Geological survey simulation and visualization platform based on 3D modeling

Abstract

The invention discloses a geological survey simulation and visualization platform based on three-dimensional modeling, and relates to the field of geological survey technology. The invention determines the geological area of ​​three-dimensional modeling and sets control points; collects various types of geological data in real time in the area; obtains geological images, three-dimensional point cloud data and three-dimensional coordinates of control points; data processing includes key feature point extraction, point cloud registration and point pair matching, conversion of point cloud coordinates, denoising and thinning; marks control points in point cloud data and records their coordinate information; generates a conversion correction model of geodetic coordinates and pixel coordinates according to the geodetic coordinates and pixel coordinates of the control points; prepares a three-dimensional model, identifies geological features, draws geological markers, records geological information, and integrates information; reads the three-dimensional coordinates of the geological marker line, calculates the geodetic coordinates of the geological marker line using the three-dimensional coordinate conversion model, and extends to form a three-dimensional geological model surface with geodetic coordinates according to the corresponding geological information.

Description

Technical Field

[0001] The present invention belongs to the field of geological survey technology, and in particular relates to a geological survey simulation and visualization platform based on three-dimensional modeling. Background Art

[0002] Geological survey is an important part of environmental science research, which involves the prevention and control of geological disasters, as well as the monitoring and control of environmental pollution. Through geological survey, we can understand the current status of the geological environment and provide technical support for environmental protection and ecological restoration.

[0003] Traditional survey methods often rely on plane drawings and simple two-dimensional models to describe geological structures. However, with the increasing complexity and scale of geological engineering projects, this traditional two-dimensional method has become increasingly difficult to meet actual needs. Summary of the invention

[0004] In order to overcome the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a geological survey simulation and visualization platform based on three-dimensional modeling.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A geological survey simulation and visualization platform based on 3D modeling, which includes data acquisition module, data processing module, coordinate conversion module, 3D modeling module and visualization module;

[0007] A data acquisition module is used to collect geological data in real time within a specified geological area and transmit the data to a data processing module;

[0008] Data processing module, used to realize key feature point extraction, point cloud registration and point pair matching, coordinate conversion, denoising and thinning of point cloud data;

[0009] A coordinate transformation module is used to match the control points in the point cloud data with their coordinates in the geodetic coordinate system and generate a coordinate transformation model;

[0010] A 3D modeling module, used to construct a 3D geological model and mark geological features in the model;

[0011] The visualization module is used to provide a three-dimensional visualization interface, display the three-dimensional geological model, and allow users to perform interactive operations.

[0012] Preferably, the platform workflow is as follows:

[0013] Step 1: Determine the geological area for 3D modeling and set control points; collect various types of geological data in real time in the area, including temperature, humidity, soil type, terrain steepness, and vegetation coverage; extract key features based on the collected geological data, generate geological hazard coefficients and personnel risk coefficients; calculate the geological hazard coefficients and personnel risk coefficients for each candidate risk point; sort the N1 candidate risk points from large to small according to the comprehensive risk coefficient Z, and select the first N2 candidate risk points as monitoring points;

[0014] Step 2: Obtain geological images, 3D point cloud data and 3D coordinates of control points;

[0015] Step 3: Data processing, including key feature point extraction, point cloud registration and point pair matching, point cloud coordinate conversion, denoising and thinning;

[0016] Step 4: Mark the control points in the point cloud data and record their coordinate information;

[0017] Step 5: Generate a conversion correction model of geodetic coordinates and pixel coordinates according to the geodetic coordinates and pixel coordinates of the control points;

[0018] Step 6: Prepare a 3D model, identify geological features, draw geological markers, record geological information, and integrate the information;

[0019] Step 7: Read the three-dimensional coordinates of the geological marker line, use the three-dimensional coordinate conversion model to calculate the geodetic coordinates of the geological marker line, and extend it to form a three-dimensional geological model surface with geodetic coordinates based on the corresponding geological information.

[0020] Preferably, step one further comprises:

[0021] Calculate the geological hazard coefficient D of each candidate risk point, where the calculation formula of D is as follows:

[0022] ;

[0023] Among them, ci is the soil type risk coefficient of the preset candidate risk point corresponding to the soil type, M is the soil density, Q is the terrain steepness, F is the vegetation coverage, H1 is the initial height of the groundwater level; b1, b2, b3 and b4 are all preset proportional coefficients;

[0024] Calculate the personnel risk coefficient R of each candidate risk point, where the calculation formula of R is as follows:

[0025] ;

[0026] Wherein, j is the number of the densely populated area, Gj is the altitude difference between the candidate risk point and the jth densely populated area, Lj is the horizontal straight-line distance between the candidate risk point and the jth densely populated area; b5 and b6 are both preset proportional coefficients;

[0027] Calculate the comprehensive risk coefficient Z of each candidate risk point, where the calculation formula of Z is as follows:

[0028] ;

[0029] Among them, b7 and b8 are both preset proportional coefficients.

[0030] Preferably, the step 2 of obtaining geological images, three-dimensional point cloud data and three-dimensional coordinates of control points is as follows:

[0031] The 3D point cloud data is acquired through the 3D laser scanner; the panoramic camera is used to capture the geological texture information, quickly obtain the panoramic image of the geology, make up for the deficiency of the point cloud data, and assist in modeling; the GNSS RTK technology is used to acquire the 3D coordinates of the control points.

[0032] Preferably, the key feature point extraction step in step three includes:

[0033] Taking all individual point cloud data as the origin, n local coordinate systems are established, and all adjacent point cloud data that can be retrieved in the local coordinate system are weighted;

[0034] The corresponding covariance matrix is ​​established for all neighboring point cloud data in each local coordinate system, and the eigenvalue decomposition algorithm is used to obtain the eigenvalue of the covariance matrix;

[0035] The feature point screening threshold is set, and when the eigenvalue decomposition result meets the conditions, it is regarded as a local ISS key feature point of the point cloud dataset.

[0036] Preferably, the key feature point extraction step in step 3 is as follows:

[0037] The point cloud dataset is ; With all individual point cloud data is the origin, where ; Establish n local coordinate systems and set As the weight, all adjacent point cloud data that can be retrieved in the local coordinate system are weighted. The calculation formula is as follows:

[0038] ; ;

[0039] in, for The coordinates in the local coordinate system, for Neighboring point cloud data The coordinates in the local coordinate system, Search radius for feature points;

[0040] For Establish the corresponding covariance matrix for all neighboring point cloud data in the local coordinate system of the origin , The calculation formula is as follows:

[0041] ;

[0042] in, is the symbol for matrix transpose;

[0043] After obtaining the covariance matrices of multiple local coordinate systems in the distribution space of the point cloud data set, the eigenvalue decomposition algorithm is used to obtain the eigenvalues ​​of the covariance matrices in different local coordinate systems;

[0044] Each covariance matrix The eigenvalue calculation results are arranged in descending order, and the first three eigenvalue decomposition results are retained to form the feature set ;

[0045] Set feature point screening threshold , ; When the decomposition result of the random feature point feature set meets the following conditions, it is marked as a local ISS key feature point of the point cloud dataset:

[0046] , ;

[0047] In the three-dimensional point cloud data, by extracting key feature points, the redundancy of the three-dimensional point cloud data is reduced and the point cloud recognition efficiency is optimized.

[0048] Preferably, step five includes:

[0049] Get the three-dimensional coordinates of each control point and its corresponding geodetic coordinates;

[0050] Construct a four-dimensional vector, where the first three dimensions of each vector are the three-dimensional coordinates X, Y, and Z values ​​of the control point, and the fourth dimension is the latitude, longitude, and elevation values ​​of the geodetic coordinates;

[0051] The least squares approximation interpolation method is used to construct a three-dimensional interpolation model, and three three-dimensional interpolation models are obtained, corresponding to the correction of the three coordinate dimensions of X, Y, and Z respectively.

[0052] Preferably, step seven also includes:

[0053] Read the three-dimensional coordinates X, Y, and Z of the geological marker line from the three-dimensional geological model;

[0054] Use the three-dimensional coordinate conversion model to convert the coordinates of the three-dimensional geological marker line into the latitude, longitude and elevation of the geodetic coordinates;

[0055] According to the geological information, a three-dimensional geological model surface with geodetic coordinates is extended.

[0056] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0057] 1. The present invention defines the geological risk factor D and the personnel risk factor R, and calculates the comprehensive risk factor Z according to the preset proportional coefficient, so as to sort and select the candidate risk points. It is not limited to the calculation of a single geological risk factor or a single personnel risk factor, but through the comprehensive evaluation of the geological risk factor and the personnel risk factor, the comprehensive risk factor Z is obtained, so as to more comprehensively evaluate the risk level of the geological area.

[0058] 2. The present invention establishes a local coordinate system, performs weighted processing on the point cloud data in the local coordinate system, and then uses the eigenvalue decomposition algorithm to obtain the eigenvalues ​​of the covariance matrix, thereby screening out key feature points.

[0059] 3. The present invention utilizes the three-dimensional coordinates of the control points and the geodetic coordinates to construct a three-dimensional interpolation model through the least squares approximation interpolation method, thereby realizing the conversion from point cloud coordinates to geodetic coordinates and improving the accuracy of coordinate conversion; the three-dimensional visualization interface makes the geological features more intuitive and easy to understand, supports users to perform interactive operations, and facilitates in-depth understanding and analysis of geological features. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 The module diagram of the geological survey simulation and visualization platform based on three-dimensional modeling of the present invention is shown;

[0062] Figure 2 The work flow chart of the geological survey simulation and visualization platform based on three-dimensional modeling of the present invention is shown. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] Embodiment 1:

[0065] See also Figure 1 As shown, the geological survey simulation and visualization platform based on three-dimensional modeling of this embodiment includes a data acquisition module, a data processing module, a coordinate conversion module, a three-dimensional modeling module and a visualization module.

[0066] Data acquisition module: The hardware configuration includes 3D laser scanner, panoramic camera, GNSS RTK receiver, and sensor; it is responsible for real-time collection of various types of geological data in the designated geological area, including but not limited to temperature, humidity, soil type, terrain steepness, vegetation coverage, etc., and storing the data; the collected data is transmitted to the data processing module for further processing.

[0067] Data processing module: used to realize key feature point extraction, point cloud registration and point pair matching, coordinate conversion, denoising and thinning functions of point cloud data. Feature point extraction is to extract key feature points from the point cloud data set through feature point search algorithm to optimize point cloud recognition efficiency; point cloud registration is to splice point cloud data of multiple control points into a complete point cloud through key feature point registration; coordinate conversion is to convert the spliced ​​point cloud data to the required coordinate system; denoising and thinning is to remove noise in the point cloud and thin the point cloud through resampling function to meet modeling requirements.

[0068] Coordinate transformation module: used to match the control points in the point cloud data with their coordinates in the geodetic coordinate system and generate a coordinate transformation model; use methods such as polynomial fitting or affine transformation to generate a coordinate transformation model, and use this model to transform all point cloud data into the geodetic coordinate system.

[0069] 3D modeling module: used to construct 3D geological models and mark geological features in the models. Based on the geodetic coordinates and pixel coordinates of the control points, a conversion correction model between geodetic coordinates and pixel coordinates is generated, and a 3D geological model surface with geodetic coordinates is formed based on the geological information. Geological features such as rock layers, cracks, faults, etc. are identified and marked in the 3D model, and detailed geological information of each geological marker is recorded and integrated into the 3D model.

[0070] Visualization module: used to provide a three-dimensional visualization interface, display the three-dimensional geological model, and allow users to perform interactive operations. The interactive operation provides a toolbar that allows users to zoom in, zoom out, rotate the model, and add, edit, and delete geological marks.

[0071] The beneficial effects of this embodiment are as follows: by integrating advanced data acquisition, processing, coordinate conversion, three-dimensional modeling and visualization technology, the efficiency and accuracy of geological survey work are improved.

[0072] Embodiment 2:

[0073] See also Figure 2 As shown, the workflow of the geological survey simulation and visualization platform based on three-dimensional modeling in this embodiment is as follows:

[0074] Step 1: Determine the geological area for 3D modeling and set control points.

[0075] Various types of geological data are collected in real time within the 3D modeled geological area, including but not limited to temperature, humidity, soil type, terrain steepness, and vegetation coverage.

[0076] Based on the collected geological data, key features are extracted to generate geological hazard factors and personnel risk factors.

[0077] Calculate the geological risk factor D and personnel risk factor R of each candidate risk point;

[0078] The calculation formula of geological risk factor D is as follows:

[0079] ;

[0080] Among them, ci is the soil type risk coefficient of the preset candidate risk point corresponding to the soil type, M is the soil density, Q is the terrain steepness, F is the vegetation coverage, H1 is the initial height of the groundwater level; b1, b2, b3 and b4 are all preset proportional coefficients;

[0081] Among them, the calculation formula of personnel risk coefficient R is as follows:

[0082] ;

[0083] Wherein, j is the number of the densely populated area, Gj is the altitude difference between the candidate risk point and the jth densely populated area, Lj is the horizontal straight-line distance between the candidate risk point and the jth densely populated area; b5 and b6 are both preset proportional coefficients; the comprehensive risk coefficient Z of each candidate risk point is calculated, and the calculation formula of the comprehensive risk coefficient Z is as follows:

[0084] ;

[0085] Among them, b7 and b8 are preset proportional coefficients;

[0086] Sort the N1 candidate risk points from large to small according to the comprehensive risk coefficient Z, and select the first N2 candidate risk points as monitoring points.

[0087] Based on geological characteristics and personnel risk data, monitoring points are selected from candidate risk points.

[0088] A monitoring station is set up at the selected monitoring point, and m control points are set up in the area, and the coordinate information of the control points is recorded.

[0089] Step 2: Obtain geological images, 3D point cloud data and 3D coordinates of control points.

[0090] The 3D point cloud data is acquired by collecting point cloud data through a 3D laser scanner; the panoramic camera is used to capture geological texture information and quickly obtain panoramic geological images, which can make up for the lack of point cloud data and assist in modeling; the GNSSRTK technology is used to collect the 3D coordinates of the control points.

[0091] Step 3: Data processing.

[0092] S301, extracting key feature points.

[0093] The point cloud dataset is ; With all individual point cloud data is the origin, where ; Establish n local coordinate systems, As the weight, all adjacent point cloud data that can be retrieved in the local coordinate system are weighted. The calculation formula is as follows:

[0094] ; ;

[0095] in, for The coordinates in the local coordinate system, for Neighboring point cloud data The coordinates in the local coordinate system, Search radius for feature points.

[0096] For Establish the corresponding covariance matrix for all neighboring point cloud data in the local coordinate system of the origin , The calculation formula is as follows:

[0097] ;

[0098] After obtaining the covariance matrices of multiple local coordinate systems in the distribution space of the point cloud data set, the eigenvalue decomposition algorithm is used to obtain the eigenvalues ​​of the covariance matrices in different local coordinate systems.

[0099] Each covariance matrix The eigenvalue calculation results are arranged in descending order, and the first three eigenvalue decomposition results are retained to form the feature set ; Set the feature point screening threshold , ; When the decomposition result of the random feature point feature set meets the following conditions, it can be regarded as a local ISS key feature point of the point cloud dataset:

[0100] , ;

[0101] Through the above method, the key feature points in the three-dimensional point cloud data can be extracted, the redundancy of the three-dimensional point cloud data can be reduced, and the point cloud recognition efficiency can be optimized.

[0102] S302: Point cloud registration and point pair matching.

[0103] The point cloud data of m control points are aligned and spliced ​​into a whole point cloud through key feature points.

[0104] S303: Convert the point cloud coordinates.

[0105] The point cloud after registration and splicing has no definite absolute coordinates. The point cloud data is converted to the required coordinate system through m control points.

[0106] S304, denoising and thinning.

[0107] In the process of point cloud scanning, a large amount of point cloud noise is inevitably included. The redundant data and noise are deleted to keep only the point cloud required for modeling.

[0108] S305. Process geological images using Photoshop software.

[0109] Step 4: Mark the control points in the point cloud data and record their coordinate information.

[0110] Match control points in point cloud data to their coordinates in the geodetic coordinate system.

[0111] Generate coordinate transformation models using methods such as polynomial fitting or affine transformation.

[0112] Using the generated coordinate transformation model, all point cloud data are transformed into the geodetic coordinate system.

[0113] Step 5: Generate a conversion correction model between geodetic coordinates and pixel coordinates based on the geodetic coordinates and pixel coordinates of the control points.

[0114] S501, obtaining the three-dimensional coordinates of each control point and its corresponding geodetic coordinates, the number of control points of each geological image is at least 3.

[0115] Mark control points in the point cloud data and record the 3D coordinates (X, Y, Z) of these control points.

[0116] Use high-precision surveying equipment (such as GNSS RTK) to obtain the geodetic coordinates (latitude, longitude, elevation) of each control point.

[0117] S502. Construct a four-dimensional vector.

[0118] Construct a four-dimensional vector. The first three dimensions of each vector are the three-dimensional coordinate values ​​(X, Y, Z) of the control point, and the fourth dimension is the value of the geodetic coordinates (latitude, longitude, elevation).

[0119] S503: construct a three-dimensional interpolation model using the least squares approximation interpolation method.

[0120] The first three components of the four-dimensional vector of the same type are used as independent variables, and the fourth component is used as the dependent variable;

[0121] Use the least squares approximation interpolation method to construct a three-dimensional interpolation model;

[0122] Three three-dimensional interpolation models are obtained, corresponding to the correction of the three coordinate dimensions of X, Y, and Z respectively.

[0123] Step 6: Prepare the 3D model.

[0124] Ensure that the 3D model has been built and the data in the model can clearly reflect the geological characteristics. Use professional geological modeling software to load the 3D model.

[0125] Identify geological features that need to be marked in the 3D model, such as rock layers, cracks, faults, etc., which can be assisted by features such as color, texture, and depth;

[0126] Use the drawing tools provided by the 3D modeling software to draw geological markers in the model, such as lines, polygons, etc. The markers should accurately reflect the actual location and shape of the geological features;

[0127] For each geological mark drawn, record detailed geological information, including the name, type, location, trend, dip, etc. of the geological feature, and attach the information to the corresponding geological mark using labels, annotations or other means;

[0128] Integrate all geological markers and their geological information into the three-dimensional model to create a detailed information list or database for later query and analysis.

[0129] Step 7: Read the three-dimensional coordinates of the geological marker line, use the three-dimensional coordinate conversion model to calculate the geodetic coordinates of the geological marker line, and extend it to form a three-dimensional geological model surface with geodetic coordinates based on the corresponding geological information.

[0130] Read the 3D coordinates (X, Y, Z) of the geological marker line from the 3D geological model;

[0131] Using the established three-dimensional coordinate conversion model, the coordinates of the three-dimensional geological marker line are converted into geodetic coordinates (latitude, longitude, elevation);

[0132] Verify whether the converted geodetic coordinates are reasonable and consistent with the field measurement results;

[0133] According to the geological information corresponding to the geological marking line (such as rock layer type, thickness, inclination angle, etc.), it is entered into the model. Using the geological information, through three-dimensional interpolation or other geometric modeling techniques, a three-dimensional geological model surface with geodetic coordinates is extended to adjust the model surface and ensure the consistency and rationality of the model.

[0134] The beneficial effects of this embodiment are: improving the efficiency and accuracy of geological survey work, and realizing geological survey simulation and visualization.

[0135] The weights of the present invention are used to measure the degree of influence of different factors or variables on a certain result or decision. The definition of weights refers to the numerical value assigned to each factor when comparing and evaluating multiple factors to reflect its importance or priority. By setting weights reasonably, it can help programs or systems make decisions or predictions more accurately.

[0136] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0137] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A geological survey simulation and visualization platform based on 3D modeling, characterized by: The platform includes a data acquisition module, a data processing module, a coordinate conversion module, a three-dimensional modeling module and a visualization module; A data acquisition module is used to collect geological data in real time within a specified geological area and transmit the data to a data processing module; Data processing module, used to realize key feature point extraction, point cloud registration and point pair matching, coordinate conversion, denoising and thinning of point cloud data; A coordinate transformation module is used to match the control points in the point cloud data with their coordinates in the geodetic coordinate system and generate a coordinate transformation model; A 3D modeling module, used to construct a 3D geological model and mark geological features in the model; Visualization module, used to provide a 3D visualization interface, display the 3D geological model, and allow users to perform interactive operations; The platform workflow is as follows: Step 1: Determine the geological area for 3D modeling and set control points; collect various types of geological data in real time in the area, including temperature, humidity, soil type, terrain steepness, and vegetation coverage; extract key features based on the collected geological data and generate geological hazard coefficients and personnel risk coefficients; Calculate the geological hazard factor and personnel risk factor for each candidate risk point; Sort the N1 candidate risk points from large to small according to the comprehensive risk coefficient Z, and select the first N2 candidate risk points as monitoring points; Step 2: Obtain geological images, 3D point cloud data and 3D coordinates of control points; Step 3: Data processing, including key feature point extraction, point cloud registration and point pair matching, point cloud coordinate conversion, denoising and thinning; Step 4: Mark the control points in the point cloud data and record their coordinate information; Step 5: Generate a conversion correction model of geodetic coordinates and pixel coordinates according to the geodetic coordinates and pixel coordinates of the control points; Step 6: Prepare a 3D model, identify geological features, draw geological markers, record geological information, and integrate the information; Step 7: Read the three-dimensional coordinates of the geological marker line, calculate the geodetic coordinates of the geological marker line using the three-dimensional coordinate conversion model, and extend it to form a three-dimensional geological model surface with geodetic coordinates according to the corresponding geological information; The key feature point extraction step in step 3 is as follows: The point cloud dataset is ; All individual point cloud data is the origin, where ; Establish n local coordinate systems and set As the weight, all adjacent point cloud data that can be retrieved in the local coordinate system are weighted. The calculation formula is as follows: ； ； in, is the weight; for The coordinates in the local coordinate system, for Neighboring point cloud data The coordinates in the local coordinate system, Search radius for feature points; For Establish the corresponding covariance matrix for all neighboring point cloud data in the local coordinate system of the origin , The calculation formula is as follows: ； in, is the symbol for matrix transpose; After obtaining the covariance matrices of multiple local coordinate systems in the distribution space of the point cloud data set, the eigenvalue decomposition algorithm is used to obtain the eigenvalues ​​of the covariance matrices in different local coordinate systems; Each covariance matrix The eigenvalue calculation results are arranged in descending order, and the first three eigenvalue decomposition results are retained to form the feature set ; When the decomposition result of the random feature point feature set meets the following conditions, it will be marked as a local ISS key feature point of the point cloud dataset: ， ； in, , To set the feature point screening threshold; In the three-dimensional point cloud data, by extracting key feature points, the redundancy of the three-dimensional point cloud data is reduced and the point cloud recognition efficiency is optimized.

2. The geological survey simulation and visualization platform based on three-dimensional modeling according to claim 1, characterized in that: The step one also includes: Calculate the geological hazard coefficient D of each candidate risk point, where the calculation formula of D is as follows: ； Among them, ci is the soil type risk coefficient of the preset candidate risk point corresponding to the soil type, M is the soil density, Q is the terrain steepness, F is the vegetation coverage, H1 is the initial height of the groundwater level; b1, b2, b3 and b4 are all preset proportional coefficients; Calculate the personnel risk coefficient R of each candidate risk point, where the calculation formula of R is as follows: ； Wherein, j is the number of the densely populated area, Gj is the altitude difference between the candidate risk point and the jth densely populated area, Lj is the horizontal plane straight-line distance between the candidate risk point and the jth densely populated area; b5 and b6 are both preset proportional coefficients; Calculate the comprehensive risk coefficient Z of each candidate risk point, where the calculation formula of Z is as follows: ； Among them, b7 and b8 are both preset proportional coefficients.

3. The geological survey simulation and visualization platform based on three-dimensional modeling according to claim 1, characterized in that: The step 2 of obtaining geological images, 3D point cloud data and 3D coordinates of control points is as follows: The 3D point cloud data is acquired through the 3D laser scanner; the panoramic camera is used to capture the geological texture information, quickly obtain the panoramic image of the geology, make up for the defect of insufficient point cloud data, and assist in modeling; the GNSS RTK technology is used to collect the 3D coordinates of the control points.

4. The geological survey simulation and visualization platform based on three-dimensional modeling according to claim 1, characterized in that: The key feature point extraction step in step 3 includes: Taking all individual point cloud data as the origin, n local coordinate systems are established, and all adjacent point cloud data that can be retrieved in the local coordinate system are weighted; The corresponding covariance matrix is ​​established for all neighboring point cloud data in each local coordinate system, and the eigenvalue decomposition algorithm is used to obtain the eigenvalue of the covariance matrix; The feature point screening threshold is set, and when the eigenvalue decomposition result meets the conditions, it is regarded as a local ISS key feature point of the point cloud dataset.

5. The geological survey simulation and visualization platform based on three-dimensional modeling according to claim 1, characterized in that: The step five comprises: Get the three-dimensional coordinates of each control point and its corresponding geodetic coordinates; Construct a four-dimensional vector, where the first three dimensions of each vector are the three-dimensional coordinates X, Y, and Z values ​​of the control point, and the fourth dimension is the latitude, longitude, and elevation values ​​of the geodetic coordinates; The least squares approximation interpolation method is used to construct a three-dimensional interpolation model, and three three-dimensional interpolation models are obtained, corresponding to the correction of the three coordinate dimensions of X, Y, and Z respectively.

6. The geological survey simulation and visualization platform based on three-dimensional modeling according to claim 1, characterized in that: The step seven also includes: Read the three-dimensional coordinates X, Y, and Z of the geological marker line from the three-dimensional geological model; Use the three-dimensional coordinate conversion model to convert the coordinates of the three-dimensional geological marker line into the latitude, longitude and elevation of the geodetic coordinates; According to the geological information, a three-dimensional geological model surface with geodetic coordinates is extended.

Images