Collaborative modeling method and device for multi-view disaster detection information of underground shielded space

Abstract

The invention discloses an underground shielded space multi-view disaster detection information collaborative modeling method and device, and belongs to the technical field of underground space public safety. The method comprises the following steps: collecting air-sky-ground-hole multi-source data; the method comprises the following steps: dividing three-dimensional space objects into dot-shaped, linear, planar and body-shaped three-dimensional space objects, designing five geometric elements of vertexes, edges, rings, surfaces and bodies, and establishing a topological data model; dividing a rock stratum and a soil layer by adopting a multi-layer DEM stratum skeleton modeling method, and generating a complete three-dimensional stratum model through a volume element subdivision method; the invention discloses a discrete fracture network refined modeling method based on a rectangular joint model. A three-dimensional discrete fracture network is generated; according to the method, space-space-ground-hole multi-source data are fused, and a multi-layer and multi-source data collaborative three-dimensional underground space model is constructed. According to the method, 'air-space-ground-hole 'multi-source data is reduced to the same space coordinate system under the same standard, and the problem of multi-scale data space fusion is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of underground space public safety, and specifically relates to a method and device for collaborative modeling of multi-view disaster detection information in underground sheltered spaces. Background Art

[0002] The catastrophic processes that result from major underground disasters are complex and dangerous, and there is currently a lack of systematic scientific understanding of their response mechanisms. Different types of underground space exhibit complex patterns of hazard development, with shallower spaces being overloaded and deeper spaces being highly concealed and often consisting of long tunnels. This leads to difficulties in disaster detection and monitoring, inaccurate early warnings, and uninformed decision-making, resulting in a long-term passive response. Countries around the world primarily use geophysical prospecting and drilling to detect structures that can cause sudden water and mud bursts, but existing technology and equipment present numerous challenges, hindering early detection, early positioning, and early identification. Core disaster detection technologies and equipment, such as multi-source remote sensing, continue to face challenges, and digital models of complex underground geological structures are difficult to construct. Therefore, there is an urgent need to develop collaborative modeling technologies for three-dimensional visual detection information on the morphology, depth, structure, and orientation of underground sheltered spaces. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention provides a collaborative modeling method and device for multi-view disaster detection information in underground sheltered spaces. It adopts multi-layer DEM stratum skeleton modeling, discrete fracture network refined modeling based on rectangular joint model and other methods to convert the "air-sky-ground-hole" multi-source data into the same spatial coordinate system under the same standard, thereby solving the problem of multi-scale data space fusion.

[0004] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0005] A collaborative modeling method for multi-view disaster detection information in underground sheltered spaces, the method comprising:

[0006] Step 1: Collect multi-source data from air, space, ground and borehole;

[0007] Step 2: Classify 3D space objects into four types: point, line, surface, and volume. Design five geometric elements: vertex, edge, ring, surface, and volume. Establish a topological data model.

[0008] Step 3: Use the multi-layer DEM stratigraphic skeleton modeling method to divide the rock and soil layers to form the skeleton structure of the three-dimensional stratigraphic model. Combined with the topological data model, a complete three-dimensional stratigraphic model is generated through the voxel segmentation method. DEM stands for digital elevation model.

[0009] Step 4: Combine the topological data model and generate a three-dimensional discrete fracture network based on the discrete fracture network refined modeling method of the rectangular joint model;

[0010] Step 5: Based on the three-dimensional stratum model and the three-dimensional discrete fracture network, the multi-source data of air, space, ground and hole are integrated to construct a three-dimensional underground space model with multi-level and multi-source data coordination.

[0011] In another aspect, the present invention provides a collaborative modeling device for multi-view disaster detection information in underground sheltered spaces, comprising:

[0012] Acquisition module, used to collect multi-source data from air, space, ground and borehole;

[0013] The topology module is used to classify three-dimensional space objects into four types: points, lines, surfaces, and volumes. It designs five geometric elements: vertices, edges, loops, surfaces, and volumes, and establishes a topological data model.

[0014] The first generation module is used to divide the rock and soil layers using a multi-layer DEM stratum skeleton modeling method to form a skeleton structure of a three-dimensional stratum model, and to generate a complete three-dimensional stratum model using a voxel segmentation method;

[0015] The second generation module is used to generate a three-dimensional discrete fracture network based on a discrete fracture network refined modeling method of a rectangular joint model;

[0016] The output module is used to integrate air-space-ground-pore multi-source data based on the three-dimensional stratum model and the three-dimensional discrete fracture network to construct a three-dimensional underground space model with multi-level and multi-source data collaboration.

[0017] In a third aspect, the present invention provides an electronic device comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned collaborative modeling method for multi-view disaster detection information in underground sheltered spaces.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned collaborative modeling method for multi-view disaster detection information in underground sheltered spaces.

[0019] Beneficial effects:

[0020] This invention proposes a comprehensive technical solution for monitoring and analyzing the geological environment of underground space disasters. By analyzing stratigraphic data, underground structure information, semi-aerial electromagnetic images, distributed fiber optic data, and high-frequency electromagnetic drilling data, and based on the differences in the physical principles of these data, the geological environment that predisposes to typical water and mud bursts and surrounding rock collapses is studied, and the geological body information of underground space is summarized. Utilizing a topological data model, intelligent identification methods for complex stratigraphic and rock mass structures are studied, and a unified data description standard and basic data system are established to resolve the problem of conflicting descriptions of data from different sources. A unified spatial coordinate reduction standard is proposed to integrate multi-scale data into the same topological model, achieving full spatial coverage and eliminating data conflicts. Through cross-division of rock and soil layers and voxel decomposition, a continuous geometric representation of the stratigraphic structure is constructed to support the precise positioning of water burst channels. Based on a rectangular joint model and a chain splicing algorithm, a three-dimensional fracture network that conforms to geological statistical laws is generated to improve the reliability of surrounding rock stability analysis. The fusion model supports real-time data updates and dynamically adjusts topological relationships to quickly respond to disaster evolution. Combined with real-time deformation data from fiber optic sensors, this technology can identify precursors to rock collapse in advance, shortening warning times by over 50% compared to traditional methods. This technology is compatible with existing detection equipment, eliminating the need for hardware modifications and enabling low-cost upgrades through algorithm-level integration. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flow chart of the collaborative modeling method for multi-view disaster detection information in underground sheltered spaces of the present invention;

[0022] Figure 2 It is a graph of relationships between objects in a topological data model;

[0023] Figure 3 These are two forms of multi-layer DEM intersection grids, where (a) is a schematic diagram of the ground plane intersection as the opposite side, and (b) is a schematic diagram of the ground plane intersection as the adjacent side;

[0024] Figure 4 Schematic diagram of the volumetric division of the intersecting strata, where (a) is a schematic diagram of the intersection of the strata as the opposite side, and (b) is a schematic diagram of the intersection of the strata as the adjacent side;

[0025] Figure 5 A schematic diagram of random number generation for joint occurrence;

[0026] Figure 6 Define schematic diagrams for the rectangular vertices of the joint element;

[0027] Figure 7 Schematic diagram of joint numbering in space;

[0028] Figure 8 Schematic diagram of chain generation algorithm for complex joint networks;

[0029] Figure 9 Generate schematic diagrams for joint-fracture network models;

[0030] FIG10( a ) shows a three-dimensional underground space model formed based on the method of the present invention;

[0031] Figure 10(b) shows a two-dimensional cross-section of the three-dimensional underground space model;

[0032] Figure 11 Schematic diagram of the collaborative modeling device for multi-view disaster detection information in underground sheltered spaces according to the present invention.

[0033] The figures are marked as: first point 1, second point 2, third point 3, fourth point 4. DETAILED DESCRIPTION

[0034] The present invention will be further described below with reference to the accompanying drawings and examples.

[0035] The present invention provides a collaborative modeling method and device for multi-view disaster detection information in underground sheltered spaces. Based on a topological data model, it studies intelligent identification methods for complex strata and rock mass structures, establishes a unified data description standard and basic data system for different source data on the geological environment of engineering collapse areas, and solves the problem of conflicting descriptions of geological environment by different source data. It adopts multi-layer DEM stratum skeleton modeling, discrete fracture network refined modeling based on rectangular joint model, and other methods to reduce the potential field data obtained by different observation methods to the same spatial coordinate system under the same standard, solving the problem of multi-scale data space fusion. Figure 1 As shown, specifically including:

[0036] Step 1: Collect multi-source data from air, space, ground and boreholes, including strata, rock mass structural surfaces, tunnel structures, remote sensing data (air), semi-aerial electromagnetic data (air), distributed optical fiber data (ground) and borehole detection data (holes).

[0037] Step 2: Divide the three-dimensional space objects into four types: point, line, surface and body, design five geometric elements: vertex, edge, ring, surface and body, and establish a data description standard system, namely the topological data model; Figure 2 As shown in the figure, multi-source data such as stratigraphic data, rock structure surface data, remote sensing data, semi-aerial electromagnetic data, distributed optical fiber data and borehole detection data are fully considered, and three-dimensional space objects are divided into four basic types: point objects, line objects, surface objects and body objects. Five basic geometric elements, namely vertices, edges, rings, faces and bodies, are designed, and the topological relationship between vertices, edges, rings, faces and bodies is constructed, and a topological data model is established. The engineering geological body model and engineering structure model are expressed according to the body-face-ring-edge-vertex organization, which can meet the needs of model visualization and can also perform geometric analysis and semantic analysis on the model.

[0038] Step 3: Use the multi-layer DEM stratigraphic skeleton modeling method to divide the rock and soil layers to form the skeleton structure of the three-dimensional stratigraphic model, and generate a complete three-dimensional stratigraphic model through the voxel decomposition method; Based on the multi-layer DEM stratigraphic skeleton modeling: Based on the single-body lithologic elements, use the DEM fitting interpolation method to interpolate or fit the boundary points of each rock and soil layer. Then, cross-divide the multi-layer DEM according to the properties of the rock and soil layers to form the skeleton structure of the three-dimensional stratigraphic model that is strictly divided according to the lithology. Since each layer of DEM has a completely consistent reference system and can be accurately matched and one-to-one corresponding, hexahedral voxels can be formed where the strata do not intersect, and voxels are generated according to the voxel decomposition method at the places where the strata intersect, and finally a complete three-dimensional stratigraphic model is formed that is divided into a single body according to the rock and soil medium elements.

[0039] Step 4: A discrete fracture network refinement modeling method based on a rectangular joint model is used to generate a three-dimensional discrete fracture network. Based on the anisotropic characteristics of complex rock structures such as joints, a rectangular representation hypothesis of structural surfaces is introduced. Considering the spatial position correlation relationship between adjacent structural surfaces, a three-dimensional joint network reconstruction technology based on a chain splicing algorithm is proposed. Refined modeling of discrete fracture networks based on a rectangular joint model is carried out to generate a three-dimensional discrete fracture network.

[0040] Step 5: Based on the 3D stratigraphic model and the 3D discrete fracture network, multi-source data from air, space, ground, and boreholes are integrated to construct a multi-layered, multi-source 3D underground spatial model. UAV LiDAR data is introduced using surface elevation constraints. Combined with subsurface geotechnical constraints, distributed fiber optic sensing, semi-airborne electromagnetic data, and geological borehole data are integrated to construct a multi-layered, multi-source 3D underground spatial model. This model leverages the synergy of multi-source data from air (semi-airborne electromagnetic), space (UAV LiDAR), ground (distributed fiber optic sensing), and boreholes (geological boreholes) to achieve a comprehensive, high-precision description of the underground space, ultimately forming a holistic 3D stratigraphic model based on the integration of air, space, ground, and boreholes.

[0041] In step 3, the stratum is constructed based on the DEM fitting interpolation method; the digital elevation model (DEM), also known as the digital terrain model (DTM), is a continuous representation method for spatial undulations. Since DTM implies the meaning of topographic landscape, DEM is often used to simply represent elevation. The representation methods of DEM are mainly divided into four methods: fitting interpolation, contour line, grid DEM, and irregular triangulated network DEM (TIN). Interpolation is the core issue of the digital elevation model. DEM interpolation is to calculate the elevation value of the point to be determined based on the elevation of several adjacent reference points. Any interpolation method is based on the continuous smoothness of the original terrain undulation, or in other words, the adjacent data points have a great correlation, so it is possible to interpolate the elevation of the point to be determined from the adjacent data points. According to the distribution range of the interpolation points, interpolation can be divided into three categories: overall interpolation, block interpolation and point-by-point interpolation. According to the relationship between the binary function approximation mathematical surface and the reference point, interpolation can be divided into two types: pure two-dimensional interpolation and surface fitting interpolation.

[0042] Spatial interpolation or fitting is performed on the boundary points of the sampled rock and soil layers to obtain a DEM for each rock and soil layer, thereby depicting the distribution of different stratigraphic layers in three-dimensional space. This results in a multi-layer DEM. Based on the properties of the rock and soil layers, multiple single-layer DEMs are cross-partitioned to form the skeleton structure of the three-dimensional stratigraphic model. Notably, the reference systems used by these multi-layer DEMs are completely consistent and accurately matched, providing more than one elevation value corresponding to the [X, Y] coordinates in each grid. Ideally, if multiple stratigraphic layers do not intersect, a local topological model can be constructed using hexahedral voxels. For example, grids on adjacent layers can be combined in a one-to-one fashion to generate a single hexahedral voxel. Topological relationships are then established to form a local topological model (LTM). Ultimately, the multi-layer DEMs form a complete three-dimensional stratigraphic model. However, in practical applications, stratigraphic interfaces inevitably intersect and overlap. Therefore, intersecting stratigraphic layers are gridded, and a complete three-dimensional stratigraphic model is generated using voxel-based methods. Therefore, it is usually necessary to make corresponding judgments on each layer first and complete the stratum division. Only on this basis can a three-dimensional stratum model be established. Specifically, after forming a multi-layer DEM and before performing stratum division, the concept of body has not yet been formed. Moreover, since the DEM in the present invention is obtained by interpolation of a regular grid, the only surface formed is a quadrilateral. The topological relationship established at this time is mainly the relationship between the node-side, the edge-node-face and the face-edge. When determining the coordination relationship between strata, the intersecting grids need to be subdivided. The intersection in a quadrilateral face is like Figure 3 (a) Figure 3 There are two forms of (b). Figure 3As shown, first find the intersection points E and F of the two ground planes in the grid ABCD, and then use the topological relationship between the surface and the edge to determine which edge the E and F fall on. Figure 3 (a) Figure 3 Which of the two options (b) is used? The above-mentioned stratigraphic division method is then used to determine the selection of intersecting stratigraphic layers. As shown in the figure, the stratigraphic layers after division are quadrilaterals □ABCD and □B'C'FE, or triangles ΔB'FE. Once the spatial distribution of each stratigraphic layer is determined, the grids can be connected to form voxels based on the good top-to-bottom correspondence between the multiple DEM layers.

[0043] After multi-layer DEM modeling, we completed the finite-to-mutually-exclusive-to-complete volume partitioning based on the geotechnical medium, forming the basic framework of the 3D stratigraphic model. However, tunnel structures also exist in the subsidence area, so these special volumes need to be incorporated into the already basic stratigraphic model. This step essentially involves intersecting and splicing the volume elements in the already basic stratigraphic model with these complex volume objects, ensuring continuity and coordination between the elements in the model after intersection and splicing.

[0044] like Figure 4 (a), for Figure 3 In the (a) splitting case, the intersection line EF splits the quadrilateral □ABCD into quadrilateral □AEFD and quadrilateral □EBCF, which meets the agreement on geometric elements, so there is no need to split it further for the time being. However, the body B'C'CBEF formed between the ground planes is not one of the four element forms agreed upon by the present invention and must be split. After connecting EC and EC', B'C'CBEF is split into a quadrangular pyramid B'C'CBE and a tetrahedron EC'CF. At the same time, in order to ensure the coordination between the elements, the corresponding edges of the upper and lower planes must be connected. For example Figure 4 As shown in (a), the MI of the previous level is connected. The hexahedron EBCFMHIN is now split into two triangular prisms EBCMHI and ECFMIN. Thus, the unprocessed volume B'C'CBEF and hexahedron ABCDGHIJ are split into one hexahedron AEFDGMNJ, two triangular prisms EBCMHI and ECFMIN, one tetrahedron EC'CF, and one square pyramid B'C'CBE. This ensures continuity and coordination between volume elements, laying a good foundation for finite element mesh conversion.

[0045] for Figure 3 As for the face splitting case (b), there are two different body intersection situations, but the splitting operations are similar. This invention only describes one of the situations. Figure 4(b) First, the quadrilateral □ABCD needs to be triangulated along the intersection line EF, forming four triangles ΔAED, ΔDEF, ΔEBF, and ΔDFC. Then, the edges between the elements are divided and connected in a corresponding manner to form elements that meet the requirements. The original hexahedron ABCDGHIJ is then divided into four triangular prisms AEDGMI, EFDMNJ, EFBMHN, and FCDNIJ. Simultaneously, a tetrahedron EB'BF is generated between the two strata. This also satisfies the division principles of continuity and coordination.

[0046] In step 4, in order to generate a discrete fracture network in a specific space, an engineering geological survey is first required to determine the number of joint groups in the area. and the spacing between each group of joints , long trace , Bridge Length The distribution law and maximum and minimum values ​​of each joint group are used to determine the number of structural planes contained in the space and stored in the matrix middle:

[0047] ,

[0048] in, represents a joint set;

[0049] ,

[0050] ,

[0051] in, Indicates The number of structural surfaces in the direction; Indicates the side length of the cube circumscribing the generated structural surface area; denote the mean of spacing, trace length, and bridge length, respectively;

[0052] According to the number of each group of structural surfaces, random numbers that obey the distribution rules of spacing, trace length, and bridge length are generated to define the geometric characteristics of the structural surface; and the generated random numbers are stored in the matrix In, such as Figure 5 As shown. Among them:

[0053] ,

[0054] ,

[0055] in, Storage Random number of group joint spacing; Storage Length of joint group Direction random number; Storage Length of joint group Direction random number; Storage Group joint bridge length Direction random number; Storage Group joint bridge length Direction random number;( ).

[0056] The rectangular model is used to generate joint units, and the spatial position of each structural surface is located by four-dimensional coordinates; the orientation of the four vertices is defined as follows: Figure 6 As shown, when the coordinates of the first point 1 are known Then, the coordinates of the second point 2, the third point 3, and the fourth point 4 can be obtained based on the coordinates of the first point 1 and the side length of the rectangle, and are expressed as:

[0057] ,

[0058] ,

[0059] ,

[0060] in, is the number of the structural surface in the joint group.

[0061] Therefore, for each structural surface, it is only necessary to obtain the coordinates of the first point 1 to store the information of the entire structural surface according to the above formula.

[0062] The four-dimensional coordinates are used to define the number of each structural surface, such as Figure 7 The first number is the number of the joint group to which the structural surface belongs, and the last three numbers are the positions of the structural surface in space, similar to the xyz coordinates in a three-dimensional coordinate system. , the x coordinate of the first point 1 is determined by the structural surface The x-coordinate of point 1 and its trace length and bridge length in the x-direction are determined, and its y-coordinate is determined by the structural surface The y coordinate of the structure is determined by the trace length and bridge length in the y direction, and the z coordinate is determined by the structure surface That is, the spatial position of any structural surface is determined by the coordinates and spacing of its adjacent structural surfaces, the distribution of trace length, and bridge length.

[0063] Based on the chain splicing algorithm, the three-dimensional discrete fracture network of the entire region is generated in sequence in spatial order. For each joint group, a local coordinate system is established. First, the first structural surface is generated at the coordinate origin, and then the first structural surface is generated according to the coordinate origin. Figure 8 The order of the joints is generated in space.

[0064] Introducing four-dimensional matrix 、 、 Record number is The coordinate value of structural surface point 1 in the local coordinate system.

[0065] Therefore, the coordinates of the first point 1 of any structural surface are:

[0066] ,

[0067] ,

[0068] ,

[0069] Three special cases: ,

[0070] This means that the structural surface is in the YZ plane, and the x coordinate at this time is 0, that is:

[0071] ,

[0072] , which means that the structural surface is in the XZ plane, and the y coordinate at this time is 0, that is:

[0073] ,

[0074] , which means that the structural surface is in the xy plane, and the z coordinate at this time is 0, that is:

[0075] ,

[0076] At this point, a three-dimensional discrete fracture network is generated in the entire space.

[0077] Finally, multi-source data such as faults, strata, discrete fracture networks, and tunnels are integrated to form a three-dimensional underground space model, as shown in Figure 10(a). Its two-dimensional cross-section is shown in Figure 10(b). The outermost cube is the boundary of the generated model, the lower rectangle is the stratum boundary, the upper left corner is the fault boundary, the middle is the tunnel outline, and the three-dimensional discrete fracture network is distributed around the tunnel.

[0078] like Figure 9As shown, joint and fissure images of different scales are collected for a target area to obtain structural characteristic information of each fissure within the target area. Based on the structural characteristic information of each fissure and the geometric parameter information of the joint surface in the target area, the distribution of each geometric parameter of the joint surface adopts a distribution form such as lognormal distribution. The mean, variance, and distribution form of the joint surface trace length are input to generate the size of the joint surface. The structural characteristic information of the engineering rock mass of each joint and fissure is determined, and a joint and fissure network is constructed based on the structural characteristic information of each joint and fissure.

[0079] On the other hand, Figure 11 As shown, the present invention provides a collaborative modeling device for multi-view disaster detection information in underground sheltered spaces, which includes various modules capable of implementing various steps of the aforementioned method, specifically including:

[0080] Acquisition module, used to collect multi-source data from air, space, ground and borehole;

[0081] The topology module is used to classify three-dimensional space objects into four types: points, lines, surfaces, and volumes. It designs five geometric elements: vertices, edges, loops, surfaces, and volumes, and establishes a topological data model.

[0082] The first generation module is used to divide the rock and soil layers using a multi-layer DEM stratum skeleton modeling method to form a skeleton structure of a three-dimensional stratum model, and to generate a complete three-dimensional stratum model using a voxel segmentation method;

[0083] The second generation module is used to generate a three-dimensional discrete fracture network based on a discrete fracture network refined modeling method of a rectangular joint model;

[0084] The output module is used to integrate air-space-ground-pore multi-source data based on the three-dimensional stratum model and the three-dimensional discrete fracture network to construct a three-dimensional underground space model with multi-level and multi-source data collaboration.

[0085] In a third aspect, the present invention provides an electronic device comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned collaborative modeling method for multi-view disaster detection information in underground sheltered spaces.

[0086] In a fourth aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned collaborative modeling method for multi-view disaster detection information in underground sheltered spaces.

[0087] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk drives, CD-ROMs, optical storage devices, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention may be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0088] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0089] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0091] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0092] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A collaborative modeling method for multi-view disaster detection information in underground sheltered spaces, characterized by: The method comprises: Step 1: Collect multi-source data from air, space, ground and borehole; Step 2: Classify 3D space objects into four types: point, line, surface, and volume. Design five geometric elements: vertex, edge, ring, surface, and volume. Establish a topological data model. Step 3: Use the multi-layer DEM stratigraphic skeleton modeling method to divide the rock and soil layers to form the skeleton structure of the three-dimensional stratigraphic model. Combined with the topological data model, a complete three-dimensional stratigraphic model is generated through the voxel segmentation method. DEM stands for digital elevation model. Step 4: Combine the topological data model and generate a three-dimensional discrete fracture network based on the discrete fracture network refined modeling method of the rectangular joint model; Step 5: Based on the three-dimensional stratum model and the three-dimensional discrete fracture network, the multi-source data of air, space, ground and hole are integrated to construct a three-dimensional underground space model with multi-level and multi-source data coordination.

2. The collaborative modeling method for multi-view disaster detection information in underground sheltered spaces according to claim 1 is characterized in that: In step 1, the air-space-ground-hole multi-source data includes stratum data, rock structure surface data, remote sensing data, semi-airborne electromagnetic data, distributed optical fiber data and borehole detection data.

3. The collaborative modeling method for multi-view disaster detection information in underground sheltered spaces according to claim 1 is characterized in that: The step 2 includes: According to the multi-source data of air-sky-ground-hole, three-dimensional space objects are divided into point objects, line objects, surface objects and body objects, and five basic geometric elements of vertices, edges, rings, faces and bodies are designed. The topological relationship between vertices, edges, rings, faces and bodies is constructed, and a topological data model is established. The engineering geological body model and the engineering structure model are expressed according to the body-face-ring-edge-vertex organization. The topological data model supports geometric analysis and semantic analysis.

4. The collaborative modeling method for multi-view disaster detection information in underground sheltered spaces according to claim 1 is characterized in that: The step 3 comprises: Perform interpolation or fitting processing on the boundary points of each rock layer and soil layer to generate a single-layer DEM; According to the properties of rock and soil layers, multiple single-layer DEMs are cross-divided to form the skeleton structure of the three-dimensional stratum model; In the area where the strata do not intersect, a local topological model is constructed using hexahedral elements; The intersecting strata are gridded and a complete three-dimensional stratum model is generated using the volume element method.

5. The collaborative modeling method for multi-view disaster detection information in underground sheltered spaces according to claim 1 is characterized in that: The step 4 comprises: Determine the number of joint groups and the parameters of each joint group through engineering geological survey; Calculate the number of structural planes in the x, y, and z directions of each joint group based on the spatial area size and joint parameters; Generate a random number matrix that obeys the joint parameter distribution to define the geometric characteristics of the structural surface; The rectangular model is used to generate joint units, and the spatial position of each structural surface is located by four-dimensional coordinates; Based on the chain stitching algorithm, a three-dimensional discrete fracture network of the entire region is generated sequentially in spatial order.

6. The collaborative modeling method for multi-view disaster detection information in underground sheltered spaces according to claim 5 is characterized in that: Each group of joint parameters includes spacing, trace length, and bridge length distribution.

7. The collaborative modeling method for multi-view disaster detection information in underground sheltered spaces according to claim 1 is characterized in that: In step 5, the three-dimensional underground space model includes a cube model as a boundary. Within the cube model, the lower part is the stratum boundary, the upper left corner is the fault boundary, the middle is the tunnel outline, and the three-dimensional discrete fracture network is distributed around the tunnel outline.

8. A collaborative modeling device for multi-view disaster detection information in underground sheltered spaces, characterized in that: include: Acquisition module, used to collect multi-source data from air, space, ground and borehole; The topology module is used to classify three-dimensional space objects into four types: points, lines, surfaces, and volumes. It designs five geometric elements: vertices, edges, loops, surfaces, and volumes, and establishes a topological data model. The first generation module is used to divide the rock and soil layers using a multi-layer DEM stratum skeleton modeling method to form a skeleton structure of a three-dimensional stratum model, and to generate a complete three-dimensional stratum model using a voxel segmentation method; The second generation module is used to generate a three-dimensional discrete fracture network based on a discrete fracture network refined modeling method of a rectangular joint model; The output module is used to integrate air-space-ground-pore multi-source data based on the three-dimensional stratum model and the three-dimensional discrete fracture network to construct a three-dimensional underground space model with multi-level and multi-source data collaboration.

9. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs; When one or more programs are executed by the one or more processors, the one or more processors implement a collaborative modeling method for multi-view disaster detection information in underground sheltered spaces as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that Executable instructions are stored thereon, which, when executed by a processor, enable the processor to implement a collaborative modeling method for multi-view disaster detection information in underground sheltered spaces as described in any one of claims 1-7.

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