CN117332664A - Geological disaster digital twin live-action display method based on physical calculation result - Google Patents
Geological disaster digital twin live-action display method based on physical calculation result Download PDFInfo
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Abstract
A geological disaster digital twin live-action display method based on a physical calculation result belongs to the field of geological disaster monitoring and early warning, and specifically comprises the following steps: constructing a landslide deformation characteristic family library; constructing a crack extraction module; constructing a live-action display module; obtaining physical information and physical parameters of a target landslide body and crack distribution of the target landslide body; establishing a landslide geological model for forming a landslide area according to the physical information and physical parameters of the target landslide body; screening calculation parameters and establishing a calculation model; inputting expected working conditions into the calculation model, obtaining a finite element calculation result cloud picture, and obtaining a crack system under the expected working conditions by utilizing the crack extraction module; and according to the landslide geological model of the landslide region of the target landslide body and the crack system under the expected working condition, utilizing the live-action display module to display digital twin live-action based on a physical calculation result.
Description
Technical Field
The invention belongs to the field of geological disaster monitoring and early warning, and particularly relates to a geological disaster digital twin live-action display method based on a physical calculation result.
Background
The geological disaster monitoring and early warning system is a system which monitors occurrence and evolution of geological disasters (such as earthquakes, landslides, debris flows and the like) by utilizing sensors and data acquisition and analysis technologies and timely sends early warning information so as to protect life and property safety of people. The step of utilizing the early warning system for early warning generally comprises the main links of sensor layout, data acquisition, data transmission, data processing and analysis, early warning judgment and release, coping and emergency response and the like. The design and implementation of the geological disaster monitoring and early warning system need to comprehensively consider the characteristics of geological environment, sensor technology, data processing and analysis algorithm, communication technology, disaster development mechanism and the like.
The Digital twinning (Digital twinning) is a virtual representation of a physical entity or system, and is used for realizing monitoring, simulation and optimization of the entity or system by integrating sensor data, simulation and analysis and other technologies, and the main steps comprise the steps of data acquisition, data processing and integration, digital twinning model construction, visual design, data presentation and interaction, analysis and optimization and the like.
The features and embodiments of the two techniques determine that the two techniques have a good degree of agreement in terms of the combination of techniques. In fact, in recent years, the application of the digital twin technology in geological disaster monitoring and early warning is deepened continuously, and the Chinese patent ' a landslide monitoring system and method based on the digital twin technology ' (ZL 202210885360.9) ' a collapse monitoring system and method based on the digital twin technology ' (ZL 202211065374.2) ' a debris flow monitoring system and method based on digital twin ' (ZL 202211170033.1) ' respectively face different disaster types, and discloses a construction path and method of a digital twin body aiming at landslide, collapse and debris flow, which belongs to an integral solution of the geological disaster digital twin field facing different disaster types. In the chinese patent, "a debris flow monitoring system and method based on digital twinning" (ZL 202211170033.1), the actual VR display of the debris flow simulation calculation result is mentioned, but no specific technical route and details are mentioned.
The objective of the digital twin calculation result live-action visualization is to present the data and calculation results of the digital twin model in a visual manner so that a user can intuitively understand and analyze the state, behavior and performance of a physical entity or system. The traditional CAE numerical simulation mainly forms other field cloud pictures such as a displacement cloud picture, a stress cloud picture, a strain cloud picture and the like, and the cloud pictures can only be used for observation and use by professional persons, and can not enable non-professional users to feel entity changes corresponding to calculation results, so that the requirement of digital twin calculation result visualization is not met; however, in an emergency response scene of monitoring and early warning of geological disasters, commanders and decision makers of emergency consultants often need to have a non-specialized and visual geological disaster simulation result presentation so as to make a forever decision and command. The situation above constitutes a practical requirement for visual visualization of the digital twin calculation results of geological disasters.
On the other hand, some video animations for dynamically restoring the real scenes in the geological disaster occurrence process in the prior art are based on the knowledge and learning of the geological disaster occurrence mechanism, and are manually made into animations and images, so that a restoring creator envisages the geological disaster development process; the real-time computing result is not based on the content of the real-time computing result, so that the real-time computing cloud image is separated from the reference basis of the computing cloud image to develop the real-time computing result, and therefore, the real-time computing cloud image does not belong to the category of digital twin products of geological disasters and can not meet the requirement of real-time visualization of the digital twin computing result of geological disasters.
In summary, the digital twin technology is gradually paid attention to in the field of geological disasters, and digital twin solutions based on various disasters are continuously developed; digital twinning calculation result live-action visualization is an important dimension of digital twinning, but the prior art does not have technical details about the aspect of geological disaster digital twinning calculation result live-action visualization, and obvious short plates exist.
Disclosure of Invention
The invention aims to provide a real-scene display method for digital twin calculation results of geological disasters, which is mainly aimed at landslide disasters in the field of geological disasters, and aims to solve the problems that in the prior art, a cloud chart of simulation calculation results is single in display and cannot meet requirements of emergency decision making and command non-specialized but visual real-scene display in monitoring and early warning of the geological disasters.
The invention provides a geological disaster digital twin live-action display method based on a physical calculation result, which specifically comprises the following steps:
obtaining physical information and physical parameters of a target landslide body and crack distribution of the target landslide body;
establishing a landslide geological model according to the physical information and physical parameters of the target landslide body; screening calculation parameters and establishing a calculation model;
inputting a specific working condition into the calculation model to obtain a finite element calculation result cloud picture, and obtaining a crack system under the specific working condition by using a crack extraction module;
the crack extraction module is used for extracting the crack space distribution and the attribute parameters of the cracks in the landslide according to the finite element calculation result cloud picture input by the user to form a crack system;
according to the landslide geological model of the target landslide body and the crack system under the specific working condition, a live-action display module is utilized to display digital twin live-action based on a physical calculation result;
the real landslide display module is used for generating a real landslide model according to the input landslide geological model, calling a landslide deformation characteristic family library according to the input crack system under the specific working condition, and developing Boolean operation on the real landslide model to generate a landslide deformation real-scene model.
Further, the landslide deformation characteristic family base is constructed according to landslide categories and the deformation characteristics of landslide.
Further, aiming at a target landslide body, according to physical information and physical parameters of the target landslide body, obtaining a corresponding finite element boundary condition when the target landslide body is induced to slide.
Further, the specific working condition is an expected working condition for the target landslide body.
Further, the specific working condition is a working condition which is obtained according to landslide mechanism analysis and induces the landslide of the target landslide body.
Further, the optimization steps of the calculation model are as follows: according to landslide mechanism analysis, working conditions for inducing the landslide of the target landslide body are obtained, initial calculation is conducted through the calculation model, initial calculation results are obtained, the initial calculation results are input into the crack extraction module, an initial crack system is extracted, and the calculation model is adjusted and optimized through comparison of the initial crack system and obtained landslide body crack distribution of the target landslide body.
Further, training a crack identification model based on machine learning by using an indoor physical test result and a finite element calculation result of the landslide physical model, and constructing a crack extraction module.
Further, the construction of the crack extraction module mainly comprises the following steps:
applying various working conditions and constraints to a landslide physical model through an indoor physical test, observing and recording deformation crack distribution of the landslide physical model, forming an internal image of the landslide by CT scanning, and calibrating and recording crack positions, crack types and crack size information in the internal image;
establishing a finite element landslide model with the same scale as the indoor physical test, applying various working conditions and constraints corresponding to the indoor physical test on the finite element landslide model, performing finite element calculation, generating a finite element calculation result, and forming a finite element calculation result cloud picture;
and establishing a crack identification model based on machine learning, and training the crack identification model by using the indoor physical test result and the finite element calculation result to obtain a crack extraction module.
The method is based on a calculated result cloud picture, carries out crack extraction based on the cloud picture, and synchronously maps an extracted crack system with disaster (landslide) live-action data to realize the purpose of digital twin calculation result live-action visualization of the geological disaster. The invention solves the problem to be solved in the prior art, complements the defect of the digital twinning of the geological disasters on the basis of the live-action visual display of the calculation result, and sweeps the obstacle of the digital twinning technology on the application visualization of the geological disasters. The method of the invention realizes real-time mapping relation between CAE calculation cloud image and entity scene, can make the live-action display result more scientific, and belongs to the important basis of digital twin technology. After the calculation result data is displayed in the live-action mode, the mapping result of the live-action is conveniently and directly projected into the live-action model, and a decision maker can conveniently and directly evaluate the result, so that the emergency command decision is facilitated.
Drawings
For a more complete understanding of the present invention, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a schematic flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of a landslide deformation characteristic family library according to the present invention;
FIG. 3 is a schematic diagram of an initial fracture system according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a fracture system under expected conditions in accordance with an embodiment of the present invention.
Detailed Description
For the purposes of explanation, specific details, and effective applications of the present invention are set forth in order to facilitate understanding and practice by those of ordinary skill in the art, as will be further described in detail below in connection with the embodiments of the invention and the accompanying drawings. It is apparent that the examples described herein are for illustration and explanation of the present invention only and are not intended to be limiting.
The invention provides a geological disaster digital twin live-action display method based on a physical calculation result, which utilizes a finite element module, a crack extraction module and a live-action display module, inputs expected working conditions into the finite element module, outputs a finite element calculation result, and inputs the finite element calculation result into the crack extraction module to obtain a crack system; and the live-action display module generates live-action according to the crack system, pushes live-action display to a user terminal, and finally achieves the final purpose of live-action display of the digital twin calculation result.
The expected conditions include, but are not limited to, various conditions that are expected, that occur with a high probability recently, and that are set by deduction, such as various conditions that may occur with different durations and intensities of rainfall, manual loading, dropping of the reservoir water level at a certain rate, and three types of boundary conditions under these conditions.
The finite element calculation module mainly comprises all physical and mathematical models related to calculation, such as a finite element geometric model, a mesh subdivision model, a seepage model, a fracture model and the like.
The crack extraction module belongs to a large model formed based on a deep learning technology. And comparing the finite element calculation result with the geotechnical test result according to a large number of geotechnical test results and synchronous finite element calculation results, taking the comparison result as a training data set and a verification set, and forming a crack extraction module through deep learning training.
The live-action display module is an image display module based on computer graphics, inputs the live-action display module into a landslide geological model and a crack system, outputs the live-action display module into a landslide deformation live-action corresponding to the crack system, and combines technologies such as display, texture lamination and rendering to increase a vivid effect.
The method for displaying the digital twin live action of the geological disaster based on the physical calculation result is described in detail below, and the flow of the method is shown in fig. 1, and specifically comprises the following steps:
and S01, constructing a landslide deformation characteristic family base according to landslide categories and landslide deformation characteristics.
The landslide is determined, and the landslide can be divided into a push type landslide and a traction type landslide according to the occurrence type of the landslide. Based on the two types of landslide, the deformation of the landslide can be further divided into a trailing edge pull crack, a trailing edge wall, a landslide ridge, a flank shear expansion crack (goose's-series), a leading edge bulge crack and the like according to the deformation characteristics of the whole life stage of the landslide, and can be seen in fig. 2.
The landslide deformation characteristic family library comprises but is not limited to the deformation characteristics, and can be dynamically enriched according to other possible landslide types.
Each of the deformation features of the landslide has an attribute feature. Taking a crack as an example, the attribute features include a crack type, a crack depth (longitudinal depth of crack development), a development direction (inclination angle of downward development of the crack and crack tendency), a crack morphology (crack normal vector), a crack boundary, a relational operation between cracks (crack merging compatibility and penetration), and the like.
S02, training a crack identification model based on machine learning by utilizing an indoor physical test and a finite element calculation result of a landslide physical model, and constructing a crack extraction module; the crack extraction module is used for extracting the crack space distribution and various attribute parameters of the crack in the landslide according to the finite element calculation result cloud image of the landslide under various working conditions input by a user, so as to form a crack system.
Specifically, the method may comprise the steps of:
step S201, data collection and preprocessing.
And (3) designing an indoor engineering test, applying various working conditions and constraints (not less than 10 combinations) to a landslide physical model (the scaling ratio is not less than 1:1000) through the indoor physical test, observing and recording deformation crack distribution of the landslide physical model, forming an internal image of the landslide body by utilizing CT scanning, and calibrating and recording crack position, crack category and crack size information in the CT scanning formed image.
Meanwhile, a finite element landslide model with the same scale as the indoor engineering test is established, various working conditions corresponding to the indoor physical test are applied to the finite element landslide model, finite element calculation is conducted, calculation results of displacement, strain, stress and the like of the finite element landslide model are generated, and a finite element calculation result cloud picture is formed.
And comparing the results recorded by the indoor physical test with the cloud image of the finite element calculation result, wherein the comparison contents comprise crack positions, crack types, crack sizes and the like.
And S202, establishing a crack identification model based on machine learning, and training the crack identification model by using the indoor physical test and the finite element calculation result to obtain a crack extraction module.
Based on the comparison result obtained in the step S201, a convolutional neural network (Convolutional Neural Networks, CNN) and a support vector machine (Support Vector Machines, SVM) algorithm are adopted, a crack recognition model training system based on machine learning is established, 85% of data in the comparison result data set is used as a training set, and the crack recognition model is trained; and using 15% of data in the comparison result data set as a verification set to ensure that the crack identification model has good generalization capability.
And continuously improving the crack identification model according to the verification result and the actual application feedback, and improving the identification performance of the crack identification model.
Further, after the fracture training model is trained by utilizing the data set, super-parameter adjustment can be performed according to the requirement so as to obtain better performance.
The crack extraction module is formed by the crack identification model after training and adjustment.
The crack extraction module is used for extracting the crack space distribution and various attribute parameters of the crack in the landslide according to the finite element calculation result cloud image of the landslide under various working conditions input by a user. And storing the crack types, the spatial distribution and other attribute parameters in the landslide in a list form, and assisting in a deformation characteristic diagram to jointly form a crack system.
Meanwhile, when the crack extraction module forms the crack system, incomplete crack attributes can be supplemented and completed according to the landslide deformation characteristic family base after crack distribution and attributes are identified.
S03, constructing a live-action display module; the real landslide display module is used for generating a real landslide model according to the input landslide geological model, calling the landslide deformation characteristic family base formed in the step S01 according to the input crack system, developing Boolean operation on the real landslide model, generating a landslide deformation real-scene model, and realizing standardized rapid display.
And integrally displaying the landslide deformation live-action model by adopting an artist algorithm, adding a lighting model and a texture veneer on the basis, rendering and updating a graph, and enhancing cracks to be displayed in the landslide deformation live-action model more visually.
The landslide deformation live-action model can display landslide live-action under a corresponding crack system, can be displayed in three dimensions, and supports functions of visual angle switching, rotation, amplification and the like.
And S04, obtaining physical information, physical parameters and landslide body crack distribution of a target landslide body, and obtaining a corresponding finite element boundary condition when the target landslide body is induced to slide.
And obtaining physical information and physical parameters of the target landslide body. Site surveys and the like may be employed. Obtaining a contour map in the range of the target landslide body through site survey, wherein the contour map comprises a landslide boundary, each deformation area range, a landslide free surface, a main sliding direction and the like which are delineated by taking the contour map as a base map; obtaining a point cloud data map through unmanned aerial vehicle aerial survey; arranging a plurality of core drilling holes along the main sliding surface of the target sliding body and two directions perpendicular to the main sliding surface to form a drilling bar graph; and performing a geotechnical test on the soil sample of the target landslide body to obtain physical parameters of the target landslide body, thereby forming a geotechnical test table.
And acquiring landslide body crack distribution of the target landslide body. The landslide body crack distribution comprises a strong deformation area, a weak deformation area, crack development characteristics of each deformation area and main control crack characteristics from whole to part. And the landslide body crack distribution takes each crack in the target landslide body as a investigation record object, and each crack comprises various characteristics of the crack, including crack positions, crack widths, crack lengths, crack extension directions and the like.
In addition, combining engineering geology experience and computational mechanics knowledge, estimating and recording the corresponding finite element boundary condition when the target landslide body induces sliding from the angle analysis of landslide inducement.
And S05, establishing a finite element geological model for forming a landslide area according to the physical information and physical parameters of the target landslide body obtained in the step S04, screening calculation parameters and establishing a calculation model.
Acquiring elevation data of the ground surface of the landslide area by utilizing the point cloud data graph collected in the step S04; estimating stratum data of the landslide area through borehole histogram line interpolation formed in the step S04; and calibrating the range and the boundary of the landslide body in the elevation data by combining the on-site field survey data.
Obtaining parameters required for numerical simulation of landslide mass, such as cohesion, by using the geotechnical test chart in step S04cShear strengthtAngle of internal frictionfWeight per unitg Etc.
And establishing a finite element geological model for forming the landslide area according to the data. Modeling may be performed using finite element geologic modeling software commonly used in the art, such as GOCAD three-dimensional geologic modeling software. And (3) forming a ground model by importing elevation data, embedding the range and boundary data of the landslide body calibrated by investigation to establish the contour of the landslide body, importing drilling coordinate points and a stratum histogram, and operating an interpolation algorithm to form a finite element geological model of the landslide region.
And performing grid subdivision on the finite element geological model of the landslide area, and converting the subdivision grid information into a grid model for finite element calculation for standby.
The above model is imported into finite element computing software, and boundary conditions are set in the finite element computing software according to the finite element boundary conditions obtained in step S04.
And S06, adjusting and optimizing the calculation model by using an initial calculation result and the landslide body crack distribution, and generating an original crack system according to the adjusted and optimized calculation model.
And (3) loading the working condition of the induced landslide obtained according to landslide mechanism analysis by utilizing the calculation model obtained in the step (S05), expanding initial calculation, inputting the result of the initial calculation into the crack extraction module, and extracting an initial crack system.
Comparing the initial crack system with the landslide body crack distribution obtained in the step S04, calibrating the calculation model by taking the landslide body crack distribution as a standard, and carrying out calculation again based on the newly-debugged calculation model and comparing until the calculation result is basically consistent with the landslide body crack distribution, and ending the step. When the calculation model is rated, the debugging direction can be physical parameters of the model, the structural surface of the model is increased, the mesh subdivision of the key area is encrypted, the boundary condition is adjusted and the like.
Preferably, since the calculation of the model is complicated, the calculation may be performed automatically by machine learning.
And adjusting the optimized calculation model and finally adopting a finite element calculation platform to form the finite element module.
And generating an original crack system by using the adjusted and optimized calculation model.
S07, inputting expected working conditions into the finite element model, obtaining a finite element calculation result cloud picture, and obtaining a crack system under the expected working conditions by using the crack extraction module.
And analyzing the target landslide body, and analyzing various expected and recently largely-occurring working conditions and boundary conditions under the working conditions to form expected working conditions from the angles of future data and possibly generated damage types. The various conditions include, for example, various conditions that may occur with different durations and different intensity rains, manual stacking, reservoir level changes that drop at a rate, and the like.
Inputting the expected working condition into the finite element module and carrying out calculation to form a calculation result comprising a displacement cloud picture, a stress cloud picture and a strain cloud picture, and inputting the calculation result into the crack extraction module to generate a crack system under the expected working condition.
And S08, carrying out digital twin live-action display based on a physical calculation result by utilizing the live-action display module according to the finite element geological model of the landslide area and the crack system under the expected working condition.
Inputting the finite element geologic model of the landslide area generated in the step S05 into the live-action display module constructed in the step S03. Inputting the original crack system obtained in the step S06 into the live-action display module, and calling the family library constructed in the step S01 by the live-action display module in combination with the input crack system to perform standardized display to generate live-action display 1; inputting the crack system under the expected working condition obtained in the step S07 into the live-action display module, and generating the live-action display 2 by the same principle.
Pushing the two live-action displays to a user side to complete a display task.
In order to more clearly express the method and steps of the present invention, the following examples are given to illustrate the method of the present invention in detail.
Examples
In southwest, a certain oversized landslide A exists, and through preliminary evaluation, danger is caused by the fact that the landslide A possibly slides again under the condition of a certain expected working condition, decision departments need to pre-judge landslide development situations under emergency conditions, so that the landslide development situations can be carried out in advance. Through project economic cost performance analysis, the digital twin body for building the landslide by adopting the method is determined, the deformation of the landslide is deduced by utilizing a simulation means, and the real-scene display of the calculated result is developed based on the simulation calculation result so as to present the reference of a decision team.
The steps S01 to S03 are performed according to the above steps, and are not described herein.
And S04, obtaining physical information, physical parameters and landslide body crack distribution of a target landslide body, and obtaining a corresponding finite element boundary condition when the target landslide body is induced to slide.
The basic conditions of the A landslide are established by adopting an on-site investigation mode: the typical reservoir type landslide is positioned on the coast of the reservoir, and the typical traction type landslide is characterized in that the slope of the front edge part of the landslide slides due to sudden drop of the reservoir water level to form a preliminary temporary surface, and the temporary surface creates favorable traction conditions for the ancient landslide to lead the ancient landslide to slide towards the direction of the landslide.
1 part of geophysical prospecting result data is collected, and the geophysical prospecting data shows that the landslide body has a thickness of 7-16m, a rear edge is thinner, a middle bulge cover is thicker, and a lower cover is moderate.
1 part of drilling result data (5 drill holes) is collected, and the drill hole data show that the landslide A stratum mainly comprises three parts: the surface layer is powdery clay and palettes; the mudstone of the Xigeda group is softened by water and is slippery soil; the bottom is quartz amphibole.
And (3) completing site mapping by using an unmanned plane to form 1 part of site digital elevation model (Digital Elevation Model, DEM) data.
Site investigation shows that two groups of cracks are found behind the landslide body, three groups of cracks are found in front of the landslide body, and the main sliding direction of the landslide A and the boundary of the landslide A are marked; and a strong deformation area is found on the west side of the landslide A, a tensioning crack is formed on the rear side of the strong deformation area, and feathered shearing cracks are formed in local areas on two sides of the landslide A.
And determining the initial calculation boundary condition of the landslide by combining the relevant experience of computational mechanics and engineering geology.
And S05, establishing a finite element geological model for forming a landslide area according to the physical information and physical parameters of the target landslide body obtained in the step S04, screening calculation parameters and establishing a calculation model.
The embodiment adopts GOCAD geological modeling software in the oil and gas field. And interpolating to form each stratum lithology interface by using the given 5 drilling layering data, and generating a solid model according to the stratum interfaces. Natural deadweight physical parameters in landslide body geotechnical test, such as cohesive force c, shear strength tau, internal friction angle phi, volume weight r and the like, are obtained through investigation data, and are shown in the following table.
Physical parameters of landslide body in Table example I
And S06, adjusting and optimizing the calculation model by using an initial calculation result and the landslide body crack distribution, and generating an original crack system according to the adjusted and optimized calculation model.
Through analysis, a higher reservoir water level exists in front of the bank slope where the landslide A is located, the reservoir water level has a back pressure effect on the bank slope initially, the whole bank slope is stable, and a vertical water pressure is applied to the front edge of the calculation model established in the step S05. And (3) calculating, wherein the front edge of the bank slope does not slide from the perspective of the displacement cloud picture, and the calculation model in the step S05 accords with the working condition of the step.
The investigation result in the step S04 shows that when the water level of the reservoir suddenly drops, the front edge of the bank slope where the landslide A is located is in a temporary empty state, the local area of the front edge slides under a good temporary empty condition and is accompanied by tension cracks, and the middle part of the front edge is accompanied by bulge. Therefore, the water pressure load is withdrawn at the front edge of the landslide model, gravity is applied, and meanwhile, the volume weight of the soil body after the water level is lowered is adjusted, so that calculation is carried out. The first calculation was found to result in no effect of front deformation stretch-draw. And adjusting the calculation model according to the calculation result. According to empirical analysis, the main reason for the occurrence of the deformation area is that the soil body in the area has low strength, and the stress is concentrated under the load condition to generate larger deformation. Therefore, a structural surface is added directly behind the local area of the calculation model. And obtaining ideal calculation results through several rounds of debugging.
Comparing the calculation result with the cracks calibrated in the step S04, the phenomenon is found to be identical with a crack system found by field investigation, wherein the cracks JFL1 and JFL2 at the two positions of the trailing edge and the cracks JFL1, JFL2 and JFL3 at the front edge 3 exist in the calculation result respectively. The final crack distribution diagram and a list of each object (cracks and deformation elements) are generated, forming the original crack system, as shown in fig. 3. In the figure, 1-landslide boundary; 2-landslide mass; 3-1-crack SBL1;3-2—crack sbl2; 4-1-crack SFL1; 4-2-crack SFL2; 4-3-crack SFL3; 5-deformation zone; 6-flank shearing crack; 7-trailing edge tension fracture; 8, a bump; 9-main slip direction.
S07, inputting expected working conditions into the finite element model, obtaining a finite element calculation result cloud picture, and obtaining a crack system under the expected working conditions by using the crack extraction module.
And analyzing the landslide body A, and analyzing various expected and recently-generated working conditions with a large probability of deduction setting from the angles of future data and possibly generated damage types.
The expected working conditions are established as follows: in the non-water storage period of 9 to 10 months, continuous rainfall of 800mm occurs, and rainfall infiltrates from cracks at the rear, so that the shear strength of the upper Xigeda group stratum is obviously reduced; meanwhile, the reservoir at the bottom is in a non-water storage period, so that the temporary surface at the lower part has no water pressure back pressure effect, and a good temporary condition is created for sliding of the landslide body. The original fracture system may be significantly changed, and a new fracture system may be formed.
And inputting the expected working condition into a finite element module and carrying out calculation to form a calculation result comprising a displacement cloud picture, a stress cloud picture and a strain cloud picture, inputting the calculation result into a crack extraction module, and forming a crack system under the expected working condition through the action of the module, wherein the crack system is shown in figure 4. In the figure, 1-landslide boundary; 2-landslide mass; 3-1-crack SBL1;3-2—crack sbl2; 4-1-crack SFL1; 4-2-crack SFL2; 4-3-crack SFL3; 4-crack SFL4 (addition); 5-deformation zone; 6-flank shearing crack; 7-trailing edge tension fracture; 8, a bump; 9-main slip direction.
And S08, carrying out digital twin live-action display based on a physical calculation result by utilizing the live-action display module according to the finite element geological model of the landslide area and the crack system under the expected working condition.
Referring to fig. 3 and fig. 4, after the expected working condition is input, analysis shows that the change of the landslide a crack system is expanded into the cracks JBL1 and JBL2 at the rear edge, wherein the crack JBL2 is expanded and then communicated with the rear edge tensioning crack of the deformation zone, the change of the crack SFL1 at the front edge is not obvious, the SFL2 and the SFL3 are both expanded and then communicated, a new crack SFL4 is generated at the front edge under the new working condition, and the middle bulge zone is further bulged.
And inputting the crack system into a live-action display module. Inputting a crack system of the original crack system into a live-action display module to generate a live-action display 1. Inputting a crack system under expected working conditions into a live-action display module to generate a live-action display 2. Pushing the two live-action displays to a user side. The user terminal displays a large screen for the municipal disaster emergency center. And finishing the display task.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description of embodiments, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Multiple units or devices of the system may also be implemented by one unit or device in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Claims (8)
1. A geological disaster digital twin live-action display method based on a physical calculation result specifically comprises the following steps:
obtaining physical information and physical parameters of a target landslide body and crack distribution of the target landslide body;
establishing a landslide geological model according to the physical information and physical parameters of the target landslide body; screening calculation parameters and establishing a calculation model;
inputting a specific working condition into the calculation model to obtain a finite element calculation result cloud picture, and obtaining a crack system under the specific working condition by using a crack extraction module;
the crack extraction module is used for extracting the crack space distribution and the attribute parameters of the cracks in the landslide according to the finite element calculation result cloud picture input by the user to form a crack system;
according to the landslide geological model of the target landslide body and the crack system under the specific working condition, a live-action display module is utilized to display digital twin live-action based on a physical calculation result;
the real landslide display module is used for generating a real landslide model according to the input landslide geological model, calling a landslide deformation characteristic family library according to the input crack system under the specific working condition, and developing Boolean operation on the real landslide model to generate a landslide deformation real-scene model.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the landslide deformation characteristic family base is constructed according to landslide categories and the deformation characteristics of landslide.
3. The method of claim 1, wherein the step of determining the position of the substrate comprises,
and aiming at a target landslide body, obtaining a finite element boundary condition corresponding to the sliding of the target landslide body according to physical information and physical parameters of the target landslide body.
4. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the specific working condition is an expected working condition aiming at the target landslide body.
5. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the specific working condition is a working condition which is obtained according to landslide mechanism analysis and induces the landslide of the target landslide body.
6. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the optimization steps of the calculation model are as follows: according to landslide mechanism analysis, working conditions for inducing the landslide of the target landslide body are obtained, initial calculation is conducted through the calculation model, initial calculation results are obtained, the initial calculation results are input into the crack extraction module, an initial crack system is extracted, and the calculation model is adjusted and optimized through comparison of the initial crack system and obtained landslide body crack distribution of the target landslide body.
7. The method of claim 1, wherein the step of determining the position of the substrate comprises,
and training a crack identification model based on machine learning by utilizing an indoor physical test result and a finite element calculation result of the landslide physical model, and constructing a crack extraction module.
8. The method according to claim 1 or 7, wherein,
the construction of the crack extraction module mainly comprises the following steps:
applying various working conditions and constraints to a landslide physical model through an indoor physical test, observing and recording deformation crack distribution of the landslide physical model, forming an internal image of the landslide by CT scanning, and calibrating and recording crack positions, crack types and crack size information in the internal image;
establishing a finite element landslide model with the same scale as the indoor physical test, applying various working conditions and constraints corresponding to the indoor physical test on the finite element landslide model, performing finite element calculation, generating a finite element calculation result, and forming a finite element calculation result cloud picture;
and establishing a crack identification model based on machine learning, and training the crack identification model by using the indoor physical test result and the finite element calculation result to obtain a crack extraction module.
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