Tunnel lining service performance index visualization method based on data interaction

Abstract

The invention discloses a tunnel lining service performance index visualization method based on data interaction, and relates to the technical field of data visualization, and the method comprises the steps: obtaining geologic structure model parameters and lining structure design parameters, and generating a numerical calculation model; monitoring points are established in the numerical calculation model, internal force data of the structure are obtained, and analysis and extraction of key data streams in the calculation process are synchronously achieved by monitoring the operation state of the model in real time. And post-processing the internal force data of the structure, and establishing an initial visualization model through numerical gradient processing to obtain a mapping relation among the internal force, the safety coefficient and the graphical representation. Color gradient processing is carried out on the initial visualization model in combination with an RGB color model, a target visualization model is obtained, the target visualization model is used for visualizing the structural internal force data and the structural safety coefficient, structural internal force calculation precision and visual visualization of a calculation result are both considered, the analysis efficiency is improved, and meanwhile the analysis accuracy of the structural internal force is improved. And the problem of single visualization is also avoided.

Description

Technical Field

[0001] This application relates to the field of data visualization technology, and in particular to a method for visualizing the service performance indicators of tunnel linings based on data interaction. Background Technology

[0002] In the field of underground and tunnel engineering, structural safety assessment is a core step in ensuring the stability of the project. Currently, the load-structure method and the ground structure method are mainly used for mechanical analysis. The load-structure method simplifies the effect of the surrounding rock on the support structure as a load, and then analyzes the internal forces and deformations of the support structure. The ground structure method, based on continuum mechanics, models the surrounding rock and support structure as a whole, and solves the stress-strain distribution through numerical simulation, thus more realistically reflecting the pressure on the surrounding rock.

[0003] The value of surrounding rock pressure mainly relies on empirical formulas, which are difficult to accurately reflect the actual situation. The geological conditions, surrounding rock properties and construction methods of different projects vary significantly. Traditional assessment methods are difficult to adapt to complex and ever-changing geological environments and cannot take into account both the accuracy of structural internal force calculation and the intuitive visualization of calculation results, thus affecting the accurate assessment of structural safety.

[0004] Therefore, how to balance the accuracy of structural internal force calculation with the intuitive visualization of calculation results is a problem that urgently needs to be solved. Summary of the Invention

[0005] The main purpose of this application is to provide a visualization method for the service performance indicators of tunnel lining based on data interaction, which aims to solve the technical problem that it is impossible to balance the accuracy of structural internal force calculation and the intuitive visualization of calculation results in complex environments.

[0006] To achieve the above objectives, this application proposes a method for visualizing tunnel lining service performance indicators based on data interaction. The method includes:

[0007] Obtain geological structure model parameters and lining structure design parameters, and generate a numerical calculation model;

[0008] Monitoring points are established in the numerical calculation model to obtain structural internal force data;

[0009] The internal force data of the structure are post-processed, and an initial visualization model is established through numerical gradient processing;

[0010] The initial visualization model is processed by color gradient processing using the RGB color model to obtain the target visualization model, which is used to visualize structural internal force data and structural safety factor.

[0011] In one embodiment, the step of post-processing the structural internal force data and establishing an initial visualization model through numerical gradient processing includes:

[0012] The internal force data of the structure is sliced ​​to obtain a discrete coordinate set and a tilt angle set;

[0013] The internal force data of the structure is extracted according to the preset command to obtain a discrete internal force dataset;

[0014] The discrete internal force dataset is input into the safety factor calculation model to obtain the safety factor set;

[0015] The rectangle width is calculated based on the discrete coordinate set, and the rectangle width includes the internal force rectangle width and the safety factor rectangle width.

[0016] The rectangle length set is determined based on the preset canvas size, the discrete internal force dataset, and the safety factor set.

[0017] An initial visualization model is determined based on the discrete coordinate set, the tilt angle set, the rectangle width, and the rectangle length set.

[0018] In one embodiment, the step of slicing the structural internal force data to obtain a discrete coordinate set and a tilt angle set includes:

[0019] The internal force data of the structure is sliced, and the node coordinate data is read.

[0020] Linear interpolation is used to generate a discrete coordinate set from the node coordinate data;

[0021] The tilt angle between adjacent coordinate points is calculated based on the discrete coordinate set, and a tilt angle set is generated.

[0022] In one embodiment, the step of determining the initial visualization model based on the discrete coordinate set and tilt angle set, the rectangle width, and the rectangle length set includes:

[0023] Select target nodes from the discrete coordinate set, and establish a local coordinate system with the spatial coordinates of the target nodes as the origin;

[0024] The axial direction of the local coordinate system is dynamically determined based on the set of tilt angles, and a set of corner coordinates is obtained. The corner coordinates are used to characterize the rectangular profile of the internal force gradient.

[0025] The initial visualization model is determined by combining the rectangle width and the rectangle length set, based on the local coordinate system and the corner coordinate set.

[0026] In one embodiment, the step of performing color gradient processing on the initial visualization model using the RGB color model to obtain a target visualization model, wherein the target visualization model is used to visualize structural internal force data and structural safety factors, includes:

[0027] Based on the RGB color model, a gradient color mark sequence is generated using a discretized gradient control algorithm;

[0028] Based on the initial visualization model, a continuous dataset is extracted to determine the target numerical range;

[0029] The target numerical range is divided into multiple continuous sub-intervals;

[0030] Establish a mapping relationship between the multiple continuous sub-intervals and the gradient color mark sequence;

[0031] The initial visualization model is filled with color according to the mapping relationship to obtain the target visualization model.

[0032] In one embodiment, the step of generating a gradient color stop sequence based on the RGB color model using a discretized gradient control algorithm includes:

[0033] Based on the RGB color model, the starting color stop is set to the first color;

[0034] The color channel intensity value is increased incrementally by a preset step size using a discretized gradient control algorithm.

[0035] When the first color channel reaches a preset saturation threshold, the first increment is transferred to the second color channel, where the first increment is the increment corresponding to the first color channel.

[0036] When the second color channel reaches a preset saturation threshold, the second increment is transferred to the third color channel to generate a gradient color stop sequence. The terminating color stop of the gradient color stop sequence is the third color, and the second increment is the increment corresponding to the second color channel.

[0037] In one embodiment, the step of filling the initial visualization model with color according to the mapping relationship to obtain the target visualization model includes:

[0038] Based on each rectangular contour in the initial visualization model, determine the corresponding target internal force value and target safety factor value;

[0039] The sub-interval number is determined based on the target internal force value and the target safety factor value, respectively.

[0040] The target color code is indexed by the sub-interval number;

[0041] Call the rectangular primitive drawing interface of the graphics library, set the fill color parameter to the target color mark, and obtain the target visualization model.

[0042] In one embodiment, the step of obtaining geological structure model parameters and lining structure design parameters to generate a numerical calculation model includes:

[0043] A data interaction interface is constructed, which is used to transmit model parameters, modeling calculations, and data processing.

[0044] The geological structure model parameters and lining structure design parameters are obtained through the data interaction interface, and a numerical calculation model is generated.

[0045] In one embodiment, the step of obtaining geological structure model parameters and lining structure design parameters to generate a numerical calculation model further includes:

[0046] Based on the preset embedded command template and the geological structure model parameters and lining structure design parameters, a one-sided mesh of the numerical calculation model is generated.

[0047] Based on the single-sided grid of the numerical calculation model, the other half of the model corresponding to the single-sided grid is symmetrically generated through a mapping function to obtain the numerical calculation model.

[0048] Furthermore, to achieve the above objectives, this application also proposes a data interaction-based visualization device for tunnel lining service performance indicators, the device comprising:

[0049] The structural parameter acquisition module is used to acquire geological structure model parameters and lining structure design parameters to generate a numerical calculation model;

[0050] The structural internal force monitoring module is used to establish monitoring points in the numerical calculation model and acquire structural internal force data.

[0051] The initial model building module is used to post-process the internal force data of the structure and build an initial visualization model through numerical gradient processing.

[0052] The target model building module is used to perform color gradient processing on the initial visualization model in conjunction with the RGB color model to obtain the target visualization model, which is used to visualize the structural internal force data and structural safety factor.

[0053] One or more technical solutions proposed in this application have at least the following technical effects:

[0054] By acquiring geological structure model parameters and lining structure design parameters, a numerical calculation model is generated. Integrating these parameters, a high-precision numerical calculation model is constructed, overcoming the errors of empirical formulas in the load-structure method and accurately reflecting the interaction between surrounding rock and lining in complex geological environments. Based on the pre-set geological structure model and lining structure design parameters, the numerical calculation model is automated for parameter assignment and excavation calculations. This enables monitoring of internal forces and safety factors at monitoring sections under multiple excavation steps, improving computational efficiency.

[0055] By establishing monitoring points in the numerical calculation model to obtain structural internal force data, the core deficiency of the geological structure method in being unable to output specific numerical values ​​of unit internal forces is resolved. The monitoring points allow for real-time monitoring of the model's operational status, simultaneously enabling the parsing and extraction of key data streams during the calculation process.

[0056] Post-processing of structural internal force data involves establishing an initial visualization model through numerical gradient processing, automatically calculating the safety factor of the lining structure, and establishing a mapping relationship between internal forces, safety factors, and graphical representations. This generates rectangular primitives representing internal forces and safety factors, along with their precise positioning parameters, transforming abstract internal force data into spatial distribution graphics. This improves the accuracy of internal force calculations and makes the calculation results more intuitive.

[0057] By combining the RGB color model with color gradient processing of the initial visualization model, a target visualization model is obtained. This target visualization model is used to visualize structural internal force data and structural safety factors, simultaneously presenting the internal force distribution trend and hazard level. This overcomes the limitations of traditional methods that can only display stress cloud diagrams, solves the problem of visualization singularity, and achieves a precise correlation between structural mechanical parameters and visualization representation. By defining mapping rules from numerical results to color attributes, a dual-mode visualization of the lining structure's response state is ultimately achieved. Attached Figure Description

[0058] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0059] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a flowchart illustrating the first embodiment of the data interaction-based visualization method for tunnel lining service performance indicators of this application.

[0061] Figure 2 This is a schematic diagram of the environment initialization interface configuration interface in an embodiment of this application;

[0062] Figure 3 This is a schematic diagram of the geological structure model of an embodiment of this application;

[0063] Figure 4 This is a schematic diagram of the custom model configuration interface in an embodiment of this application;

[0064] Figure 5This is a schematic diagram illustrating the design steps of an embodiment of this application;

[0065] Figure 6 This is a schematic diagram of the mesh for the numerical calculation model in an embodiment of this application;

[0066] Figure 7 This is a schematic diagram of the excavation process in an embodiment of this application;

[0067] Figure 8 This is a flowchart illustrating the second embodiment of the data interaction-based visualization method for tunnel lining service performance indicators in this application.

[0068] Figure 9 This is a schematic diagram of a partial coordinate system in an embodiment of this application;

[0069] Figure 10 This is a flowchart illustrating the third embodiment of the data interaction-based visualization method for tunnel lining service performance indicators in this application.

[0070] Figure 11 This is a schematic diagram illustrating the mapping relationship between numerical values ​​and colors in an embodiment of this application;

[0071] Figure 12 This is a schematic diagram of the lining bending moment at 30m depth, as described in an embodiment of this application.

[0072] Figure 13 This is a schematic diagram of the lining axial force at a depth of 30m in an embodiment of this application;

[0073] Figure 14 This is a schematic diagram of the lining shear force at an excavation depth of 30m in an embodiment of this application;

[0074] Figure 15 This is a schematic diagram of the lining safety factor at an excavation depth of 30m, as described in an embodiment of this application.

[0075] Figure 16 This is a schematic diagram illustrating the variation of bending moment with the number of excavation steps in an embodiment of this application;

[0076] Figure 17 This is a schematic diagram illustrating the variation of axial force with the number of excavation steps in an embodiment of this application;

[0077] Figure 18 This is a schematic diagram illustrating the variation of shear force with the number of excavation steps in an embodiment of this application;

[0078] Figure 19 This is a schematic diagram illustrating the change of the safety factor with the number of excavation steps in an embodiment of this application;

[0079] Figure 20 This is a schematic diagram of the module structure of the data interaction-based tunnel lining service performance index visualization device according to an embodiment of this application.

[0080] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0081] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0082] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0083] In engineering fields such as underground structures and tunnels, structural safety assessment is of paramount importance. The load-structure method and the soil structure method are two commonly used analytical methods, but both have certain limitations.

[0084] The load-structure method simplifies the effect of surrounding rock on the support structure into loads, and then analyzes the internal forces and deformations of the support structure. However, the value of surrounding rock pressure mainly relies on empirical formulas, which are difficult to accurately reflect the actual situation. In addition, due to the significant differences in geological conditions, surrounding rock properties, and construction methods among different projects, this method is difficult to adapt to complex and variable geological environments, resulting in large deviations in the safety factor calculation results, which in turn affects the accurate assessment of structural safety.

[0085] The stratigraphic structure method, based on the principles of continuum mechanics, analyzes the surrounding rock and support structure as a whole, considering their interaction to more realistically simulate the distribution of surrounding rock pressure. This method obtains more accurate values ​​of surrounding rock pressure by establishing a reasonable constitutive model and boundary conditions, and then using numerical calculations to solve for stress and strain distribution. However, it cannot provide specific numerical values ​​of internal forces within elements (such as bending moment and axial force) or directly output safety factors, making it difficult to quickly assess structural safety. It requires additional complex calculations and analyses, increasing the difficulty and uncertainty of engineering analysis.

[0086] Given the limitations of traditional technologies in balancing the accuracy of structural internal force calculations with the intuitive visualization of calculation results, as well as the low efficiency, cumbersome process, and limited visualization of lining structure design methods, an integrated technical solution is urgently needed.

[0087] It should be noted that the embodiments of this application can be implemented through a data interaction interface system. This system can be simulation software, such as FLAC3D (Fast Lagrangian Analysis of Continua). FLAC3D is an extension of a two-dimensional finite difference program, capable of simulating the three-dimensional structural stress characteristics and plastic flow analysis of soil, rock, and other materials. It employs an explicit Lagrangian algorithm and hybrid, discrete partitioning techniques, enabling very accurate simulation of plastic failure and flow of materials.

[0088] Based on this, embodiments of this application provide a method for visualizing tunnel lining service performance indicators based on data interaction, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the data interaction-based visualization method for tunnel lining service performance indicators in this application.

[0089] In this embodiment, the method for visualizing tunnel lining service performance indicators based on data interaction includes steps S10 to S40:

[0090] Step S10: Obtain the geological structure model parameters and lining structure design parameters, and generate a numerical calculation model.

[0091] It should be noted that step S10 includes constructing a data interaction interface. This interface system is used for transmitting model parameters, modeling calculations, and data processing. Geological structure model parameters and lining structure design parameters are obtained through the data interaction interface, and a numerical calculation model is generated. Specifically, a Python-Fish (FLAC3D) data interaction interface can be constructed. Python-Fish can be understood as a programming language combining Python and Fish. Python is a commonly used programming language, while Fish is the internal programming language of FLAC3D, allowing users to customize model behavior and implement complex material models and boundary conditions. The Python-Fish (FLAC3D) data interaction interface is used to complete model parameter transmission, automatic modeling calculations, and automatic data processing. Initialization of the computing environment requires configuration. For example, a graphical user interface (GUI) built using the PySimpleGUI framework (used to simplify graphical user interface development in Python) dynamically sets the project root directory as the base storage path for model files, calculation logs, and intermediate results. A uniquely identifiable project name is created to avoid data confusion and ensure data isolation in a multi-task concurrent environment. The installation path of the FLAC3D solver core module is bound using Python's os.path module. os.path is a Python function used to handle file paths.

[0092] For example, the environment initialization interface configuration is as follows: Figure 2As shown, the graphical GUI interface sets "D: / PYFLAC" as the root path for storing project files. To ensure the program runs correctly, the working path cannot contain Chinese characters. The project name can be "pyflac", ensuring its uniqueness and avoiding conflicts with other files. A dynamic path mapping is established with the FLAC3D solver core module installation directory; that is, the FLAC3D solver path is entered, which can be "D: / Program Files / Itasca / FLAC3D700 / exe64 / flac3d700_console.exe".

[0093] It should be noted that different parameters need to be input depending on the chosen modeling system construction method. For example, geological structure model parameters can be understood as rock mass constitutive model parameters, which are stratigraphic lithological layering information entered based on geological exploration data. These parameters may include Hoek-Brown criterion parameters such as rock mass weight, elastic modulus, Poisson's ratio, geological strength index, strength criterion (Hoek-Brown) constant, and uniaxial compressive strength. Lining structure design parameters can be understood as the mechanical properties of the lining structure, i.e., inputting the structural parameters of the lining, covering key parameters of the lining system such as Poisson's ratio, elastic modulus, and unit weight. Based on the above input parameters, modeling calculation commands are automatically generated to construct a numerical calculation model.

[0094] For example, a tunnel model with a burial depth of 40m and dimensions of 90m×100×90m is selected. The tunnel cross-section is a three-centered circle. The constitutive model is the Hoek-Brown model. The Poisson's ratio of the rock mass is 0.22, the elastic modulus is 1e9Pa, and the unit weight is 2500N / m³. 3 The geological strength index is 14, the uniaxial compressive strength is 1e6 Pa, the Hoe k-Brown constant is 10, the lining structural parameters are Poisson's ratio 0.2, the elastic modulus 30e9, the unit weight 2500, and the thickness 0.5 m. The tunnel interior is grouped as "tunnel," and the tunnel exterior is grouped as "rock." The model is as follows: Figure 3 As shown. Based on this, a custom model is used for modeling and calculation. Custom model configuration and parameter input are referenced. Figure 4 The inputs include rock mass constitutive model parameters, lining structure mechanical properties, reinforcement scheme, model file (path: D: / PYFLAC / geo-model-2.sav) and node coordinate file (path: C: / Users / lenovo / Desktop / coordinates.xlsx).

[0095] Step S20: Establish monitoring points in the numerical calculation model and obtain structural internal force data.

[0096] It should be noted that monitoring points can be established using the commands "struct.near" and "struct.shell.resultant". Structural internal force data can include lining displacement, stress, internal forces, bending moments, axial forces, and shear forces. Specifically, the "struct.near" command establishes monitoring points at appropriate locations in the model. The "struct.near" command intelligently identifies adjacent structural elements, automatically retrieving and returning the structural element in the numerical model whose centroid is closest to a given spatial coordinate point. If no structural elements are defined in the model, a null value is returned. This allows for real-time recording of lining displacement, stress, and internal forces during tunnel excavation and support. The "struct.shell.resultant" command can directly retrieve the element stress results at a specified node index, based on the most recently calculated values, and obtain the bending moments, axial forces, and shear forces of the shell element where the monitoring point is located.

[0097] For example, the `open()` function in the Python programming language can be used to write a pre-written FLAC3D command sequence to a temporary text file (.txt format). To ensure that the generated file can be correctly recognized and executed by the FLAC3D solver, the file extension of the temporary text file needs to be changed from ".txt" to ".dat". After changing the file extension, the `subprocess` module in the Python standard library is used to call the FLAC3D solver to execute the ".dat" format command file generated after the extension change, producing an output file containing calculated internal force data.

[0098] Step S30: Post-process the structural internal force data and establish an initial visualization model through numerical gradient processing.

[0099] It should be noted that numerical gradient processing can be a visual mapping based on the spatial distribution characteristics of mechanical data, which represents the relative magnitude of values ​​through changes in the length of geometric figures. The initial visualization model can be generated by numerical gradient processing into vector graphics, which include positioning parameters and geometric attributes.

[0100] Step S40: Combine the RGB color model to perform color gradient processing on the initial visualization model to obtain the target visualization model. The target visualization model is used to visualize the structural internal force data and structural safety factor.

[0101] It should be noted that the RGB (Red, Green, Blue) color model is the most basic additive color model in digital image processing, representing any color by mixing different proportions of the three primary colors: red, green, and blue. Color gradient processing can be understood as establishing a mapping relationship between numerical values ​​and colors in the RGB color space, and is an enhanced rendering method for color filling of geometric shapes. The target visualization model can be understood as a composite graphic that integrates numerical gradients and color gradients, simultaneously expressing the relative magnitude of structural internal forces and the structural safety factor. For example, a numerical simulation and visualization method based on the Python-Fish (FLAC3D) data interaction interface system is used to address the problems of low efficiency, cumbersome process, and limited visualization in existing lining structure design methods. Its design steps are as follows: Figure 5 As shown.

[0102] In this embodiment, a numerical calculation model is generated by acquiring geological structure model parameters and lining structure design parameters, overcoming the errors of empirical formulas in the load-structure method and improving calculation efficiency. Monitoring points are established in the numerical calculation model to acquire structural internal force data, simultaneously enabling the parsing and extraction of key data streams during the calculation process. Post-processing of the structural internal force data involves establishing an initial visualization model through numerical gradient processing, transforming abstract internal force data into spatial distribution graphics. This improves the accuracy of internal force calculations and makes the calculation results more intuitive. Color gradient processing is applied to the initial visualization model using an RGB color model to obtain a target visualization model. This target visualization model is used to visualize structural internal force data and structural safety factors, solving the problem of visualization uniformity and achieving a precise correlation between structural mechanical parameters and visual representation.

[0103] In one embodiment, step S10 further includes: generating a one-sided grid of the numerical calculation model based on a preset embedded command template, geological structure model parameters, and lining structure design parameters; and generating the other half of the model corresponding to the one-sided grid by a mapping function based on the one-sided grid of the numerical calculation model to obtain the numerical calculation model.

[0104] For example, based on a preset embedded command template and user-input geotechnical engineering numerical model parameters (including geometric dimensions, material properties, and boundary conditions), a Python script automatically generates a FLAC3D command flow through a parametric modeling mechanism. The generated mesh diagram of the numerical calculation model is shown in the reference. Figure 6 Mesh plots can be created using the Extrusion tool, which can perform mesh generation only on the right side of the numerical computation model, and then use the "reflect" function to symmetrically generate the other half of the model.

[0105] In this embodiment, the mesh generation operation is performed only on the right side of the numerical calculation model, and the "reflect" function is used to generate the other half of the model, which greatly improves the efficiency of computational modeling.

[0106] In one implementation, specifically for step S20, a key monitoring section can be preset at a longitudinal distance of 30m from the lining structure, and an internal force monitoring unit can be deployed to dynamically capture the mechanical response of the support structure during the excavation process. Through the construction procedure time step control logic, the script automatically triggers data extraction for the following four key nodes: the initial completion time of the support structure (t0), the mechanical equilibrium state after completing three excavation steps (t3), the mechanical equilibrium state after completing five excavation steps (t5), and the mechanical equilibrium state after completing ten excavation steps (t6). 10 The excavation diagram is as follows: Figure 7 As shown, the diagonal lines represent the surrounding rock, the white areas represent the tunnel excavation, and the horizontal lines represent the tunnel lining.

[0107] Specifically, during tunnel excavation, when the excavation reaches section 1 (which serves as the monitoring section), support measures are immediately implemented, and a monitoring network is rationally arranged to record the mechanical equilibrium state of the monitoring section at this time. After completing three consecutive excavation steps, the tunnel reaches section 2, and the mechanical equilibrium state of the monitoring section is recorded again. After completing five consecutive excavation steps, the tunnel reaches section 3, and the mechanical equilibrium state of the monitoring section is recorded again. After completing ten consecutive excavation steps, the tunnel reaches section 4, and the mechanical equilibrium state of the monitoring section is recorded again. The internal force data at each stage are output through the built-in Fish function in FLAC3D, and a Python interface is used for automated extraction for subsequent safety assessment of the support structure.

[0108] In this embodiment, by pre-setting key monitoring sections at the longitudinal position of the lining structure and deploying internal force monitoring units, the mechanical response of the support structure during excavation can be dynamically captured. Through the construction process time step control logic, the script automatically triggers the data extraction of key nodes. The fully automated process improves data acquisition and calculation efficiency, laying a solid foundation for the subsequent data input of the target visualization model.

[0109] Reference Figure 8 , Figure 8 This is a flowchart illustrating the second embodiment of the data interaction-based visualization method for tunnel lining service performance indicators in this application. Figure 1 The first embodiment shown presents a second embodiment of the method for visualizing tunnel lining service performance indicators based on data interaction, as proposed in this application.

[0110] In the second embodiment, step S30 includes:

[0111] Step S301: Slice the internal force data of the structure to obtain a discrete coordinate set and an inclination angle set.

[0112] It should be noted that the raw data output from the numerical simulation can be partitioned and analyzed to extract key information. Specifically, step S301 includes: slicing the structural internal force data and reading the nodal coordinate data; using linear interpolation on the nodal coordinate data to generate a discrete coordinate set; and calculating the tilt angle between adjacent coordinate points based on the discrete coordinate set to generate a tilt angle set.

[0113] It should be noted that the discrete coordinate set can be understood as a set of spatial points generated through linear interpolation, used to accurately describe the geometric shape of the structure. The tilt angle set can be understood as the set of normal vector direction angles calculated based on the cross product of vectors from three adjacent points. For example, Python can be used to slice the structural internal force data file, read the content containing nodal coordinates, and use linear interpolation to obtain the discrete coordinate set D = {(x1,y1),(x2,y2),…,(x...}. n ,y n Using the line segment connecting any two adjacent points P in the coordinate set as a reference, determine the normal of that line segment and calculate its inclination angle. Specifically, for any three adjacent points P in the coordinate set... i-1 (x i-1 ,y i-1 ), P i (x i ,y i ) and P i+1 (x i+1 ,y i+1 The line segment P is determined by methods such as vector cross product. i-1 P i and P i P i+1 The direction vector of P is then used to obtain the normal vector of the plane enclosed by the two line segments. It is necessary to explain P. i-1 (x i-1 ,y i-1 ), P i (x i ,y i ) and P i+1 (x i+1 ,y i+1 The coordinates are taken from the discrete coordinate set D. The angle between the normal and the x-axis is calculated through the adjacent coordinate points of the element, and this angle is defined as the tilt angle θ of the point. i The calculation formula is expressed as formula (1).

[0114]

[0115] It should be noted that the corresponding angle set Θ = {θ1, θ2, ..., θ} n}

[0116] Step S302: Extract the internal force data of the structure according to the preset command to obtain a discrete internal force dataset.

[0117] It should be noted that the discrete internal force dataset includes bending moment sets, axial force sets, and shear force sets. For example, the Python pandas package can be used to automatically read internal force files and store them in a structured format, resulting in a discrete internal force dataset, including the bending moment set M = {M1, M2, ..., M...}. n}, Axial force collection N =

[0118] N1,N2,…,N n} and shear set Q={Q1,Q2,…,Q n}

[0119] Step S303: Input the discrete internal force dataset into the safety factor calculation model to obtain the safety factor set.

[0120] It should be noted that the safety factor set can be understood as a set of cross-sectional safety assessment parameters calculated based on the limit state design method. For example, a mathematical model for calculating the safety factor can be established based on mechanics theory. The stored internal force data is used as the input parameters of the model, combined with the reinforcement scheme, and the cross-sectional safety factor is calculated using the limit state design method, resulting in the safety factor set F = {F1, F2, ..., F...}. n}

[0121] Step S304: Calculate the rectangle width based on the discrete coordinate set. The rectangle width includes the internal force rectangle width and the safety factor rectangle width.

[0122] It should be noted that since the outlines are the same, the rectangle widths are consistent. Both the width of the internal force rectangle and the width of the safety factor rectangle can be expressed as b. For example, as in formula (2), two adjacent points are A... i and A i+1 Then the width b is the Euclidean distance between the two points.

[0123]

[0124] In formula (2), Represented as A i x and y coordinates Represented as A i+1 The x and y coordinates. It is necessary to explain A. i and A i+1 Taken from the discrete coordinate set D.

[0125] Step S305: Determine the rectangle length set based on the preset canvas size, discrete internal force dataset, and safety factor set.

[0126] Examples include formulas (3) to (6).

[0127]

[0128] It should be noted that the lengths of the internal force rectangle and the safety factor rectangle are determined based on the maximum display length l0 (selected according to the canvas size), the length of the internal force rectangle is determined based on the ratio of the internal force of the unit section to the maximum internal force, and the length of the safety factor rectangle is determined based on the ratio of the safety factor of the unit section to the maximum safety factor. In formulas (3) to (6), The length of the rectangle representing the bending moment, M i M represents the bending moment of the element section. max Indicates the maximum bending moment; The length of the rectangle representing the axial force, N i Represents the axial force at the element section, N max Indicates the maximum axial force; The length of the rectangle representing the shear force, Q i Q represents the shear force at the element section. max Indicates the maximum shear force; The length of the rectangle representing the safety factor, F i F represents the safety factor of the unit section. max This indicates the maximum safety factor. It constitutes a long set with degree L.

[0129] Step S306: Determine the initial visualization model based on the discrete coordinate set, tilt angle set, rectangle width, and rectangle length set.

[0130] It should be noted that step S306 includes: selecting target nodes from the discrete coordinate set, establishing a local coordinate system with the spatial coordinates of the target nodes as the origin; dynamically determining the axis of the local coordinate system according to the tilt angle set to obtain the corner coordinate set, the corner coordinates being used to characterize the rectangular outline of the internal force gradient; and determining the initial visualization model based on the local coordinate system and the corner coordinate set by combining the rectangle width and the rectangle length set.

[0131] For example, given the set of node coordinates A = {A1, A2, ..., A...} n}, tilt angle set Θ, rectangle width d, and length set degree L, with node A i A local coordinate system is established with the origin O' as the spatial coordinate system. The axis of the local coordinate system is determined based on the tilt dynamics, and the coordinates of the four corner points of the rectangular profile representing the internal force gradient are calculated. For different data relationships, a system is established as follows: Figure 9 The local coordinate system is shown. In the figure, the rectangle has a length of l. iThe rectangle has a width of d, and P is the point to the right of the origin of the local coordinate system. i,1 Point P, in counter-clockwise order, the corner points of the rectangle are P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, P11, P12, P13, P14, P15, P16, P17, P18, P1 ...6 i,1 P i,2 P i,3 P i,4 The coordinates of each point are calculated using formulas (7) to (14).

[0132]

[0133] In formulas (7) to (14), formulas (7) and (8) represent P i,1 The coordinate calculation formulas, formulas (9) and (10) represent P i,,2 The coordinate calculation formulas, formulas (11) and (12) represent P i,,3 The coordinate calculation formulas, formulas (13) and (14) represent P i,,4 The formula for calculating coordinates. The coordinate set R formed by the four corner points of the rectangle is calculated. * ={P i,j The formula |1≤i≤100,1≤j≤4} uses the positions of the four corner points of a rectangle as input variables. It uses Python's matplotlib.pyplot package to plot the rectangle and generate a rectangular graphic representing the internal force gradient. Specifically, the internal force gradient is visualized by using different rectangle lengths.

[0134] In this embodiment, a high-precision geometric skeleton is constructed through discrete coordinate interpolation and dynamic calculation of the tilt angle. The tilt angle can eliminate the projection distortion of the tunnel surface and improve the adaptability of complex structures. By utilizing an automated internal force extraction and safety factor calculation model, the limitations of traditional methods in outputting unit internal forces and safety factors are overcome. Furthermore, spatial gradient encoding of mechanical parameters is achieved through rectangular width and length sets, and oriented rectangular contours are generated in the local coordinate system. Parametric driving of the entire process from modeling to visualization can adapt to complex geological conditions.

[0135] Reference Figure 10 , Figure 10 This is a flowchart illustrating the third embodiment of the data interaction-based visualization method for tunnel lining service performance indicators in this application. Figure 8 The second embodiment shown presents a third embodiment of the method for visualizing tunnel lining service performance indicators based on data interaction, as proposed in this application.

[0136] In the third embodiment, step S40 includes:

[0137] Step S401: Based on the RGB color model, a gradient color mark sequence is generated using a discretized gradient control algorithm.

[0138] It should be noted that the discretized gradient control algorithm can be a piecewise progressive color channel intensity adjustment algorithm, which achieves non-linear hue transition by transferring increments. The gradient color mark sequence can be understood as an ordered set of RGB color marks generated by the discretized gradient control algorithm. Specifically, step S401 includes: based on the RGB color model, setting the starting color mark as the first color; increasing the color channel intensity value by a preset step size using the discretized gradient control algorithm; when the first color channel reaches a preset saturation threshold, transferring the first increment to the second color channel, where the first increment is the increment corresponding to the first color channel; when the second color channel reaches the preset saturation threshold, transferring the second increment to the third color channel, generating a gradient color mark sequence, where the ending color mark of the gradient color mark sequence is the third color, and the second increment is the increment corresponding to the second color channel.

[0139] It should be noted that the first color can be blue, the second color can be green, and the third color can be red. Specifically, the initial color stop is set to pure blue (R:0, G:0, B:1); the color channel intensity values ​​are incremented by a preset step size using a discretized gradient control algorithm; when the blue channel reaches a saturation threshold, the increment is transferred to the green channel; when the green channel reaches a saturation threshold, the increment is transferred to the red channel; thus generating a gradient color stop sequence with a final color stop of pure red (R:1, G:0, B:0).

[0140] For example, in the RGB standard color gamut space, nine typical colors are systematically and uniformly selected as reference color scales. The RGB color model is an additive color model, and the intensity values ​​of its color channels (Red R, Green G, Blue B) are all in the range of [0,1]; where (0,0,0) corresponds to black and (1,1,1) corresponds to white. To enhance visual contrast, the hue H is gradient-adjusted: the hue range linearly transitions from pure blue (RGB:(0,0,1)) to pure red (RGB:(1,0,0)); a discretized gradient control algorithm is used to generate nine-level gradient color scales (C1~C9): the intensity value of each color channel increases in increments of 0.5; when a channel value reaches its upper limit, its increment is transferred to the previous channel according to its weight. It can be understood that the upper limit is 1. For example, after the blue channel is saturated, the increment is transferred to the green channel. This algorithm achieves continuous hue change through the segmented progression of intensity values ​​between channels, simulating nonlinear color response characteristics.

[0141] It should be noted that this embodiment supports replacement with other color encoding strategies to meet the needs of different visualization scenarios.

[0142] Step S402: Extract continuous datasets based on the initial visualization model and determine the target numerical range.

[0143] It should be noted that the continuous dataset includes all internal force and safety factor values ​​in the initial visualization model, and the target numerical range includes the extreme value range of the continuous dataset. This means that the target numerical range also includes the target numerical ranges for different internal forces and the target numerical ranges for safety factors. For example, based on the collected internal force dataset and safety factor dataset, their upper and lower boundary values ​​are clearly defined.

[0144] Step S403: Divide the target value range into multiple continuous sub-intervals.

[0145] For example, based on the boundary value range, the continuous numerical interval is evenly divided into nine equidistant sub-intervals (I1 to I9).

[0146] Step S404: Establish the mapping relationship between multiple continuous sub-intervals and the gradient color mark sequence.

[0147] It should be noted that the mapping relationship between continuous sub-intervals and gradient color mark sequences can be understood as a one-to-one correspondence binding rule between sub-intervals and color marks. For example, the mapping relationship can be referred to formula (15).

[0148]

[0149] The nine sub-intervals (I1~I9) are bound one-to-one according to their numerical ascending order, generating a nine-level gradient color stop (C1~C9), which is then established as follows: Figure 11 The mapping relationship between numerical values ​​and colors shown demonstrates the hexadecimal color encoding corresponding to each color mark, the standardized RGB triplet, and the mapping relationship between each color mark level and the discretized numerical range.

[0150] Step S405: Fill the initial visualization model with color according to the mapping relationship to obtain the target visualization model.

[0151] It should be noted that step S405 includes: determining the corresponding target internal force value and target safety factor value based on each rectangular outline in the initial visualization model; determining the sub-interval number according to the target internal force value and target safety factor value; indexing the corresponding target color mark through the sub-interval number; calling the rectangular primitive drawing interface of the graphics library, setting the fill color parameter to the target color mark, and obtaining the target visualization model.

[0152] It should be noted that the rectangular primitive drawing interface can be a vector graphics programming interface provided by a graphics library. For example, based on the mapping relationship between multiple consecutive sub-intervals and gradient color stop sequences, the following rendering process can be executed: based on the sub-interval number I... i The color code C corresponding to the index i Call the rectangular primitive drawing interface of the graphics library and set the fill color parameter to C. iThe RGB color scale is used. Finally, the tunnel cross-section outline is drawn in the color scale mapping area to achieve a precise correlation between structural mechanical parameters and visualization. The final generated visualization of internal force distribution is shown below. Figures 12 to 15 As shown.

[0153] In this embodiment, a discrete gradient control algorithm is used to generate gradient color stops that conform to the characteristics of human visual perception, overcoming the color gradation confusion caused by traditional linear gradients. By combining equal-width sub-interval partitioning with numerical and color mapping, precise conversion from mechanical parameters to color space is achieved. Furthermore, a rectangular primitive interface is used to complete data-driven automated rendering, ultimately generating a dual-channel visualization model that integrates length gradient (relative magnitude of internal force) and color gradient (absolute danger level), achieving a precise correlation between structural mechanical parameters and visual representation.

[0154] In one implementation, after obtaining the target visualization model, it is applied to the monitoring of the cross-section. The graph showing the variation of crown internal forces and safety factor with the number of excavation steps is shown below. Figures 16 to 19 As shown. The monitoring section was monitored at t0 (initial time), t3, t5, and t... 10 The mechanical equilibrium state at four specific moments was analyzed, with the arch crown as the key analysis point. Based on monitoring data at this point, curves depicting the changes in internal forces (axial force N, bending moment M, shear force Q) and safety factor at these four moments were plotted. The analysis results show that the bending moment, shear force, and safety factor exhibit a monotonically decreasing trend over time, while the axial force shows a non-linear change characteristic of first increasing and then decreasing. These calculation results provide important quantitative analytical basis for structural design optimization in tunnel engineering, stability assessment of support structures during construction, and long-term operational safety evaluation.

[0155] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the data interaction-based visualization method for tunnel lining service performance indicators. Any simple modifications based on this technical concept are within the scope of protection of this application.

[0156] This application also provides a visualization device for tunnel lining service performance indicators based on data interaction; please refer to [reference needed]. Figure 20 The data interaction-based tunnel lining service performance visualization device includes:

[0157] The structural parameter acquisition module 10 is used to acquire geological structure model parameters and lining structure design parameters to generate a numerical calculation model.

[0158] The structural internal force monitoring module 20 is used to establish monitoring points in the numerical calculation model and acquire structural internal force data.

[0159] The initial model building module 30 is used to post-process the internal force data of the structure and build an initial visualization model through numerical gradient processing.

[0160] The target model building module 40 is used to perform color gradient processing on the initial visualization model in combination with the RGB color model to obtain the target visualization model, which is used to visualize the structural internal force data and structural safety factor.

[0161] The tunnel lining service performance index visualization device based on data interaction provided in this application adopts the tunnel lining service performance index visualization method based on data interaction in the above embodiments, which can solve the technical problem that it is impossible to balance the accuracy of structural internal force calculation and the intuitive visualization of calculation results in complex environments. Compared with the prior art, the beneficial effects of the tunnel lining service performance index visualization device based on data interaction provided in this application are the same as the beneficial effects of the tunnel lining service performance index visualization method based on data interaction provided in the above embodiments, and other technical features in the tunnel lining service performance index visualization device based on data interaction are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.

[0162] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for visualizing the service performance indicators of tunnel lining based on data interaction, characterized in that, The method includes: Obtain geological structure model parameters and lining structure design parameters, and generate a numerical calculation model; Monitoring points are established in the numerical calculation model to obtain structural internal force data; The internal force data of the structure are post-processed, and an initial visualization model is established through numerical gradient processing; The initial visualization model is processed by color gradient processing using the RGB color model to obtain the target visualization model, which is used to visualize structural internal force data and structural safety factor.

2. The method as described in claim 1, characterized in that, The step of post-processing the structural internal force data and establishing an initial visualization model through numerical gradient processing includes: The internal force data of the structure is sliced ​​to obtain a discrete coordinate set and a tilt angle set; The internal force data of the structure is extracted according to the preset command to obtain a discrete internal force dataset; The discrete internal force dataset is input into the safety factor calculation model to obtain the safety factor set; The rectangle width is calculated based on the discrete coordinate set, and the rectangle width includes the internal force rectangle width and the safety factor rectangle width. The rectangle length set is determined based on the preset canvas size, the discrete internal force dataset, and the safety factor set. An initial visualization model is determined based on the discrete coordinate set, the tilt angle set, the rectangle width, and the rectangle length set.

3. The method as described in claim 2, characterized in that, The step of slicing the internal force data of the structure to obtain a discrete coordinate set and a tilt angle set includes: The internal force data of the structure is sliced, and the node coordinate data is read. Linear interpolation is used to generate a discrete coordinate set from the node coordinate data; The tilt angle between adjacent coordinate points is calculated based on the discrete coordinate set, and a tilt angle set is generated.

4. The method as described in claim 2, characterized in that, The step of determining the initial visualization model based on the discrete coordinate set, the tilt angle set, the rectangle width, and the rectangle length set includes: Select target nodes from the discrete coordinate set, and establish a local coordinate system with the spatial coordinates of the target nodes as the origin; The axial direction of the local coordinate system is dynamically determined based on the set of tilt angles, and a set of corner coordinates is obtained. The corner coordinates are used to characterize the rectangular profile of the internal force gradient. The initial visualization model is determined by combining the rectangle width and the rectangle length set, based on the local coordinate system and the corner coordinate set.

5. The method according to any one of claims 1 to 4, characterized in that, The step of performing color gradient processing on the initial visualization model using the RGB color model to obtain the target visualization model, which is used to visualize structural internal force data and structural safety factors, includes: Based on the RGB color model, a gradient color mark sequence is generated using a discretized gradient control algorithm; Based on the initial visualization model, a continuous dataset is extracted to determine the target numerical range; The target numerical range is divided into multiple continuous sub-intervals; Establish a mapping relationship between the multiple continuous sub-intervals and the gradient color mark sequence; The initial visualization model is filled with color according to the mapping relationship to obtain the target visualization model.

6. The method as described in claim 5, characterized in that, The step of generating a gradient color stop sequence based on the RGB color model using a discretized gradient control algorithm includes: Based on the RGB color model, the starting color stop is set to the first color; The color channel intensity value is increased incrementally by a preset step size using a discretized gradient control algorithm. When the first color channel reaches a preset saturation threshold, the first increment is transferred to the second color channel, where the first increment is the increment corresponding to the first color channel. When the second color channel reaches a preset saturation threshold, the second increment is transferred to the third color channel to generate a gradient color stop sequence. The terminating color stop of the gradient color stop sequence is the third color, and the second increment is the increment corresponding to the second color channel.

7. The method as described in claim 5, characterized in that, The step of filling the initial visualization model with color according to the mapping relationship to obtain the target visualization model includes: Based on each rectangular contour in the initial visualization model, determine the corresponding target internal force value and target safety factor value; The sub-interval number is determined based on the target internal force value and the target safety factor value, respectively. The target color code is indexed by the sub-interval number; Call the rectangular primitive drawing interface of the graphics library, set the fill color parameter to the target color mark, and obtain the target visualization model.

8. The method as described in claim 1, characterized in that, The steps of obtaining geological structure model parameters and lining structure design parameters, and generating a numerical calculation model, include: A data interaction interface is constructed, which is used to transmit model parameters, modeling calculations, and data processing. The geological structure model parameters and lining structure design parameters are obtained through the data interaction interface, and a numerical calculation model is generated.

9. The method as described in claim 8, characterized in that, The step of obtaining geological structure model parameters and lining structure design parameters, and generating a numerical calculation model, further includes: Based on the preset embedded command template and the geological structure model parameters and lining structure design parameters, a one-sided mesh of the numerical calculation model is generated. Based on the single-sided grid of the numerical calculation model, the other half of the model corresponding to the single-sided grid is symmetrically generated through a mapping function to obtain the numerical calculation model.

10. A visualization device for tunnel lining service performance indicators based on data interaction, characterized in that, The device includes: The structural parameter acquisition module is used to acquire geological structure model parameters and lining structure design parameters to generate a numerical calculation model; The structural internal force monitoring module is used to establish monitoring points in the numerical calculation model and acquire structural internal force data. The initial model building module is used to post-process the internal force data of the structure and build an initial visualization model through numerical gradient processing. The target model building module is used to perform color gradient processing on the initial visualization model in conjunction with the RGB color model to obtain the target visualization model, which is used to visualize the structural internal force data and structural safety factor.

Images