Multi-view disaster detection information collaborative modeling method and device for underground shelter space

Abstract

The application discloses a method and device for collaborative modeling of multi-view disaster detection information of underground shelter space, and belongs to the technical field of underground space public safety. The method comprises the following steps: collecting air-space-ground-hole multi-source data; dividing three-dimensional space objects into four types of point, line, surface and volume, designing five geometric elements of vertex, edge, ring, surface and volume, and establishing a topological data model; adopting a multi-layer DEM stratum skeleton modeling method, dividing rock strata and soil strata, and generating a complete three-dimensional stratum model through a volume element subdivision method; generating a three-dimensional discrete fracture network based on a discrete fracture network refinement modeling method of a rectangular joint model; and fusing the air-space-ground-hole multi-source data to construct a multi-level and multi-source data collaborative three-dimensional underground space model. The application calculates the air-space-ground-hole multi-source data to the same space coordinate system under the same standard, and solves the problem of multi-scale data space fusion.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of underground space public safety, and particularly relates to a multi-view disaster detection information collaborative modeling method and device for underground shelter space. BACKGROUND

[0002] The major disaster derived disaster change process of underground space is complex and dangerous, and at present, the response mechanism thereof lacks systematic and scientific understanding. Different types of underground space derived disaster modes are complex, the load of shallow space is too heavy, deep space is highly concealed and is mostly long hole lines, which leads to great difficulty in disaster detection and monitoring, inaccurate early warning, blind decision-making, and long-term passive prevention and control. Major countries in the world mainly use means such as geophysical prospecting and drilling to detect water and mud inrush disaster structures, but the existing technology and equipment have many problems, and it is difficult to achieve "early discovery, early positioning and early identification". The core disaster detection technology and equipment such as multi-source remote sensing still face difficulties, and it is difficult to construct a digital model of complex geological bodies in underground space. Therefore, it is urgent to develop a three-dimensional visual detection information collaborative modeling technology for the shape, depth, structure and direction of underground shelter space disasters. SUMMARY

[0003] To solve the above technical problems, the application provides a multi-view disaster detection information collaborative modeling method and device for underground shelter space, which uses multi-layer DEM stratum skeleton modeling, and a discrete fracture network refinement modeling method based on a rectangular joint model to reduce multi-source data to the same space coordinate system under the same standard, thereby solving the problem of multi-scale data space fusion.

[0004] To achieve the above purpose, the technical scheme adopted by the application is as follows:

[0005] A multi-view disaster detection information collaborative modeling method for underground shelter space, the method comprising:

[0006] Step 1, collecting air-space-ground-hole multi-source data;

[0007] Step 2, dividing three-dimensional space objects into four types of point, line, surface and volume, designing five types of geometric elements of vertex, edge, ring, surface and volume, and establishing a topological data model;

[0008] Step 3, using a multi-layer DEM stratum skeleton modeling method to divide rock strata and soil strata to form the skeleton structure of the three-dimensional stratum model, combining the topological data model, and generating a complete three-dimensional stratum model through a volume element subdivision method; DEM represents a digital elevation model;

[0009] Step 4, combining the topological data model, generating a three-dimensional discrete fracture network based on a discrete fracture network refinement modeling method of a 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 space-air-ground-hole is fused to construct a three-dimensional underground space model with multi-level and multi-source data collaboration.

[0011] In another aspect, the present application provides an underground shelter space multi-view disaster detection information collaborative modeling device, comprising:

[0012] The acquisition module is configured to acquire multi-source data of space-air-ground-hole.

[0013] The topology module is configured to divide three-dimensional space objects into four types of point, line, surface and volume, design five geometric elements of vertex, edge, loop, surface and volume, and establish a topological data model.

[0014] The first generation module is configured to divide rock strata and soil layers by using a multi-layer DEM stratum skeleton modeling method, form a skeleton structure of a three-dimensional stratum model, and generate a complete three-dimensional stratum model by using a volume element subdivision method.

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

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

[0017] In a third aspect, the present application 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 above-mentioned underground shelter space multi-view disaster detection information collaborative modeling method.

[0018] In a fourth aspect, the present application provides a computer-readable storage medium having stored executable instructions, which when executed by a processor, enable the processor to implement the above-mentioned underground shelter space multi-view disaster detection information collaborative modeling method.

[0019] Advantages:

[0020] The present application is directed to the geological environment monitoring and analysis of underground space disasters, and proposes a set of comprehensive technical solutions. By analyzing stratum data, underground structure information, semi-airborne electromagnetic images, distributed optical fiber data and high-frequency electromagnetic borehole data, based on the differences in physical principles of these data, the disaster-prone geological environment of typical water and mud inrush and surrounding rock collapse disasters is studied, and the underground space geological body information is summarized. Using the topological data model, the intelligent identification method of complex stratum and rock mass structure is studied, a unified data description standard and basic data system are established, and the problem of conflict between different source data description is solved. A unified spatial coordinate reduction standard is proposed, which integrates multi-scale data into the same topological model, realizes full-space coverage, and eliminates data conflicts. Through the cross division of rock-soil layers and the division of body elements, the continuous geometric expression of stratum structure is constructed, which supports the accurate positioning of water inrush channels. Based on the rectangular joint model and chain splicing algorithm, a three-dimensional fracture network conforming to the geological statistical law is generated, which improves the reliability of surrounding rock stability analysis. The fusion model supports real-time data updating, and through dynamic adjustment of topological relationship, it quickly responds to disaster evolution. Combined with real-time deformation data of optical fiber sensing, the precursors of rock mass collapse are identified in advance, and the warning time is shortened by more than 50% compared with traditional methods. The present application is compatible with existing detection equipment and does not need to modify hardware, and realizes low-cost upgrade through algorithm layer integration. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 The flow chart of the underground shelter space multi-view disaster detection information collaborative modeling method of the present application;

[0022] Figure 2 The relationship diagram between objects in the topological data model;

[0023] Figure 3 Two forms of multi-layer DEM intersection grid, wherein (a) is a schematic diagram of stratum intersection point as opposite side, and (b) is a schematic diagram of stratum intersection point as adjacent side;

[0024] Figure 4 Body division schematic of stratum intersection, wherein (a) is a schematic diagram of stratum intersection point as opposite side, and (b) is a schematic diagram of stratum intersection point as adjacent side;

[0025] Figure 5 Random number generation schematic of joint occurrence;

[0026] Figure 6 Rectangular vertex definition schematic of joint unit;

[0027] Figure 7 Joint numbering schematic in space;

[0028] Figure 8 Chain generation algorithm principle diagram of complex joint network;

[0029] Figure 9 generating a schematic diagram for the joint fissure network model;

[0030] Fig. 10(a) is a three-dimensional underground space model formed based on the method of the present application;

[0031] Fig. 10(b) is a two-dimensional section of the three-dimensional underground space model;

[0032] Figure 11 Fig. 1 is a schematic diagram of the underground shelter space multi-view disaster detection information collaborative modeling device of the present application.

[0033] Wherein, the reference signs are: the first point 1, the second point 2, the third point 3, and the fourth point 4. DETAILED DESCRIPTION

[0034] The present application will be further described below in combination with the drawings and examples.

[0035] The present application provides an underground shelter space multi-view disaster detection information collaborative modeling method and device, based on a topological data model, studies intelligent identification methods for complex strata and rock mass structures, establishes a unified data description standard and a basic data system for different source data on engineering collapse area geological environment, solves the problem of conflict in geological environment description by different source data; multi-layer DEM stratum skeleton modeling, discrete fissure network refinement modeling based on rectangular joint model, and other methods are used to reduce the 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. As shown in Figure 1 specifically includes:

[0036] Step 1, collect multi-source data of air-space-ground-hole; including strata, rock mass structure surface, tunnel structure, remote sensing data (space), semi-airborne electromagnetic data (space), distributed optical fiber data (ground) and borehole detection data (hole) and other multi-source data.

[0037] Step 2, divide the three-dimensional space object into four types of point, line, surface and volume, design five geometric elements of vertex, edge, ring, surface and volume, and establish a data description standard system, i.e. a topological data model; as shown in Figure 2 , fully considering strata data, rock mass structure surface data, remote sensing data, semi-airborne electromagnetic data, distributed optical fiber data and borehole detection data and other multi-source data, dividing the three-dimensional space object into four basic types of point object, line object, surface object and volume object, designing five basic geometric elements of vertex, edge, ring, surface and volume, constructing the topological relationship between vertex, edge, ring, surface and volume, and establishing a topological data model; according to the body-surface-ring-edge-vertex organization to express the engineering geological body model and the engineering structure model, which can meet the demand of model visualization, and can also carry out geometric analysis and semantic analysis on the model.

[0038] Step 3, using multi-layer DEM stratum skeleton modeling method, the rock stratum and soil layer are divided, the skeleton structure of three-dimensional stratum model is formed, and the complete three-dimensional stratum model is generated by the body element subdivision method; based on multi-layer DEM stratum skeleton modeling: based on single body lithology element, the DEM fitting interpolation method is used to interpolate or fit the division points of each rock stratum and soil layer. Then, according to the properties of rock stratum and soil layer, the multi-layer DEM is cross-divided, forming the skeleton structure of three-dimensional stratum model strictly divided according to lithology. Because each layer DEM has a completely consistent reference system, and can accurately match and one-to-one correspondence, hexahedral body elements can be formed at the place where the stratum does not intersect, and according to the body element subdivision method, body elements are generated at the place corresponding to the stratum intersection, and finally a complete three-dimensional stratum model is formed according to the single body division of rock-soil medium elements.

[0039] Step 4, the discrete fracture network refinement modeling method based on rectangular joint model is generated; based on the anisotropic characteristics of joint and other complex rock mass structures, the rectangular representation assumption of structural plane is introduced, considering the spatial position correlation of adjacent structural planes, the three-dimensional joint network reconstruction technology based on chain splicing algorithm is proposed, the discrete fracture network refinement modeling based on rectangular joint model is carried out, and the three-dimensional discrete fracture network is generated.

[0040] Step 5, based on three-dimensional stratum model and three-dimensional discrete fracture network, multi-source data of space-air-ground-hole are fused, and multi-level, multi-source data collaborative three-dimensional underground space model is constructed. Through the introduction of unmanned aerial vehicle LiDAR data by surface elevation constraint, combined with underground rock-soil constraint, distributed optical fiber sensing, semi-airborne electromagnetic data and geological drilling data are integrated, a multi-level, multi-source data fusion three-dimensional underground space model is constructed. The model fully utilizes the synergistic effect of multi-source data of space (semi-airborne electromagnetic), sky (unmanned aerial vehicle LiDAR), ground (distributed optical fiber sensing) and hole (geological drilling), realizes the all-around and high-precision description of underground space, and finally forms an integrated three-dimensional stratum model based on air-sky-ground-hole integration.

[0041] In step 3, the stratum is constructed based on the DEM fitting interpolation method; the digital elevation model (DEM), also known as a digital terrain model (DTM), is a continuous representation method for spatial fluctuation changes. Since the DTM implies the meaning of the terrain landscape, DEM is often used to simply represent the elevation. The representation method of DEM mainly includes four methods of fitting interpolation method, contour, grid DEM, and irregular triangle network DEM (TIN). Interpolation is the core problem of digital elevation model, and DEM interpolation is to obtain the elevation value of the to-be-determined point according to the elevation of several adjacent reference points. Any interpolation method is based on the continuity of the original terrain fluctuation, or the correlation between adjacent data points, so that the elevation of the to-be-determined point can be interpolated from the adjacent data points. According to the distribution range of the interpolation point, the interpolation can be divided into three categories of whole interpolation, block interpolation and point-by-point interpolation. According to the relationship between the binary function approximation mathematical surface and the reference point, the interpolation can be divided into two kinds of pure two-dimensional interpolation and surface fitting interpolation.

[0042] The space interpolation or fitting operation is performed on the sampling obtained division points of each rock stratum and soil layer to obtain the DEM of each rock stratum and soil layer surface, and then the distribution of different stratum surfaces in the three-dimensional space is drawn. In this way, a multi-layer DEM is formed. According to the properties of the rock stratum and the soil layer, the multiple single-layer DEMs are cross-divided to form the skeleton structure of the three-dimensional stratum model; it is worth noting that the reference systems of these multi-layer DEMs are completely consistent and can be accurately matched with each other, so as to provide more than one corresponding elevation value for the [X, Y] coordinates in each grid. In an ideal state, if the multiple stratum surfaces do not intersect, a local topological model (LTM) is constructed by hexahedral elements, such as combining the grids on each adjacent surface in a one-to-one connection form to generate a hexahedral element and establishing a topological relationship to form a local topological model (LTM), and finally the three-dimensional stratum model is formed by the multi-layer DEM. However, in actual application, intersection and splicing between stratum interfaces inevitably occur. Therefore, the intersecting strata are grid-divided, and the complete three-dimensional stratum model is generated by the element division method. Therefore, it is usually necessary to first make a corresponding judgment on each stratum surface to complete the stratum division. On this basis, the three-dimensional stratum model can be established. Specifically, after the multi-layer DEM is formed, before the stratum division, the concept of body has not been formed. Moreover, since the DEM in the present application is obtained by regular grid interpolation, the formed surface is only in the form of quadrilateral. At this time, the topological relationship established is mainly the relationship between nodes, edges and surfaces. When the coordination relationship between the strata is determined, the intersecting grids need to be divided. There are two forms of (a) and (b) in the intersection of a quadrilateral surface. Figure 3 Figure 3 Figure 3 ​​As shown, first, find the intersection points E and F of the two locations in the grid ABCD. Then, based on the topological relationship between the faces and edges, determine the cases where the edges containing E and F belong to which of the following categories? Figure 3 of (a), Figure 3 Which of (b) is correct? Then, based on the stratigraphic division method described above, the selection of intersecting strata is determined. As shown in the figure, the divided strata are quadrilaterals □ABCD and □B'C'FE or triangles ΔB'FE. After the spatial distribution of each stratum is determined, the grid can be connected to form volume elements based on the good vertical correspondence between multiple DEMs.

[0043] After multi-layer DEM modeling, a finite-mutually exclusive-complete volume partitioning based on the soil and rock media was completed, forming the basic framework of the 3D stratigraphic model. However, tunnel structures still exist in the engineering subsidence area, so it is necessary to introduce these special volumes into the already partitioned stratigraphic model. The main operation in this step is actually to perform intersection and splicing between the volume elements in the already partitioned stratigraphic model and these complex volume objects, and it is important to ensure the continuity and coordination between the volume elements in the model after intersection and splicing.

[0044] like Figure 4 (a), for Figure 3 In case (a) of the subdivision, the intersection line EF divides quadrilateral □ABCD into quadrilaterals □AEFD and □EBCF, satisfying the convention for geometric elements, and therefore does not need further subdivision. However, the volume B'C'CBEF formed between the ground planes is not one of the four volume element forms specified in this invention and must be subdivided. By connecting EC and EC', B'C'CBEF is subdivided into a square pyramid B'C'CBE and a tetrahedron EC'CF. Simultaneously, to ensure the coordination between volume elements, the corresponding edges of the upper and lower planes also need to be connected. For example... Figure 4 As shown in (a), the MIs of the previous layer are connected, at which point the hexahedron EBCFMHIN is divided into two triangular prisms EBCMHI and ECFMIN. In this way, the volume B'C'CBEF and the hexahedron ABCDGHIJ before processing are divided into one hexahedron AEDGMNJ, two triangular prisms EBCMHI and ECFMIN, one tetrahedron EC'CF and one square pyramid B'C'CBE. At the same time, the continuity and coordination between the volume elements are ensured, laying a good foundation for finite element mesh transformation.

[0045] for Figure 3 Regarding the (b) type of surface partitioning, there are two different volume intersection cases, but their partitioning operations are similar. This invention only describes one of these cases, see [link to documentation]. Figure 4(b). First, the quadrilateral □ABCD is triangulated according to the intersection line EF, forming four triangles ΔAED, ΔDEF, ΔEBF and ΔDFC, and then the edges between the body elements are divided and the upper and lower corresponding edges are connected to form the body elements meeting the requirements, so that the first body element hexahedron ABCDGHIJ is divided into four triangular prisms AEDGMI, EFDMNJ, EBFMHN and FCDNIJ, and one tetrahedron EB'BF is generated between the two strata. Similarly, the division principles of continuity and coordination are met.

[0046] In step 4, to generate a discrete fracture network in a specific space, first, an engineering geological survey is conducted to determine the number of joint sets in the region and the distribution rules and maximum and minimum values of the spacing , trace length , and bridge length between the joint sets in each joint set, and the number of structural planes contained in each joint set in the space is determined and stored in the matrix :

[0047] ,

[0048] wherein represents the joint set;

[0049] ,

[0050] ,

[0051] wherein represents the number of structural planes in the direction; represents the side length of the circumscribed cube of the structural plane region; respectively represent the mean value of the spacing, trace length, and bridge length;

[0052] According to the number of each joint set, random numbers conforming to the distribution rules of the spacing, trace length, and bridge length are generated to define the geometric characteristics of the structural planes, and the generated random numbers are stored in the matrix , as shown in Figure 5 . Wherein:

[0053] ,

[0054] ,

[0055] wherein stores the random number of the spacing of the first joint set; stores the random number of the trace length of the first joint set in the direction; stores the random number of the bridge length of the first Group joint trace length Random number in direction; Storage of the first Jointed bridge length Random number in direction; Storage of the first Jointed bridge length Random number in direction; ).

[0056] Joint elements are generated using a rectangular model, and the spatial position of each structural surface is located using four-dimensional coordinates; the orientation of the four vertices is defined as follows: Figure 6 As shown, therefore, 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 from the coordinates of the first point 1 and the side length of the rectangle, and are represented as follows:

[0057] ,

[0058] ,

[0059] ,

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

[0061] Therefore, for each structural surface, only the coordinates of the first point 1 need to be obtained, and the information of the entire structural surface can be stored according to the above formula.

[0062] The numbering of each structural surface is defined using four-dimensional coordinates, such as... Figure 7 As shown. The first number indicates the joint group to which the structural surface belongs, and the last three numbers indicate the position of the structural surface in space, similar to the x, y, and z coordinates in a three-dimensional coordinate system. For any structural surface... The x-coordinate of its first point 1 is determined by the structural surface. The x-coordinate of point 1 is determined by its trace length in the x-direction and the bridge length, while its y-coordinate is determined by the structural surface. Its y-coordinate is determined by its trace length in the y-direction and the bridge length, while its z-coordinate is determined by the structural surface. The z-coordinate and its spacing determine the position of any structural surface. That is, the spatial position of any structural surface is determined by the distribution patterns of the coordinates and spacing of its adjacent structural surfaces, trace length, and bridge length.

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

[0064] Introducing four-dimensional matrix , , The coordinate value of the structure surface point 1 with record number in the local coordinate system.

[0065] Therefore, the coordinate of the first point 1 of any structure surface is:

[0066] ,

[0067] ,

[0068] ,

[0069] Three special cases: ,

[0070] That is, the structure surface is in the YZ plane, and the x coordinate is 0 at this time, that is:

[0071] ,

[0072] That is, the structure surface is in the XZ plane, and the y coordinate is 0 at this time, that is:

[0073] ,

[0074] That is, the structure surface is in the XY plane, and the z coordinate is 0 at this time, that is:

[0075] ,

[0076] Up to now, a three-dimensional discrete fracture network has been generated in the entire space.

[0077] Finally, integrate multi-source data such as faults, strata, discrete fracture networks, and tunnels to form a three-dimensional underground space model, as shown in FIG. 10(a), and its two-dimensional profile is shown in FIG. 10(b), wherein 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 contour, and the three-dimensional discrete fracture network is distributed around the tunnel.

[0078] As Figure 9As shown, different scale joint fissure images are collected for the target area, structural feature information of each fissure in the target area is acquired, according to the structural feature information of each fissure in the target area and the geometric parameter information of the joint surface, the distribution of each geometric parameter of the joint surface adopts a lognormal distribution or the like, the mean value, variance and distribution form of the trace length of the joint surface are input, and the size of the joint surface is generated. The engineering rock mass structure feature information of each joint fissure is determined, and the joint fissure network is constructed according to the engineering rock mass structure feature information of each fissure.

[0079] In another aspect, as shown in the drawings, the present application provides an underground shelter space multi-view disaster detection information collaborative modeling device, each module of which can realize each step of the foregoing method, specifically comprising: Figure 11

[0080] The acquisition module is used for acquiring air-space-ground-borehole multi-source data.

[0081] The topology module is used for dividing three-dimensional space objects into four types of point, line, surface and volume, designing five geometric elements of vertex, edge, loop, surface and volume, and establishing a topological data model.

[0082] The first generation module is used for dividing rock strata and soil strata by using a multi-layer DEM stratum skeleton modeling method, forming a skeleton structure of a three-dimensional stratum model, and generating a complete three-dimensional stratum model by using a volume element subdivision method.

[0083] The second generation module is used for generating a three-dimensional discrete fissure network based on a discrete fissure network refinement modeling method of a rectangular joint model.

[0084] The output module is used for fusing air-space-ground-borehole multi-source data based on the three-dimensional stratum model and the three-dimensional discrete fissure network, and constructing a three-dimensional underground space model with multi-level and multi-source data collaboration.

[0085] In a third aspect, the present application 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 foregoing underground shelter space multi-view disaster detection information collaborative modeling method.

[0086] In a fourth aspect, the present application provides a computer readable storage medium having stored executable instructions, which when executed by a processor, can enable the processor to implement the foregoing underground shelter space multi-view disaster detection information collaborative modeling method.

[0087] ​Those skilled in the art will appreciate that embodiments of the present application can be readily used as software, hardware, or a combination of software and hardware. In a software embodiment, the methods can be tangibly embodied in a machine-readable storage medium having stored thereon instructions that can be used to program a computer to perform any of the methods. The software implementation can be initialized by loading and executing a set of instructions arranged to perform one of the methods into the computer's memory. Alternatively, hard-wired circuitry can be used in place of, or in combination with, software instructions. Thus, the

[0088] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams. Figure 1 means for performing each of the functions specified in the flowchart illustrations and / or block diagrams.

[0089] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams. Figure 1 means for performing each of the functions specified in the flowchart illustrations and / or block diagrams.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in one or more of the flowchart illustrations and / or block diagrams. Figure 1 means for performing each of the functions specified in the flowchart illustrations and / or block diagrams.

[0091] While preferred embodiments of the application have been described, modifications and variations can be apparent to those skilled in the art once aware of the general underlying concepts. Accordingly, the appended claims are intended to embrace all such modifications and variations as fall within the scope of the application.

[0092] It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

Claims

1. A method for collaborative modeling of multi-view disaster detection information of underground sheltered space, characterized in that, The method comprises: Step 1, collecting air-space-ground-hole multi-source data, wherein the air-space-ground-hole multi-source data comprises stratum data, rock mass structural plane data, remote sensing data, semi-airborne electromagnetic data, distributed optical fiber data and borehole detection data; Step 2, dividing three-dimensional space objects into four types of point, line, surface and volume, designing five geometric elements of vertex, edge, loop, surface and volume, and establishing a topological data model; comprising: According to the air-space-ground-hole multi-source data, three-dimensional space objects are divided into point objects, line objects, surface objects and volume objects, five basic geometric elements of vertex, edge, loop, surface and volume are designed, the topological relationship between vertex, edge, loop, surface and volume is constructed, a topological data model is established, and the engineering geological body model and the engineering structure model are organized and expressed according to the volume-surface-loop-edge-vertex, and the topological data model supports geometric analysis and semantic analysis; Step 3, adopting a multi-layer DEM stratum skeleton modeling method, dividing rock strata and soil strata to form a skeleton structure of a three-dimensional stratum model, combining the topological data model, and generating a complete three-dimensional stratum model through a volume element subdivision method; DEM represents a digital elevation model; Step 4, combining the topological data model, and generating a three-dimensional discrete fracture network based on a discrete fracture network refinement modeling method of a rectangular joint model; Step 5, based on the three-dimensional stratum model and the three-dimensional discrete fracture network, fusing the air-space-ground-hole multi-source data, and constructing a three-dimensional underground space model with multi-level and multi-source data collaboration.

2. The underground shelter multi-view disaster detection information collaborative modeling method according to claim 1, characterized in that, The step 3 comprises: Interpolating or fitting each rock stratum and soil stratum boundary point to generate a single-layer DEM; According to the properties of the rock strata and soil strata, a plurality of single-layer DEMs are cross-divided to form a skeleton structure of a three-dimensional stratum model; In the non-intersecting area of the strata, a local topological model is constructed by using a hexahedral volume element; The intersecting strata are grid-subdivided, and a complete three-dimensional stratum model is generated through a volume element subdivision method.

3. The method of claim 1, wherein the method further comprises: The step 4 comprises: Determining the number of joint sets and the parameters of each joint set through engineering geological investigation; According to the spatial region size and the joint parameters, the number of structural planes in the x, y and z directions of each joint set is calculated; A random number matrix conforming to the joint parameter distribution is generated to define the geometric characteristics of the structural planes; A rectangular model is used to generate a joint unit, and the spatial positions of the structural planes are located through four-dimensional coordinates; Based on a chain splicing algorithm, a three-dimensional discrete fracture network of the whole region is generated in spatial order.

4. The underground shelter multi-view disaster detection information collaborative modeling method according to claim 3, characterized in that, Each set of joint parameters comprises interval, trace length and bridge length distribution.

5. The underground shelter multi-view disaster detection information collaborative modeling method according to claim 1, characterized in that, In the step 5, the three-dimensional underground space model comprises a cubic model as a boundary, in which the lower part is a stratum boundary, the upper left corner is a fault boundary, the middle part is a tunnel contour, and the three-dimensional discrete fracture network is distributed around the tunnel contour.

6. An underground shelter multi-view disaster detection information collaborative modeling device, characterized in that, Comprising: A collection module for collecting air-space-ground-hole multi-source data, wherein the air-space-ground-hole multi-source data comprises stratum data, rock mass structural plane data, remote sensing data, semi-airborne electromagnetic data, distributed optical fiber data and borehole detection data; A topology module is configured to divide three-dimensional space objects into four types of point, line, surface and volume, design five geometric elements of vertex, edge, loop, surface and volume, and establish a topology data model. According to the space-air-ground-hole multi-source data, three-dimensional space objects are divided into point objects, line objects, surface objects and volume objects, five basic geometric elements of vertex, edge, loop, surface and volume are designed, topology relationships among the vertex, edge, loop, surface and volume are constructed, a topology data model is established, and an engineering geological body model and an engineering structure model are organized and expressed in accordance with the volume-surface-loop-edge-vertex, and the topology data model supports geometric analysis and semantic analysis; A first generation module is configured to divide rock strata and soil strata by using a multi-layer DEM stratum skeleton modeling method, form a skeleton structure of a three-dimensional stratum model, and generate a complete three-dimensional stratum model by using a body element subdivision method; A second generation module is configured to generate a three-dimensional discrete fracture network by using a discrete fracture network refinement modeling method based on a rectangular joint model; An output module is configured to fuse the space-air-ground-hole multi-source data based on the three-dimensional stratum model and the three-dimensional discrete fracture network, and construct a three-dimensional underground space model with multi-level and multi-source data collaboration.

7. An electronic device, comprising: comprise: one or more processors; a memory for storing one or more programs; wherein the one or more programs, when executed by the one or more processors, enable the one or more processors to implement the underground sheltered space multi-view disaster detection information collaborative modeling method of any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, executable instructions are stored thereon, which, when executed by a processor, enable the processor to implement the underground sheltered space multi-view disaster detection information collaborative modeling method of any one of claims 1-5.

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