A method, apparatus, equipment, and medium for predicting the extent of earthquake-induced landslide disasters.

Abstract

This invention belongs to the technical field of earthquake disaster prediction, and specifically relates to a method, apparatus, equipment, and medium for predicting the range of earthquake landslide disasters. A method for predicting the range of earthquake landslide disasters includes: acquiring the occurrence data of a target rock mass, first material information, and morphological information and second material information of a target sliding bed; determining the energy dissipation of the target rock mass during landslides at different earthquake amplitudes based on the occurrence data, the first material information, and the second material information; determining the dynamic friction weakening coefficient of the target rock mass during landslides at different earthquake amplitudes based on the first material information and the second material information; and determining the ideal sliding range of the target rock mass based on the energy dissipation of the landslides at different earthquake amplitudes and the dynamic friction weakening coefficient. This significantly improves the prediction accuracy of the disaster-causing range of long-distance landslides in high-altitude rock masses, and significantly enhances the physical accuracy and computational precision of the prediction results.

Description

Technical Field

[0001] This invention belongs to the technical field of earthquake disaster prediction, and specifically relates to a method, device, equipment and medium for predicting the extent of earthquake landslide disasters. Background Technology

[0002] Earthquakes often trigger rockfalls, which pose a significant threat to surrounding buildings and residents. Therefore, accurately predicting the impact range of rockfalls can effectively protect the lives and property of residents.

[0003] Current technologies for predicting the extent of landslide disasters typically simplify unstable rock masses into discrete rigid blocks or granular accumulations, neglecting the process by which intact rock masses transform from continuous to discontinuous media (i.e., disintegration and fragmentation) under earthquakes or impacts. This simplification ignores the significant amount of fracture surface energy (energy dissipation term) consumed during the fragmentation process, leading to an overestimation of the residual kinetic energy of the rock mass in energy conservation calculations. Consequently, the predicted extent of the disaster is often overestimated or distorted.

[0004] Furthermore, the slides used in traditional experiments are either stationary or vibrating as a whole, which cannot simulate the "acoustic fluidization" phenomenon caused by the continuous vibration of seismic waves along the sliding path. This phenomenon significantly reduces the friction coefficient between particles, causing the actual disaster area to far exceed the theoretical calculation. Summary of the Invention

[0005] This invention provides a method for predicting the extent of earthquake-induced landslide disasters, comprising: acquiring the occurrence data of a target rock mass, first material information, and morphological information and second material information of a target sliding bed; determining the energy dissipation of the target rock mass during landslides at different earthquake amplitudes based on the occurrence data, first material information, and second material information; determining the dynamic friction weakening coefficient of the target rock mass during landslides at different earthquake amplitudes based on the first material information and second material information; determining the ideal sliding range of the target rock mass based on the energy dissipation of the landslides at different earthquake amplitudes and the dynamic friction weakening coefficient; and predicting the disaster extent of the target rock mass during landslides on the target sliding bed based on the ideal sliding range and the morphological information. This method can accurately calculate the enormous fracture surface energy that is ignored by traditional methods, effectively eliminating the prediction deviation of the disaster extent caused by overestimating residual kinetic energy, and accurately acquiring the difficult-to-measure dynamic friction weakening factor, thereby significantly improving the prediction accuracy of the disaster extent of long-distance landslides in high-altitude rock masses and significantly enhancing the physical accuracy and calculation precision of the prediction results.

[0006] To address the aforementioned technical problems, this application proposes five aspects.

[0007] In a first aspect, this application provides a method for predicting the extent of earthquake landslide disasters, comprising: acquiring the occurrence data of a target rock mass, first material information, and morphological information and second material information of a target sliding bed; determining the energy dissipation of the target rock mass during landslides at different earthquake amplitudes based on the occurrence data, the first material information, and the second material information; determining the dynamic friction weakening coefficient of the target rock mass during landslides at different earthquake amplitudes based on the first material information and the second material information; determining the ideal sliding range of the target rock mass based on the energy dissipation of the landslides at different earthquake amplitudes and the dynamic friction weakening coefficient; and predicting the disaster extent of the target rock mass during landslides on the target sliding bed based on the ideal sliding range and the morphological information.

[0008] In some embodiments, determining the energy dissipation of the target rock mass during landslides at different seismic amplitudes based on the occurrence data, the first material information, and the second material information includes: determining a target seismic amplitude within a preset amplitude range; determining a reference rock mass similar to the target rock mass in a preset database based on the occurrence data; determining material difference information based on the first material information and the second material information; determining a target fractal dimension of the reference rock mass in the database based on the target seismic amplitude and the material difference information; and determining the target energy dissipation of the target rock mass during landslides at the target seismic amplitude based on the target fractal dimension.

[0009] In some embodiments, determining the dynamic friction weakening coefficient of the target rock mass during the collapse and slide process under different seismic amplitudes based on the first material information and the second material information includes: determining the target vibration weakening factor of the reference rock mass in the database based on the target seismic amplitude and the first material information and the second material information; and determining the dynamic friction weakening coefficient of the target rock mass when it collapses and slides under the target seismic amplitude based on the target vibration weakening factor.

[0010] In some embodiments, predicting the disaster range of the target rock mass when it collapses on the target slide bed based on the ideal sliding range and the morphological information includes: determining the equivalent sliding distance of the target slide bed based on the morphological information; and determining the disaster range of the target rock mass when it collapses on the target slide bed based on the equivalent sliding distance and the ideal sliding range.

[0011] In some embodiments, the method further includes: constructing a rock mass model conforming to a target attitude and a first target material; constructing a slide bed model conforming to a second target material and capable of simulating traveling wave effects; applying vibration acceleration to each section of the slide bed model according to a preset seismic amplitude; collecting the landslide deposits of the rock mass model and measuring the sliding distance of the landslide deposits after the rock mass model passes through the vibrating slide bed model; determining the fractal dimension of the rock mass model based on the landslide deposits; determining the vibration weakening factor of the rock mass model when it lands on the slide bed model based on the sliding distance and the fractal dimension; constructing the database based on the target attitude, the first target material, the second target material, the preset seismic amplitude, the fractal dimension, and the vibration weakening factor; wherein the database stores vibration weakening factors and fractal dimensions under different attitudes, different first target materials, different second target materials, and different seismic amplitudes.

[0012] In some embodiments, the slide model includes: multiple vibration unit plates, exciters, and a control unit; the multiple vibration unit plates are arranged laterally, and adjacent vibration unit plates are connected by a flexible material, so that each vibration unit plate represents the vibration of a region; an exciter is installed under each vibration unit plate, and the exciter is used to make the vibration unit plate vibrate; the control unit is connected to the exciter and is used to determine the vibration phase and vibration amplitude generated by each exciter according to a preset seismic amplitude, and control the start time and vibration level of each exciter according to the vibration phase and vibration amplitude.

[0013] In some embodiments, determining the ideal sliding range of the target rock mass based on the fracturing energy dissipation and the dynamic friction weakening coefficient under different seismic amplitudes is achieved by the following formula: in, and Let be the total mass of the rock mass and the initial centroid height of the rock mass, respectively. Energy is consumed for crushing. The sliding distance of the rock mass is given by the formula. The ideal sliding distance of the rock mass is the solution required in this application. The dynamic friction weakening coefficient, Let the angle between the target slide and the horizontal plane be denoted as . This is the acceleration due to gravity.

[0014] Secondly, this application proposes a device for predicting the extent of earthquake landslide disasters, comprising: a first acquisition module for acquiring the occurrence data, first material information, and morphological information and second material information of a target rock mass; a first determination module for determining the energy dissipation of the target rock mass during landslides at different earthquake amplitudes based on the occurrence data, the first material information, and the second material information; a second determination module for determining the dynamic friction weakening coefficient of the target rock mass during landslides at different earthquake amplitudes based on the first material information and the second material information; a third determination module for determining the ideal sliding range of the target rock mass based on the energy dissipation of the landslides at different earthquake amplitudes and the dynamic friction weakening coefficient; and a first execution module for predicting the disaster extent of the target rock mass during landslides on the target sliding bed based on the ideal sliding range and the morphological information.

[0015] Thirdly, this application proposes a computer electronic production apparatus, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the methods described in the first aspect.

[0016] Fourthly, this application proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the first aspects.

[0017] Fifthly, this application proposes a computer program product comprising a computer program that, when executed by a processor, implements the steps of the method described in any of the first aspects.

[0018] This invention provides a method for predicting the extent of earthquake-induced landslide disasters, comprising: acquiring the occurrence data of a target rock mass, first material information, and morphological information and second material information of a target sliding bed; determining the energy dissipation of the target rock mass during landslides at different earthquake amplitudes based on the occurrence data, first material information, and second material information; determining the dynamic friction weakening coefficient of the target rock mass during landslides at different earthquake amplitudes based on the first material information and second material information; determining the ideal sliding range of the target rock mass based on the energy dissipation of the landslides at different earthquake amplitudes and the dynamic friction weakening coefficient; and predicting the disaster extent of the target rock mass during landslides on the target sliding bed based on the ideal sliding range and the morphological information. This method can accurately calculate the enormous fracture surface energy that is ignored by traditional methods, effectively eliminating the prediction deviation of the disaster extent caused by overestimating residual kinetic energy, and accurately acquiring the difficult-to-measure dynamic friction weakening factor, thereby significantly improving the prediction accuracy of the disaster extent of long-distance landslides in high-altitude rock masses and significantly enhancing the physical accuracy and calculation precision of the prediction results. Attached Figure Description

[0019] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0020] Figure 1 The main flowchart of a method for predicting the extent of earthquake landslide disasters provided in this application embodiment; Figure 2 A main flowchart of a database formation method provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a slide bed model provided in an embodiment of this application; Figure 4 A main structural block diagram of an earthquake landslide disaster range prediction device provided in an embodiment of this application; Figure 5 This is a structural block diagram of a computer electronic production equipment provided in an embodiment of this application. Detailed Implementation

[0021] Earthquakes often trigger rockfalls, which pose a significant threat to surrounding buildings and residents. Therefore, accurately predicting the impact range of rockfalls can effectively protect the lives and property of residents.

[0022] Current technologies for predicting the extent of landslide disasters typically simplify unstable rock masses into discrete rigid blocks or granular accumulations, neglecting the process by which intact rock masses transform from continuous to discontinuous media (i.e., disintegration and fragmentation) under earthquakes or impacts. This simplification ignores the significant amount of fracture surface energy (energy dissipation term) consumed during the fragmentation process, leading to an overestimation of the residual kinetic energy of the rock mass in energy conservation calculations. Consequently, the predicted extent of the disaster is often overestimated or distorted.

[0023] Furthermore, the slides used in traditional experiments are either stationary or vibrating as a whole, which cannot simulate the "acoustic fluidization" phenomenon caused by the continuous vibration of seismic waves along the sliding path. This phenomenon significantly reduces the friction coefficient between particles, causing the actual disaster area to far exceed the theoretical calculation.

[0024] To address the aforementioned technical problems, this invention proposes a method for predicting the extent of earthquake landslide disasters. The implementation details of the disaster extent prediction method in this embodiment are described below. The following implementation details are provided for ease of understanding and are not essential for implementing this solution.

[0025] Example 1: like Figure 1As shown, this application provides a method for predicting the extent of earthquake-induced landslide disasters. This method is applicable to electronic production equipment, which can be a server, mobile terminal, computer, cloud platform, etc. The data processing functionality provided in this application embodiment can be implemented by the processor of the electronic production equipment calling program code, wherein the program code can be stored in a computer storage medium. The method for predicting the extent of the disaster includes: Step S1: Obtain the occurrence data of the target rock mass, the first material information, the morphological information of the target slide, and the second material information.

[0026] Because rock masses fracture during landslides, and the degree of fracture depends on the rock mass's occurrence, material composition, and the material of the slide bed it contacts, determining the hazard extent of a landslide requires considering energy consumption during fragmentation. Therefore, this application requires obtaining the occurrence data and material information of the target rock mass, as well as the material information of the target slide bed. To distinguish between the material information of the rock mass and the slide bed, this application refers to the rock mass's material information as "first material information" and the slide bed's material information as "second material information." The direction of a rock mass landslide is not fixed; there can be multiple landslide directions. Therefore, this application refers to the path traversed by one selected landslide direction as the target slide bed. Furthermore, the hazard extent of the landslide is also affected by the structure of the slide bed; therefore, this application also obtains the morphological information of the target slide bed.

[0027] In this application, the attitude data of the target rock mass and the morphological information of the target slide bed can be determined by unmanned aerial vehicle photogrammetry or 3D laser scanning. The attitude data includes dip direction, dip angle, spacing, and trace length. The material information of the target rock mass and the target slide bed can be obtained through sampling.

[0028] Step S2: Determine the energy consumption of the target rock mass during collapse and landslide at different seismic amplitudes based on the occurrence data, the first material information, and the second material information.

[0029] This application argues that the rock mass collapse process is a process of converting gravitational potential energy into energy dissipation from fracturing, frictional heat energy, and residual kinetic energy. Therefore, the energy conservation equation for the rock mass collapse process in this application is: That is: initial total potential energy = energy consumed by breaking + frictional heat energy + residual kinetic energy.

[0030] Since the movement of the rock mass ceases when the landslide ends, the residual kinetic energy is zero. When the residual kinetic energy is zero, the sliding distance of the rock can be considered the hazard range in this application. Furthermore, after the landslide, the fragmented rock fragments concentrate in one area. Because the surface of the broken rock is extremely complex and difficult to measure directly, this invention utilizes fractal dimension. This macroscopic statistical parameter transforms the microscopic crack surface area into a macroscopically measurable particle size distribution parameter, thereby enabling the measurement of crushing energy consumption. The precise quantification solves the problem that traditional methods cannot quantitatively calculate energy loss during earthquake landslides.

[0031] Therefore, the formula for calculating the energy consumption of crushing in this application is as follows: in: The specific surface energy of the material (J / m²) The total mass (kg) of the landslide mass; Density of rock mass material (kg / m³); The characteristic particle size (m) of the largest fragment after crushing; The characteristic particle size (m) of the smallest fragment after crushing; Let fractal dimension be the fractal dimension of the rock mass. Although the energy consumption of the rock mass can be calculated using the above formula, the fractal dimension, the size of the largest and smallest fragments are affected by the rock mass's orientation and the earthquake amplitude.

[0032] Therefore, in some embodiments, step S2, "determining the energy dissipation of the target rock mass during collapse at different seismic amplitudes based on the occurrence data, the first material information, and the second material information," includes: Step S21: Determine the target earthquake amplitude within the preset amplitude range.

[0033] Since the purpose of this application is to make predictions, and the earthquake amplitude that the rock mass will experience during the actual collapse is unknown, this application needs to calculate the fracturing energy consumption under each possible earthquake amplitude, that is, the fracturing energy consumption under each earthquake amplitude within a preset amplitude range. Therefore, when calculating the fracturing energy consumption under one earthquake amplitude, the target earthquake amplitude needs to be determined first.

[0034] Step S22: Determine a reference rock mass similar to the target rock mass in a preset database based on the occurrence data.

[0035] In this application, a database was established using simulation methods. This database stores rock mass models with various attitudes under different seismic amplitudes under simulated conditions, as well as fractal dimensions, maximum and minimum grain sizes, based on both primary and secondary material information. Therefore, to calculate the energy dissipation of the target rock mass during fracture under the target seismic amplitude, it is necessary to first identify a rock mass model with similar or identical attitude characteristics to the target rock mass in the database; this is the reference rock mass in this application.

[0036] Step S23: Determine the material difference information based on the first material information and the second material information.

[0037] The fragmentation of the rock mass during the collapse and slide process is also affected by the difference between the rock mass hardness and the slide bed hardness. Therefore, this application also needs to determine the material difference information based on the first material information and the second material information, that is, to determine the material difference between the rock mass and the slide bed.

[0038] Step S24: Determine the target fractal dimension of the reference rock mass in the database based on the target seismic amplitude and the material difference information.

[0039] After identifying the reference rock mass, its fractal dimension, maximum grain size, and minimum grain size can be found in the database under the target earthquake amplitude and material differences. Since the reference and target rock masses share similar or identical occurrence characteristics, the fractal dimension of the reference rock mass can be used as the fractal dimension of the target rock mass. The maximum and minimum grain sizes of the reference rock mass are then proportionally scaled to determine the maximum and minimum grain sizes of the target rock mass's fragments after the collapse.

[0040] Step S25: Determine the target fractal energy consumption when the target rock mass collapses and slides under the target seismic amplitude based on the target fractal dimension.

[0041] After obtaining the fractal dimension, maximum grain size, and minimum grain size of the target rock mass from the database, the energy consumption of the target rock mass under the target earthquake amplitude can be calculated according to the above formula for calculating the energy consumption of the rock mass.

[0042] Step S3: Determine the dynamic friction weakening coefficient of the target rock mass during the collapse process under different seismic amplitudes based on the first material information and the second material information.

[0043] During the rock mass collapse and slide, due to the influence of earthquake, the rock mass or rock block and the slide bed are not only subjected to friction under gravity. Part of the influence of gravity is canceled out by the vibration generated by the earthquake. Therefore, the actual frictional heat energy of the rock mass during the collapse and slide is greater than the frictional heat energy converted from the potential energy of the rock mass. However, since the energy transferred to the rock mass by the earthquake will eventually be broken and consumed by friction, the dynamic friction weakening coefficient of the rock mass needs to be calculated when calculating the frictional heat energy in this application.

[0044] In some embodiments, step S3, "determining the dynamic friction weakening coefficient of the target rock mass during the collapse process under different seismic amplitudes based on the first material information and the second material information," includes: Step S31: Determine the target vibration weakening factor of the reference rock mass in the database based on the target earthquake amplitude, the first material information, and the second material information.

[0045] The dynamic friction weakening coefficient in this application is used to characterize the frictional force of rock mass under the combined effects of seismic vibration and gravity. The formula for calculating the dynamic friction weakening coefficient in this application is as follows: In the formula, The dynamic friction weakening coefficient, The static reference friction coefficient, As a vibration weakening factor, It is the acceleration due to gravity. For the target earthquake amplitude, Let be a step function related to the gliding speed, when the speed The value is 1 if the earthquake occurs, and 0 otherwise. The vibration weakening factor in the formula refers to the impact of earthquakes on rock masses. Rock masses of different materials are affected by earthquake amplitudes differently. Moreover, the slide bed transmits earthquake amplitudes, rather than generating them. Therefore, slide beds of different materials are also affected by earthquake amplitudes differently.

[0046] The database in this application also includes vibration weakening factors for slides of different materials and rock masses of different materials under the target earthquake amplitude. Therefore, to calculate the dynamic friction weakening coefficient in this application, it is necessary to determine the vibration weakening factor in the database based on the target earthquake amplitude, the material of the rock mass, and the material of the slide.

[0047] Step S32: Determine the dynamic friction weakening coefficient of the target rock mass when it collapses under the target earthquake amplitude based on the target vibration weakening factor.

[0048] Once the vibration weakening factor is determined, the dynamic friction weakening factor can be calculated using the above formula based on the vibration weakening factor and the static friction coefficients determined by the rock mass material and the slide material.

[0049] Step S4: Determine the ideal sliding range of the target rock mass based on the energy consumption of fracturing and the dynamic friction weakening coefficient under different earthquake amplitudes.

[0050] Since this application requires predicting the disaster area, and the disaster area is related to the sliding distance of the rock mass during collapse, and the sliding distance of the rock mass is the moment when the kinetic energy is zero, the energy conservation equation at this time can be expressed as: Since frictional heat energy is an integral process, the energy balance equation in integral form in this application is as follows: Expanding the dynamic friction term After transformation, the final solution is obtained: In the formula, and Let represent the total mass of the rock mass and the initial centroid height of the rock mass, respectively. Of course, in actual calculations... It can be reduced, so accurate rock mass measurements are not required in this application. Energy is consumed for crushing. The sliding distance of the rock mass. Given the static friction coefficient, For the target earthquake amplitude, A step function related to the gliding speed. This is the vibration weakening factor. In the formula... This application seeks to determine the rock mass sliding distance. However, since the sliding distance in this formula refers to the sliding distance under the contact condition between the rock mass and the sliding bed, the actual contact condition between the rock mass and the sliding bed in landslides is affected by the morphology and structure of the sliding bed, and is not an ideal sliding state throughout the entire process. Therefore, this application will... This is called the ideal sliding distance. The ideal sliding distance under different target earthquake amplitudes can form the ideal sliding range.

[0051] Step S5: Predict the disaster range when the target rock mass collapses on the target sliding bed based on the ideal sliding range and the morphological information.

[0052] In some embodiments, step S5, "predicting the disaster range of the target rock mass when it collapses on the target sliding bed based on the ideal sliding range and the morphological information," includes: Step S51: Determine the equivalent sliding distance of the target slide based on the morphological information.

[0053] Step S52: Determine the disaster range when the target rock mass collapses through the target slide bed based on the equivalent sliding distance and the ideal sliding range.

[0054] Because rock masses cannot maintain continuous contact with the slide surface during a rockfall as ideally would, and because actual slide surfaces are not continuous and stable, this application requires determining the equivalent sliding distance of the rock mass on the target slide surface based on its morphological information. In other words, when a rock mass collapses on the target slide surface, its actual sliding distance can be represented by an equivalent sliding distance. Once the equivalent sliding distance is determined, the disaster area can be determined based on the equivalent sliding distance and the ideal sliding range.

[0055] This invention provides a method for predicting the extent of earthquake-induced landslide disasters, comprising: acquiring the occurrence data of a target rock mass, first material information, and morphological information and second material information of a target sliding bed; determining the energy dissipation of the target rock mass during landslides at different earthquake amplitudes based on the occurrence data, first material information, and second material information; determining the dynamic friction weakening coefficient of the target rock mass during landslides at different earthquake amplitudes based on the first material information and second material information; determining the ideal sliding range of the target rock mass based on the energy dissipation of the landslides at different earthquake amplitudes and the dynamic friction weakening coefficient; and predicting the disaster extent of the target rock mass during landslides on the target sliding bed based on the ideal sliding range and the morphological information. This method can accurately calculate the enormous fracture surface energy that is ignored by traditional methods, effectively eliminating the prediction deviation of the disaster extent caused by overestimating residual kinetic energy, and accurately acquiring the difficult-to-measure dynamic friction weakening factor, thereby significantly improving the prediction accuracy of the disaster extent of long-distance landslides in high-altitude rock masses and significantly enhancing the physical accuracy and calculation precision of the prediction results.

[0056] Example 2: In this application, when predicting the extent of a disaster, a pre-established database is required, and this database is formed through simulation. Therefore, in this embodiment, as... Figure 2 As shown, this application will further describe the method for forming the database.

[0057] Step S61: Construct a rock mass model that conforms to the target occurrence and the first target material.

[0058] The structural surface attitude data of the real unstable rock mass, i.e. the target attitude, including dip, dip angle, spacing and trace length, are obtained by using UAV photogrammetry or 3D laser scanning. A 3D fracture network digital model is generated by Monte Carlo simulation. Based on a three-dimensional fracture network digital model, fracture surface entities are extracted, and an internal fracture skeleton with non-through rock bridge characteristics is 3D printed using photosensitive resin material. A fractured skeleton is placed in a mold, and a brittle similar material is cast. This brittle similar material is a mixture of aggregate, weighting agent, binder, and modifier, with the following mass percentage ratio: 40%-48% quartz sand, 35%-45% barite powder, 7%-25% high-strength gypsum powder, and the remainder being water and trace amounts of glycerol. By adjusting the ratio of gypsum to aggregate, the compressive strength of the model material can be adjusted between 0.2 MPa and 1.5 MPa, and the elastic modulus between 100 MPa and 500 MPa, meeting the requirements of dynamic similarity tests with a geometric scale of 1:100 to 1:200. After initial setting, the material is cured under constant temperature and humidity to form a simulated rock mass model with a specific ratio of compressive strength (0.2-1.5 MPa) to elastic modulus.

[0059] The model remains intact under static conditions, but under seismic inertial forces or impacts, stress will preferentially concentrate at the tip of the skeleton, causing the model to crack, propagate, and disintegrate along a preset path, thus accurately measuring the energy dissipation during the transition of the rock mass model from continuous to discontinuous medium.

[0060] Step S62: Construct a slide model that conforms to the second target material and can simulate the traveling wave effect.

[0061] In some embodiments, such as Figure 3 As shown, the slide bed model includes: multiple vibration unit plates 100, exciter 200, and control unit.

[0062] Multiple vibration unit plates 100 are arranged laterally, and adjacent vibration unit plates 100 are connected by a flexible material, so that each vibration unit plate 100 represents the vibration of a region. An exciter 200 is installed below each vibration unit plate 100, and the exciter 200 is used to cause the vibration unit plate 100 to vibrate. The control unit is connected to the exciter 200 and is used to determine the vibration phase and vibration amplitude generated by each exciter 200 according to a preset seismic amplitude, and to control the start-up time and vibration level of each exciter 200 according to the vibration phase and vibration amplitude.

[0063] The sliding path is designed as a segmented independent vibration unit, meaning the segmented traveling wave excitation loading step employs a multi-stage independent vibration slide system. The slide is longitudinally divided along the slope into N independent vibration unit plates 100 (N1, N2, ..., Ns), with adjacent unit plates sealed together using a flexible polymer film. Each vibration unit plate 100 has an independent exciter 200 connected to its bottom, all controlled by a central controller. The central controller controls the vibration frequency of each exciter 200. and phase delay The model loads seismic waveforms including near-field pulsed seismic waves and far-field long-duration seismic waves, and the vibration direction of each element is perpendicular to the slope surface to simulate the bottom surface projection effect caused by seismic P waves or SV waves.

[0064] Step S63: Apply vibration acceleration to each section of the slide model according to the preset earthquake amplitude.

[0065] Step S64: After the rock mass model passes through the vibrating slide model, collect the landslide deposits of the rock mass model and measure the sliding distance of the landslide deposits.

[0066] Step S65: Determine the fractal dimension of the rock mass model based on the landslide deposits.

[0067] After the slide bar model and rock mass model are completed, during the simulation, the vibrators on the slide bar are activated first, causing each vibration unit plate on the slide bar to vibrate with a preset phase and amplitude. At this point, the rock mass model is controlled to become unstable and slide down, disintegrating and fracturing upon impact with the slope (first energy distribution). The fragmented rock mass then moves at high speed on the continuously vibrating slide (second energy distribution). The final accumulation morphology, particle size distribution, and maximum sliding distance are recorded. .

[0068] After the experiment, all landslide deposits were collected and sieved using standard square-hole gravel sieves and fine aggregate sieves to determine the full gradation. The cumulative mass distribution of each particle size range was statistically analyzed. The fractal dimension of the fractured rock mass was calculated using the mass-particle size distribution relationship. Specifically: Establish and coordinate system (where (where the particle size is 0.5), perform linear fitting, and extract the slope. Calculate the fractal dimension ,in, Let r be the cumulative mass of rock debris with a particle size of r. For total mass.

[0069] Step S66: Determine the vibration weakening factor of the rock mass model when it collapses on the sliding bed model based on the sliding distance and the fractal dimension.

[0070] At this point, the integral form of the energy balance equation expands to include the dynamic friction term. Then, the calculation formula is as follows: Since the furthest gliding distance can be measured during the simulation, it is included in the above formula. and are known quantities, namely the total mass of the model and the initial centroid height, respectively. The total crushing energy consumption is calculated using fractal formulas after screening crushed stone with known quantities through experiments. It is a known quantity, obtained by measuring the actual position where the rock mass stops during a physical experiment. Given a known quantity, the static friction coefficient. Given a known quantity, the known traveling wave excitation acceleration applied to the slide. For an unknown target quantity, the vibration weakening factor.

[0071] Therefore, in order to complete the prediction in the actual prediction process, it is necessary to calculate the vibration weakening factor under the first target material and the second target material when establishing the database. Substitute these values ​​into the above formula to invert and calculate the vibration weakening factor.

[0072] Step S67: Construct the database based on the target orientation, the first target material and the second target material, the preset seismic amplitude, the fractal dimension and the vibration weakening factor.

[0073] After calculating the vibration weakening factor, the vibration weakening factor, maximum grain size, minimum grain size, and fractal dimension are correlated with the rock mass model's attitude characteristics, material, slide material, and the earthquake amplitude used in the experiment, and recorded in the database to form the database used in Example 1.

[0074] In this application, during the database construction process, 3D printing technology is used to construct a non-penetrating fracture skeleton, which is then filled with brittle, similar materials to simulate the dynamic fracturing characteristics of earthquakes and to simulate the energy consumption of rock mass fracturing under real earthquake conditions. The slideway consists of multiple independent vibration units, and the traveling wave effect of seismic waves propagating along the slope is simulated through phase delay control. By controlling the vibration phase difference of each unit, the "traveling wave" of seismic waves propagating from the mountaintop to the foot of the mountain is simulated, ensuring that the debris flow is in a "bumpy suspension" state throughout the slide. An energy balance formula between the fracturing energy consumption E_frac and the dynamic friction coefficient μ_vib is introduced to correct the prediction error of the disaster-causing range.

[0075] During the database construction process, a rock mass model with an embedded 3D-printed non-penetrating fracture skeleton was constructed to realistically simulate the process of unstable rock mass disintegrating from continuous medium into loose debris flow under earthquake action. Combined with a fractal theory-based quantitative algorithm for fracture energy consumption, the huge fracture surface energy (resistance term) that was ignored by traditional methods was accurately calculated, effectively eliminating the prediction deviation of the disaster range caused by overestimation of residual kinetic energy, and significantly improving the physical realism and calculation accuracy of the prediction model.

[0076] The established segmented phase-excited sliding bed system can simulate the traveling wave effect of seismic waves propagating along the slope, realistically reproducing the acoustic fluidization and drag reduction phenomenon induced by continuous vibration at the bottom of the sliding path. The "physical calibration-formula inversion" verification model established based on the principle of energy conservation can accurately obtain the difficult-to-measure dynamic friction weakening factor, thereby significantly improving the prediction accuracy of the disaster-causing range of long-distance landslides in high-altitude rock masses.

[0077] Example 3: Based on the foregoing embodiments, this application provides a device for predicting the extent of earthquake landslide disasters. The various modules and units included in the device can be implemented by a processor in a computer device; of course, they can also be implemented by specific logic circuits. In the implementation process, the processor can be a central processing unit (CPU), a microprocessor unit (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0078] like Figure 4 As shown, a device for predicting the extent of earthquake landslide disasters includes: a first acquisition module 1, a first determination module 2, a second determination module 3, a third determination module 4, and a first execution module 5.

[0079] The first acquisition module 1 is used to acquire the occurrence data of the target rock mass, the first material information, and the morphological information and second material information of the target slide bed. The first determination module 2 is used to determine the energy dissipation of the target rock mass during landslides at different seismic amplitudes based on the occurrence data, the first material information, and the second material information. The second determination module 3 is used to determine the dynamic friction weakening coefficient of the target rock mass during landslides at different seismic amplitudes based on the first material information and the second material information. The third determination module 4 is used to determine the ideal sliding range of the target rock mass based on the energy dissipation of the landslides at different seismic amplitudes and the dynamic friction weakening coefficient. The first execution module 5 is used to predict the disaster range of the target rock mass during landslides on the target slide bed based on the ideal sliding range and the morphological information.

[0080] The modules in the aforementioned earthquake landslide disaster range prediction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the device in hardware form or independently of it, or stored in the memory of the processing device in software form, so that the processor can call and execute the operations corresponding to each module. It should be noted that the module division in this embodiment is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods.

[0081] Example 4: Thirdly, this application provides a computer electronic production device, such as... Figure 5 As shown, it includes: at least one processor 901; and a memory 902 communicatively connected to the at least one processor 901; wherein the memory 902 stores instructions executable by the at least one processor 901, the instructions being executed by the at least one processor 901 to enable the at least one processor 901 to execute a method for predicting the extent of earthquake landslide disasters in the above embodiments.

[0082] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0083] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0084] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0085] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0086] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0087] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0088] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0089] Example 5: Fourthly, this application proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the first aspects.

[0090] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0091] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0092] Example 6: Fifthly, this application proposes a computer program product comprising a computer program / instructions that, when executed by a processor, implement the steps of the method described in any of the first aspects.

[0093] The data query system in this application is capable of communicating with various existing systems. Moreover, the data query system has preset data processing rules for the data in each system, which are used to convert the raw data queried from each system into data in a unified format and display it to the user. The system in this application has multiple communication protocols, which can meet the needs of communicating with multiple systems.

[0094] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0095] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0096] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0097] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0098] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0099] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a controller to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0100] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein.

[0101] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.

Claims

1. A method for predicting the extent of earthquake-induced landslide disasters, characterized in that, include: Acquire the occurrence data, primary material information, morphological information, and secondary material information of the target rock mass and the target slide. Based on the occurrence data, the first material information, and the second material information, determine the energy consumption of the target rock mass during collapse and slide at different seismic amplitudes; The step of determining the energy dissipation of the target rock mass during collapse and landslide at different seismic amplitudes based on the occurrence data, the first material information, and the second material information includes: Determine the target earthquake amplitude within the preset amplitude range; Based on the occurrence data, a reference rock mass similar to the target rock mass is determined in a preset database; Material difference information is determined based on the first material information and the second material information; The target fractal dimension of the reference rock mass is determined in the database based on the target seismic amplitude and the material difference information. Determine the target fractal dimension to determine the target energy consumption of rock mass when it collapses and slides under the target seismic amplitude; The method further includes: Construct a rock mass model that conforms to the target occurrence and the primary target material; Construct a slide bed model that conforms to the second target material and can simulate the traveling wave effect; The vibration acceleration of each section of the slide model is given according to the preset earthquake amplitude; After the rock mass model passes through the vibrating slide model, the landslide deposits of the rock mass model are collected, and the sliding distance of the landslide deposits is measured. The fractal dimension of the rock mass model is determined based on the landslide deposits. The vibration weakening factor of the rock mass model when it collapses on the sliding bed model is determined based on the sliding distance and the fractal dimension. The database is constructed based on the target orientation, the first target material and the second target material, the preset seismic amplitude, the fractal dimension and the vibration weakening factor; The database contains vibration weakening factors and fractal dimensions for different attitudes, different first target materials, different second target materials, and different earthquake amplitudes. The dynamic friction weakening coefficient of the target rock mass during the collapse and slide process under different seismic amplitudes is determined based on the first material information and the second material information. The ideal sliding range of the target rock mass is determined based on the energy consumption of fracturing and the dynamic friction weakening coefficient under different earthquake amplitudes. Based on the ideal sliding range and the morphological information, predict the disaster range when the target rock mass collapses and slides on the target sliding bed.

2. The method according to claim 1, characterized in that, The step of determining the dynamic friction weakening coefficient of the target rock mass during landslides under different seismic amplitudes based on the first material information and the second material information includes: Based on the target seismic amplitude and the first and second material information, the target vibration weakening factor of the reference rock mass is determined in the database. The dynamic friction weakening coefficient of the target rock mass when it collapses under the target earthquake amplitude is determined based on the target vibration weakening factor.

3. The method according to claim 1, characterized in that, The method of predicting the disaster range of the target rock mass when it collapses on the target sliding bed based on the ideal sliding range and the morphological information includes: The equivalent sliding distance of the target slide is determined based on the morphological information; The disaster range when the target rock mass collapses through the target slideway is determined based on the equivalent sliding distance and the ideal sliding range.

4. The method according to claim 1, characterized in that, The slide model includes: multiple vibration unit plates, exciter, and control unit; Multiple vibration unit plates are arranged horizontally, and adjacent vibration unit plates are connected by a flexible material, so that each vibration unit plate represents the vibration of a region. Each of the vibration unit plates is equipped with an exciter underneath, which is used to cause the vibration unit plate to vibrate. The control unit is connected to the exciter and is used to determine the vibration phase and vibration amplitude generated by each exciter according to the preset earthquake amplitude, and to control the start-up time and vibration level of each exciter according to the vibration phase and vibration amplitude.

5. The method according to claim 1, characterized in that, The ideal sliding range of the target rock mass is determined based on the fracturing energy consumption and the dynamic friction weakening coefficient under different seismic amplitudes, by the following formula: in, and Let be the total mass of the rock mass and the initial centroid height of the rock mass, respectively. Energy is consumed for crushing. The sliding distance of the rock mass is given by the formula. To find the ideal sliding distance of the rock mass, the ideal sliding distance under different target seismic amplitudes can form the ideal sliding range. The dynamic friction weakening coefficient, Let the angle between the target slide and the horizontal plane be denoted as . This is the acceleration due to gravity.

6. A device for predicting the extent of earthquake landslide disasters, characterized in that, include: The first acquisition module is used to acquire the occurrence data of the target rock mass, the first material information, the morphological information of the target slide, and the second material information. The first determining module is used to determine the energy consumption of the target rock mass when it collapses and slides under different seismic amplitudes, based on the occurrence data, the first material information and the second material information. The first determining module is also used for: Determine the target earthquake amplitude within the preset amplitude range; Based on the occurrence data, a reference rock mass similar to the target rock mass is determined in a preset database; Material difference information is determined based on the first material information and the second material information; The target fractal dimension of the reference rock mass is determined in the database based on the target seismic amplitude and the material difference information. Determine the target fractal dimension to determine the target energy consumption of rock mass when it collapses and slides under the target seismic amplitude; Also used for: Construct a rock mass model that conforms to the target occurrence and the primary target material; Construct a slide bed model that conforms to the second target material and can simulate the traveling wave effect; The vibration acceleration of each section of the slide model is given according to the preset earthquake amplitude; After the rock mass model passes through the vibrating slide model, the landslide deposits of the rock mass model are collected, and the sliding distance of the landslide deposits is measured. The fractal dimension of the rock mass model is determined based on the landslide deposits. The vibration weakening factor of the rock mass model when it collapses on the sliding bed model is determined based on the sliding distance and the fractal dimension. The database is constructed based on the target orientation, the first target material and the second target material, the preset seismic amplitude, the fractal dimension and the vibration weakening factor; The database contains vibration weakening factors and fractal dimensions for different attitudes, different first target materials, different second target materials, and different earthquake amplitudes. The second determining module is used to determine the dynamic friction weakening coefficient of the target rock mass during the collapse and slide process under different seismic amplitudes based on the first material information and the second material information. The third determining module is used to determine the ideal sliding range of the target rock mass based on the energy consumption of fracturing and the dynamic friction weakening coefficient under different earthquake amplitudes. The first execution module is used to predict the disaster range when the target rock mass collapses and slides on the target sliding bed based on the ideal sliding range and the morphological information.

7. A computer electronic production equipment, characterized in that, It includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 5.

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