based on a preset correction algorithm to obtain the corresponding target tunnel data. Specifically, it is used to: acquire inertial navigation data in the target railway tunnel, including at least attitude data and odometer increment data, based on the inertial navigation trolley traveling in the target railway tunnel; and process the inertial navigation data based on the preset correction algorithm to obtain target tunnel data including the three-dimensional coordinates of the central axis of the target railway tunnel and the corresponding roll data and yaw angle information.

[0139] In an exemplary embodiment, the data acquisition module 41 is used to process inertial navigation data based on a preset correction algorithm to obtain corresponding target tunnel data. Specifically, it is used to: process inertial navigation data based on the preset correction algorithm, using preset sampling interval data as a correction window, to obtain corresponding target tunnel data; the overlap rate of adjacent correction windows is b, 30%≤b≤60%; the preset sampling interval data is determined based on the length of the unit tunnel three-dimensional model in the tunnel extension direction.

[0140] In an exemplary embodiment, the data acquisition module 41 is further configured to: acquire the correction residuals of the inertial navigation data and the target tunnel data associated with the correction window; and, if the correction residuals exceed a preset residual threshold, expand the correction window and re-execute the steps of processing the inertial navigation data based on the preset correction algorithm to obtain the corresponding target tunnel data.

[0141] The various modules in the aforementioned railway tunnel modeling device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the operations corresponding to each module.

[0142] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 12 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for modeling railway tunnels. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad set on the casing of the computer device, or an external keyboard, touchpad, or mouse, etc. It should be noted that using a computer device as a terminal is only one optional implementation method provided in this application, but this application is not limited to this. The computer device can also be a server, or a combination of a terminal and a server connected in communication.

[0143] Those skilled in the art will understand that Figure 12The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0144] In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement steps related to a method for modeling railway tunnels.

[0145] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements steps related to a method for modeling railway tunnels.

[0146] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements steps related to a method for modeling railway tunnels.

[0147] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0148] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0149] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0150] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for modeling railway tunnels, characterized in that, The method includes: The inertial navigation data collected by the inertial navigation system on board the inertial navigation trolley during its journey along the target railway tunnel is obtained, and the inertial navigation data is processed based on a preset correction algorithm to obtain the corresponding target tunnel data. Obtain a three-dimensional model of the unit tunnel constructed based on the design drawings of the target railway tunnel; A preset modeling script is invoked to determine the Z-axis deviation angle difference between the target tunnel data and the unit tunnel 3D model, and the Y-axis rotation angle corresponding to the roll data included in the target tunnel data, based on the target tunnel data and the unit tunnel 3D model. Based on the Z-axis deviation angle difference value and the Y-axis rotation angle, the three-dimensional rotation angle of the tunnel model is determined, and a complete three-dimensional tunnel model of the target railway tunnel is constructed based on the three-dimensional rotation angle of the tunnel model.

2. The method according to claim 1, characterized in that, The step of determining the Z-axis deflection angle difference between the target tunnel data and the unit tunnel 3D model based on the target tunnel data and the unit tunnel 3D model includes: The target tunnel data includes yaw angle information and the Z-axis rotation angle of the unit tunnel 3D model. Based on the yaw angle information and the Z-axis rotation angle, the Z-axis yaw angle difference value corresponding to the two is determined.

3. The method according to claim 1, characterized in that, The method further includes: The design drawing data of the target railway tunnel obtained includes the tunnel design parameters of the target railway tunnel, such as the tunnel cross-sectional dimensions, lining thickness, and reinforcement spacing. A three-dimensional model of a unit tunnel with a preset unit length is created based on the tunnel design parameters.

4. The method according to claim 1, characterized in that, The acquisition of inertial navigation data collected by the onboard inertial navigation system during the trolley's journey along the target railway tunnel, and the processing of the inertial navigation data based on a preset correction algorithm to obtain the corresponding target tunnel data, includes: Acquire inertial navigation data, including at least attitude data and odometer increment data, based on the inertial navigation system on board, during the inertial navigation vehicle's journey along the target railway tunnel; The inertial navigation data is processed based on a preset correction algorithm to obtain target tunnel data, including the three-dimensional coordinates of the centerline of the target railway tunnel and the corresponding roll data and yaw angle information.

5. The method according to claim 1, characterized in that, The process of processing the inertial navigation data based on a preset correction algorithm to obtain the corresponding target tunnel data includes: Based on a preset correction algorithm, the inertial navigation data is processed using a preset sampling interval as a correction window to obtain the corresponding target tunnel data; the overlap rate of adjacent correction windows is b, 30%≤b≤60%; the preset sampling interval is determined based on the length of the unit tunnel three-dimensional model in the tunnel extension direction.

6. The method according to claim 5, characterized in that, After processing the inertial navigation data based on a preset correction algorithm to obtain the corresponding target tunnel data, the method further includes: Obtain the correction residuals of the inertial navigation data and the target tunnel data associated with the correction window; If the correction residual exceeds a preset residual threshold, the correction window is expanded, and the inertial navigation data is processed again based on the preset correction algorithm to obtain the corresponding target tunnel data.

7. A modeling device for railway tunnels, characterized in that, The device includes: The data acquisition module is used to acquire inertial navigation data collected by the onboard inertial navigation system during the inertial navigation trolley's journey along the target railway tunnel, and to process the inertial navigation data based on a preset correction algorithm to obtain the corresponding target tunnel data. The data acquisition module is also used to acquire a three-dimensional model of the unit tunnel constructed based on the design drawing data of the target railway tunnel; The data processing module is used to call a preset modeling script to determine the Z-axis deviation angle difference between the target tunnel data and the unit tunnel 3D model, and the Y-axis rotation angle corresponding to the roll data included in the target tunnel data, based on the target tunnel data and the unit tunnel 3D model. The tunnel modeling module is used to determine the three-dimensional rotation angle of the tunnel model based on the Z-axis deflection angle difference value and the Y-axis rotation angle, and to construct a complete three-dimensional tunnel model of the target railway tunnel based on the three-dimensional rotation angle of the tunnel model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images