Geomechanical data visualization method and system based on mixed data model

Abstract

The invention relates to the technical field of geomechanical data research, in particular to a geomechanical data visualization method and system based on a mixed data model. According to the technical scheme, the method comprises the steps of data collection and arrangement, mixed data model construction, visual algorithm design and application and visual result display and interaction. The data collection and arrangement comprises the step of collecting geomechanical data including geologic structure data, rock mechanics parameter data and ground stress data. According to the method, geologists can be assisted to deeply understand the complex relationship between geologic structure evolution and rock mechanical behaviors, and powerful data visualization support is provided for development of geologic theories; geological risks can be predicted in advance, an engineering design scheme is optimized, and engineering safety and economic benefits are improved; through accurate visualization and deep analysis of geomechanical data, the possibility and the potential influence range of disaster occurrence can be evaluated more accurately, and key technical support is provided for disaster early warning and prevention and control measure formulation.

Description

Technical Field

[0001] The present invention relates to the technical field of geomechanical data research, and in particular to a geomechanical data visualization method and system based on a hybrid data model. Background Art

[0002] In geomechanics research, data sources are extensive and complex, including geological structure data, rock mechanics parameter data, geostress data, etc. Traditional data visualization methods often have some defects, such as: a single data model is difficult to fully and accurately express the multi-dimensional characteristics of geomechanics data, resulting in loss of data details; when processing large-scale geomechanics data, the visualization efficiency is low and the key information in the data cannot be quickly and effectively displayed; the correlation between different types of data is not sufficiently mined, making the geomechanics analysis not in-depth and comprehensive enough.

[0003] Therefore, this application proposes a geomechanical data visualization method and system based on a hybrid data model. Summary of the invention

[0004] The purpose of the present invention is to address the problem that the geomechanical data analysis in the background technology is not in-depth and comprehensive enough, and to propose a geomechanical data visualization method and system based on a hybrid data model.

[0005] In a first aspect, the present application provides a geomechanical data visualization method based on a hybrid data model, comprising the following steps: S1. Data collection and collation: Collect geomechanical data including geological structure data, rock mechanics parameter data and geostress data; S2. Hybrid data model construction, including: S201, data classification and preprocessing: the geomechanical data are divided into discrete data and continuous data, the discrete data are coded, and the continuous data are normalized according to a formula; S202, discrete data model construction: a discrete data model is constructed using a data structure based on graph theory, with discrete elements such as geological structures and faults as nodes, edges are constructed based on their spatial relationships, and geological structure type codes and location coordinate attributes are set for the nodes; S203, continuous data model construction: using a three-dimensional space grid model to represent continuous data, determining the spatial range of the geological body and dividing it into small cubic units, and filling the continuous data into corresponding units according to spatial positions; S204, model association: establishing an index mechanism to associate discrete data models with continuous data models; S3. Visualization algorithm design and application, including: Multi-scale visualization: The macro-scale threshold range is set to [0, 0.3], and simplified geometric graphics are used to display the overall outline of the geological structure and the large-scale geostress trend. The graphics are drawn based on the geological structure node location information in the discrete data model and the macro-geostress trend data in the continuous data model. The mesoscale threshold range is set to (0.3, 0.7], and the surface of the geological body is represented by a semi-transparent surface. The continuous data is mapped to the surface color and texture, and the key structural information of the discrete data is annotated. The surface construction is based on the geological structure boundary information in the discrete data model and the rock mechanics parameter data in the continuous data model. The micro-scale threshold range is set to (0.7, 1], and the volume rendering technology is used to visualize the small cubic units in the continuous data model. The rock mechanical property data are mapped to color and transparency. The volume rendering is performed based on the data association visualization algorithm based on ray tracing. S4. Visualization and interaction of results: Develop a visualization software platform, which includes a data import module, a model building module, a visualization setting module, an interactive operation module, and a data query module. Each module works together to realize the visualization and interactive operation functions of geomechanical data.

[0006] Optionally, the normalization formula is: ; in, is the normalized value, is the original data value, and These are the minimum and maximum values ​​of the data type, respectively.

[0007] Optionally, in step S3, the data association visualization algorithm: adopts a data association visualization algorithm based on ray tracing, and the formula is: ; ; ; in, is the screen coordinate The final pixel color value at is the number of sampling points for ray tracing, The light is The location of the sampling points, and is the sampling step size, is the ray from the starting point to the sampling point The transmittance, The medium in the light propagation path The extinction coefficient on It is the sampling point The light intensity at It is the sampling point The color of the object at a location is determined by both discrete data and continuous data. The relationship between discrete data and continuous data is demonstrated through the propagation process of light in the mixed data model.

[0008] Optionally, the data import module specifically includes: Design user interface to provide file selection dialog box supporting multiple common geomechanical data file formats; The background program automatically calls the corresponding parsing function according to the selected file format to read the data into the memory, and processes the data according to the data classification and preprocessing rules to build discrete data lists and continuous data arrays.

[0009] Optionally, the model building module specifically includes: Using the processed discrete data to build a discrete data model based on graph theory, including creating node objects and setting attributes such as geological structure type code and location coordinates, creating edge objects based on spatial relationships and associating corresponding nodes; Create a three-dimensional spatial grid object, determine the range and resolution, fill the data in the continuous data array into the corresponding grid cells, and establish an index association with the discrete data model.

[0010] Optionally, the visualization setting module specifically includes: Provide a multi-scale visualization switch button to call the corresponding visualization function according to the selected scale threshold range; In the macro-scale visualization settings, users are allowed to customize the color, size, transparency and other display properties of simplified geometric figures, as well as the style and annotation information of the geostress direction arrows; The mesoscale visualization settings interface provides surface color mapping scheme selection, texture image upload and adjustment functions, and font, size, and color settings for discrete structural information annotations; Microscale visualization settings allow users to adjust the transfer function parameters of volume rendering, including color-data value mapping curves and transparency-data value mapping curves.

[0011] Optionally, the interactive operation module specifically includes: Implement the mouse wheel zoom function, and adjust the camera's field of view according to the scroll direction and amplitude by monitoring the mouse wheel events; Press and drag the left mouse button to perform translation operation, and update the camera position coordinates according to the calculated mouse drag displacement; Press and drag the right mouse button to rotate the camera, and adjust the camera rotation angle according to the mouse drag angle information.

[0012] Optionally, the data query module specifically includes: When the user clicks anywhere on the visualization interface, the three-dimensional coordinate information corresponding to the clicked position is obtained; Searching in the hybrid data model based on the coordinate information, querying the node information of the discrete data model where the location is located and the data value in the grid unit of the continuous data model; The query results are displayed to the user in a pop-up window or information panel, including the data name, value and brief description.

[0013] In a second aspect, the present application provides a geomechanical data visualization system based on a hybrid data model, comprising: The data classification and preprocessing unit is used to receive geomechanical data and classify it into discrete data and continuous data, encode the discrete data, and normalize the continuous data, and the normalization is based on the normalization formula: ; implement; A hybrid data model building unit, which builds a discrete data model based on graph theory, takes discrete elements such as geological structures and faults as nodes, builds edges based on spatial relationships, and uses a three-dimensional spatial grid model to build a continuous data model, and establishes an index association between the two; The visualization algorithm execution unit sets a multi-scale threshold range, which is divided into macroscale [0, 0.3], mesoscale (0.3, 0.7] and microscale (0.7, 1]. At the macroscale, simplified geometric figures are used to display the overall outline of the geological structure and the large-scale geostress trend. At the mesoscale, semi-transparent surfaces are used to represent the surface of the geological body and map the continuous data to display the differences in rock mechanical parameters and annotate the key structural information of the discrete data. At the microscale, volume rendering technology is used to visualize the small cubic units in the continuous data model to display the internal mechanical properties of the rock, and the correlation between discrete data and continuous data is displayed through the propagation process of light in the hybrid data model. The visualization system functional module integration unit includes a data import module, a visualization setting module, an interactive operation module and a data query module.

[0014] Optionally, in the visualization system functional module integration unit, The data import module has a design user interface and provides a file selection dialog box that supports a variety of common geomechanical data file formats, and the background program can automatically call the corresponding parsing function according to the selected file format to read the data into the memory, and process the data according to the data classification and preprocessing rules to construct a discrete data list and a continuous data array; The visualization setting module can provide a multi-scale visualization switching button, call the corresponding visualization function according to the selected scale threshold range, and allow users to perform corresponding visualization settings at different scales, including allowing users to customize the color, size, transparency and other display properties of simplified geometric figures in the macro-scale visualization setting, as well as the style and annotation information of the ground stress direction arrow. The meso-scale visualization setting interface provides surface color mapping scheme selection, texture image upload and adjustment functions, and font, size, and color settings for discrete structural information annotation. The micro-scale visualization setting allows users to adjust the transfer function parameters of volume rendering, including color-data value mapping curves and transparency-data value mapping curves. The interactive operation module implements the mouse wheel zoom function, by monitoring the mouse wheel event, adjusting the camera field of view according to the scroll direction and amplitude, pressing and dragging the left mouse button to implement the translation operation, updating the camera position coordinates according to the calculated mouse drag displacement, pressing and dragging the right mouse button to implement the rotation operation, and adjusting the camera rotation angle according to the mouse drag angle information; When the user clicks any position in the visualization interface, the data query module obtains the three-dimensional coordinate information corresponding to the clicked position, searches in the hybrid data model based on the coordinate information, queries the node information of the discrete data model where the position is located and the data value in the grid unit of the continuous data model, and displays the query results to the user in the form of a pop-up window or information panel, including the data name, value and brief description.

[0015] Compared with the prior art, the present invention has at least one of the following beneficial technical effects: Through data classification and preprocessing, complex and diverse geomechanical data can be effectively sorted out and standardized. Whether it is discrete geological structure data or continuous rock mechanics parameters and geostress data, they can exist in a unified and suitable form for calculation and analysis, laying the foundation for subsequent accurate modeling. The construction and association of discrete data models and continuous data models breaks the limitations of traditional single data models, closely combines the spatial distribution of geological structures with the physical properties of rocks, makes the expression of data more comprehensive and three-dimensional, greatly improves the integrity of the understanding of geological bodies, and helps to more accurately grasp the complex relationships and laws within geological bodies.

[0016] In terms of visualization, the overall geological structure and large-scale geostress situation are presented in simple and intuitive simplified geometric graphics, allowing researchers to quickly gain a macro and global understanding of the study area, quickly locate key areas and trends, and provide directional guidance for further in-depth analysis.

[0017] At the meso-scale, translucent surfaces are used to delicately display the secondary structure and changes in rock mechanical parameters inside the geological body. Combined with discrete data annotation, the details and local features inside the geological body can be clearly presented, which helps to analyze the relationship between local geological phenomena and mechanical properties.

[0018] By using volume rendering technology at a microscopic scale to delve into the microscopic world inside the rock, the mechanical properties data of the rock are vividly mapped into color and transparency based on the data association visualization algorithm based on ray tracing, accurately revealing the intrinsic connection between the internal microstructure and mechanical properties of the rock, providing a powerful means for in-depth research on the micromechanical mechanism of rock.

[0019] Through the coordinated work of various modules in the visualization system functional module integration unit, users are provided with a powerful and convenient operating experience. The versatility of the data import module ensures that geomechanical data from various sources and formats can be widely accepted, lowering the threshold for data acquisition. The rich functions of the visualization setting module enable users to flexibly customize the visualization effects according to specific research needs or personal preferences. Whether it is the display style of macro graphics or the parameter adjustment of micro volume drawing, they can control it at will, enhancing the system's adaptability to different application scenarios.

[0020] The present invention can help geologists gain a deeper understanding of the complex relationship between geological structural evolution and rock mechanical behavior, and provide powerful data visualization support for the development of geological theory; it can predict geological risks in advance, optimize engineering design plans, and improve engineering safety and economic benefits; through precise visualization and in-depth analysis of geomechanical data, it can more accurately assess the possibility of disasters and the potential impact range, and provide key technical support for disaster warning and the formulation of prevention and control measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a structural diagram of a geomechanical data visualization method and system based on a hybrid data model; Figure 2 The schematic diagram of the structure of a geomechanical data visualization method and system based on a hybrid data model. DETAILED DESCRIPTION

[0022] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0023] Example 1 like Figure 1 As shown, the present invention proposes a geomechanical data visualization method based on a hybrid data model, which includes four steps: data collection and organization, hybrid data model construction, visualization algorithm design and application, and visualization result display and interaction. Each step is described in detail below.

[0024] 1. Data collection and organization First, we collect a wide range of geomechanical data, including geological structural data, fault distribution, fold morphology, rock mechanical parameter data, elastic modulus, Poisson's ratio, compressive strength, and geostress data, including size and direction. These data may come from geological exploration reports, field measurement instruments, laboratory test results, and existing geological databases. We conduct preliminary sorting and verification of the collected data to ensure the accuracy and completeness of the data.

[0025] 2. Hybrid Data Model Construction Data classification and preprocessing: Geomechanical data are divided into two categories: discrete data and continuous data. Discrete data such as geological structure type and fault number are coded. Different types of faults are coded as 1, 2, and 3 respectively. Different geological structures including anticlines and synclines are also given specific codes so that computers can recognize and process them.

[0026] For continuous data, rock elastic modulus and ground stress values, normalization is used. The minimum value in this data type is , the maximum value is , then the normalization formula is: ; Through normalization, the numerical range of continuous data is uniformly mapped to between 0 and 1, which facilitates subsequent calculation and visualization processing.

[0027] Discrete data model construction: A discrete data model is constructed using a data structure based on graph theory. Discrete elements such as geological structures and faults are used as nodes of the graph. Each independent fault segment is a node, and the core area of ​​each fold is also a node. Then the edges of the graph are constructed based on the spatial relationship between these elements. If two faults intersect or are adjacent to each other in space, an edge is established between the nodes representing the two faults; if a fold structure is spatially associated with a fault and the fault passes through the fold, an edge is constructed between the corresponding nodes. At the same time, attributes are set for each node, including the geological structure type code (such as the previous code) and location coordinate information (determined by the coordinate positioning method in the geographic information system).

[0028] Continuous data model construction: Use a three-dimensional space grid model to represent continuous data. First determine the spatial scope of the geological body to be studied, for example, determine a three-dimensional space area based on the geographical boundaries of the exploration area and the research depth. Then divide the area into several small cube units. The accuracy of the division can be set according to the detail level of the data and the visualization requirements. For example, the side length of each small cube unit can be set to 1 meter, 5 meters or other suitable length units. Fill the continuous data into the corresponding small cube units according to their corresponding spatial positions. The rock elastic modulus data at a certain position is stored in the small cube unit corresponding to the position, thereby constructing a distribution model of continuous data in three-dimensional space.

[0029] Model association: By establishing an index mechanism, the discrete data model and the continuous data model are associated and integrated. In this way, in subsequent operations, it is convenient to quickly locate the data in the related continuous data model based on the geological structure information in the discrete data model, and vice versa, to achieve information interaction and collaborative work between the two data models.

[0030] 3. Visualization Algorithm Design and Application Multi-scale visualization The multi-scale threshold range is set to divide the visualization of geomechanical data into macroscale, mesoscale and microscale. The macroscale threshold range is [0, 0.3]. At this scale, the overall outline of the geological structure and the large-scale geostress trend are mainly displayed. Simplified geometric graphics are used for visualization, such as using large polygons to represent the approximate shape of the geological body and arrows to represent the direction of the main geostress. The drawing of these simplified geometric graphics is based on the location information of the geological structure nodes in the discrete data model and the macroscopic geostress trend data in the continuous data model. For example, the length and direction of the arrow are determined according to the average size and direction of the geostress in the region, and the vertex coordinates of the polygon are determined according to the approximate range of the geological body.

[0031] The mesoscale threshold range is (0.3, 0.7]. At this scale, some secondary structures and local stress changes inside the geological structure are displayed. A semi-transparent surface is used to represent the surface of the geological body. The surface is constructed based on the geological structure boundary information in the discrete data model and the rock mechanics parameter data in the continuous data model. By mapping the continuous data (rock elastic modulus) to the color and texture of the surface, the surface color can be set to a darker color (red indicates a high elastic modulus area) in the area with a larger elastic modulus. At the same time, the density of the texture can be set according to the change of the data to represent the difference in rock mechanics parameters. In addition, the key structural information in the discrete data is marked on the surface, including the fault name and fold type. The position of the mark is determined according to the node position in the discrete data model, and the style of the mark (font, size, color) can be set according to the needs.

[0032] The micro-scale threshold range is (0.7, 1], and the mechanical properties and microstructural information inside the rock are displayed in detail at this scale. For each small cubic unit in the continuous data model, volume rendering technology is used for visualization according to the data stored in it. The elastic modulus, porosity and other data of the rock are mapped to different colors and transparencies. The area with higher porosity is set to higher transparency, and the area with higher elastic modulus is set to brighter color (yellow indicates a microscopic area with high elastic modulus). The volume rendering technology determines the final display effect by calculating the light propagation of each small cubic unit, which is based on the data association visualization algorithm based on ray tracing.

[0033] Data association visualization algorithm A data association visualization algorithm based on ray tracing is used to mine the association relationship between discrete data and continuous data. The algorithm formula is: ; ; ; in, is the screen coordinate The final pixel color value at is the number of sampling points for ray tracing, The light is The location of the sampling points, and is the sampling step size, is the ray from the starting point to the sampling point The transmittance, The medium in the light propagation path The extinction coefficient on It is the sampling point The light intensity at It is the sampling point The color of the object at a location is determined by both discrete data and continuous data. The relationship between discrete data and continuous data is demonstrated through the propagation process of light in the mixed data model.

[0034] in, is the screen coordinate The final pixel color value at the coordinate position, that is, the color that is finally displayed on the screen, is obtained by comprehensively calculating the information of each sampling point through which the light passes during the ray tracing process. It is the number of sampling points for ray tracing. In order to accurately simulate the propagation effect of light in a geological body, it is necessary to perform sampling calculations at multiple points along the direction of the light. The more sampling points there are, the more accurate the calculation results will be, but the amount of calculation will also be greater. It can be adjusted according to actual needs and system performance. The light is The position of the sampling point, whose coordinates are obtained by adding the starting point coordinates to the product of the sampling step length and the sampling sequence number, and is the sampling step length, which determines the distance interval between the sampling points and affects the accuracy and calculation efficiency of ray tracing. is the transmittance of light from the starting point to the sampling point, indicating the degree of attenuation of light when it passes through the medium (geological body) during propagation. It is calculated by an exponential function, where is the extinction coefficient of the medium on the light propagation path. The extinction coefficient is related to the material composition and density factors of the geological body. Different rock types and geological structural areas may have different extinction coefficients. The longer the distance the light propagates, the lower the transmittance, that is, the weaker the light. is the light intensity at the sampling point, simulating the light effect on the light at this point. For example, the light on the surface of the geological body is strong and the light inside is weak. The setting of light intensity can enhance the realism of the visualization effect. is the color of the object at the sampling point, which is determined by discrete data and continuous data. For example, the type of geological structure in discrete data may affect the basic color tone of the object. The fault area is set to a blue tone, and the fold area is set to a green tone. The rock mechanical parameters in the continuous data, including elastic modulus and porosity, further adjust the brightness and saturation details of the color. Through the propagation process of light in the hybrid data model, when the light encounters the geological structure in the discrete data model, the light is reflected, refracted and blocked according to the properties of the geological structure. When the light encounters a fault, it may be reflected and refracted, changing the propagation direction. At the same time, the color and transparency of the area where the light passes are determined according to the data in the continuous data model, thereby intuitively showing the relationship between different types of data.

[0035] 4. Visualization Results Display and Interaction Visual software platform operation A special visualization software platform is developed to integrate the hybrid data model constructed above and the designed visualization algorithm into the platform. The platform has a user-friendly interface, including functional modules such as data import, model construction, visualization settings, and interactive operations. After the user imports the sorted geomechanical data into the platform, the platform automatically builds a hybrid data model according to the previous steps, and performs multi-scale visualization and data association visualization settings according to the user's needs.

[0036] Interactive Function Usage Use the interactive tools provided by the platform, such as zoom, pan, and rotate functions, to operate the visualization results. Use the mouse wheel to zoom in and out. Roll the wheel forward to zoom in on the visualization image to view the details of the geomechanical data; roll it backward to zoom out on the image to view the overall overview. Press and drag the left mouse button to pan the image, making it easier to observe data in different areas. Press and drag the right mouse button to rotate the image around different axes to observe the distribution of geological structures and rock mechanical parameters from different angles.

[0037] Data query and analysis On the visualization interface, users can click on any location, and the platform will automatically query the detailed geomechanical data information at that location. Through the hybrid data model indexing mechanism established earlier, the discrete data model node information (such as geological structure type, related description) and the data values ​​in the continuous data model grid unit (such as rock elastic modulus, ground stress value) of the location can be quickly obtained. The query results will be presented to the user in an intuitive way, such as popping up an information window or displaying data details in a specific area of ​​the interface, including data name, value and brief description, to help users gain an in-depth understanding of the specific situation of geomechanical data at different locations, so as to conduct further geological analysis and research.

[0038] Example 2 like Figure 2 As shown, this embodiment provides a geomechanical data visualization system based on a hybrid data model, including a data classification and preprocessing unit, a hybrid data model construction unit, a visualization algorithm execution unit, and a visualization system functional module integration unit.

[0039] The data classification and preprocessing unit is used to receive geomechanical data and classify it into discrete data and continuous data, encode the discrete data, and normalize the continuous data, and the normalization is based on the normalization formula: ; implement; In addition, the hybrid data model construction unit constructs a discrete data model based on graph theory, takes discrete elements such as geological structures and faults as nodes, constructs edges according to spatial relationships, and uses a three-dimensional spatial grid model to construct a continuous data model, and establishes an index association between the two.

[0040] It is worth noting that the visualization algorithm execution unit sets a multi-scale threshold range, which is divided into macroscale [0, 0.3], mesoscale (0.3, 0.7] and microscale (0.7, 1]. At the macroscale, simplified geometric figures are used to display the overall outline of the geological structure and the large-scale geostress trend. At the mesoscale, semi-transparent surfaces are used to represent the surface of the geological body and map continuous data to display the differences in rock mechanical parameters and annotate the key structural information of discrete data. At the microscale, volume rendering technology is used to visualize the small cubic units in the continuous data model to display the internal mechanical properties of the rock, and the correlation between discrete data and continuous data is displayed through the propagation process of light in the hybrid data model. The visualization system functional module integration unit includes a data import module, a visualization setting module, an interactive operation module and a data query module. Each module is described in detail below.

[0041] The data import module has a designed user interface and provides a file selection dialog box that supports a variety of common geomechanical data file formats. The background program can automatically call the corresponding parsing function according to the selected file format to read the data into the memory, and process the data according to the data classification and preprocessing rules to build discrete data lists and continuous data arrays. The visualization setting module can provide a multi-scale visualization switching button, call the corresponding visualization function according to the selected scale threshold range, and allow users to perform corresponding visualization settings at different scales, including macro-scale visualization settings that allow users to customize the color, size, transparency and other display properties of simplified geometric figures, as well as the style and annotation information of the ground stress direction arrow. The meso-scale visualization setting interface provides surface color mapping scheme selection, texture image upload and adjustment functions, and font, size, and color settings for discrete structural information annotation. The micro-scale visualization setting allows users to adjust the transfer function parameters of volume rendering, including color-data value mapping curves and transparency-data value mapping curves. The interactive operation module implements the mouse wheel zoom function. By monitoring the mouse wheel event, the camera field of view is adjusted according to the scroll direction and amplitude. The left mouse button is pressed and dragged to achieve translation operation, and the camera position coordinates are updated according to the calculated mouse drag displacement. The right mouse button is pressed and dragged to achieve rotation operation, and the camera rotation angle is adjusted according to the mouse drag angle information. When the user clicks anywhere on the visualization interface, the data query module obtains the three-dimensional coordinate information corresponding to the clicked position, searches in the hybrid data model based on the coordinate information, queries the node information of the discrete data model where the position is located and the data value in the grid unit of the continuous data model, and displays the query results to the user in the form of a pop-up window or information panel, including the data name, value and brief description.

[0042] In this system, geomechanical data can be effectively classified and standardized through data classification and preprocessing units, which can ensure the standardization and consistency of data and lay a solid foundation for subsequent accurate analysis and visualization. The hybrid data model construction unit cleverly integrates discrete and continuous data, accurately presents the intrinsic relationship between geological structure and rock mechanical parameters, and fully displays the overall structure and local details of the geological body, greatly improving the integrity and accuracy of data expression.

[0043] In this embodiment, the multi-scale threshold setting and corresponding visualization strategy of the visualization algorithm execution unit can quickly grasp the overall picture of the geological structure and the large-scale geostress trend at the macro scale, deeply analyze the internal structure of the geological body and the distribution of rock mechanical properties at the meso scale, and carefully explore the micromechanical mechanism inside the rock at the micro scale, which effectively improves the visualization efficiency, helps users quickly focus on key information, and meets the diverse needs of different research and application scenarios for data display.

[0044] Through the mutual cooperation of various modules in the visualization system functional module integration unit, the convenient data acquisition and preliminary processing functions of the data import module facilitate the rapid input and organization of data; the powerful personalized setting capabilities of the visualization setting module enable users to flexibly adjust the visualization effect according to specific needs, enhancing the adaptability and ease of use of the system; the smooth zoom, translation and rotation functions of the interactive operation module provide users with an interactive experience of multi-angle and all-round observation of geomechanical data, which helps to deeply understand the content of the data; the efficient information retrieval and display functions of the data query module allow users to instantly obtain detailed data information of a specific location, providing strong support for accurate analysis and decision-making.

[0045] Through data classification and preprocessing, complex and diverse geomechanical data can be effectively sorted out and standardized. Whether it is discrete geological structure data or continuous rock mechanics parameters and geostress data, they can exist in a unified and suitable form for calculation and analysis, laying the foundation for subsequent accurate modeling. The construction and association of discrete data models and continuous data models breaks the limitations of traditional single data models, closely combines the spatial distribution of geological structures with the physical properties of rocks, makes the expression of data more comprehensive and three-dimensional, greatly improves the integrity of the understanding of geological bodies, and helps to more accurately grasp the complex relationships and laws within geological bodies.

[0046] In terms of visualization, the overall geological structure and large-scale geostress situation are presented in simple and intuitive simplified geometric graphics, allowing researchers to quickly gain a macro and global understanding of the study area, quickly locate key areas and trends, and provide directional guidance for further in-depth analysis.

[0047] At the meso-scale, translucent surfaces are used to delicately display the secondary structure and changes in rock mechanical parameters inside the geological body. Combined with discrete data annotation, the details and local features inside the geological body can be clearly presented, which helps to analyze the relationship between local geological phenomena and mechanical properties.

[0048] Through the use of volume rendering technology at the microscopic scale, we can go deep into the microscopic world inside the rock. According to the data association visualization algorithm based on ray tracing, the mechanical property data of the rock is vividly mapped into color and transparency, accurately revealing the intrinsic relationship between the microscopic structure and mechanical properties of the rock, and providing a powerful means for in-depth research on the micromechanical mechanism of the rock. This multi-scale progressive visualization meets the needs of different research depths and precisions from macro to micro, greatly improves the efficiency and effectiveness of data visualization, and avoids analysis difficulties caused by loss of data details or information overload.

[0049] Through the coordinated work of various modules in the visualization system functional module integration unit, users are provided with a powerful and convenient operation experience. The versatility of the data import module ensures that geomechanical data from various sources and formats can be widely accepted, lowering the threshold for data acquisition. The rich functions of the visualization setting module enable users to flexibly customize the visualization effect according to specific research needs or personal preferences. Whether it is the display style of macro graphics or the parameter adjustment of micro volume drawing, they can control it at will, enhancing the adaptability of the system to different application scenarios. The convenience of the interactive operation module, such as mouse wheel zoom, left key translation, right key rotation and other functions, allows users to observe and explore geomechanical data from any angle and distance just like operating geological models in real scenes, greatly improving the flexibility and depth of data exploration, and helping to discover the geological laws and characteristics hidden behind the data. The efficiency of the data query module enables users to quickly obtain detailed data information when focusing on a specific location. Whether it is discrete geological structure attributes or continuous rock mechanics parameter values, they can be presented in real time, providing a reliable basis for accurate analysis and decision-making, and greatly improving the work efficiency of geological research and engineering applications.

[0050] The present invention can help geologists to have a deeper understanding of the complex relationship between geological structural evolution and rock mechanical behavior, and provide powerful data visualization support for the development of geological theory; in the field of geological engineering, such as oil exploration, mining, underground engineering construction, etc., it can help engineers predict geological risks in advance, optimize engineering design plans, and improve engineering safety and economic benefits; in the prevention and control of geological disasters, through the precise visualization and in-depth analysis of geomechanical data, it can more accurately assess the possibility of disasters and the potential impact range, and provide key technical support for disaster warning and prevention and control measures. In summary, the geomechanical data visualization method and system based on the hybrid data model have irreplaceable and important value in many fields related to geology, and have strongly promoted the development and progress of geological science and engineering technology.

[0051] The above specific embodiments are only several optional embodiments of the present invention. Based on the technical solutions of the present invention and the relevant inspirations of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

Claims

1. A geomechanical data visualization method based on a hybrid data model, characterized in that: The following steps are involved: S1. Data collection and collation: Collect geomechanical data including geological structure data, rock mechanics parameter data and geostress data; S2. Hybrid data model construction, including: S201, data classification and preprocessing: the geomechanical data are divided into discrete data and continuous data, the discrete data are coded, and the continuous data are normalized according to a formula; S202, discrete data model construction: a discrete data model is constructed using a data structure based on graph theory, with discrete elements such as geological structures and faults as nodes, edges are constructed based on their spatial relationships, and geological structure type codes and location coordinate attributes are set for the nodes; S203, continuous data model construction: using a three-dimensional space grid model to represent continuous data, determining the spatial range of the geological body and dividing it into small cubic units, and filling the continuous data into corresponding units according to spatial positions; S204, model association: establishing an index mechanism to associate discrete data models with continuous data models; S3. Visualization algorithm design and application, including: Multi-scale visualization: The macro-scale threshold range is set to [0, 0.3], and simplified geometric graphics are used to display the overall outline of the geological structure and the large-scale geostress trend. The graphics are drawn based on the geological structure node location information in the discrete data model and the macro-geostress trend data in the continuous data model. The mesoscale threshold range is set to (0.3, 0.7], and the surface of the geological body is represented by a semi-transparent surface. The continuous data is mapped to the surface color and texture, and the key structural information of the discrete data is annotated. The surface construction is based on the geological structure boundary information in the discrete data model and the rock mechanics parameter data in the continuous data model. The micro-scale threshold range is set to (0.7, 1], and the volume rendering technology is used to visualize the small cubic units in the continuous data model. The rock mechanical property data are mapped to color and transparency. The volume rendering is performed based on the data association visualization algorithm based on ray tracing. S4. Visualization and interaction of results: Develop a visualization software platform, which includes a data import module, a model building module, a visualization setting module, an interactive operation module, and a data query module. Each module works together to realize the visualization and interactive operation functions of geomechanical data.

2. A geomechanical data visualization method based on a hybrid data model according to claim 1, characterized in that: The normalization formula is: ； in, is the normalized value, is the original data value, and These are the minimum and maximum values ​​of the data type, respectively.

3. The geomechanical data visualization method based on a hybrid data model according to claim 1, characterized in that: In step S3, the data association visualization algorithm: adopts the data association visualization algorithm based on ray tracing, and the formula is: ； ； ； in, is the screen coordinate The final pixel color value at is the number of sampling points for ray tracing, The light is The location of the sampling points, and is the sampling step size, is the ray from the starting point to the sampling point The transmittance, The medium in the light propagation path The extinction coefficient on It is the sampling point The light intensity at It is the sampling point The color of the object at a location is determined by both discrete data and continuous data. The relationship between discrete data and continuous data is demonstrated through the propagation process of light in the mixed data model.

4. The geomechanical data visualization method based on a hybrid data model according to claim 1, characterized in that: The data import module specifically includes: Design user interface to provide file selection dialog box supporting multiple common geomechanical data file formats; The background program automatically calls the corresponding parsing function according to the selected file format to read the data into the memory, and processes the data according to the data classification and preprocessing rules to build discrete data lists and continuous data arrays.

5. The geomechanical data visualization method based on a hybrid data model according to claim 1, characterized in that: The model building module specifically includes: Using the processed discrete data to build a discrete data model based on graph theory, including creating node objects and setting attributes such as geological structure type code and location coordinates, creating edge objects based on spatial relationships and associating corresponding nodes; Create a three-dimensional spatial grid object, determine the range and resolution, fill the data in the continuous data array into the corresponding grid cells, and establish an index association with the discrete data model.

6. The geomechanical data visualization method based on a hybrid data model according to claim 1, characterized in that: The visualization setting module specifically includes: Provide a multi-scale visualization switch button to call the corresponding visualization function according to the selected scale threshold range; In the macro-scale visualization settings, users are allowed to customize the color, size, transparency and other display properties of simplified geometric figures, as well as the style and annotation information of the geostress direction arrows; The mesoscale visualization settings interface provides surface color mapping scheme selection, texture image upload and adjustment functions, and font, size, and color settings for discrete structural information annotations; Microscale visualization settings allow users to adjust the transfer function parameters of volume rendering, including color-data value mapping curves and transparency-data value mapping curves.

7. The geomechanical data visualization method based on a hybrid data model according to claim 1, characterized in that: The interactive operation module specifically includes: Implement the mouse wheel zoom function, and adjust the camera's field of view according to the scrolling direction and amplitude by monitoring the mouse wheel events; Press and drag the left mouse button to perform translation operation, and update the camera position coordinates according to the calculated mouse drag displacement; Press and drag the right mouse button to rotate the camera, and adjust the camera rotation angle according to the mouse drag angle information.

8. The geomechanical data visualization method based on a hybrid data model according to claim 1, characterized in that: The data query module specifically includes: When the user clicks anywhere on the visualization interface, the three-dimensional coordinate information corresponding to the clicked position is obtained; Searching in the hybrid data model based on the coordinate information, querying the node information of the discrete data model where the location is located and the data value in the grid unit of the continuous data model; The query results are displayed to the user in a pop-up window or information panel, including the data name, value and brief description.

9. A geomechanical data visualization system based on a hybrid data model, characterized in that: include: The data classification and preprocessing unit is used to receive geomechanical data and classify it into discrete data and continuous data, encode the discrete data, and normalize the continuous data, and the normalization is based on the normalization formula: ； implement; A hybrid data model construction unit, which constructs a discrete data model based on graph theory, takes discrete elements such as geological structures and faults as nodes, constructs edges based on spatial relationships, and uses a three-dimensional spatial grid model to construct a continuous data model, and establishes an index association between the two; The visualization algorithm execution unit sets a multi-scale threshold range, which is divided into macroscale [0, 0.3], mesoscale (0.3, 0.7] and microscale (0.7, 1]. At the macroscale, simplified geometric figures are used to display the overall outline of the geological structure and the large-scale geostress trend. At the mesoscale, semi-transparent surfaces are used to represent the surface of the geological body and map the continuous data to display the differences in rock mechanical parameters and annotate the key structural information of the discrete data. At the microscale, volume rendering technology is used to visualize the small cubic units in the continuous data model to display the internal mechanical properties of the rock, and the correlation between discrete data and continuous data is displayed through the propagation process of light in the hybrid data model. The visualization system functional module integration unit includes a data import module, a visualization setting module, an interactive operation module and a data query module.

10. A geomechanical data visualization system based on a hybrid data model according to claim 9, characterized in that: In the visualization system functional module integration unit, The data import module has a design user interface and provides a file selection dialog box that supports a variety of common geomechanical data file formats, and the background program can automatically call the corresponding parsing function according to the selected file format to read the data into the memory, and process the data according to the data classification and preprocessing rules to construct a discrete data list and a continuous data array; The visualization setting module can provide a multi-scale visualization switching button, call the corresponding visualization function according to the selected scale threshold range, and allow users to perform corresponding visualization settings at different scales, including allowing users to customize the display properties of simplified geometric figures and the style and annotation information of the ground stress direction arrow in the macro-scale visualization setting. The meso-scale visualization setting interface provides surface color mapping scheme selection, texture image upload and adjustment functions, and font, size, and color settings for discrete structural information annotation. The micro-scale visualization setting allows users to adjust the transfer function parameters of volume rendering, including color-data value mapping curves and transparency-data value mapping curves; The interactive operation module implements the mouse wheel zoom function, by monitoring the mouse wheel event, adjusting the camera field of view according to the scroll direction and amplitude, pressing and dragging the left mouse button to implement the translation operation, updating the camera position coordinates according to the calculated mouse drag displacement, pressing and dragging the right mouse button to implement the rotation operation, and adjusting the camera rotation angle according to the mouse drag angle information; When the user clicks any position in the visualization interface, the data query module obtains the three-dimensional coordinate information corresponding to the clicked position, searches in the hybrid data model based on the coordinate information, queries the node information of the discrete data model where the position is located and the data value in the grid unit of the continuous data model, and displays the query results to the user in the form of a pop-up window or information panel, including the data name, value and brief description.

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