A tunnel overburden rock three-dimensional structure feature analysis method and system

Abstract

The application provides a tunnel overburden rock three-dimensional structure feature analysis method and system, and relates to the technical field of tunnel structure analysis. The method comprises the following steps: extracting the geometric features and topological features of each fissure unit in the fissure network model; determining the engineering importance grade of each fissure unit according to the geometric features and topological features; according to the engineering importance grade of each fissure unit, hierarchically organizing the three-dimensional fissure network model to form a multi-granularity hierarchical data structure; according to the preset engineering analysis purpose, determining the data form of the current analysis requirement, and extracting the fissure subset or equivalent parameter of the corresponding granularity; and performing feature analysis according to the extracted fissure subset or equivalent parameter. The method of the application solves the problems of low calculation efficiency and poor applicability of the existing detailed fissure model in different engineering applications, and improves the efficiency, accuracy and practicability of the model in the dynamic construction process of the tunnel engineering analysis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tunnel structure analysis, in particular to a tunnel overburden three-dimensional structure feature analysis method and system. BACKGROUND

[0002] In the planning and design stage of tunnel engineering, accurately mastering the three-dimensional fissure distribution characteristics inside the overburden is a key link to ensure engineering safety and optimize design schemes. The engineering team comprehensively uses various geological exploration techniques to obtain the structure information of underground rock mass. For example, through drilling core sampling, rock core samples along the drilling path can be obtained, which can provide high-precision lithology, mineral composition and local fissure occurrence information. Geological radar detection can provide high-resolution two-dimensional profile images along the survey line, which has an advantage in detecting fissures, cavities and other abnormal bodies in the shallow overburden. Seismic wave exploration, including reflection or refraction wave technology, can penetrate deeper strata and obtain macroscopic geological structure information, such as large faults and rock mass boundaries. In some cases, when the tunnel excavation surface or the ground surface has rock outcrops, high-precision laser scanning or photogrammetry technology is also used to obtain high-density point cloud data to accurately capture the geometric shape of the outcrop fissures.

[0003] Based on these multi-source heterogeneous exploration data, engineering technicians finally build a detailed three-dimensional fissure network model through complex preprocessing, data fusion and geological interpretation. This model usually contains tens of thousands or even hundreds of thousands of discrete fissure units, each unit has accurate geometric information such as spatial coordinates, occurrence, size, opening, roughness, and complex topological connection relationships such as intersection points, overlapping areas and endpoints. This detailed model is the final output of fissure distribution analysis and can theoretically reflect the true complexity of the fissures inside the overburden.

[0004] However, in actual engineering applications, this highly detailed three-dimensional fissure network model directly used for subsequent engineering numerical simulation and analysis will face significant challenges.

[0005] Firstly, due to the large number of fissures contained in the model and the complex geometric topological relationship, directly inputting it into traditional numerical simulation software for calculation will lead to an exponential increase in calculation amount. This makes the simulation process time-consuming and even unable to be completed within the engineering time window, seriously affecting the efficiency of design iteration. For example, a model containing millions of fissure surfaces needs to define grids and boundary conditions for each fissure surface when performing hydraulic coupling calculation, resulting in too large number of grids and equation groups, which exceeds the bearing capacity of existing computing resources.

[0006] Second, different types of engineering analysis have different requirements for the details of the fracture model. For example, when evaluating the long-term stability of the overall surrounding rock of a tunnel, more attention may be paid to regional large-scale fault structures and the connectivity of the main fracture network, while the geometric details of small fractures are not sensitive; but when evaluating the local anchor support effect or analyzing the instability mechanism of the surrounding rock of the working face, the microscopic features such as fracture opening, filling type, and roughness of the fracture wall need to be accurate to the millimeter level. The highly detailed single fracture model generated by existing methods cannot automatically or efficiently provide fracture representation of different scales or different precision according to different engineering analysis requirements. This means that engineers need to manually simplify or adjust the model, which is time-consuming and prone to subjective errors.

[0007] Third, detailed fracture models also have difficulties in data transmission, storage, and visualization. The huge amount of data makes the model file volume huge, making it difficult to efficiently transmit and share between different software platforms. When visualizing, excessive dense fracture information can cause visual confusion, making it difficult for engineers to quickly identify key fracture systems or high-risk areas, reducing the practicality and interpretability of the model. For example, when displaying all small fractures in a three-dimensional view, the entire model will appear too crowded to distinguish the main structural planes.

[0008] Fourth, for different engineering purposes, fracture network models need to be converted from geometric representation to equivalent physical parameter representation. For example, when simulating regional groundwater seepage, it is often necessary to equivalent complex discrete fracture networks to permeability tensors or equivalent permeability coefficients of continuous media. However, due to the presence of a large number of non-main fracture and locally disconnected fractures in detailed fracture models, existing methods have difficulty in effectively filtering out key fractures that play a dominant role in macroscopic physical behavior such as seepage and deformation, and automatically excluding or simplifying those fractures that contribute little. This results in low accuracy of equivalent parameters or low efficiency of the equivalent process itself. For example, if all fractures are included in the equivalent calculation, a large amount of redundant information will be introduced, making the calculation results deviate from the actual situation; if manually selected, it depends on the experience of engineers, and lacks objective standards.

[0009] Fifth, in the entire life cycle of tunnel engineering, the analysis requirements for fracture distribution are dynamically changing. In the early design stage, a macroscopic fracture distribution profile may be needed; in the construction stage, attention needs to be paid to local fracture evolution caused by construction disturbance; in the operation stage, it may be necessary to evaluate the fracture propagation under long-term load.

[0010] The single detailed fracture model constructed by the existing method, although containing rich original information, lacks a mechanism to automatically generate or extract fracture sub-models or equivalent models of different abstraction levels and different key features according to these dynamically changing needs. This forces engineers to start from scratch to perform complex cropping, simplification or parameterization of the original detailed model each time a specific analysis is needed, increasing the workload and reducing the timeliness of the analysis. For example, if the local stability of a certain area needs to be quickly evaluated, but only a super-large model containing all the fractures in the entire area is accessible, the efficiency of extracting the required local information and performing a simplified analysis will be very low. SUMMARY

[0011] The purpose of the present application is to provide a tunnel overburden three-dimensional structure feature analysis method and system, which solves the problem of low calculation efficiency and poor applicability of the existing detailed fracture model in different engineering applications, and improves the efficiency, accuracy and practicality of the model in the dynamic construction process.

[0012] In a first aspect, the present application provides a tunnel overburden three-dimensional structure feature analysis method, comprising the following steps:

[0013] Obtain a three-dimensional fracture network model of the tunnel overburden, and extract the geometric features and topological features of each fracture element in the fracture network model;

[0014] Determine the engineering importance level of each fracture element according to the geometric features and topological features;

[0015] According to the engineering importance level of each fracture element, combined with the topological connection relationship between each fracture element, the three-dimensional fracture network model is hierarchically organized to form a multi-granularity hierarchical data structure;

[0016] According to the preset engineering analysis purpose, determine the data form of the current analysis requirement, and based on the determined data form, extract the fracture subset or equivalent parameter of the corresponding granularity from the multi-granularity hierarchical data structure;

[0017] According to the extracted fracture subset or equivalent parameter, perform feature analysis.

[0018] The tunnel overburden three-dimensional structure feature analysis method provided by the present application aims to construct a multi-granularity hierarchical data structure of the three-dimensional fracture network of the tunnel overburden, and realize adaptive extraction of fracture subsets or equivalent physical parameters based on predefined engineering analysis purposes. The core is to quantize the engineering importance of the fracture and hierarchically organize the original detailed fracture model, thereby avoiding processing redundant data in different engineering analyses and improving the analysis efficiency and model applicability.

[0019] In a second aspect, the present application provides a tunnel overburden three-dimensional structure feature analysis system, comprising:

[0020] An acquisition module is used to obtain a three-dimensional fracture network model of the tunnel overburden and extract the geometric and topological features of each fracture unit in the fracture network model;

[0021] A determination module is used to determine the engineering importance level of each fracture unit based on geometric and topological characteristics;

[0022] The hierarchical organization module is used to organize the three-dimensional fracture network model into layers according to the engineering importance level of each fracture unit and the topological connection relationship between each fracture unit to form a multi-granularity hierarchical data structure;

[0023] An extraction module is used to determine the data format required for the current analysis according to the preset engineering analysis purpose, and based on the determined data format, extract the fracture subset or equivalent parameters of the corresponding granularity from the multi-granularity hierarchical data structure;

[0024] The analysis module is used to perform feature analysis based on the extracted fracture subset or equivalent parameters.

[0025] From the above, it can be seen that the three-dimensional structural characteristic analysis method of tunnel overburden provided by the present invention overcomes the common problem of low computational efficiency when existing methods are directly applied to diversified engineering analyses such as surrounding rock stability, groundwater seepage, and support effect. It can automatically and efficiently extract fracture representations or equivalent physical parameters that meet different scales, precisions, and abstraction levels from the detailed model according to different engineering analysis purposes, so as to improve the practicality, interpretability, and analysis accuracy of the model in dynamic engineering application scenarios.

[0026] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the embodiments of the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flow chart of a method for analyzing three-dimensional structural characteristics of tunnel overburden provided by an embodiment of the present invention.

[0028] Figure 2 A schematic structural diagram of a system for analyzing three-dimensional structural characteristics of tunnel overburden provided by an embodiment of the present invention.

[0029] Description of labels:

[0030] 100, acquisition module; 200, determination module; 300, hierarchical organization module; 400, extraction module; 500, analysis module. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0032] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0033] With reference to the accompanying drawings, Figure 1 the present application provides a tunnel overburden rock three-dimensional structure feature analysis method, comprising the following steps:

[0034] acquiring a three-dimensional fracture network model of the tunnel overburden rock, and extracting geometric features and topological features of each fracture unit in the fracture network model;

[0035] determining the engineering importance grade of each fracture unit according to the geometric features and the topological features;

[0036] according to the engineering importance grade of each fracture unit, combining the topological connection relationship between each fracture unit, and layering the three-dimensional fracture network model to form a multi-granularity hierarchical data structure;

[0037] the multi-granularity hierarchical data structure comprises:

[0038] a microscopic discrete fracture layer, used for storing geometric information and attributes of each fracture unit;

[0039] a fracture cluster and main control fracture zone layer, used for aggregating fracture units with similar occurrence, close spatial position or strong connectivity to form a fracture cluster, and used for identifying a main control fracture zone which plays a leading role in surrounding rock stability or groundwater seepage path;

[0040] a regional equivalent parameter layer, used for equivalent the fracture network in a specific region to macroscopic physical parameters;

[0041] According to a preset engineering analysis purpose, a data form of a current analysis requirement is determined, and based on the determined data form, a fracture subset or an equivalent parameter of a corresponding granularity is extracted from a multi-granularity hierarchical data structure; the fracture subset includes a fracture unit in a specific region extracted from a micro-discrete fracture layer, and / or a fracture cluster extracted from a fracture cluster and master fracture zone layer; the equivalent parameter includes a macroscopic physical parameter in a specific region extracted from a regional equivalent parameter layer.

[0042] According to the extracted fracture subset or equivalent parameter, feature analysis is performed.

[0043] The method first acquires a three-dimensional fracture network model of the tunnel overburden rock (the reconstruction of the model can refer to the existing three-dimensional geological model of the coal mine roadway, and the three-dimensional geological model is a digital model that uses computer technology to predict unknown strata through geology and statistics, etc., to visually display the internal structure, geometric shape, etc. of the geological body in a three-dimensional space form. For example, according to actual needs, test drill holes are arranged, and test drill hole data is acquired by using a drill hole camera in-situ test technology, then a virtual vertical drill hole is obtained by linking the test drill holes along the working face, and then the strata are manually compared and divided according to the drill hole strata information, and finally Kriging spatial interpolation is performed on the strata surface in the three-dimensional space, and the surface is formed into a body, so as to realize the reconstruction of the three-dimensional geological model), and the geometric features and topological features of each fracture unit in the fracture network model are extracted. These features include the spatial position, size, occurrence, opening degree, etc. of the fractures, and the topological relationship between the fractures, such as connectivity and intersection, which are the basic data for subsequent analysis. Based on these geometric features and topological features, the engineering importance grade of each fracture unit is determined, and the influence degree of the fracture unit on the surrounding rock stability or groundwater seepage, etc. is evaluated, so as to provide a basis for subsequent processing.

[0044] The working principle of the present application is that, first, the characteristics of each fracture unit in the original three-dimensional fracture network model obtained from the multi-source heterogeneous exploration data are comprehensively extracted, and the "engineering importance index" of each fracture unit is quantified based on these characteristics. Then, according to the index and the topological connectivity between the fractures, the huge fracture data set is intelligently organized into a multi-granularity hierarchical structure with different degrees of abstraction and precision levels, including the most detailed micro-discrete fracture layer, the aggregated fracture cluster and master fracture zone layer, and the macroscopic equivalent physical parameter layer. When specific engineering analysis is needed, the system automatically selects and extracts the data granularity or equivalent parameter most suitable for the current analysis requirement from the above multi-granularity hierarchical structure according to a set of adaptive extraction rules, rather than processing the entire huge original model, thereby greatly reducing the calculation amount and improving the efficiency.

[0045] The core innovation of this application lies in that, by introducing a multi-granularity hierarchical data structure, a three-dimensional fracture network model with a large number of units and connection relationships is organized into data representations with different levels of abstraction and granularity, and combined with a method of extracting corresponding granularity data on demand according to the purpose of engineering analysis, the problems of slow computing speed, difficulty in adapting the model to different analysis needs, and difficulty in data management caused by directly processing models with large amounts of data in the existing technology are solved, and the effect of fast processing speed and the ability to adapt to different needs for three-dimensional structural feature analysis of tunnel overburden is achieved.

[0046] Specifically, after determining the engineering importance level of each fracture unit, the three-dimensional fracture network model is hierarchically organized based on the topological connectivity between the fracture units to form a multi-granularity hierarchical data structure. This structure comprises a microscopic discrete fracture layer, which stores the geometric information and attributes of each fracture unit, including all unit properties and topological relationships, while preserving the original data details. A fracture cluster and dominant fracture zone layer is used to aggregate fracture units with similar occurrence, close spatial location, or strong connectivity into fracture clusters. This layer is used to identify dominant fracture zones that play a dominant role in surrounding rock stability or groundwater seepage paths, providing structural information between the micro and macro scales. Finally, a regional equivalent parameter layer is used to equate the fracture network within a specific region to macroscopic physical parameters, providing a macroscopic representation of physical properties. These three layers together constitute a multi-granularity characterization system from the micro to the macro scale. During engineering analysis, the data format required for the current analysis, i.e., the granularity of the required data, is determined based on the predetermined engineering analysis objectives. Based on this data format, a corresponding fracture subset or equivalent parameter of the granularity is extracted from the multi-granularity hierarchical data structure. Fracture subsets can be extracted from a microscopic discrete fracture layer, i.e., fracture units within a specific region, or from fracture clusters extracted from the fracture cluster and master fracture zone layers. Equivalent parameters are extracted from the regional equivalent parameter layer. This on-demand extraction approach avoids the need to process the entire, data-intensive model. Finally, based on the extracted fracture subsets or equivalent parameters, characteristic analysis is performed, and calculations and evaluations are performed using appropriate engineering analysis methods or numerical simulation tools.

[0047] As an implementation, the scheme of the application is implemented as follows: assuming that it is necessary to conduct groundwater seepage analysis on a certain area of the tunnel overburden rock. First, a three-dimensional fracture network model of the area is constructed through geological exploration data, and the spatial position, size, occurrence, opening degree and other geometric characteristics of each fracture surface and the topological characteristics such as the connection relationship between them are extracted. Based on these characteristics, the engineering importance level of each fracture surface is determined in combination with specific factors such as fracture opening degree and connectivity. Subsequently, using this information, a multi-granularity hierarchical data structure is constructed. The microscopic discrete fracture layer stores detailed information of each fracture surface. The fracture cluster and the master fracture zone layer aggregate fracture surfaces with similar occurrence or connectivity into clusters through a clustering algorithm, and identify the dominant seepage channels. The regional equivalent parameter layer calculates the equivalent permeability coefficient tensor of the region in different directions through the volume average or representative unit method. When regional seepage analysis is needed, according to the engineering analysis purpose of "groundwater seepage analysis", the required data form is determined to be the regional equivalent parameters, and the analysis region is specified. The system extracts the equivalent permeability coefficient tensor of the specific region from the regional equivalent parameter layer of the multi-granularity hierarchical data structure. Finally, the extracted equivalent permeability coefficient tensor is input into the continuous medium seepage simulation software to calculate the regional groundwater flow field and pressure distribution, thereby completing the feature analysis.

[0048] Through the above scheme, the application realizes data organization and management of a three-dimensional fracture network model with a large number of units and connection relationships by constructing and utilizing a multi-granularity hierarchical data structure. According to different engineering analysis purposes, different granularity of fracture data or equivalent parameters can be extracted as needed, avoiding the computational burden caused by directly processing a large amount of data model, and significantly improving the analysis processing speed. This on-demand extraction and hierarchical representation makes the model adaptable to different scales and different types of engineering analysis requirements, improving the application value of the model. At the same time, by equivalent to macroscopic parameters for discrete fracture network, the input data form that meets the needs of large-scale numerical simulation is provided, solving the problem of difficult effective screening of important fractures when converting from geometric representation to physical parameters. Overall, the method provides a fast processing speed and adaptable three-dimensional structural feature analysis method for tunnel overburden rock.

[0049] Further, it further comprises the steps of:

[0050] Receiving subsequent detection data, and locally updating the fracture network model of the corresponding spatial region in the multi-granularity hierarchical data structure according to the subsequent detection data.

[0051] The application also has dynamic adaptability. By providing a local correction interface, new exploration data can be integrated in real time during engineering progress, and incremental updates are only made to the affected local area, ensuring that the model always reflects the latest geological conditions, thereby supporting dynamic analysis of crack distribution throughout the tunnel engineering life cycle.

[0052] Subsequent exploration data refers to geological information or monitoring data about overburden crack distribution or state newly obtained after the initial construction of the three-dimensional crack network model and the formation of the multi-granularity hierarchical data structure, which can be obtained in the form of drilling data, geophysical exploration data, monitoring data (such as displacement, stress, and seepage monitoring data), etc. Local update refers to using newly obtained subsequent exploration data to modify, supplement, or correct the crack network model in a specific spatial area corresponding to the new data in the already constructed multi-granularity hierarchical data structure, which can be achieved by adding new crack elements, modifying the geometry or attribute information of existing crack elements, adjusting the topological connection relationship between crack elements, etc.

[0053] By introducing the step of receiving subsequent exploration data and performing local update, the entire analysis method can adapt to the dynamic changes of the project. When new exploration data is received, the system can identify the spatial area corresponding to these data. Subsequently, using these new data, the crack network model in the specific spatial area in the multi-granularity hierarchical data structure is modified. This modification is local, avoiding the reconstruction or global processing of the entire large model. Since the update is directly applied to the crack network model in the multi-granularity hierarchical data structure, it means that the underlying microscopic discrete crack information will be modified or supplemented. More importantly, this local update can affect the upper layer through the association and organization within the data structure. For example, changes in microscopic cracks may change the morphology, number, or connectivity of crack clusters, and may also affect the identification results of the master crack zone. Further, these changes will be reflected in the calculation of regional equivalent parameters, making the macroscopic physical parameters of the upper layer more accurately represent the latest overburden state. This local update and layer-by-layer influence mechanism ensures that the multi-granularity hierarchical data structure can always reflect the latest geological conditions, thereby enabling subsequent feature analysis based on the structure to be more accurate and timely. Compared with analysis based only on the initial model, this scheme can use dynamically obtained new information to significantly improve the reliability and practicality of the analysis results, better supporting dynamic adjustment of engineering decisions.

[0054] By constructing a multi-granularity hierarchical characterization system of the fracture network of the overburden rock of a tunnel and combining with adaptive extraction according to engineering analysis requirements, the problem of low calculation efficiency and poor applicability of existing detailed fracture models in different engineering applications is effectively solved. Specifically, it greatly improves the calculation efficiency of engineering analysis, realizes automatic and efficient extraction of fracture characterization of different scales, precision and abstraction levels, enhances the practicality and interpretability of the model in different engineering scenarios, improves the accuracy and design optimization capability of engineering analysis, and ensures that the model can be dynamically updated and locally corrected in the life cycle of a tunnel project, thereby significantly improving the scientificity and efficiency of tunnel engineering planning, design and construction.

[0055] In certain embodiments, according to the engineering importance level of each fracture unit, combined with the topological connection relationship between each fracture unit, the three-dimensional fracture network model is hierarchically organized to form a fracture cluster and a main fracture zone in a multi-granularity hierarchical data structure:

[0056] According to the geometric characteristics, topological characteristics and rock mass medium properties of each fracture unit, the coupling strength between the fracture units in the hydraulic is determined;

[0057] According to the coupling strength, the functional connectivity path between the fracture units having hydraulic connectivity is identified;

[0058] The fracture units located on the functional connectivity path are aggregated to form a fracture cluster.

[0059] The coupling strength refers to the degree of mutual influence or transmission between the fracture units in the hydraulic, which can be realized by using the hydraulic conductivity rate calculated based on the fracture opening, the permeability of the filler, the roughness of the fracture surface, etc., or using the mechanical stiffness or stress transmission coefficient calculated based on the fracture lap area, the rock bridge strength, the fracture surface closure degree, etc. Among them, the functional connectivity path refers to a continuous or semi-continuous channel formed by a series of fracture units in the three-dimensional fracture network through the coupling action in the hydraulic, which has a significant influence on the overall water flow movement or stress distribution, which can be identified by using a graph algorithm (such as minimum spanning tree, maximum flow minimum cut) combined with a coupling strength threshold or a path cumulative coupling value.

[0060] In the process of hierarchically organizing the three-dimensional fracture network model into fracture cluster and master fracture zone layer in the multi-granularity hierarchical data structure according to the engineering importance grade of each fracture unit and the topological connection relationship between each fracture unit, the hydraulic coupling strength between fracture units is quantified and analyzed to more accurately identify the functional connected path that has a dominant effect on the engineering behavior. Specifically, first, the interaction strength between any two fracture units in the hydraulic is calculated or evaluated according to the geometric properties (such as size, opening) of each fracture unit itself, the topological relationship (such as connection type, connection number) and the physical properties (such as bedrock permeability, filler type) of the rock mass medium in which it is located. This process goes beyond simple geometric proximity or intersection judgment, but also considers the physical state and medium characteristics of the fracture, thus more truly reflecting the effectiveness of water flow or stress transmission between fractures. Second, based on the calculated coupling strength, the sequence or set of fracture units that can form an effective hydraulic channel with high coupling strength is identified in the entire fracture network. These sequences or sets form functional connected paths. This step filters out the "ineffective" connections that are geometrically connected but have weak coupling, and focuses on the key paths that really affect the macro behavior. Finally, all fracture units located in these identified functional connected paths are aggregated to form fracture clusters. The fracture clusters formed in this way are no longer just a simple collection in space or occurrence, but functional units composed of fracture units that are functionally closely related and jointly bear the hydraulic transmission function. Through this aggregation based on functional connected paths, the scheme can construct a hierarchical data structure that better reflects the actual engineering behavior of the fracture network. Combined with the basic scheme of hierarchical organization according to the engineering importance grade and topological relationship, it can more effectively capture the key structure and functional characteristics of complex fracture networks, and provide a more accurate and representative data basis for subsequent engineering analysis of different granularities.

[0061] For example, in determining the hydraulic coupling strength between fracture elements, the equivalent hydraulic conductivity between adjacent or overlapping fracture elements can be calculated for hydraulic coupling, which can be determined based on the average aperture, roughness of the fractures, and the geometry of the connection area; for mechanical coupling, the equivalent stiffness at the connection of fracture elements can be calculated, which can take into account the closure of the fracture surface, the mechanical properties of the fillers, and the size and strength of the rock bridge. In identifying the functionally connected paths according to the coupling strength, the fracture network can be abstracted as a graph, with fracture elements as nodes and connections between fracture elements as edges, and the weight of the edges is set as the calculated coupling strength. Then, graph search algorithms such as Dijkstra or Floyd-Warshall can be applied to find the "most open" or "stiffest" paths in terms of hydraulic, or network flow algorithms can be used to identify the path set that contributes most to the overall hydraulic connectivity or mechanical stability. In aggregating fracture elements located on the functionally connected paths to form fracture clusters, all fracture elements on the identified functionally connected paths can be marked, and then connected component analysis is performed on these marked fracture elements to aggregate the mutually connected marked elements into a fracture cluster.

[0062] By determining the hydraulic coupling strength between fracture elements according to the geometric characteristics, topological characteristics, and rock mass medium properties of each fracture element, and identifying the functionally connected paths based on this, and then aggregating the fracture elements located on these paths to form fracture clusters, the present scheme can overcome the limitations of aggregation relying solely on geometric or simple topological relationships. The fracture clusters formed in this way can more accurately represent the actual hydraulic channels or stress transfer structures in the rock mass, making the fracture clusters in the multi-granularity hierarchical data structure and the master fracture zone layer more reflect the real functional behavior of the fracture network. This helps to improve the accuracy and reliability of subsequent engineering analysis (such as groundwater seepage simulation, surrounding rock stability calculation), and avoids analysis bias caused by the inconsistency between fracture network hierarchical organization and actual engineering response.

[0063] In some embodiments, when the region equivalent parameter layer in the multi-granularity hierarchical data structure is formed by layering the three-dimensional fracture network model according to the engineering importance level of each fracture element and the topological connection relationship between each fracture element:

[0064] By analyzing the geometric characteristics, topological characteristics, and spatial distribution regularity of each fracture element in a particular region, the structural characteristics of the fracture network are obtained;

[0065] According to the structural characteristics of the fracture network, the anisotropy characteristics and scale effect of the particular region are determined;

[0066] According to the anisotropy characteristics and scale effect, the macroscopic physical parameters of the particular region are calculated.

[0067] The structural characteristics of fracture network refer to the overall geometric, topological and spatial arrangement characteristics presented by the set of discrete fracture elements in a specific region, which can be characterized by quantitative indicators such as fracture density, connectivity, dominant occurrence, fracture length distribution, aperture distribution, roughness distribution, fracture network tensor, etc. The anisotropy characteristics refer to the differences in the physical properties of rock mass in different spatial directions, which can be described by tensors such as permeability tensor, elastic tensor, strength tensor, etc. The scale effect refers to the phenomenon that the macroscopic physical properties of rock mass change with the scale of the analysis region, which can be characterized by the representative element volume (REV) or the variation curve of equivalent parameters with scale. The macroscopic physical parameters refer to the parameters used to describe the overall physical behavior of rock mass at the macroscopic scale, which can be represented by equivalent permeability coefficient, equivalent permeability tensor, equivalent elastic modulus, equivalent Poisson's ratio, equivalent strength parameters, etc.

[0068] This proposal provides a specific method for forming regional equivalent parameter layers within a multi-granular hierarchical data structure, aiming to address the inability of existing technologies to reflect the anisotropy and scale effects of overburden fracture networks. Based on the engineering importance of each fracture unit and the topological connectivity between them, a three-dimensional fracture network model is organized into layers to form a multi-granular hierarchical data structure. This approach analyzes the geometric, topological, and spatial distribution patterns of each fracture unit within a specific region to determine the structural characteristics of the fracture network. This step, which forms the basis for equivalent calculations, comprehensively considers the geometric information, topological relationships, and spatial arrangement of the fractures within the region, quantitatively describing the overall structural characteristics of the fracture network within that region. Based on these acquired structural characteristics, this proposal further determines the anisotropy and scale effects of specific regions. This step recognizes that fracture networks often exhibit significant anisotropy (i.e., physical properties vary in different directions) and scale effects (i.e., physical properties vary with the scale of the analysis area), and quantifies these characteristics based on structural analysis. Accurately determining anisotropic characteristics and scale effects is crucial for the subsequent calculation of macroscopic physical parameters that reflect the actual rock mass behavior. Finally, macroscopic physical parameters for a specific region are calculated based on the determined anisotropic characteristics and scale effects. Unlike existing methods that may simply perform equivalent calculations based on fracture geometry, this approach explicitly requires the calculation of macroscopic physical parameters based on the anisotropic characteristics and scale effects determined in the previous step. This means that the calculation process fully considers the dimensional variations of the fracture network and the influence of the analysis scale on the results. This equivalent calculation method, based on structural characteristics, anisotropic characteristics, and scale effects, more accurately reflects the anisotropy and scale effects of the overburden fracture network, ensuring that the equivalent parameters represent the actual mechanical or hydraulic response of the region at the macroscale and the effectiveness of subsequent engineering analysis. By integrating this refined equivalent calculation process into the formation of a multi-granularity hierarchical data structure, this approach not only provides data representation at different granularities, but also ensures the accuracy and reliability of the regional equivalent parameter layer, providing a solid foundation for subsequent engineering analysis based on this data structure.

[0069] In one embodiment, when forming the zone equivalent parameter layer in the multi-granularity hierarchical data structure, the structural characteristics of the fracture network in a specific region can be first obtained by calculating the density, average length, average opening, dominant occurrence direction and fracture connectivity index of the fractures in the specific region. For example, the fracture surface density, volume density, connection point density, etc. can be calculated. Then, according to these structural characteristics, the anisotropy characteristics and scale effect of the region can be determined. For example, the anisotropic permeability or mechanical anisotropic direction can be determined by analyzing the dominant occurrence direction and the connectivity difference in different directions; the representative element size range can be determined by calculating the equivalent permeability coefficient or equivalent elastic modulus in regions of different sizes of cubes or spheres and analyzing the trend of the change with the size, so as to represent the scale effect. Finally, according to the determined anisotropy characteristics and scale effect, the macroscopic physical parameters of the region are calculated. For example, in the calculation of the equivalent permeability tensor, numerical simulation methods such as the coupling of the discrete fracture network (DFN) model and the finite element or finite difference method, or analytical or semi-analytical methods such as the method based on the mean field theory can be used, and the weight in the anisotropic direction or the adjustment of the model parameters are considered in the calculation process; in the determination of the equivalent elastic modulus, numerical methods such as uniaxial compression or shear test in different directions can be used, or analytical methods based on the fracture tensor theory can be used, and the influence of the scale effect is considered, for example, the representative element size is selected for calculation.

[0070] The scheme can obtain the structural characteristics of the fracture network by analyzing the geometric characteristics, topological characteristics and spatial distribution of each fracture element in a specific region; determine the anisotropy characteristics and scale effect of the specific region according to the structural characteristics of the fracture network; and calculate the macroscopic physical parameters of the specific region according to the anisotropy characteristics and scale effect. In this way, the problem that the existing method cannot reflect the anisotropy and scale effect of the overburden fracture network can be overcome, so that the equivalent parameters calculated can more accurately represent the actual mechanical or hydraulic response of the region at the macroscopic scale, thereby improving the effectiveness of subsequent engineering analysis.

[0071] In some embodiments, the step of determining the anisotropy characteristics of the specific region according to the structural characteristics of the fracture network comprises:

[0072] According to the structural characteristics of the fracture network and the engineering importance grade of each fracture element, the structural characteristics of each fracture element in the fracture network are weighted processed;

[0073] Based on the weighted structural characteristics of the fracture network, the anisotropic direction which has a dominant effect on the stability of the surrounding rock or the seepage path of groundwater is identified by analyzing the fracture connectivity, fracture density and dominant occurrence distribution in different spatial directions in the fracture network;

[0074] Anisotropy characteristics of a specific region are determined by quantifying the difference in physical properties of the specific region in the direction of anisotropy with a dominant effect.

[0075] The structural characteristics of the fracture network refer to the geometric characteristics, topological characteristics, and spatial distribution of the fracture elements. The engineering importance grade of each fracture element refers to the degree of influence of the fracture element on the overall engineering behavior of the rock mass, which is evaluated based on the geometric characteristics, topological characteristics, and medium properties of the rock mass. A rule-based scoring system or model can be used to determine the engineering importance grade. The weighted processing refers to using the engineering importance grade of the fracture element as a weight to apply to the structural characteristic data of the fracture element, so that the structural characteristics of the fracture element with a high importance grade are amplified in subsequent analysis, and the structural characteristics of the fracture element with a low importance grade are weakened. Multiplication weighting or other mathematical methods can be used to achieve this. Fracture connectivity refers to the ability of fracture elements in the fracture network to form continuous paths, which affects hydraulic transmission. It can be determined by analyzing the intersection, overlap, or close parallel relationship of fracture elements. Fracture density refers to the number or total area / volume of fractures per unit volume or unit area, which can be characterized by counting the number or area projection of fractures in different directions. Dominant occurrence distribution refers to the direction in which the occurrence of fractures in a specific region is concentrated. Statistical methods can be used to analyze this. The direction of anisotropy with a dominant effect refers to the direction in which the structural characteristics of the fracture network differ from other directions, resulting in differences in the macroscopic physical properties of the rock mass in that direction, and having a major impact on the stability of the surrounding rock or the path of groundwater seepage. Quantifying the difference in physical properties of a specific region in the direction of anisotropy with a dominant effect refers to calculating or simulating the difference between the equivalent physical parameters of the region in the dominant direction and the equivalent physical parameters of the region in the non-dominant direction after identifying the dominant anisotropy direction. This can be achieved through numerical simulation or analytical methods based on the weighted fracture network. Determining the anisotropy characteristics of a specific region refers to obtaining parameters representing the degree and direction of anisotropy of the region through the above quantification process.

[0076] The scheme provides a specific method for determining the anisotropy characteristics of a specific area, which is used to solve the problem of how to identify the anisotropy direction and degree that has a dominant effect on the macroscopic physical behavior when determining the anisotropy characteristics according to the structural characteristics of the fracture network. First, according to the structural characteristics of the fracture network and the engineering importance level of each fracture unit, the structural characteristics of each fracture unit in the fracture network are weighted. This step is the improvement point of the scheme. It not only considers the geometric and topological structural characteristics of the fracture unit itself, but also introduces the engineering importance level determined according to these characteristics as a weight. The engineering importance level reflects the degree of influence of different fracture units on the overall rock mass engineering behavior. By applying the engineering importance level to the structural characteristics of the fracture unit, those fractures that have a large influence on the macroscopic behavior are given a higher weight in the subsequent analysis, while those fractures that have a small influence are given a lower weight. This weighting process can highlight the characteristics of the key fracture system that dominates the regional macroscopic anisotropy, filter out the interference of unimportant fractures, and lay the foundation for subsequent identification of the anisotropy direction. Second, based on the weighted structural characteristics of the fracture network, the anisotropy direction that has a dominant effect on the stability of the surrounding rock or the seepage path of groundwater is identified by analyzing the connectivity, density and dominant occurrence distribution of the fractures in different spatial directions. After weighting the structural characteristics of the fractures, the connectivity, density and dominant occurrence distribution of the fractures in different spatial directions are analyzed. Since the analysis is based on the weighted characteristics, the spatial direction that has a dominant influence on the stability of the surrounding rock or the seepage path of groundwater, i.e., the dominant anisotropy direction, can be identified. The weighting process makes the identified anisotropy direction based on the more important fracture system for engineering behavior, improving the accuracy of identification. Finally, the anisotropy characteristics of the specific area are determined by quantifying the differences in physical properties of the specific area in the dominant anisotropy direction. After identifying the dominant anisotropy direction, the differences in physical properties of the region in these dominant directions and other directions need to be quantified. This quantification is based on the weighted structural characteristics of the fracture network by the engineering importance level, so it can reflect the macroscopic physical response of the rock mass in the dominant direction. Through this quantification, the anisotropy characteristics of the specific area are finally determined, providing input for the subsequent calculation of the equivalent physical parameters of the region. The implementation of the scheme enables the focus to be on the key fracture system that has a dominant influence on the engineering behavior when determining the anisotropy characteristics of a specific area based on the structural characteristics of the fracture network. The anisotropy characteristics determined based on this can reflect the physical response of the rock mass at the macroscopic scale, thereby providing input for the subsequent calculation of the equivalent parameters of the region. This equivalent parameter further enhances the representativeness of the region equivalent parameter layer, ultimately improving the accuracy and engineering applicability of the entire tunnel overburden three-dimensional structural characteristics analysis method.

[0077] In one embodiment, the determination of the anisotropy characteristics of a specific region can be achieved as follows: first, according to the size, aperture, connectivity of each fracture element and its relative position in the tunnel engineering, an engineering importance level score is calculated, for example, the larger the size, the larger the aperture, the better the connectivity, the closer to the tunnel excavation face, the higher the importance level score of the fracture. Then, the structural characteristics such as the area and length of each fracture element are multiplied by the corresponding engineering importance level score to obtain the weighted structural characteristic values. Next, in multiple preset spatial directions, the weighted fracture area density and the number of weighted connected paths are counted, and the weighted fracture occurrence distribution is analyzed to identify the direction with significantly higher weighted characteristic values than other directions as the dominant anisotropy direction. Finally, in the identified dominant direction, based on the weighted fracture network model, the equivalent permeability coefficient in this direction is calculated using numerical simulation method, and compared with the equivalent permeability coefficient perpendicular to this direction to quantify the difference, thereby determining the permeability anisotropy characteristics of the region.

[0078] By weighting the structural characteristics of each fracture element in the fracture network according to the structural characteristics of the fracture network and the engineering importance level of each fracture element, and identifying the dominant anisotropy direction based on the weighted fracture network structural characteristics, and then quantifying the physical property difference to determine the anisotropy characteristics, the present scheme can identify and quantify the engineering dominant anisotropy which has a dominant effect on the stability of surrounding rock or the seepage path of groundwater, avoiding the interference of non-key fractures, so that the determined anisotropy characteristics can better reflect the actual macroscopic physical response of the rock mass, providing basic data for subsequent calculation of regional equivalent parameters, and improving the accuracy and reliability of the analysis of the structural characteristics of the tunnel overburden rock.

[0079] In some embodiments, according to the structural characteristics of the fracture network, the step of determining the scale effect of a specific region comprises:

[0080] Under a predefined analysis scale, based on the structural characteristics of the fracture network, a subset of fracture elements corresponding to the scale is extracted, and the fracture geometric characteristics, topological characteristics and spatial distribution regularity within the subset of fracture elements are counted to obtain structural characteristic quantization indicators corresponding to the scale; the representative element size corresponding to the analysis scale is analyzed;

[0081] Based on the structural characteristic quantization indicators under each analysis scale, the equivalent physical parameters of the specific region under each analysis scale are calculated, and through the analysis of the calculation results, the variation trend of the equivalent physical parameters with the analysis scale is obtained;

[0082] According to the variation trend of the equivalent physical parameters, the scale range corresponding to the stable equivalent physical parameters is identified, and the identified scale range is determined as the representative scale range of the corresponding specific region, which serves as the representation of the scale effect of the corresponding specific region.

[0083] The analysis scale refers to the size or range of a representative unit used to investigate the structural characteristics of the fracture network and calculate equivalent physical parameters. It can be set by using a series of discrete, gradually increasing volume or area sizes. The fracture unit subset refers to the collection of all fracture units located within the corresponding representative unit at a specific analysis scale. The structural characteristic quantification index refers to the numerical value or vector obtained by statistically analyzing the fracture unit subset, which can reflect the structural characteristics of the fracture network at a specific scale. It can include fracture density, average fracture length, connectivity rate, dominant occurrence, etc. The equivalent physical parameter refers to the physical property parameter that macroscopically equivalent to the discrete fracture network in a specific region as a continuous medium. It can be represented by equivalent permeability, equivalent elastic modulus, equivalent strength parameter, etc. The variation trend refers to the numerical variation law of the equivalent physical parameter with the increase of the analysis scale. It can be visually displayed by plotting the parameter-scale curve. The representative scale range refers to the scale interval where the equivalent physical parameter tends to be flat or basically unchanged with the change of the analysis scale. In this scale range, the sensitivity of the equivalent parameter to the scale decreases, indicating that this scale is sufficient to represent the macroscopic physical behavior of the region. The characterization of the scale effect refers to the quantitative description of the dependence of the macroscopic physical properties of the fracture network on the analysis scale by identifying the representative scale range.

[0084] The present scheme provides a specific method for determining the scale effect of a specific region, aiming to solve the technical problem of how to objectively determine the representative scale range when calculating the equivalent parameters of the region. This method quantifies the structural characteristics of the fracture network and calculates the equivalent physical parameters at multiple analysis scales, and analyzes the variation law of the equivalent parameters with the scale, thereby identifying the scale range where the equivalent physical parameters tend to be stable. This stable scale range is determined as the representative scale range, which characterizes the scale effect of the region. Through this method, the scale where the equivalent physical parameters tend to be stable can be identified, thereby providing a basis for subsequent accurate calculation of macroscopic physical parameters, avoiding the problem that the equivalent parameters cannot accurately reflect the actual engineering response due to improper scale selection. As a specific implementation method for determining the scale effect of a specific region, this method, combined with the determination of anisotropy characteristics based on the structural characteristics of the fracture network, provides a basis for calculating the macroscopic physical parameters of a specific region based on anisotropy characteristics and scale effect, so that the calculated macroscopic physical parameters can more accurately reflect the real physical behavior of the fracture network, and the reliability of the equivalent parameters of the region is improved.

[0085] In one embodiment, the method of determining the scale effect of a specific region can be implemented as follows: first, a series of analysis scales are set, for example, cubes with side lengths of L1, L2, L3,..., Ln can be set as representative unit cells, where L1 < L2 <... < Ln. For each set analysis scale Li, all fracture elements located within the cube with side length Li are extracted to form a subset of fracture elements at the corresponding scale. Then, statistical analysis is performed on the subset of fracture elements to calculate the structure characteristic quantification indicators such as fracture density, average fracture length, and connectivity rate at this scale. Next, based on these structure characteristic quantification indicators, an appropriate equivalent calculation model is used to calculate the equivalent physical parameters of the specific region at this scale, such as the equivalent permeability coefficient. The above process is repeated to obtain the equivalent permeability coefficient values at all analysis scales L1 to Ln. The equivalent permeability coefficient values are plotted against the analysis scale Li to obtain a curve. By analyzing the curve, the scale range where the equivalent permeability coefficient values tend to be stable or remain unchanged is identified, for example, when the relative change rate of the equivalent permeability coefficient is less than a certain pre-set threshold, it is considered to be stable. The identified scale range is determined as the representative scale range of the specific region, and is used as the representation of the scale effect of the region.

[0086] Through the above method, the representative scale range of the specific region can be objectively determined, avoiding errors caused by subjective experience judgment or single scale analysis. This provides a reliable scale basis for subsequent calculation of macroscopic physical parameters of the specific region based on anisotropy characteristics and scale effect, so that the calculated equivalent physical parameters can more accurately reflect the macroscopic physical behavior of the fracture network, improving the accuracy and reliability of engineering analysis.

[0087] In some embodiments, according to the pre-set engineering analysis purpose, the data form of the current analysis requirement is determined, and based on the determined data form, the fracture subset or equivalent parameter of the corresponding granularity is extracted from the multi-granularity hierarchical data structure, which includes:

[0088] According to the pre-set engineering analysis purpose, the data form of the initial analysis requirement is determined, and based on the initial data form, the fracture subset or equivalent parameter of the initial granularity is extracted from the multi-granularity hierarchical data structure;

[0089] Obtaining analysis feedback information based on the initial extraction result; the analysis feedback information includes regional analysis results or user instructions;

[0090] According to the analysis feedback information, identifying the target region that needs to be adjusted in granularity, and determining the target data form of the target region; the target data form corresponds to the target granularity;

[0091] performing local incremental extraction on the target region from the multi-granularity hierarchical data structure according to the target region and the target data form, to obtain a fissure subset or equivalent parameter corresponding to the target data form;

[0092] integrating the fissure subset or equivalent parameter obtained by the local incremental extraction with the initial extraction result, to form an updated fissure subset or equivalent parameter, for subsequent feature analysis.

[0093] The analysis feedback information refers to information generated after analysis based on the initial extraction result, which is used to guide the subsequent data extraction and analysis process. It can be realized by using the risk area identification and required refinement granularity suggestion output by the automatic regional analysis algorithm, or by using the refinement or coarsening instructions for specific regions input by the user through the interactive interface. The target region refers to a specific spatial range that needs to be adjusted in data granularity according to the analysis feedback information. It can be realized by three-dimensional coordinate framing, automatic region identification based on analysis results (such as high stress area, large deformation area, high seepage area), or predefined engineering structure region (such as working face, anchor rod area). The target data form refers to the data granularity or type that needs to be adjusted for the target region. It can be realized by specifying the micro-discrete fissure layer, fissure cluster and main control fissure zone layer or regional equivalent parameter layer. The local incremental extraction refers to the process of extracting data from the multi-granularity hierarchical data structure according to the target data form only for the identified target region. It can be realized by spatial index query combined with granularity level filtering. The integration refers to merging the data obtained by the local incremental extraction with the initial extraction result to form a unified data set containing different granularity data. It can be realized by data structure merging, regional data replacement or data superposition.

[0094] The application can realize dynamic adjustment of data extraction granularity according to analysis feedback information because a feedback mechanism and a local incremental extraction process are introduced on the basis of the construction of the multi-granularity hierarchical data structure. First, according to the preliminary engineering analysis purpose, the system selects a suitable initial granularity from the multi-granularity hierarchical data structure for data extraction, which provides a basis for subsequent analysis. Then, the system obtains feedback information generated based on the initial analysis result. These feedback information can be the regions that need further attention and the required data granularity identified by the system automatically, or the adjustment instructions input by the engineer according to the preliminary result or experience. It is because of the acquisition of such feedback information that the system can perceive the current analysis state and the further needs of the user. Subsequently, the system accurately identifies the specific spatial region that needs to be adjusted in granularity and determines the target granularity to which the region needs to be adjusted. On this basis, instead of re-extracting the global data, the system utilizes the hierarchical nature and spatial indexing capability of the multi-granularity hierarchical data structure to perform local and incremental data extraction in the identified target region according to the new target granularity. This local incremental extraction method avoids processing the entire model and significantly improves the efficiency of data extraction. Finally, the system integrates the locally adjusted data with the original global or local data to form an updated data set that may contain mixed granularities. This integrated data structure not only retains the macro information required for global analysis but also provides fine information required for local key regions, which can more flexibly and efficiently support subsequent feature analysis. Through this feedback-driven local incremental extraction and integration mechanism, the application can update the data on demand, dynamically and efficiently according to the dynamically changing analysis requirements, thereby solving the problem of difficult dynamic adjustment of data granularity in the analysis process and avoiding the need for frequent manual re-extraction or model reconstruction, improving the analysis efficiency and timeliness.

[0095] In one embodiment, according to the preset engineering analysis purpose, the data form of the current analysis requirement is determined, and based on the determined data form, the step of extracting the fracture subset or equivalent parameter of the corresponding granularity from the multi-granularity hierarchical data structure can be implemented as follows: first, according to the preliminary tunnel surrounding rock stability evaluation requirement, the data form of the initial analysis requirement is determined as the regional equivalent parameter layer, and based on this, the equivalent permeability coefficient and equivalent elastic modulus field of the initial granularity covering the entire tunnel region are extracted from the multi-granularity hierarchical data structure. Then, these equivalent parameters are input into the numerical simulation software for preliminary calculation to obtain the stress distribution and deformation field of the surrounding rock. The system automatically analyzes the calculation results, identifies the regions with stress concentration or larger deformation, and marks these regions as target regions that need to be further refined. At the same time, the system determines that these target regions need more detailed data forms according to the analysis rules, for example, the fracture cluster information needs to be extracted from the fracture cluster and main control fracture zone layer, or the detailed discrete fracture element information needs to be extracted from the microscopic discrete fracture layer. Subsequently, based on the identified target regions and the determined target data form, the system uses the spatial indexing function of the multi-granularity hierarchical data structure to perform local incremental extraction from the corresponding granularity level only for these target regions to obtain the fracture cluster or discrete fracture element data within the target regions. Finally, the system integrates the fracture cluster or discrete fracture element data obtained by local incremental extraction with the global equivalent parameters obtained by initial extraction. The integration method can be to replace the original equivalent parameter representation with the extracted fracture cluster or discrete fracture element in the target region, while retaining the equivalent parameter representation in other regions to form a hybrid representation data set. This updated data set can be used for more detailed local numerical simulation or analysis of the target region, such as local support effect evaluation or instability mechanism analysis.

[0096] By determining the data form of the initial analysis requirement according to the preset engineering analysis purpose, and extracting the fracture subset or equivalent parameter of the initial granularity from the multi-granularity hierarchical data structure based on the initial data form; obtaining analysis feedback information based on the initial extraction result; identifying the target region that needs to be adjusted in granularity according to the analysis feedback information, and determining the target data form of the target region; based on the target region and the target data form, performing local incremental extraction on the target region from the multi-granularity hierarchical data structure to obtain the fracture subset or equivalent parameter of the corresponding target data form; and integrating the fracture subset or equivalent parameter obtained by local incremental extraction with the initial extraction result to form an updated fracture subset or equivalent parameter for subsequent feature analysis, the application can dynamically and on-demand adjust the granularity of data extraction according to the feedback information during the analysis process, realize local refinement of key regions or coarsening of non-key regions, avoid the need to re-extract global data or reconstruct the model every time the requirement is adjusted, significantly improve the efficiency of data extraction and analysis, and better support the iterative optimization process of tunnel engineering analysis.

[0097] Reference is made to the accompanying drawings Figure 2 The present application provides a tunnel overburden three-dimensional structure feature analysis system, comprising:

[0098] An acquisition module 100 is configured to acquire a three-dimensional fracture network model of the tunnel overburden and extract geometric features and topological features of each fracture unit in the fracture network model;

[0099] A determination module 200 is configured to determine an engineering importance level of each fracture unit according to the geometric features and the topological features;

[0100] A hierarchical organization module 300 is configured to hierarchically organize the three-dimensional fracture network model according to the engineering importance level of each fracture unit and the topological connection relationship between the fracture units, and form a multi-granularity hierarchical data structure;

[0101] An extraction module 400 is configured to determine a data form of a current analysis requirement according to a preset engineering analysis purpose, and extract a fracture subset or an equivalent parameter of a corresponding granularity from the multi-granularity hierarchical data structure based on the determined data form;

[0102] An analysis module 500 is configured to perform feature analysis according to the extracted fracture subset or the equivalent parameter.

[0103] In this document, the terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between the entities or operations.

[0104] The above description is only an example of the present application and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for analyzing three-dimensional structure characteristics of overburden rock of a tunnel, characterized by, The method comprises the following steps: obtaining a three-dimensional fracture network model of the overburden rock of a tunnel, and extracting geometric features and topological features of each fracture unit in the fracture network model; determining the engineering importance grade of each fracture unit according to the geometric features and the topological features; organizing the three-dimensional fracture network model in layers according to the engineering importance grade of each fracture unit and the topological connection relationship between the fracture units, to form a multi-granularity hierarchical data structure; determining the data form of the current analysis requirement according to a preset engineering analysis purpose, and extracting a fracture subset or an equivalent parameter of a corresponding granularity from the multi-granularity hierarchical data structure based on the determined data form; performing feature analysis according to the extracted fracture subset or the equivalent parameter.

2. The method according to claim 1, wherein, The multi-granularity hierarchical data structure comprises: a microscopic discrete fracture layer for storing geometric information and attributes of each fracture unit; a fracture cluster and main control fracture zone layer for aggregating fracture units with similar occurrence, close spatial position or strong connectivity to form a fracture cluster, and for identifying a main control fracture zone that plays a leading role in the stability of surrounding rock or the seepage path of underground water; a regional equivalent parameter layer for equivalent of the fracture network in a specific region to macroscopic physical parameters.

3. The method according to claim 2, wherein, The fracture subset comprises fracture units in a specific region extracted from the microscopic discrete fracture layer, and / or a fracture cluster extracted from the fracture cluster and main control fracture zone layer; and the equivalent parameter comprises macroscopic physical parameters in a specific region extracted from the regional equivalent parameter layer.

4. The method of claim 1, wherein, The method further comprises the following steps: receiving subsequent detection data, and performing local update on the fracture network model of a corresponding spatial region in the multi-granularity hierarchical data structure according to the subsequent detection data.

5. The method of claim 2, wherein, When organizing the three-dimensional fracture network model in layers according to the engineering importance grade of each fracture unit and the topological connection relationship between the fracture units to form the fracture cluster and main control fracture zone layer in the multi-granularity hierarchical data structure, the following is performed: determining the coupling strength of the fracture units in terms of water power according to the geometric features, the topological features and the rock mass medium attributes of the fracture units; identifying a functional connectivity path having a water power connectivity effect between the fracture units according to the coupling strength; aggregating the fracture units on the functional connectivity path to form a fracture cluster.

6. The method of claim 2, wherein, When organizing the three-dimensional fracture network model in layers according to the engineering importance grade of each fracture unit and the topological connection relationship between the fracture units to form the regional equivalent parameter layer in the multi-granularity hierarchical data structure, the following is performed: obtaining structural features of the fracture network by analyzing the geometric features, the topological features and the spatial distribution law of each fracture unit in a specific region; determining anisotropy features and scale effects of the specific region according to the structural features of the fracture network; calculating macroscopic physical parameters of the specific region according to the anisotropy features and the scale effects.

7. The method according to claim 6, wherein, The step of determining the anisotropy features of the specific region according to the structural features of the fracture network comprises: performing weighted processing on the structural features of each fracture unit in the fracture network according to the structural features of the fracture network and the engineering importance grade of each fracture unit. Based on the weighted fracture network structure characteristics, by analyzing the fracture connectivity, fracture density and dominant occurrence distribution in different spatial directions in the fracture network, the anisotropic direction which has a dominant effect on the stability of surrounding rock or the seepage path of underground water is identified; By quantifying the physical property difference of a specific region in the anisotropic direction which has a dominant effect, the anisotropy characteristics of the specific region are determined.

8. The method of claim 6, wherein, The step of determining the scale effect of the specific region according to the structure characteristics of the fracture network comprises: Based on the structure characteristics of the fracture network, the fracture element subset at a corresponding scale is extracted under a predefined analysis scale, and the fracture geometry characteristics, topological characteristics and spatial distribution rules within the fracture element subset are counted to obtain the structure characteristic quantization index at the corresponding scale; Based on the structure characteristic quantization index at each analysis scale, the equivalent physical parameters of the specific region at each analysis scale are calculated, and the variation trend of the equivalent physical parameters with the analysis scale is obtained by analyzing the calculation results; According to the variation trend of the equivalent physical parameters, the scale range corresponding to the stable equivalent physical parameters is identified, and the identified scale range is determined as the representative scale range of the corresponding specific region, which is used as the representation of the scale effect of the corresponding specific region. 9.The tunnel overburden rock three-dimensional structure feature analysis method according to claim 1, characterized in that, According to the preset engineering analysis purpose, the data form of the current analysis requirement is determined, and based on the determined data form, the fracture subset or equivalent parameter of the corresponding granularity is extracted from the multi-granularity hierarchical data structure, which comprises: According to the preset engineering analysis purpose, the data form of the initial analysis requirement is determined, and based on the initial data form, the fracture subset or equivalent parameter of the initial granularity is extracted from the multi-granularity hierarchical data structure; Obtaining analysis feedback information based on the initial extraction result; According to the analysis feedback information, the target region which needs to be adjusted in granularity is identified, and the target data form of the target region is determined; the target data form corresponds to the target granularity; Based on the target region and the target data form, local incremental extraction is performed on the target region from the multi-granularity hierarchical data structure to obtain the fracture subset or equivalent parameter corresponding to the target data form; The fracture subset or equivalent parameter obtained by local incremental extraction is integrated with the initial extraction result to form an updated fracture subset or equivalent parameter, which is used for subsequent feature analysis.

10. A system for analyzing the three-dimensional structural characteristics of tunnel overburden, characterized in that: Comprise: An acquisition module is configured to acquire a three-dimensional fracture network model of tunnel overburden rock, and extract geometric characteristics and topological characteristics of each fracture element in the fracture network model; A determination module is configured to determine an engineering importance level of each fracture element according to the geometric characteristics and the topological characteristics; A hierarchical organization module is configured to hierarchically organize the three-dimensional fracture network model according to the engineering importance level of each fracture element and the topological connection relationship between the fracture elements, and form a multi-granularity hierarchical data structure; An extraction module is configured to determine a data form of a current analysis requirement according to a preset engineering analysis purpose, and extract a fracture subset or equivalent parameter of a corresponding granularity from the multi-granularity hierarchical data structure based on the determined data form; An analysis module is configured to perform feature analysis according to the extracted fracture subset or equivalent parameter.

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