A digital evaluation platform for slope stability with multi-source information fusion at the tunnel entrance

Abstract

The present invention discloses a digital evaluation platform for slope stability with multi-source information fusion at the tunnel entrance, comprising: a data acquisition and preprocessing module for preprocessing the three-dimensional point cloud data of the tunnel entrance; a multi-source information fusion and integration module for constructing a three-dimensional geological model and performing GIS spatial analysis; a statistical slope stability evaluation module for constructing a digital elevation model and analyzing the high-risk areas of the tunnel entrance slope based on the weighted information method; a slope numerical simulation function module for extracting the slope step lines from the preprocessed three-dimensional point cloud data of the tunnel entrance, constructing a tunnel entrance solid model based on geological exploration data, and performing numerical simulation calculations on the tunnel entrance solid model by dividing grids; a statistical analysis module for obtaining a landslide risk map of the weighted information method, and the platform can efficiently evaluate the slope stability of the tunnel entrance.

Description

Technical Field

[0001] The present invention belongs to the field of tunnel portal slope stability evaluation, and relates to a digital evaluation platform for slope stability with multi-source information fusion at the tunnel portal. Background Art

[0002] During the excavation of a tunnel, the side rock mass at the tunnel portal is in a dynamic evolution process, resulting in the stability of the slope being affected by many dynamic and complex factors. Therefore, it is necessary to study the slope stability at the tunnel portal. Based on the comprehensive evaluation method of multiple indicators and multiple methods, the high-risk areas of the slope are determined, and key reinforcement and protection are carried out for the high risks. A visual slope stability evaluation platform for dynamic real-time monitoring is developed, which is conducive to reducing the risk of slope instability at the tunnel excavation portal, thereby effectively improving the engineering safety during the tunnel excavation process and saving the cost of slope reinforcement.

[0003] It is very difficult to integrate the functions of existing early warning platforms. The platforms still remain at the level of "checking and looking". However, with the leading of big data, Internet of Things and artificial intelligence technologies, the future digital slope stability evaluation platform should be based on the comprehensive tunnel portal geological exploration technologies and equipment for excavation, mining and exploration. By constructing an information data platform, dynamic information processing can be realized. Therefore, although the existing information platforms for tunnel portal slope stability evaluation collect multi-source and large amounts of data, the platforms are still in the technical bottleneck of "emphasizing collection and neglecting analysis". Most platforms use single monitoring data as the threshold to evaluate slope stability. Therefore, in view of the difficulty in predicting the slope under the situation of dynamic mining, it is the trend of the future slope stability evaluation platform to adopt multiple analysis means and multiple early warning indicators for comprehensive inversion. Summary of the Invention

[0004] The purpose of the present invention is to overcome the above-mentioned disadvantages of the prior art, and provides a digital evaluation platform for slope stability with multi-source information fusion at the tunnel portal, which can efficiently evaluate the slope stability at the tunnel portal.

[0005] To achieve the above purpose, the digital evaluation platform for slope stability with multi-source information fusion at the tunnel portal described in the present invention includes:

[0006] A data acquisition and preprocessing module, which is used for preprocessing the obtained three-dimensional point cloud data of the tunnel portal;

[0007] A multi-source information fusion and integration module, which is used for constructing a three-dimensional geological model for the preprocessed three-dimensional point cloud data of the tunnel portal and performing GIS spatial analysis;

[0008] A statistical slope stability evaluation module, which is used for constructing a digital elevation model based on the preprocessed three-dimensional point cloud data of the tunnel portal and analyzing the high-risk areas of the tunnel portal slope based on the weighted information method;

[0009] The slope numerical simulation function module is used to extract the slope step line from the pre-processed 3D point cloud data of the tunnel opening, construct the tunnel opening entity model based on the geological exploration data, and perform numerical simulation calculations on the tunnel opening entity model by dividing the grid;

[0010] The statistical analysis module is used to evaluate the digital elevation model, determine the risk factors based on the geological survey data, use the weighted information method to perform statistical analysis on the risk factors, and finally obtain the landslide risk map of the weighted information method.

[0011] The data acquisition and preprocessing module includes an unmanned aerial vehicle tilt photogrammetry module, a Lidar three-dimensional laser scanning data module and a data preprocessing submodule.

[0012] The Lidar three-dimensional laser scanning data module uses a total station scanner to perform a full-process scan of the tunnel entrance slope to obtain three-dimensional point cloud data of the tunnel entrance in the earth's absolute coordinate system.

[0013] The UAV oblique photogrammetry module uses the camera mounted on the UAV to take images of the slope of the tunnel entrance, and then constructs an oblique photography model based on Smart3D;

[0014] The data preprocessing submodule removes noise data of the three-dimensional point cloud data obtained by the Lidar three-dimensional laser scanning data module and extracts three-dimensional step lines. The voxel downsampling method is used to reduce the data size of the collected point cloud data, and progressive morphological filtering is used to filter the non-ground point cloud data.

[0015] The multi-source information fusion integration module uses SuperMap GIS software to store pre-processed tunnel slope 3D point cloud data, drone oblique photography data, deformation monitoring data of potential landslide areas and geological exploration data according to different data types, and uploads them to the data cloud based on SuperMap iServer. Based on the SuperMapWebGL development kit, the 3D model, 2D vector model and monitoring relational data are visualized in a 3D scene, and the collected 3D model is spatially analyzed based on 3D spatial analysis.

[0016] The statistical slope stability evaluation module uses a numerical simulation method and reduced strength theory to obtain the sliding failure surface of the tunnel entrance slope; based on the reduced strength method, the three-dimensional geological mathematical model of the open-pit mine is calculated to obtain the stress, strain and damage area of ​​the open-pit mine, so as to analyze and obtain the risk area of ​​the tunnel entrance slope.

[0017] The statistical analysis module uses a weighted information method that combines the analytic hierarchy process with the information method to evaluate the stability of the tunnel portal slope.

[0018] The information volume method performs grid pixel statistics on the open-pit mine landslide area and the open-pit mine area according to the GIS grid pixel statistics function, and performs statistical calculations on the landslide influencing factors based on the information volume calculation theoretical formula;

[0019] Each influencing factor and the weighted information assigned are assigned to a grid map, and the grid maps of all influencing factors are algebraically added through the raster map algebraic operation of SuperMap GIS to obtain the tunnel entrance slope risk cloud map using the weighted information method.

[0020] The monitoring and early warning information module is based on the high-risk areas obtained by comprehensive analysis using statistical and numerical simulation methods, and focuses on monitoring using microseismic and GPS displacement monitoring methods. Among them, 9 sensors are installed on the slope, and a slope microseismic data acquisition system is established for microseismic monitoring. The field data is transmitted over long distances to the monitoring and early warning center platform through network technology.

[0021] The present invention has the following beneficial effects:

[0022] During specific operation, the digital assessment platform for slope stability with multi-source information fusion at tunnel entrances described in the present invention pre-processes the acquired three-dimensional point cloud data of tunnel entrances through a data acquisition and pre-processing module, and fully combines the weight calculation of each factor of the hierarchical analysis method and the statistical objectivity of the information quantity statistical algorithm, so that the open-pit mine landslide risk assessment is simple and easy to operate with higher assessment accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a functional framework diagram of the present invention;

[0024] Figure 2 for Figure 1 Functional framework diagram of data acquisition and preprocessing module;

[0025] Figure 3 This is the schematic diagram of point cloud through filtering;

[0026] Figure 4 Schematic diagram of estimating normal vectors for point clouds;

[0027] Figure 5 This is the functional framework diagram of the multi-source information fusion integration module;

[0028] Figure 6 It is the slope and aspect analysis function map of the multi-source information fusion integration module;

[0029] Figure 7Functional diagram of scenario measurement and analysis for the multi-source information fusion and integration module;

[0030] Figure 8 Functional diagram of scenario coordinate query for the multi-source information fusion and integration module;

[0031] Figure 9 Implication element diagram of the finite element result model;

[0032] Figure 10 Export interface diagram of the finite element to hypergraph finite element result model;

[0033] Figure 11 Export effect diagram of the finite element to hypergraph finite element result model;

[0034] Figure 12 Tunnel entrance landslide risk map of the weighted information method;

[0035] Figure 13 Schematic diagram of dynamically updating DEM;

[0036] Figure 14 Location map of the microseismic monitoring area;

[0037] Figure 15 DEM data effect diagrams before and after generating the excavation point cloud data;

[0038] Figure 16 Frame diagram of ITRF05 of GPS;

[0039] Figure 17 The present invention Figure 1 Displacement monitoring equipment diagram of the monitoring and early warning information module in the present invention;

[0040] Figure 18 The present invention Figure 1 Displacement monitoring and early warning effect diagram of the monitoring and early warning information module in the present invention. Detailed implementation manners

[0041] In order to enable those skilled in the art of the present technology to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments, and are not intended to limit the scope of the present invention disclosure. In addition, in the following description, the description of known structures and technologies is omitted to avoid unnecessarily confusing the concepts disclosed in the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0042] The structural schematic diagrams according to the disclosed embodiments of the present invention are shown in the accompanying drawings. These figures are not drawn to scale, where for the purpose of clear expression, certain details are enlarged and certain details may be omitted. The shapes of various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary. In practice, there may be deviations due to manufacturing tolerances or technical limitations, and those skilled in the art can design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0043] Reference Figure 1 and Figure 2 , the digital evaluation platform for slope stability with multi-source information fusion at the tunnel entrance of the present invention includes a data acquisition and preprocessing module, a multi-source information fusion and integration module, a numerical simulation analysis module, a statistical analysis module, and a monitoring and early warning information module. The numerical simulation analysis module includes the statistical slope stability evaluation module and the slope numerical simulation function module;

[0044] The data acquisition and preprocessing module is used to preprocess the acquired three-dimensional point cloud data of the tunnel entrance and transmit it to the multi-source information fusion and integration module, the statistical slope stability evaluation module, and the slope numerical simulation function module;

[0045] The multi-source information fusion and integration module is used to construct a three-dimensional geological model for the preprocessed three-dimensional point cloud data of the tunnel entrance and perform GIS spatial analysis;

[0046] The statistical slope stability evaluation module is used to construct a digital elevation model (DEM) based on the preprocessed three-dimensional point cloud data of the tunnel entrance and analyze the high-risk areas of the tunnel entrance slope based on the weighted information method;

[0047] The slope numerical simulation function module is used to extract the slope step lines from the preprocessed three-dimensional point cloud data of the tunnel entrance, update the surface model of the tunnel entrance using three-dimensional interpolation technology, construct a solid model of the tunnel entrance based on geological exploration data, and perform numerical simulation calculations on the solid model of the tunnel entrance by dividing grids.

[0048] As Figure 2 shown, the data acquisition and preprocessing module includes an oblique photography model construction module, a three-dimensional point cloud data acquisition module, a three-dimensional point cloud data preprocessing module, a DEM model construction module, and a three-dimensional step line extraction module. The three-dimensional point cloud data acquisition module is connected to the three-dimensional point cloud data preprocessing module, and the three-dimensional point cloud data preprocessing module is respectively connected to the DEM model construction module and the three-dimensional step line extraction module.

[0049] It should be noted that the present invention obtains an oblique photography model and three-dimensional point cloud data by means of unmanned aerial vehicle (UAV) oblique photogrammetry. Among them, the three-dimensional point cloud data obtained under the absolute geodetic coordinate system has data types of X coordinate, Y coordinate, Z coordinate, and RGB value. The three-dimensional point cloud data preprocessing module performs scanning parameter setting, noise data removal, and point cloud data segmentation on the three-dimensional point cloud data obtained by laser scanning. The specific implementation steps are as follows:

[0050] 11) Conduct UAV oblique photogrammetry operations on the open-pit mine, and construct an oblique photography model in Smart3D based on the obtained high-definition photo data;

[0051] As Figure 1 shown, it should be noted that before constructing the oblique photography model, it is necessary to confirm the photo POS information and control point information in this measurement operation to ensure the accuracy of the constructed oblique photography model;

[0052] 12) Downsample the three-dimensional point cloud data obtained by UAV oblique photogrammetry, and perform voxel downsampling on the point cloud based on the downSample component in PCL C++;

[0053] 13) Denoise the data according to the downsampled three-dimensional point cloud data, filter out non-ground points such as trees, buildings, and grass, and use progressive morphological filtering as:

[0054]

[0055] where c is the grid size, s is the terrain slope parameter, w k is the window size of the k-th filtering, h0 is the initial height difference threshold, h max is the maximum height difference threshold, and h k is the height difference threshold under the current filtering window.

[0056] The specific working process of the three-dimensional step line extraction module is as follows:

[0057] 21) Based on the filtered three-dimensional point cloud, perform point cloud spacing statistics to obtain the average point cloud spacing in the XYZ three directions. Use the passthrough filter component in PCL C++ to segment the height in the Z direction. The passthrough filter clips the specified dimension and specified interval of the point cloud model, retains the points within the interval, and filters out the points outside the interval. After statistics, the minimum value of the Z axis is 0 meters, the maximum value is 401.92 meters, and the point average spacing is 0.95 meters. Taking the Z axis as the filtering dimension, the filtering interval is set to 1.5 meters, and the adjacent filtering intervals are set to 3 meters (the contour interval is 3 meters). Perform passthrough filtering on the model. The core code is as Figure 3 shown;

[0058] 22) Based on the idea of ​​shape contour extraction, each point cloud is retrieved through KdTree and its normal vector is calculated, such as Figure 4 As shown;

[0059] 23) Use the interface function isBoundaryPoint() in PCL C++ to determine whether the point is an edge point, so as to achieve the effect of edge extraction;

[0060] 24) performing curve fitting on the step line three-dimensional point cloud obtained by preprocessing, and performing curve fitting on the edge point cloud obtained by using a B-spline curve;

[0061] The construction process of the DEM model in the present invention is as follows: the three-dimensional point cloud data of the tunnel slope obtained by preprocessing is imported into Matlab in Las format, and the DEM of the three-dimensional point cloud is constructed by inverse distance weighted interpolation; the inverse distance weighted interpolation is based on the principle of similarity, that is, the closer the distance to the interpolation point is, the greater the weight is. On the contrary, when the point is outside a certain range of the interpolation point, the point can be ignored.

[0062] like Figure 5 As shown, the multi-source information fusion integration module includes a three-dimensional space analysis module, a panoramic three-dimensional roaming module and a model attribute query module:

[0063] 3D spatial analysis module, used for 3D spatial analysis based on slope and aspect analysis, scene measurement function, spatial coordinate query and terrain cut and fill analysis in SuperMap iServer;

[0064] Among them, the slope and aspect analysis is secondary developed based on the Cesium.js development kit in the SpuerMap WebGL library. The slope and aspect analysis uses the same layered coloring principle as the terrain inundation analysis to render the colors corresponding to different slope values, and the slope direction is represented by an arrow. It uses the SlopeSetting module of Cesium for function setting. This function analysis mode is divided into two types: one is to analyze the selected area; the other is to analyze the entire mine area. The demonstration effect is as follows Figure 6 As shown;

[0065] SuperMap's GIS development package Cesium has a development module MeasureHandler specifically for scene measurement functions. In the Dagushan open-pit mine GIS analysis function, the present invention divides the measurement function into three modules: distance measurement, area measurement, and height measurement. All measurements are in ground-based mode, and the demonstration effect is as follows: Figure 7 As shown;

[0066] Scene coordinate query, that is, secondary development based on Cesium's scene coordinate picking module pickPosition, the demonstration effect is as followsFigure 8 as shown

[0067] The numerical simulation analysis module constructs a geological model based on the stepped line obtained through preprocessing, conducts landslide risk based on finite element numerical calculation software, and uploads the numerical calculation result model by developing a finite element to platform data interface program, enabling the platform to have the function of numerical simulation analysis. The specific working process is as follows:

[0068] 31) Extract the coordinate points of the polyline with elevation from the preprocessed three-dimensional stepped line, and use the Delaunay triangulation algorithm to construct a triangular mesh from all the scattered point sets representing the surface features. These 3Dface surfaces without overlap and gaps are denoted as the digital terrain model (DTM);

[0069] 32) Import the DTM model into Rhino for the construction of the solid model. The construction of the geological model requires the data support of the open-pit mine profile diagram. Construct the geological model through the geological profile diagram, and divide the rock mass area and lithology of the constructed geological model according to the geological exploration data;

[0070] 33) Import the geological model into Hypermesh for mesh division. After statistics, it is obtained that the geological model has a total of 8,961,375 mesh elements;

[0071] 34) Assign parameters to the physical and mechanical properties of the rock materials in the relevant areas of the tunnel portal slope based on comprehensive geological exploration data and relevant literature. Use the gravity balance condition and the reduction strength coefficient method for numerical calculation. The boundary condition is that the four sides of the model are roller supports, and the bottom plate is selected as a fixed constraint;

[0072] 35) Develop a reduction strength algorithm through Comsol app, substitute the mesh model with the physical parameters of the rock assigned, and calculate the stress, strain, and damage result models;

[0073] 36) Based on the result model calculated by Comsol, develop its finite element data export interface program in Java language using the secondary development function of Comsol.app, and save the data in.dat format;

[0074] The storage of Comsol calculation results is divided into two categories. One is the coordinates and displacements of each node according to the numbers of all nodes Node (memory address numbers, stored in a chain array). The second is to store the node numbers, stresses, etc. of each unit according to the numbers of all calculation units Element (also memory address numbers, stored in the chain array).

[0075] During the model reconstruction process, the topological relationship between elements and nodes has been built into the COMSOL mesh modeling process. The nodes of all elements are arranged in the order shown in the following figure to obtain an array of element node numbers (already arranged in the default order). Thus, the information of the nodes on each element face can be known, and they are arranged in a counterclockwise order. According to the triple topology of node - element - face, the classification of elements in COMSOL and the default sorting of nodes in elements are the keys to establishing the mesh topological relationship.

[0076] Based on the above finite element data export principle, this invention follows: the first row: the total number of nodes; the second row: the total number of elements; the third to (total number of nodes + 2) rows: node coordinates, displacements, stresses, and strains (16 columns in each row); the (total number of nodes + 3)th row to (total number of elements + total number of nodes + 2)th row: element type, node numbers of the element. Using the secondary development function of the COMSOL app, a finite element data export interface program is developed in Java language, and the data is saved in the.dat format. As Figure 9 shown is the finite element element export code;

[0077] Among them, the.dat format file contains the following elements of the finite element simulation result model:

[0078] As Figure 10 shown, according to the first row: the total number of nodes; the second row: the total number of elements; the third to (total number of nodes + 2) rows: node coordinates, displacements, stresses, and strains (16 columns in each row); the (total number of nodes + 3)th row to (total number of elements + total number of nodes + 2)th row: element type, node numbers of the element;

[0079] 37) Use the components of SuperMap iObject.NET to develop the interface program. When the scale of the three - dimensional finite element calculation model is small, the graphics can be completely drawn according to the topology of the elements, and all the details of the three - dimensional mesh can be visually displayed. That is, a tetrahedral element shows 6 edges and 4 faces, or a block element shows 12 edges and 6 faces. Among these elements, most of them are overlapping and invisible, and the actually visible elements are often less than 1 / 100 or even 1 / 1000 of the total number.

[0080] In three - dimensional finite element data, except that each node data is unique, the edges and faces of elements overlap. Among them, the surface elements do not overlap. Therefore, sorting can be used to move the same elements to adjacent positions, and then by comparing the positions from beginning to end, all the elements with repeated content can be detected. If dealing with a large - scale element sequence, the computational amount will be huge. At this time, the quick - sort algorithm can be used to handle it.

[0081] The cutting idea for the sectional view is as follows: First, find the elements that intersect with the section, then cut each one and combine them into a complete section. In the drawing after cutting is completed, the drawing of the edges depends on the connection relationship of the points, and the drawing of the surfaces depends on the connection method of the edges. When implementing the cutting of three-dimensional finite element data, it is necessary to reconstruct a new three-dimensional topological relationship. There will be new vertices, edges, and surfaces on the section, and among them, the adjacency relationship of the edges, that is, the connection order of the vertices, is the most important. Therefore, the specific algorithm steps are as follows:

[0082] 371) Determine whether an element intersects with the section based on whether there are its nodes on both the upper and lower sides of the section;

[0083] 372) Release all the element edges to form an intersection line array, and at the same time record the edge numbers and their internal numbers in the element array;

[0084] 373) Delete duplicate elements and compact the edge array, and update the corresponding numbers in the intersecting element array;

[0085] 274) Calculate the intersection points of each edge with the section, interpolate the values at the intersection points, and record them in an intersection point array;

[0086] 375) Induce the intersection lines for each element one by one, and construct and fill the intersection point adjacency list;

[0087] 376) Traverse the intersection point adjacency list to form the element section, and then combine it into the final model section.

[0088] Based on component-based development, a three-dimensional simulation result visualization program is developed, and its demonstration effect is as Figure 11 shown;

[0089] The statistical analysis module evaluates the DEM model obtained through preprocessing, determines the risk factors based on geological exploration data, and uses the weighted information method to statistically analyze the risk factors and finally obtain the landslide risk map of the weighted information method. The specific process is as follows:

[0090] 41) According to the detail level of the on-site geological data, select 6 landslide influencing factors, namely slope, aspect, slope type, fault, elevation, and lithology;

[0091] 42) Use the analytic hierarchy process to conduct hierarchical analysis on the above 6 factors. The specific process is as follows:

[0092] 421) Divide the factors according to the decision-making level, criterion level, and target level;

[0093] 422) Conduct pairwise expert scoring on each factor according to experts or the engineering analogy method, as shown in Table 1;

[0094] 423) Compare the scores between factors, construct a comparison matrix according to the divided decision levels, and calculate the eigenvalue and eigenvector of the matrix, as shown in Table 2;

[0095] 424) Conduct a consistency analysis on the weights of the evaluation factors;

[0096] When the analytic hierarchy process calculates the eigenvector of the matrix, it also calculates the maximum eigenvalue λ corresponding to the eigenvector of each level max , and the difference between the maximum eigenvector and the matrix order (consistency index CI) can be used to measure the inconsistency of the matrix, thereby verifying the objective evaluation of the matrix, that is

[0097] CI A =(λ max -n) / (n - 1)

[0098] Among them, CIA is the consistency index; n is the order of the judgment matrix of the analytic hierarchy process;

[0099] To prevent the matrix from affecting the consistency index due to randomness, the present invention refers to the random consistency index RI table. The consistency index increases correspondingly with the increase of the uncertainty of the matrix order.

[0100] Combining the consistency index and the index that causes deviation of the random matrix, taking the ratio between the two, the final test coefficient CR is obtained. If CR < 0.1, it is considered that the weight passes the consistency analysis and meets the objective evaluation standard, and the data weight can be used; if CR > 0.1, it is considered that the consistency analysis fails, and the weight value needs to be re - obtained:

[0101] CR = CI / RI

[0102] For the two scheme layers and one decision layer of the present invention to assign weights, their consistency ratios are: for the topographic and geomorphic factor CR1 is 0, for the geological structure factor CR2 is 0, and for the total layer CR3 is 0. Therefore, the weights pass the consistency test and are credible;

[0103] 425) Normalize the eigenvector of the judgment matrix corresponding to each factor.

[0104] 43) Based on the information - quantity method, conduct raster pixel statistics and information - quantity calculation on landslide factors. The specific process is as follows:

[0105] 431) Pixel statistics of the open - pit mine area and pixel statistics of the historical landslide area;

[0106] Based on the raster statistics function in SuperMap iDesktop, it is statistically obtained that the total number of raster cells in the open-pit mine is 1,676,499. According to the landslide positions provided on-site, the historical landslide surfaces are drawn. After statistics, the raster value of the landslide area is 12,292;

[0107] 432) Statistically calculate and compute the pixel values in each factor according to the information quantity formula;

[0108]

[0109] Among them, W is the unit area within the study area; W0 is the sum of the unit areas where landslide geological disasters have occurred; Sn is the total area of the units with the same combination of factors x1, x2, x3....xn; S0 is the total area of the landslide geological disasters that have occurred in the units with the same combination of factors x1, x2, x3....xn;

[0110] 44) Calculate the weighted information quantity of each factor according to the formula and obtain the corresponding weighted information quantity thematic map, as Figure 12 shown;

[0111]

[0112] Among them, Wi is the weight calculated by the analytic hierarchy process under this factor; I(y, xi) is the value of the information quantity under this factor;

[0113] The specific process of step 44) is as follows:

[0114] 441) Based on the above weighted information quantity calculation formula, perform corresponding calculations on each factor and obtain the weighted information quantity values of each factor and the corresponding division value ranges;

[0115] 442) Reassign the values in the raster thematic map corresponding to each influencing factor according to the information quantity values in the above table and obtain the final weighted information quantity thematic map;

[0116] 443) Use the raster algebra calculation in SuperMap iDesktop to linearly superimpose the weighted information quantity thematic maps of the above factors to obtain the final weighted information quantity thematic map of the open-pit mine

[0117] 45) Divide the information quantity values of the weighted information quantity landslide risk thematic map of the tunnel portal slope. Using the natural break method, it is divided into five levels: low risk level (blue), sub-low risk level (cyan), medium risk level (green), sub-high (yellow), high risk (red), and obtain the final landslide risk map of the tunnel portal slope;

[0118] 46) Update of local terrain. Based on the SuperMap iObjects 10i.NET secondary development platform (version supermap-iobjectsdotnet-10.1.0) using the Visual Studio 2019 Communit development kit, the present invention developed the "3D terrain automatic update software based on DEM" according to the mentioned terrain automatic update idea. The specific working principle is as follows:

[0119] 461) Store the point cloud for local update in the form of an excel file and import the file into the local update terrain software;

[0120] 462) Convert the point cloud data in the dataset into a two-dimensional point dataset and perform a projection conversion on the two-dimensional dataset, generally converting it to the WCS1984 projection coordinate system (to keep it in the same projection coordinate system as the subsequent original);

[0121] 463) Generate a DEM excavation terrain based on the point cloud data within the clipping area;

[0122] 464) Perform raster map registration on the generated DEM excavation terrain to make it have the same position as the original terrain for terrain update;

[0123] 465) Based on the data update function in the component form, perform mutual data update between the local new terrain and the original terrain (in the same projection coordinate system and at the same position), thus completing the local terrain update. The final effect is as Figure 15 shown;

[0124] 47) Since the GIS platform adopts the B / S mode, the above results must be synchronously updated to the SuperMap database on this platform. Specifically, it is to generate terrain and image caches and immediately publish them to the platform server. These operations are transparent to the platform, that is, they have no impact on the platform operation. First, generate a terrain cache with normals, and the cache type is TIN terrain, and then generate an image cache with the cache type of image;

[0125] 48) Upload the cached data to the iServer server. Specifically, first enter [the IP address where the iServer server is located]:8090 / iserver in the browser. Enter the "Service Management", click "Quickly publish one or a group of services" in the quick operations, select "File type", select the workspace file in the folder where the workspace is located, and then select "REST 3D service" in the next step. Click "Finish" to complete the publishing of the workspace. All the above processes have been fully automated in this software. However, in order to pursue real-time performance, this software takes a shortcut, that is, directly overwrite the original cached data file with the generated cached data file (because file-type data is used instead of database-type data), achieving instant update and having no impact on the original system at all;

[0126] The monitoring and early warning information module focuses on monitoring the high-risk areas of tunnel portal landslides determined by the statistical analysis module and numerical simulation calculations. The engineering analogy method is used to collect similar tunnel portal landslide cases, and a database of tunnel portal landslide cases is constructed based on this. Pattern matching is carried out for similar engineering cases, and the monitoring data thresholds in the cases are comprehensively referred to finally calibrate the monitoring and early warning indicators. The specific working process is as follows:

[0127] 51) Arrange monitoring equipment for the high-risk landslide areas finally delineated by statistical and numerical simulation means. Install a microseismic monitoring system with 9 sensors (8 single-component sensors and 1 three-component sensor), and establish a slope microseismic data acquisition system for microseismic monitoring. The microseismic monitoring area is as Figure 16 shown;

[0128] 52) For ground settlement monitoring, ITRF provides a global stable reference for the GPS ground settlement monitoring network, that is, in the positioning solution process, dual-frequency monitoring stations in the monitoring network are used to jointly measure IGS stations, IGS precise ephemeris is adopted, and the initial coordinate file and velocity field of the reference station uniformly adopt the ITRF05 frame, and the GPS observation results are reduced to the ITRF05 frame. The entire system processing flow is: data acquisition, data solution and settlement analysis. The data acquisition process includes the acquisition of monitoring network data, format conversion, and the download of IGS station data and products; data solution is carried out using the monitoring network data and IGS station data and products, baseline transfer is carried out and ionospheric data is obtained, and then single-frequency data solution of the monitoring network is carried out; finally, settlement analysis is carried out on the solution results to obtain settlement information, and the settlement information is visualized. The original data and solution results are backed up, archived and published, as Figure 17 shown.

[0129] 53) Collect literature materials and integrate tunnel portal landslide events. Among them, the data includes the slope characteristics, geological conditions, instability process and monitoring materials of each landslide case;

[0130] To quantitatively describe the characteristics of landslides, according to the mechanical mechanism of open-pit slope landslides, slope structure characteristics and engineering geological conditions, the characteristic factors that play an important role in slope stability are divided into 9 items, namely landslide mode, slip surface angle, slip surface surface grade, included angle between the main structural plane and the slope surface, rock mass quality grade, slope angle, depth of unloading and loosening zone, groundwater level and rainfall.

[0131] 54) For the landslide type of this case being wedge-shaped landslide, intelligent methods are applied to identify the failure mode of rock masses of the same type of slopes. According to the characteristics of each characteristic factor, the possible situations of this characteristic factor are listed and classified respectively, and the different situations of this characteristic factor are scored according to the actual situation. The scoring results are used as characteristic values. Then, for each landslide case i, a characteristic value vector Fi can be formed. The characteristic value vector of the target landslide is G. Let the matching vector Di between the characteristic value vector of the target landslide and the characteristic value vector of landslide case i be:

[0132] D i = min(G j , F i,j ) / max(G j , F i,j )

[0133] where i = 1, 2, … n, and n is the number of landslides in the case library; j = 1, 2, … 9.

[0134] Considering the influence degree of each characteristic factor on the target landslide, the weight vector μ is determined. Let the similarity between each landslide case and the target landslide be:

[0135] S i = μD i

[0136] According to the matching similarity of engineering cases, the corresponding engineering cases are matched, and combined with their actual deformation monitoring data, the deformation monitoring threshold is determined to be 10 mm / d.

[0137] 55) Four levels of blue, yellow, orange and red are set in the platform according to the deformation monitoring threshold, as Figure 18 shown, and the corresponding deformation rate thresholds are given respectively. When the monitored deformation data is at the red level, it can be considered that the slope landslide enters an irreversible stage, a red warning is issued, and the area within 150 m nearby is closed, and the entry of personnel and equipment is prohibited.

[0138] Table 1

[0139] Degree Significance 1 The importance degrees of the two factors are equal 3 The former factor is slightly more important than the latter factor 5 The former factor is more important than the latter factor 7 The former factor is far more important than the latter 9 The former factor is extremely more important than the latter 2、4、6、8 Intermediate degree value

[0140] Table 2

[0141]

[0142] Table 3

[0143]

[0144] Table 4

[0145]

[0146]

[0147] Table 5

[0148]

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation manners of the present invention. Any modification or equivalent substitution that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A digital evaluation platform for slope stability with multi-source information fusion at the tunnel portal, characterized in that Including: A data acquisition and preprocessing module for preprocessing the acquired 3D point cloud data of the tunnel entrance; A multi-source information fusion and integration module for constructing a 3D geological model for the preprocessed 3D point cloud data of the tunnel entrance and performing GIS spatial analysis; A statistical slope stability evaluation module for constructing a digital elevation model based on the preprocessed 3D point cloud data of the tunnel entrance and analyzing the high-risk areas of the tunnel entrance slope based on the weighted information method; A slope numerical simulation function module for extracting the slope step line from the preprocessed 3D point cloud data of the tunnel entrance, constructing a tunnel entrance entity model based on geological exploration data, and performing numerical simulation calculations on the tunnel entrance entity model by dividing grids; A statistical analysis module for evaluating the digital elevation model, determining risk factors based on geological exploration data, statistically analyzing the risk factors using the weighted information method, and finally obtaining a landslide risk map of the weighted information method; The multi-source information fusion and integration module uses SuperMap GIS software to store the preprocessed 3D point cloud data of the tunnel slope, UAV oblique photography data, deformation monitoring data of potential landslide areas, and geological exploration data into the database according to different data types, uploads them to the data cloud based on SuperMap iServer, and performs a visual 3D scene display of the 3D model, 2D vector model, and monitoring relational data based on the SuperMap WebGL development kit, and performs spatial analysis on the acquired 3D model based on 3D spatial analysis; The statistical slope stability evaluation module uses the numerical simulation method and the strength reduction theory to obtain the sliding failure surface of the tunnel entrance slope.

2. The digital evaluation platform for slope stability with multi-source information fusion at the tunnel entrance according to claim 1, characterized in that, The data acquisition and preprocessing module includes a UAV oblique photography measurement module, a Lidar 3D laser scanning data module, and a data preprocessing sub-module.

3. The digital evaluation platform for slope stability with multi-source information fusion at the tunnel entrance according to claim 2, characterized in that The Lidar 3D laser scanning data module uses a total station scanner to scan the tunnel entrance slope throughout the process to obtain the 3D point cloud data of the tunnel entrance in the geodetic absolute coordinate system.

4. The digital evaluation platform for slope stability with multi-source information fusion at the tunnel portal according to claim 2, characterized in that, The UAV oblique photography measurement module uses a UAV equipped with a lens to take images of the tunnel entrance slope and then constructs an oblique photography model based on Smart3D.

5. The digital evaluation platform for slope stability with multi-source information fusion at the tunnel portal according to claim 4, characterized in that, The data preprocessing sub-module removes the noise data of the 3D point cloud data obtained by the Lidar 3D laser scanning data module and extracts the 3D step line. Among them, the voxel downsampling method is used to reduce the data size of the acquired point cloud data, and the progressive morphological filtering is used to filter the non-ground point cloud data.

6. The digital evaluation platform for slope stability with multi-source information fusion at the tunnel entrance according to claim 1, characterized in that, The statistical analysis module uses the weighted information method combining the analytic hierarchy process and the information method to evaluate the stability of the tunnel entrance slope.

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