Single landslide risk quantitative evaluation method and system

Abstract

The invention discloses a single landslide risk quantitative evaluation method and system. The method comprises the following steps: acquiring landslide topographic files and landslide rock-soil body physical parameters; the method comprises the following steps: simulating a motion intensity field in a landslide evolution process through MassFlow software to obtain the maximum motion intensity; establishing a multi-level multi-dimensional vulnerability evaluation system, and extracting disaster-bearing body index data in the landslide risk area according to the vulnerability evaluation system; according to the disaster-bearing body index data, carrying out quantitative evaluation on the vulnerability of the disaster-bearing body based on an AHP-entropy weight method coupling model to obtain vulnerability values of different disaster-bearing bodies in a landslide motion range; according to the maximum movement intensity and the vulnerability value of the disaster-bearing body, the landslide risk is quantitatively calculated based on the spatial relation between the risk and the vulnerability, and the risk partition conditions of different disaster-bearing bodies under the landslide starting damage threat are obtained. According to the method, the popularization applicability and the calculation efficiency of the quantitative evaluation method for the risk under the single landslide scale are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of landslide risk assessment, and in particular to a method and system for quantitatively assessing the risk of a single landslide. Background Art

[0002] Landslide movement evolution and risk prediction are important non-engineering measures for disaster prevention and mitigation, and are widely used in the field of landslide risk assessment. The current consensus in related fields, both domestically and internationally, is that landslide risk is primarily composed of the combined effects of landslide hazard and vulnerability of the hazard-bearing structure. In other words, the greater the landslide hazard and the weaker the hazard-bearing structure's resilience, the greater the landslide risk. Assessment scales can be divided into regional landslide risk assessment and single-unit landslide risk assessment. In recent years, many domestic and international studies have proposed relatively systematic regional landslide hazard risk assessment processes and methods, resulting in numerous research results and practical engineering applications. However, methods for quantitatively assessing the risk of single-unit landslides are relatively limited, and there is a lack of methods and examples for quantitative risk assessment of single-unit landslides. The frequent catastrophic landslides across my country in recent years have demonstrated the urgency and importance of establishing rapid risk assessment methods for single-unit landslides. Rapid and effective quantitative landslide risk assessment methods can significantly reduce the risk of casualties and economic losses.

[0003] In practical engineering applications, qualitative methods such as expert scoring are often used to assess the overall hazard of individual landslides according to relevant specifications. This assessment is then combined with a qualitative assessment of the hazard-bearing structure to ultimately produce an overall landslide risk assessment. This method is rapid and widely applicable, making it widely used in disaster prevention and mitigation. However, it also suffers from limitations such as low assessment accuracy, strong subjectivity, and a one-sided nature. Existing solutions to these problems often quantitatively calculate landslide hazard (the spatiotemporal probability of instability) and hazard-bearing structure vulnerability through methods such as mathematical statistics, numerical simulation, and physical simulation experiments. These approaches describe and restore the kinematic failure process of individual landslides in detail through various methods, but also significantly increase the data quality requirements and computational complexity for landslide hazard and hazard-bearing structure vulnerability. Consequently, these methods are not widely applicable in practical engineering scenarios. Furthermore, most methods fail to clearly establish the logical connection between landslide hazard and hazard-bearing structure vulnerability, resulting in poor interpretability of the final risk assessment results and an inability to rapidly and quantitatively visualize the risk of individual landslides.

[0004] In the prior art, patent document CN107368938A first requires a large amount of historical landslide data from the same area, obtains a preliminary landslide risk range through statistical analysis, and then corrects the range based on the DAN3D numerical simulation results. However, because DAN3D uses smoothed particle hydrodynamics (SHP) for gridless modeling, the simulation calculation time is highly complex, significantly reducing the efficiency of numerical simulation of large-scale three-dimensional landslides. In addition, DAN3D's SHP method is prone to mass diffusion when simulating landslide branching paths, resulting in deviations in the prediction of landslide accumulation range. Therefore, it is not suitable for numerical simulation of landslides in complex terrain. At the same time, this patent document uses a subjective estimation and grading method to quantitatively evaluate the spatiotemporal probability of damage to the hazard-bearing body and the vulnerability, resulting in a strong sense of human subjectivity in the final quantitative results.

[0005] In view of this, this application is hereby filed. Summary of the Invention

[0006] The technical problem to be solved by the present invention is that the current process of risk assessment at the scale of a single landslide has high requirements for evaluation data, high computational complexity, poor interpretability of evaluation results, and strong subjectivity. The purpose of the present invention is to provide a method and system for quantitatively evaluating the risk of a single landslide. The method first simulates the motion intensity field during the landslide evolution process using MassFlow software, quantitatively evaluating the hazard of the landslide motion process from a three-dimensional visual perspective. Then, the vulnerability of the hazard-bearing body is quantitatively evaluated using an AHP-entropy weight coupling model. Based on spatial relationships, the vulnerability is superimposed on the maximum motion intensity field representing the hazard, ultimately obtaining a quantitative risk assessment result under landslide instability and failure. This method simplifies the evaluation model structure and reduces data requirements, effectively improving the applicability and computational efficiency of the quantitative risk assessment method at the scale of a single landslide.

[0007] The present invention is achieved through the following technical solutions:

[0008] In a first aspect, the present invention provides a method for quantitatively evaluating the risk of a single landslide, the method comprising:

[0009] Obtain landslide terrain files and landslide rock and soil physical parameters;

[0010] Based on the landslide topography file and the physical parameters of the landslide rock and soil, the movement intensity field during the landslide evolution process was simulated using MassFlow software to obtain the maximum movement intensity; the maximum movement intensity represents the landslide hazard;

[0011] Establish a multi-level and multi-dimensional vulnerability assessment system and extract the hazard-bearing body index data within the landslide risk area based on the vulnerability assessment system;

[0012] According to the index data of the hazard-bearing body, the vulnerability of the hazard-bearing body is quantitatively evaluated based on the AHP-entropy weight method coupling model, and the vulnerability values of different hazard-bearing bodies within the range of landslide movement are obtained;

[0013] The landslide risk is quantitatively calculated based on the spatial relationship between the maximum movement intensity and the vulnerability value of the hazard-bearing body, and the risk zoning of different hazard-bearing bodies under the threat of landslide initiation and destruction is obtained.

[0014] The method of the present invention uses MassFlow simulation to focus on describing the spatiotemporal evolution process of the landslide after initiation and destruction, establishes the landslide body movement intensity field, and combines the vulnerability evaluation results of the AHP-entropy weight method model to quantitatively evaluate the landslide risk, effectively reducing the risk assessment method's requirements for parameters and data, and achieving the purpose of rapid quantitative and visual risk assessment results.

[0015] Furthermore, the landslide terrain file includes a landslide area terrain file and a sliding body terrain file;

[0016] The physical parameters of landslide rock and soil include sliding mass density, cohesion, base friction coefficient and pore water pressure.

[0017] Furthermore, the steps for obtaining the physical parameters of the landslide rock and soil are as follows:

[0018] Consult geological maps and relevant information of the landslide area and conduct on-site investigation of the landslide area;

[0019] Based on the on-site investigation, the physical parameters of the landslide rock and soil are determined in combination with relevant specifications and manuals.

[0020] Furthermore, the steps for obtaining the landslide terrain file are as follows:

[0021] Based on UAV aerial photography and 3D modeling, a regional digital elevation model (DEM) was obtained;

[0022] The raster to ASCII tool in ArcGIS was used to convert the elevation raster data in the regional digital elevation model into an ASCII text file that can be recognized by MassFlow software, and the landslide area terrain file Z was obtained;

[0023] Determine the landslide boundary based on the on-site investigation results and the digital orthophoto of the landslide; determine the spatial location of the landslide surface through the on-site field survey results;

[0024] According to the spatial location of the landslide boundary and the landslide surface, the provenance thickness contour lines with attributes were established in ArcGIS. The raster data of the sliding body thickness were calculated using the Terrain to Raster tool, and the raster data were converted into the sliding body terrain file H using the Raster to ASCII tool.

[0025] Furthermore, the movement intensity field during the landslide evolution process was simulated using MassFlow software to obtain the maximum movement intensity, including:

[0026] According to the landslide area terrain file Z and the sliding body terrain file H, a three-dimensional numerical calculation model of the landslide area terrain file and the sliding body terrain file is constructed, and corresponding boundary conditions are set for the three-dimensional numerical calculation model;

[0027] Based on the three-dimensional numerical calculation model, the three-dimensional motion simulation of the landslide after initiation and failure was realized in MassFlow software to obtain the spatiotemporal evolution characteristics of the landslide movement.

[0028] Among them, the spatiotemporal evolution characteristics include the flow velocity v, flow depth h at different time nodes during the simulation process, and the maximum motion intensity hvmax during the process.

[0029] Furthermore, the steps of extracting the disaster-prone body indicator data include:

[0030] Upload the drone-photographed landslide digital orthophoto (DOM) in QGIS software and use the vector drawing tool to select the target area on the landslide digital orthophoto.

[0031] Start the Mapflow plug-in in QGIS software, configure the task parameters and select the selected range; after the Mapflow plug-in runs and processes, the disaster-affected body layer is obtained;

[0032] For erroneous boundaries or adhered or broken hazard-bearing bodies in the hazard-bearing body layer, manual corrections are made by editing in combination with the landslide digital orthophoto to obtain vector data of the hazard-bearing bodies in the landslide hazard area.

[0033] Based on the vector data of the hazard-bearing body in the landslide hazard area, the hazard-bearing body index data are extracted by means of data collection and manual on-site investigation.

[0034] Furthermore, a multi-level and multi-dimensional vulnerability assessment system is established, including:

[0035] Based on the principles of systematicity, pertinence and operability, and in combination with the actual situation of the on-site disaster-bearing body, social vulnerability, material vulnerability, economic vulnerability, resource vulnerability, emergency disaster resistance capacity, etc. are selected as the criterion layer factors of the rating system. From the criterion layer downwards, population size, population structure, building type, building area, road and bridge type, GDP (gross domestic product), cultivated land area, cultivated land type, water system length, rescue supplies, etc. are selected as the indicator layer factors of the evaluation system to establish a multi-level and multi-dimensional vulnerability evaluation system.

[0036] Furthermore, according to the vulnerability assessment system, the hazard-bearing body index data within the landslide risk area are extracted, including:

[0037] Based on the automatic recognition function of the Mapflow plug-in in QGIS software, a vector layer of hazard-prone objects within the landslide hazard range was extracted from the high-definition digital orthophoto of the landslide. The vector layer was then modified through human-computer interaction combined with the image. Finally, image recognition, data collection, and on-site investigation were used to extract hazard-prone object indicator data such as building area, building type, and population structure corresponding to the vector layer.

[0038] Furthermore, based on the hazard-bearing body index data, the vulnerability of the hazard-bearing body was quantitatively evaluated based on the AHP-entropy weight method coupling model, and the vulnerability values of different hazard-bearing bodies within the landslide movement range were obtained, including:

[0039] The index data of the hazard-bearing body are subjected to standard normalization processing to obtain standardized values; the index data of the hazard-bearing body include the index factors of each hazard-bearing body;

[0040] According to the standardized values, the AHP hierarchical analysis method is used to conduct multiple indicator weight analyses on the criterion layer and indicator layer of the vulnerability assessment system, and the weight E between each indicator in the criterion layer and the indicator layer factor weight e under different criterion layer indicator categories are obtained;

[0041] According to the weight E and the index layer factor weight e, the AHP comprehensive weight W of different index factors is obtained. A ;

[0042] The range normalization method is used to process the index data of the disaster-prone body to obtain the range normalization value x′ ij ;

[0043] According to the standardized value of the range, the proportion of the disaster-bearing body index factor is calculated; the proportion of the disaster-bearing body index factor refers to the proportion corresponding to different disaster-bearing bodies i under a certain disaster-bearing body index j;

[0044] According to the proportion of the index factors of the disaster-prone body, the information entropy is calculated; the information entropy refers to the information entropy E of the index j. j ;

[0045] Based on information entropy E j , calculate the entropy weight W of different disaster-bearing body index factors 熵 ;

[0046] According to the preset subjective and objective weight coefficients, entropy weight W 熵 and AHP comprehensive weight W A , get the combined weight W of different indicators 组合 ;

[0047] According to the combined weight W 组合 and the range normalized value to calculate the vulnerability value V of different hazard-bearing bodies i .

[0048] Furthermore, the vulnerability value V iThe formula is:

[0049]

[0050] W 组合 =α*W A +β*W 熵

[0051] Where α is the subjective weight coefficient, and β is the objective weight coefficient. The values of α and β are determined comprehensively based on the actual situation of the study area and the data quality.

[0052] In a second aspect, the present invention provides a single landslide risk quantitative assessment system, the system comprising:

[0053] An acquisition unit, used for acquiring landslide terrain files and landslide rock and soil physical parameters;

[0054] The motion intensity field simulation unit is used to simulate the motion intensity field during the landslide evolution process using MassFlow software based on the landslide terrain file and the physical parameters of the landslide rock and soil, and obtain the maximum motion intensity; the maximum motion intensity represents the landslide hazard;

[0055] An information extraction unit is used to establish a multi-level and multi-dimensional vulnerability assessment system and extract the hazard-bearing body index data within the landslide risk area based on the vulnerability assessment system;

[0056] The vulnerability calculation unit is used to quantitatively evaluate the vulnerability of the hazard-bearing body based on the AHP-entropy weight method coupling model according to the hazard-bearing body index data, and obtain the vulnerability values of different hazard-bearing bodies within the landslide movement range;

[0057] The risk quantification assessment unit is used to quantitatively calculate the landslide risk based on the spatial relationship between the maximum movement intensity and the vulnerability value of the hazard-bearing body, and to obtain the risk zoning of different hazard-bearing bodies under the threat of landslide initiation and destruction.

[0058] In a third aspect, the present invention further provides a computer-readable storage medium storing a computer program, which implements the above-mentioned method for quantitatively evaluating the risk of a single landslide when executed by a processor.

[0059] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0060] 1. The present invention uses MassFlow software to realize the spatiotemporal evolution process of landslide instability and failure. Without considering the spatiotemporal probability of landslide failure, a landslide movement model is established through on-site investigation and survey results. The movement range and movement intensity of the landslide under the most dangerous conditions are obtained, and the overall sliding failure hazard of the landslide is quantitatively evaluated. While more accurately depicting the intensity of the landslide movement hazard, the requirements for data quality and calculation for the hazard assessment of individual landslides are reduced, effectively improving the efficiency of individual landslide hazard assessment, making this method practical and applicable.

[0061] 2. The present invention adopts the AHP-entropy weight method coupling model to evaluate the vulnerability of disaster-prone bodies. The subjective weight of expert experience is introduced through the AHP hierarchical analysis method. At the same time, the entropy weight method is used to calculate the objective weight based on the difference in indicator data. The evaluation results reflect both the actual data characteristics and professional field knowledge, avoiding the one-sidedness of a single evaluation method, effectively reducing subjective interference, and enhancing the reliability and applicability of the evaluation results.

[0062] 3. The present invention uses the spatial analysis function of ArcGIS to superimpose the movement intensity output by MassFlow and the vulnerability of the hazard-bearing body, realizing the spatial coupling of landslide hazard and vulnerability. To a certain extent, it reflects the nonlinear interactive relationship between the sliding body and the hazard-bearing body during the landslide movement process, making the risk assessment results closer to the actual situation and more interpretable. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0064] Figure 1 This is a flow chart of a method for quantitatively evaluating the risk of a single landslide according to the present invention;

[0065] Figure 2 The DEM data map of the UAV laser radar topographic mapping of the present invention;

[0066] Figure 3 This is the landslide boundary range map of the present invention;

[0067] Figure 4 This is the depth contour map of the landslide body of the present invention;

[0068] Figure 5 This is the landslide body thickness grid data map of the present invention;

[0069] Figure 6 This is the maximum flow intensity distribution diagram during the landslide movement process of the present invention;

[0070] Figure 7 This is a diagram of the vulnerability assessment system for disaster-bearing bodies of the present invention;

[0071] Figure 8 This is the distribution map of disaster-bearing bodies of the present invention;

[0072] Figure 9 This is the landslide risk distribution map of the present invention;

[0073] Figure 10 This is a structural block diagram of a single landslide risk quantitative evaluation system of the present invention. DETAILED DESCRIPTION

[0074] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0075] Example 1

[0076] like Figure 1 As shown, the present invention provides a method for quantitatively evaluating the risk of a single landslide, the method comprising:

[0077] Step 1: Obtain landslide terrain files and landslide rock and soil physical parameters; landslide terrain files include landslide area terrain files and sliding body terrain files; landslide rock and soil physical parameters include sliding body density, cohesion, base friction coefficient and pore water pressure, etc.

[0078] In this embodiment, the steps for obtaining the physical parameters of the landslide rock and soil mass are as follows:

[0079] Review geological maps and relevant information of the landslide area and conduct on-site investigations of the landslide area, including soil sampling and testing, drilling to determine the location of the sliding surface, drone aerial photography and 3D modeling, and landslide boundary measurement;

[0080] According to the simulation calculation requirements of MassFlow software, the physical parameters of landslide rock and soil are determined based on on-site investigation and combined with relevant specifications and manuals.

[0081] In this embodiment, the landslide terrain file is obtained by obtaining the terrain data of the landslide area and the sliding body, obtaining a raster-type regional digital elevation model (Digital Elevation Model) and a landslide digital orthophoto map (Digital Orthophoto Map) of the landslide area through drone aerial photography 3D modeling, and generating a 3D landslide area terrain file Z and a sliding body terrain file H in ArcGIS based on the drone aerial photography data and the field survey results. The acquisition steps are as follows:

[0082] A, based on UAV aerial photography and 3D modeling, a regional digital elevation model (DEM) was obtained;

[0083] B, using the raster-to-ASCII tool in ArcGIS, the elevation raster data in the regional digital elevation model was converted into an ASCII text file that could be recognized by the MassFlow software, and the landslide area terrain file Z was obtained;

[0084] C. Determine the landslide boundary based on the on-site investigation results and high-precision digital orthophotos of the landslide; determine the spatial location of the landslide surface through on-site field survey results;

[0085] D. Based on the spatial location of the landslide boundary and the landslide surface, the provenance thickness contour lines with attributes are established in ArcGIS; and the raster data of the sliding body thickness is calculated using the Terrain to Raster tool. It is worth noting that the above conversion calculation operation should keep the raster size and center position consistent; finally, the raster data is converted into the sliding body terrain file H using the Raster to ASCII tool.

[0086] Step 2: Based on the landslide topography file and the physical parameters of the landslide rock and soil, the movement intensity field during the landslide evolution process is simulated using MassFlow software to obtain the maximum movement intensity; the maximum movement intensity represents the landslide hazard;

[0087] In this embodiment, step 2 specifically includes:

[0088] Step 21, constructing a three-dimensional numerical calculation model of ZH based on the landslide area terrain file Z and the sliding body terrain file H, and setting corresponding boundary conditions for the three-dimensional numerical calculation model;

[0089] Step 22, based on the three-dimensional numerical calculation model, realize the three-dimensional movement simulation after the landslide starts to destroy in the MassFlow software to obtain the spatiotemporal evolution characteristics of the landslide movement; wherein, the spatiotemporal evolution characteristics include the flow velocity v, flow depth h and the maximum movement intensity hvmax at different time nodes in the simulation process.

[0090] Step 3: Establish a multi-level and multi-dimensional vulnerability assessment system and extract the hazard-bearing body index data within the landslide risk area based on the vulnerability assessment system;

[0091] In this embodiment, a multi-level and multi-dimensional vulnerability assessment system is established, including:

[0092] Based on the principles of systematicity, pertinence and operability, factors such as population size, population structure, building type, building area, road and bridge type, road and bridge length, GDP (gross domestic product), cultivated land area, cultivated land type, water system length, and rescue supplies are selected as evaluation indicators from multiple dimensions such as social vulnerability, material vulnerability, economic vulnerability, resource vulnerability, and emergency disaster resistance capacity. A multi-level and multi-dimensional vulnerability evaluation system with target layer, criterion layer, and indicator layer is established.

[0093] In this embodiment, the steps of extracting the disaster-prone body indicator data include:

[0094] Upload the drone-photographed digital orthophoto (DOM) of the landslide in QGIS, ensuring that the image coordinate system is consistent with the rest of the remote sensing data. Use the vector drawing tool to select the target area on the DOM.

[0095] Start the Mapflow plug-in in QGIS software, configure the task parameters and select the selected range; after the Mapflow plug-in runs and processes, the disaster-affected body layer is obtained;

[0096] For erroneous boundaries or adhered or broken hazard-bearing bodies in the hazard-bearing body layer, manual corrections are made by editing in combination with the landslide digital orthophoto to obtain vector data of the hazard-bearing bodies in the landslide hazard area.

[0097] Based on the vector data of the hazard-bearing body in the landslide hazard area, the hazard-bearing body index data are extracted by means of data collection and manual on-site investigation.

[0098] Step 4: Based on the hazard-bearing body index data, the vulnerability of the hazard-bearing body is quantitatively evaluated based on the AHP-entropy weight method coupling model to obtain the vulnerability values of different hazard-bearing bodies within the landslide movement range;

[0099] In this embodiment, step 4 specifically includes:

[0100] Step 41, performing standard normalization processing on the disaster-prone body index data to obtain a standardized value; the disaster-prone body index data includes index factors of each disaster-prone body;

[0101] Step 42: Based on the standardized values, the AHP (Analytical Hierarchy Process) is used to perform multiple indicator weight analyses on the criterion layer and indicator layer of the vulnerability assessment system. This includes constructing a judgment matrix, calculating the evaluation factor weights, and performing consistency tests to obtain the weights E between the criterion layer indicators of social vulnerability, material vulnerability, economic vulnerability, and emergency disaster relief, as well as the indicator layer factor weights e under different criterion layer indicator categories.

[0102] Step 43: Based on the weight E and the weight e of the indicator layer factor, the AHP comprehensive weight W of different indicator factors is obtained. A =E*e;

[0103] Step 44: Process the index data of the disaster-prone body using the range normalization method to eliminate the dimensional differences between different indicators and obtain the range normalization value x′ ij ;

[0104] Specifically, the range standardization method includes:

[0105] (1) For positive indicators with larger values and higher vulnerability, a standardized formula is used, as follows:

[0106]

[0107] (2) For negative indicators where the larger the value, the lower the vulnerability, the standardized formula is used as follows:

[0108]

[0109] In the above formula, x′ ij is the standardized value of the extreme difference, the lower right corner i is the number of the disaster-prone body, the lower right corner j represents different indicator factors, and x ij is a certain index value of a certain disaster-bearing body before standardization, x max Refers to the maximum value under a certain type of indicator, x min Refers to the minimum value under a certain type of indicator.

[0110] Step 45, calculate the proportion of the disaster-prone body index factor based on the range normalization value; the proportion of the disaster-prone body index factor refers to the proportion corresponding to different disaster-prone bodies i under a certain disaster-prone body index j; the calculation is performed according to the following formula:

[0111]

[0112] In the above formula, P ij is the proportion of different disaster-bearing bodies i under index j, x′ ij The values are standardized, the lower right corner subscript i is the disaster-prone body number, and the lower right corner subscript j represents different indicator factors.

[0113] Step 46: Calculate information entropy based on the proportion of the disaster-prone body index factor; information entropy refers to the information entropy E of index j. j , calculated as follows:

[0114]

[0115] In the above formula, E j To calculate the information entropy corresponding to index j, P ij is the proportion corresponding to different hazard-prone bodies i under index j. The lower right corner subscript i is the hazard-prone body number, and the lower right corner subscript j represents different indicator factors. It should be noted that in the calculation process, for data with a proportion of 0, 0.0001 is taken to avoid affecting the logarithmic calculation formula.

[0116] Step 47, based on information entropy E j , calculate the entropy weight W of different disaster-bearing body index factors 熵 , calculated as follows:

[0117]

[0118] Step 48: Based on the preset subjective and objective weight coefficients and entropy weight W 熵 and AHP comprehensive weight WA , get the combined weight W of different indicators 组合 ; Calculate according to the following formula:

[0119] W 组合 =α*W A +β*W 熵 (6)

[0120] In the above formula, W A is the subjective weight of the indicator calculated based on the AHP method, W 熵 The objective weight of the indicator is calculated based on the entropy weight method. The linear combination method is used to calculate the combined weight. α is the subjective weight coefficient, and β is the objective weight coefficient. The values of α and β should be determined comprehensively based on the actual situation of the study area and the data quality.

[0121] Step 49, according to the combined weight W 组合 and the range normalized value to calculate the vulnerability value V of different hazard-bearing bodies i , the calculation formula is as follows:

[0122]

[0123] Step 5: Quantitatively calculate the landslide risk based on the spatial relationship between the maximum movement intensity and the vulnerability value of the hazard-bearing body, and obtain the risk zoning of different hazard-bearing bodies under the threat of landslide initiation and destruction.

[0124] In this embodiment, step 5 uses the ArcGIS spatial analysis function to superimpose the hazard-bearing body vulnerability quantitative value on the basis of the maximum movement intensity field of the landslide body simulated by MassFlow numerical simulation, and finally obtains the risk quantitative assessment result R under the landslide instability and failure. i .

[0125] R i =V i *hvmax(i) (8)

[0126] In the above formula, R i is the risk of damage caused by landslide movement, hvmax(i) is the maximum movement intensity that the i-th hazard-bearing body endures during the landslide movement, that is, the maximum danger caused by the landslide movement to the hazard-bearing body.

[0127] During specific implementation, this embodiment takes a landslide in Wenchuan County as an example to explain the above implementation process of the present invention.

[0128] Step 1: Obtain landslide terrain files and landslide rock and soil physical parameters

[0129] (1) Consult the geological map and relevant information of the landslide area and conduct on-site investigation of the landslide area.

[0130] According to the relevant geological data collected in the landslide area, the front edge of the slope where the landslide is located is the Minjiang River. The landslide area and the area below it are typical convex slopes, which are gentle at the top and steep at the bottom. The landslide is located on the upper plate of the Maowen fault zone. The landslide had co-seismic cracks during the 2008 Wenchuan earthquake. Later, it was affected by multiple earthquakes, flood season precipitation, slope cutting and road construction. The deformation signs of the landslide suddenly accelerated in 2022, and the upper and lower deformation zones of the slope were connected, gradually becoming a potential hidden danger point for landslides. Until 2024, the landslide continued to deform, the slope was obviously offset, and the back wall and side walls became more and more obvious. At the same time, the front edge slipped to form a scraping area.

[0131] Drilling, drone aerial photography and other survey measures were adopted on site to obtain high-precision digital orthophoto DOM, digital elevation model DEM and geological rock strata data of the landslide area. At the same time, rock samples will be sent to the laboratory for indoor testing to obtain relevant physical parameters of the landslide rock and soil.

[0132] (2) According to the simulation calculation requirements of MassFlow software, the physical parameters of the landslide rock and soil are determined based on on-site field investigation and combined with relevant specifications and manuals.

[0133] Based on on-site investigations and indoor tests, and in combination with Table 6.7.5-2 of the Code for Design of Building Foundations (GB 50007-2011) and Table 4.4.2 of the Code for Design of Highway Bridge and Culvert Foundations (JTG-D62-2007), the rock and soil base friction coefficient table (see Table 1) was determined. The continuous lithology in the simulated landslide area is mainly Guanfangshan Formation schist. The data in the table can be used as an important reference for the average friction coefficient. Under the most unfavorable simulation conditions, the average base friction coefficient was determined to be 0.55. At the same time, to simplify the model parameters, the volume amplification effect and erosion were not considered.

[0134] Table 1. Rock and soil base friction coefficient

[0135] Name of rock and soil mass Base friction coefficient silt, clay 0.25 Sand (silt sand, fine sand, coarse sand, gravel) 0.30～0.40 Gravel soil (breccia, gravel, pebbles, crushed stone) 0.4～0.5 Soft rock (slate, mudstone, etc.) 0.4·0.6 hard rock 0.6～0.7

[0136] According to the topography of the on-site landslide area, a certain landslide is a high-level large-scale soil landslide, which can enter a high-speed sliding state after the initial destruction. Therefore, referring to relevant literature and combining the composition and structure of the sliding body material, the landslide cohesion is determined to be 12kPa; from existing research, most landslides are triggered by heavy rainfall. During the startup phase, due to the infiltration of rainfall and the shear crushing and reorganization of soil particles, the stress borne by the particle skeleton will be quickly transferred to the pore water, thereby forming excess pore water pressure. However, in the movement phase of the Massflow simulation, the landslide body has shown fluidized movement characteristics, so the influence of pore water pressure is weakened. At the same time, in order to avoid considering the influence of the nonlinear change of excess pore water pressure during the movement process on the overall simulation, the excess pore water pressure is taken as 0 in this simulation; the sliding body density is finally taken as 2000kg / m based on the results of indoor test.3 .

[0137] (3) Obtain landslide terrain files (landslide area terrain file Z and sliding body terrain file H)

[0138] A professional drone equipped with LiDAR was used to conduct aerial mapping of the landslide area, covering an area of 1.54 km. 2 1:1000 topographic survey, thus obtaining high-precision digital elevation model DEM data of the study area (see Figure 2 ) and digital orthophoto DOM, import the DEM raster data into ArcGIS software, and use the raster to ASCII tool to convert the elevation raster data into an ASCII text file that can be recognized by MassFlow, which is the landslide area terrain file Z; at the same time, based on the collected data, combined with the on-site investigation, the landslide boundary conditions, morphological characteristics and deformation and damage characteristics are obtained, so as to clarify the landslide boundary and circle it in ArcGIS using the high-precision digital orthophoto DOM (see Figure 3 ), combined with on-site investigation and drilling, it is known that the height of the rear wall of the landslide is between 3-11m, the height of the side wall is between 0.5-7m, and the thickness of the sliding body above the front shear outlet is between 5-20m. The sliding body shows the characteristics of thin sides and thick middle, thin at the rear edge and thick at the front edge. The average thickness of the sliding body is estimated to be 20m, and the thickest part is about 44m. The sliding body isobaths are drawn in ArcGIS based on the sliding body thickness and topography (see Figure 4 ), the depth attribute is assigned to the sliding body contour line in combination with the sliding body thickness, and then the thickness raster data of a landslide is calculated by the terrain to raster tool (see Figure 5 ), and finally converted into the sliding body terrain file H through the raster to ASCII tool.

[0139] Step 2: Based on the landslide topography file and the physical parameters of the landslide rock and soil, the movement intensity field during the landslide evolution process is simulated by MassFlow software to obtain the maximum movement intensity;

[0140] 3D simulation of landslide movement based on MassFlow: Based on the 3D terrain files Z and H obtained in the previous step, select the ZH 3D numerical calculation model in MassFlow software, set the calculation type to single layer single phase, select open boundary conditions, read Z and H to establish the numerical simulation model, and determine the model to use a 3m square grid according to the simulation range size, and input the sliding material density as 2000kg / m 3The coulomb model is used to simulate the movement of the landslide fluid. The input cohesion is 12 kPa, the base friction coefficient is set to 0.55, and the excess pore water pressure coefficient is set to 0. Finally, a variable time step is used, the total calculation time is 250 s, the initial step is 0.01 s, and the courant number is 0.25. Finally, the three-dimensional movement simulation after the landslide starts to destroy is realized. The spatiotemporal evolution characteristics of the fluid during the sliding movement can be obtained, including the flow velocity v (m / s) and flow depth h (m) at different time nodes, as well as the maximum flow intensity hvmax (m 2 / s)(See Figure 6 ).

[0141] Step 3: Establish a multi-level and multi-dimensional vulnerability assessment system and extract the hazard-bearing body index data within the landslide risk area based on the vulnerability assessment system;

[0142] First, based on the on-site investigation, a multi-level and multi-dimensional vulnerability assessment system for disaster-prone objects is established. The disaster-prone objects within the range of landslide movement are mainly residential buildings of villagers. Village roads with high vulnerability and low value are not considered. At the same time, there are no resources such as arable land, nor are there emergency disaster relief engineering measures or material reserves. Based on the above situation, this embodiment selects three indicators of social vulnerability, material vulnerability, and economic vulnerability as the criterion layer, and selects five indicators of population size, population structure, building area, building type, and gross domestic product as the indicator layer (see Figure 7 ).

[0143] Upload the drone aerial digital orthophoto (DOM) in QGIS software to ensure that the image coordinate system is consistent with the rest of the remote sensing data. Use the vector drawing tool to select the target area on the optical remote sensing image. Start the Mapflow plug-in in QGIS. Since the disaster-prone objects are mainly residential buildings, the "Buildings" model is used. The selected range is selected for extraction. After running the process, the disaster-prone object layer is obtained. For the disaster-prone objects with incorrect boundaries or adhesion or fracture, the optical remote sensing image is combined with editing to make manual corrections. A total of 22 residential buildings are identified, of which 7 are within the danger range of the landslide 3D motion simulation (see Figure 2). Figure 8 ); Data collection and manual on-site investigation were used to extract the relevant evaluation index data of the disaster-prone body, including population size, population structure, building type, building area, and annual GDP. Among them, population size, building type, building area, and GDP were obtained through on-site investigation and data review. For population structure, the age of residents in the buildings was collected, and the percentage of people over 60 years old was counted. In addition, non-quantitative data such as building type were relatively assigned according to the actual situation (see Table 2).

[0144] Table 2 Original data of disaster-prone body indicators

[0145]

[0146]

[0147] Step 4: Based on the hazard-bearing body index data, the vulnerability of the hazard-bearing body is quantitatively evaluated based on the AHP-entropy weight method coupling model to obtain the vulnerability values of different hazard-bearing bodies within the landslide movement range;

[0148] Based on the original indicator data collected in step 3, the indicator data are normalized using range normalization to eliminate dimensional differences between different indicators. Indicators such as population size and population structure are positive indicators with higher values indicating higher vulnerability, so they are processed using formula (1). Building type is a negative indicator with higher values indicating lower vulnerability, so formula (2) is used. The processed data are shown in Table 3 below.

[0149] Table 3 Index data after range standardization

[0150]

[0151] According to the established vulnerability assessment system for disaster-bearing bodies, the AHP hierarchical analysis is performed on the three-dimensional indicators of the criterion layer and the five indicators of the indicator layer to calculate the weights. Taking the three-dimensional indicators of the criterion layer as an example, the indicator factor scales are first compared by pairwise comparison to construct a judgment matrix. After calculation, the eigenvalue λ of the judgment matrix is obtained. max =3.0858, the consistency result CR = 0.0825 < 0.1, the result meets the consistency test requirements, and the social vulnerability weight E is obtained. 社 =0.6738, material vulnerability weight E 物 =0.2255, economic vulnerability weight E 经 =0.1007. The above calculation steps are adopted to carry out AHP hierarchical analysis on the indicator factors of the indicator layer under different criterion layer dimensions to calculate the indicator layer factor weight e. The calculation results are shown in Table 4 below.

[0152] Table 4 AHP weight calculation table

[0153] Standards layer Weight E Indicator layer Weight e <![CDATA[W A =E*e]]> social vulnerability 0.6738 Population 0.55 0.3706 population structure 0.45 0.3032 Material vulnerability 0.2255 Building Type 0.6 0.1353 Building area 0.4 0.0902 Economic vulnerability 0.1007 Annual GDP 1 0.1007

[0154] After obtaining the subjective weights using the AHP hierarchical analysis method, the entropy weight method based on objective data analysis is used to calculate the weights of the indicator factors. Based on the standardized indicator data, the proportions of different indicator factors are first calculated according to formula (3): ij ,

[0155] Taking population as an example, its P ij The calculation process is as follows:

[0156] Disaster-bearing body No. 1: 0.50 / (0.5+0.83+0.17+0.33+0.5+0+1)=0.15

[0157] Disaster-bearing body No. 2: 0.83 / (0.5+0.83+0.17+0.33+0.5+0+1)=0.25

[0158] Disaster-bearing body No. 3: 0.17 / (0.5+0.83+0.17+0.33+0.5+0+1)=0.05

[0159] Disaster-bearing body No. 4: 0.33 / (0.5+0.83+0.17+0.33+0.5+0+1)=0.10

[0160] Disaster-bearing body No. 5: 0.50 / (0.5+0.83+0.17+0.33+0.5+0+1)=0.15

[0161] Disaster-bearing body No. 6: 0 / (0.5+0.83+0.17+0.33+0.5+0+1)=0

[0162] Disaster-bearing body No. 7: 1 / (0.5+0.83+0.17+0.33+0.5+0+1)=0.30

[0163] Population information entropy E j The calculation is as follows:

[0164]

[0165] Then calculate the index information entropy E through formula (4) j Finally, the entropy weight W of the index factor is calculated by formula (5) 熵 According to the above calculation steps, the population, building type, building area, annual GDP and other indicators are calculated repeatedly. The calculation results are shown in Table 5 below.

[0166] Table 5 Entropy weight calculation table

[0167]

[0168]

[0169] The combined weights were calculated according to formula (6), with the subjective weight coefficient α set at 0.4 and the objective weight coefficient β set at 0.6. The combined weights of different index factors were obtained (see Table 6). The vulnerability index V of the seven hazard-bearing bodies within the landslide movement hazard range was then calculated according to formula (7). i , the calculation results are shown in Table 7 below.

[0170] Table 6 Calculation table of index factor combination weights

[0171]

[0172] Table 7 Calculation results of vulnerability of hazard-bearing bodies

[0173]

[0174] Step 5: Quantitatively calculate the landslide risk based on the spatial relationship between the hazard (i.e., maximum movement intensity) and the vulnerability of the hazard-bearing body, and obtain the risk zoning of different hazard-bearing bodies under the threat of landslide initiation and destruction, i.e., the quantitative landslide risk assessment result.

[0175] From the perspective of time, the landslide movement intensity obtained in step 4 is the maximum value during the movement (i.e., the maximum movement intensity). Therefore, based on the building surface file of the hazard-bearing body extracted in step 5, the ArcGIS zoning statistical tool is used to extract the average movement intensity within the building range of different hazard-bearing bodies on the basis of the historical maximum movement intensity field. This is used as the hazard of different hazard-bearing bodies under the threat of landslide movement. On this basis, the hazard and vulnerability are superimposed using formula (8) to obtain the risk R of the hazard-bearing body under the threat of landslide. i (See Table 8).

[0176] Table 8 Calculation results of risk of disaster-bearing bodies

[0177]

[0178] The natural breakpoint method was used to classify the risk of the seven hazard-bearing bodies within the landslide threat range into low risk, medium risk, and high risk. Among them, hazard-bearing body No. 1 was low risk, hazard-bearing bodies No. 4 and No. 6 were medium risk, and hazard-bearing bodies No. 2, 3, 5, and 7 were high risk. In addition, hazard-bearing bodies No. 8-22 were outside the simulated movement range of the landslide, so their hazard values were 0. However, from a safety perspective, they were classified as low risk. Finally, a quantitative evaluation of the risk of the hazard-bearing body under the overall instability and failure of the single landslide was achieved, and a risk spatial distribution map was obtained (see Figure 1). Figure 9 ).

[0179] The present invention uses the spatial analysis function of ArcGIS to superimpose the maximum movement intensity output by MassFlow and the vulnerability of the hazard-bearing body, realizing the spatial coupling of landslide hazard and vulnerability. To a certain extent, it reflects the nonlinear interactive relationship between the sliding body and the hazard-bearing body during the landslide movement process, making the risk assessment results closer to the actual situation and more interpretable.

[0180] Example 2

[0181] like Figure 10 As shown, the difference between this embodiment and embodiment 1 is that this embodiment provides a single landslide risk quantitative assessment system, which corresponds one-to-one to the single landslide risk quantitative assessment method of embodiment 1; the system includes:

[0182] An acquisition unit, used for acquiring landslide terrain files and landslide rock and soil physical parameters;

[0183] The motion intensity field simulation unit is used to simulate the motion intensity field during the landslide evolution process using MassFlow software based on the landslide terrain file and the physical parameters of the landslide rock and soil, and obtain the maximum motion intensity; the maximum motion intensity represents the landslide hazard;

[0184] An information extraction unit is used to establish a multi-level and multi-dimensional vulnerability assessment system and extract the hazard-bearing body index data within the landslide risk area based on the vulnerability assessment system;

[0185] The vulnerability calculation unit is used to quantitatively evaluate the vulnerability of the hazard-bearing body based on the AHP-entropy weight method coupling model according to the hazard-bearing body index data, and obtain the vulnerability values of different hazard-bearing bodies within the landslide movement range;

[0186] The risk quantification assessment unit is used to quantitatively calculate the landslide risk based on the spatial relationship between hazard and vulnerability according to the maximum movement intensity and the vulnerability value of the hazard-bearing body, and to obtain the risk zoning of different hazard-bearing bodies under the threat of landslide initiation and destruction.

[0187] The execution process of each unit can be carried out according to the process steps of a single landslide risk quantitative assessment method in Example 1, and will not be described in detail in this embodiment.

[0188] At the same time, the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned method for quantitatively evaluating the risk of a single landslide.

[0189] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0190] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0191] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0192] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0193] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A quantitative evaluation method for the risk of a single landslide, characterized in that: The method includes: Obtain landslide terrain files and landslide rock and soil physical parameters; According to the landslide topography file and the physical parameters of the landslide rock and soil, the movement intensity field during the landslide evolution process is simulated by MassFlow software to obtain the maximum movement intensity; the maximum movement intensity represents the landslide hazard; Establish a multi-level and multi-dimensional vulnerability assessment system, and extract the hazard-bearing body index data within the landslide risk area based on the vulnerability assessment system; According to the index data of the hazard-bearing body, the vulnerability of the hazard-bearing body is quantitatively evaluated based on the AHP-entropy weight method coupling model to obtain the vulnerability values of different hazard-bearing bodies within the range of landslide movement; The landslide risk is quantitatively calculated based on the spatial relationship between the maximum movement intensity and the vulnerability value of the hazard-bearing body, and the risk zoning of different hazard-bearing bodies under the threat of landslide initiation and destruction is obtained.

2. A method for quantitatively assessing the risk of a single landslide according to claim 1, characterized in that: The landslide terrain file includes a landslide area terrain file and a sliding body terrain file; The physical parameters of the landslide rock and soil mass include sliding mass density, cohesion, base friction coefficient and pore water pressure.

3. A method for quantitatively assessing the risk of a single landslide according to claim 2, characterized in that: The steps for obtaining the physical parameters of the landslide rock and soil mass are as follows: Consult geological maps and relevant information of the landslide area and conduct on-site investigation of the landslide area; Based on the on-site investigation, the physical parameters of the landslide rock and soil are determined in combination with relevant specifications and manuals.

4. A method for quantitatively assessing the risk of a single landslide according to claim 2, characterized in that: The steps for obtaining the landslide terrain file are as follows: Based on UAV aerial photography and 3D modeling, a regional digital elevation model was obtained; Using the raster-to-ASCII tool in ArcGIS, the elevation raster data in the regional digital elevation model is converted into an ASCII text file that can be recognized by MassFlow software to obtain a landslide area terrain file; Determine the landslide boundary based on the field survey results and the landslide digital orthophoto; Determine the spatial location of the landslide surface through on-site field survey results; According to the spatial location of the landslide boundary and the landslide surface, the source thickness contour lines with attributes are established in ArcGIS; The raster data of the sliding body thickness is calculated by using the terrain to raster tool, and the raster data is converted into a sliding body terrain file by using the raster to ASCII tool.

5. A method for quantitatively assessing the risk of a single landslide according to claim 4, characterized in that: The movement intensity field during the landslide evolution process was simulated using MassFlow software to obtain the maximum movement intensity, including: Constructing a three-dimensional numerical calculation model of the landslide area terrain file and the sliding body terrain file according to the landslide area terrain file and the sliding body terrain file, and setting corresponding boundary conditions for the three-dimensional numerical calculation model; Based on the three-dimensional numerical calculation model, the three-dimensional motion simulation of the landslide after initiation and destruction is realized in MassFlow software to obtain the spatiotemporal evolution characteristics of the landslide movement; The spatiotemporal evolution characteristics include the flow velocity and flow depth at different time nodes during the simulation process and the maximum movement intensity during the process.

6. The method for quantitatively assessing the risk of a single landslide according to claim 1, wherein: The step of extracting the disaster-prone body indicator data includes: Upload the digital orthophoto of the landslide taken by drone in QGIS software, and use the vector drawing tool to select the target area on the digital orthophoto of the landslide; Start the Mapflow plug-in in QGIS software, configure the task parameters and select the selected range; after the Mapflow plug-in runs and processes, the disaster-affected body layer is obtained; Correcting the erroneous boundaries or the adhered or broken hazard-bearing bodies in the hazard-bearing body layer by editing in combination with the landslide digital orthophoto to obtain vector data of the hazard-bearing bodies in the landslide hazard area; Based on the vector data of the hazard-bearing body in the landslide hazard area, the hazard-bearing body index data are extracted by means of data collection and manual on-site investigation.

7. The method for quantitatively assessing the risk of a single landslide according to claim 1, characterized in that: According to the index data of the hazard-bearing body, the vulnerability of the hazard-bearing body is quantitatively evaluated based on the AHP-entropy weight method coupling model, and the vulnerability values of different hazard-bearing bodies within the landslide movement range are obtained, including: Performing standard normalization processing on the disaster-prone body index data to obtain a standardized value; the disaster-prone body index data includes index factors of each disaster-prone body; According to the standardized values, the AHP hierarchical analysis method is used to perform multiple indicator weight analyses on the criterion layer and indicator layer of the vulnerability assessment system to obtain the weight E between the indicators of the criterion layer and the indicator layer factor weight e under different criterion layer indicator categories; According to the weight E and the index layer factor weight e, the AHP comprehensive weight W of different index factors is obtained. A ; The range normalization method is used to process the index data of the disaster-prone body to obtain the range normalization value x′ ij ; Calculate the proportion of the disaster-prone body index factor according to the range normalization value; the proportion of the disaster-prone body index factor refers to the proportion corresponding to different disaster-prone bodies i under a certain disaster-prone body index j; According to the proportion of the index factors of the disaster-prone body, the information entropy is calculated; the information entropy refers to the information entropy E of the index j. j ; Based on the information entropy E j , calculate the entropy weight W of different disaster-bearing body index factors 熵 ; According to the preset subjective and objective weight coefficients, entropy weight W 熵 and AHP comprehensive weight W A , get the combined weight W of different indicators 组合 ; According to the combined weight W 组合 and the range normalized value to calculate the vulnerability value V of different hazard-bearing bodies i .

8. A method for quantitatively assessing the risk of a single landslide according to claim 7, characterized in that: The vulnerability value V of the disaster-bearing body i The formula is: W 组合 =α*W A +β*W 熵 Where α is the subjective weight coefficient, and β is the objective weight coefficient. The values of α and β are determined comprehensively based on the actual situation of the study area and the quality of the data.

9. A quantitative assessment system for the risk of a single landslide, characterized by: The system includes: An acquisition unit, used for acquiring landslide terrain files and landslide rock and soil physical parameters; a motion intensity field simulation unit, configured to simulate the motion intensity field during the landslide evolution process using MassFlow software based on the landslide topography file and the physical parameters of the landslide rock and soil mass, to obtain a maximum motion intensity; the maximum motion intensity represents the landslide hazard; An information extraction unit is used to establish a multi-level and multi-dimensional vulnerability assessment system, and extract the hazard-bearing body index data within the landslide risk area based on the vulnerability assessment system; a vulnerability calculation unit for quantitatively evaluating the vulnerability of the hazard-bearing body based on the hazard-bearing body index data and the AHP-entropy weight method coupling model to obtain vulnerability values of different hazard-bearing bodies within the range of landslide movement; The risk quantification evaluation unit is used to quantitatively calculate the landslide risk according to the spatial relationship between the maximum movement intensity and the vulnerability value of the hazard-bearing body, and obtain the risk zoning of different hazard-bearing bodies under the threat of landslide initiation and destruction.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, a method for quantitatively evaluating the risk of a single landslide is implemented as described in any one of claims 1 to 8.

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