Landslide disaster evolution analysis method and system based on coupling model

Abstract

The embodiment of the invention discloses a landslide disaster evolution analysis method and system based on a coupling model, and the method comprises the steps: carrying out the multi-source data integration of geological structure monitoring data, landform distribution data and hydrological environment monitoring data of a target seismic region, and generating a comprehensive monitoring data set; based on the data set, establishing an interaction association relationship of the target equation set, and constructing a geology-landform-hydrology coupling model; performing dynamic simulation and quantitative evaluation on the instability probability and the landslide scale of the potential landslide mass by using the coupling model according to a preset scene parameter combination, and generating an image-text annotation quantitative evaluation report; and finally, carrying out vulnerability analysis and risk assessment on the disaster-bearing body based on the report, generating a comprehensive guidance report containing a disaster-bearing body vulnerability spatial distribution diagram, landslide risk prevention and control measure suggestions and a regional space planning optimization scheme, and providing a scientific basis for landslide disaster prevention and control and regional planning.

Description

Technical Field

[0001] This invention relates to the field of geological hazard analysis technology, specifically to a method and system for landslide hazard evolution analysis based on a coupled model. Background Technology

[0002] High-intensity earthquake zones are located in unique geological structures, and their special geological background makes these areas extremely active and dangerous areas prone to landslides. Frequent high-intensity earthquakes are the primary driving force triggering landslides, capable of causing large-scale, chain-reaction instability of soil and rock masses in an instant. Simultaneously, abundant rainfall and active surface and groundwater in the region work together to soften the soil and rock over a long period, raising the groundwater level and generating dynamic water pressure, continuously reducing slope stability.

[0003] Landslides can cause severe damage, threatening not only the lives of local residents but also causing enormous destruction to urban infrastructure, roads, and vital lifeline projects. Therefore, accurate and scientific landslide risk assessments are urgently needed.

[0004] However, existing technologies lack effective integration of multi-source data in landslide disaster analysis, which makes it impossible to fully and accurately grasp the synergistic effects between various factors. Moreover, most existing technologies only consider the influence of a single or a few factors, which cannot fully reflect the complex interaction between geological, geomorphological and hydrological factors, making the prediction and assessment of landslide disasters inaccurate and incomplete. Summary of the Invention

[0005] This invention provides a method and system for landslide disaster evolution analysis based on a coupled model.

[0006] In a first aspect, embodiments of the present invention provide a landslide hazard evolution analysis method based on a coupled model, applied to a landslide hazard evolution analysis system based on a coupled model, the method comprising: Multi-source data integration and processing are performed on geological structure monitoring data, topographic distribution data, and hydrological environment monitoring data of the target seismic zone to generate a comprehensive monitoring dataset. Based on the comprehensive monitoring dataset, a geological-geomorphological-hydrological coupled model reflecting the synergistic effects of multiple factors is constructed by establishing the interaction and correlation relationships of the target equation set; the target equation set includes geological process equations, geomorphological evolution equations, and hydrodynamic equations. Based on the preset combination of scenario parameters, the geological-geomorphological-hydrological coupling model is used to dynamically simulate and quantitatively evaluate the instability probability and scale of potential landslides in the target seismic zone, and generate a graphic and text-annotated quantitative evaluation report containing the spatial distribution characteristics, temporal development trend and impact range of the landslides. Based on the aforementioned graphic and textual annotation quantitative assessment report, vulnerability analysis and risk assessment are performed on the disaster-bearing bodies within the target seismic zone, generating a comprehensive guidance report that includes a spatial distribution map of the vulnerability of disaster-bearing bodies, suggestions for landslide risk prevention and control measures, and regional spatial planning optimization schemes.

[0007] Secondly, embodiments of the present invention provide a landslide disaster evolution analysis system based on a coupled model, comprising: processor; Storage device, on which computer programs are stored, When the computer program is executed by the processor, the processor implements any of the aforementioned methods for landslide disaster evolution analysis based on the coupled model.

[0008] This invention provides a readable storage medium storing a program or instructions, which, when executed by a processor, implement the steps of the landslide disaster evolution analysis method based on a coupled model.

[0009] This invention integrates multi-source data, including geological structure monitoring data, topographic distribution data, and hydrological environment monitoring data, in the target seismic zone to generate a comprehensive monitoring dataset. Based on this dataset, a geological-geomorphological-hydrological coupled model reflecting the synergistic effects of multiple factors is constructed. This model fully considers the interactions between geological, geomorphological, and hydrological factors, and compared to traditional models, it can more realistically simulate the occurrence and development of landslide disasters. Using this coupled model, potential landslide bodies are dynamically simulated and quantitatively assessed based on preset scenario parameter combinations. The generated graphic and text-annotated quantitative assessment report includes key information such as the spatial distribution characteristics, temporal development trend, and impact range of the landslide, making landslide disaster prediction more accurate and comprehensive. Based on this report, vulnerability analysis and risk assessment are conducted on the affected bodies, and a comprehensive guidance report is generated. This provides a scientific and systematic decision-making basis for landslide disaster prevention and control and regional spatial planning, reducing the losses to affected bodies caused by landslide disasters and improving the region's disaster resistance capacity. Attached Figure Description

[0010] Figure 1 This is a flowchart of a landslide disaster evolution analysis method based on a coupled model, provided in an embodiment of the present invention.

[0011] Figure 2 This is a block diagram of the landslide disaster evolution analysis device based on a coupled model provided in an embodiment of the present invention.

[0012] Figure 3 This is a schematic diagram of the basic structure of a landslide disaster evolution analysis system based on a coupled model, provided in an embodiment of the present invention. Detailed Implementation

[0013] See Figure 1 As shown, this figure is a flowchart of a landslide disaster evolution analysis method based on a coupled model provided by an embodiment of the present invention. This method can be applied to a landslide disaster evolution analysis system based on a coupled model. Figure 1 As shown, the method may include steps 110-140.

[0014] Step 110: Perform multi-source data integration processing on the geological structure monitoring data, topographic distribution data, and hydrological environment monitoring data of the target seismic zone to generate a comprehensive monitoring dataset.

[0015] In the landslide disaster evolution analysis scenario, the target seismic zone, located in a special geological structural zone, experiences intense geological tectonic activity, complex topography, and variable hydrological environment. These factors interact to lead to frequent landslide disasters. To accurately assess the landslide risk in this area, comprehensive and accurate data must first be obtained. Geological structural monitoring data can be obtained through geological mapping and geophysical exploration. Geological mapping can identify the distribution and characteristics of faults, folds, and other structures in detail, while geophysical exploration, such as electrical resistivity tomography, seismic methods, and gravity methods, can detect information on underground geological structures, the mechanical properties of soil and rock masses, and changes in groundwater levels. Topographic distribution data can be obtained using remote sensing interpretation technology, utilizing satellite and aerial remote sensing imagery to quickly acquire information on large-area topographic relief, slope, and aspect, and can also identify potential landslide bodies and water system distributions. Hydrological environment monitoring data is collected through water level monitoring points and flow monitoring stations set up in the target seismic zone, including information on groundwater level changes and surface water flow.

[0016] Because data from different sources may differ in format, accuracy, and coordinate system, multi-source data integration is necessary. First, data quality control is performed to check the completeness, accuracy, and consistency of the data, eliminating erroneous or outlier data. Then, standardization is carried out to unify the data format, accuracy, and coordinate system. For example, fault strike data from geological structure monitoring data and elevation data from topographic distribution data are converted to the same coordinate system. Through data integration, these multi-source data are merged into a unified data platform to generate a comprehensive monitoring dataset. This dataset contains detailed information on the geology, topography, hydrology, and other aspects of the target seismic zone.

[0017] Step 120: Based on the comprehensive monitoring dataset, a geological-geomorphological-hydrological coupling model reflecting the synergistic effects of multiple factors is constructed by establishing the interaction and correlation relationships of the target equation set; the target equation set includes geological process equations, geomorphological evolution equations, and hydrodynamic equations.

[0018] In a first preferred embodiment, step 120 includes: Step 121: Extract geological structural deformation rate data, geomorphological change data, and hydrological flow change data from the comprehensive monitoring dataset.

[0019] In the comprehensive monitoring dataset, geological structure deformation rate data reflects the activity level of geological structures. This data can be extracted from geological structure monitoring data, for example, through analysis and calculation of fault displacement monitoring data. Specifically, by performing differential processing on fault displacement data at different time points to obtain the displacement change of the fault in each time period, and then dividing by the time interval, the geological structure deformation rate can be obtained. Geomorphological change data reflects the dynamic changes in topography and can be obtained by comparing topographic and geomorphological distribution data from different periods. For example, by comparing and analyzing remote sensing images from different times, and using image recognition and processing techniques, the changes in topographic and geomorphological parameters such as slope, aspect, and elevation can be calculated, thus obtaining geomorphological change data. Hydrological flow change data reflects the dynamic characteristics of the hydrological environment and can be extracted from hydrological environment monitoring data. For example, by analyzing groundwater level monitoring data and surface water flow monitoring data, the rates of change in groundwater level and surface water flow can be calculated, yielding hydrological flow change data.

[0020] Step 122: Input the geological structure deformation rate data into the geological process equation to calculate the degree of geological stress accumulation; input the geomorphological change data into the geomorphological evolution equation to calculate the geomorphological stability index; input the hydrological flow change data into the hydrodynamic equation to calculate the hydraulic erosion intensity.

[0021] The geological deformation rate data is input into the geological action equation, which comprehensively considers factors such as the type of geological structure, deformation rate, and mechanical properties of the soil and rock mass to calculate the degree of geological stress accumulation. Geological structural deformation causes changes in stress within the soil and rock mass, and this stress gradually increases over time. The geological action equation calculates the cumulative amount of geological stress by integrating the geological structural deformation rate and combining it with mechanical parameters such as the elastic modulus of the soil and rock mass.

[0022] Data on landform changes are input into the geomorphological evolution equation, which then incorporates factors such as slope, aspect, soil and rock properties, and vegetation cover to calculate a geomorphological stability index. Slope and aspect affect the gravitational potential energy and sliding force of the soil and rock mass, while soil and rock properties determine their resistance to landslides. Vegetation cover reinforces the slope. By comprehensively analyzing these factors and using methods such as fuzzy evaluation, the geomorphological evolution equation calculates the geomorphological stability index. A higher index indicates a more stable landform and a relatively lower probability of landslides; conversely, a lower index indicates a higher risk of landslides.

[0023] Hydrological flow variation data is input into the hydrodynamic equation, which considers factors such as rainfall intensity, rainfall duration, and groundwater permeability coefficient to calculate the intensity of hydraulic erosion. Rainfall infiltration increases the water content of soil and rock, reducing their shear strength. Simultaneously, groundwater flow generates dynamic water pressure, which erodes the soil and rock. The hydrodynamic equation, through analysis of hydrological flow variation data and incorporating relevant parameters of rainfall and groundwater, calculates rainfall infiltration and dynamic water pressure, thereby obtaining the intensity of hydraulic erosion.

[0024] Step 123: Establish a first interaction relationship between the degree of geological stress accumulation and the geomorphological stability index, which is represented by the effect function of geological structural deformation on geomorphological features; establish a second interaction relationship between the geomorphological stability index and the intensity of hydraulic erosion, which is represented by the effect function of geomorphological features on hydrological pathways; establish a third interaction relationship between the intensity of hydraulic erosion and the degree of geological stress accumulation, which is represented by the effect function of hydrological erosion on the integrity of geological structures.

[0025] When establishing the first interaction relationship between the degree of geological stress accumulation and the geomorphological stability index, geological structural deformation alters the topography and thus affects geomorphological stability. For example, fault displacement and folding caused by geological tectonic activity can change the slope and aspect of mountains, increasing the sliding force on soil and rock masses and reducing geomorphological stability. This relationship is described by a function of the effect of geological structural deformation on geomorphology. This function takes the degree of geological stress accumulation as input, considers the mode and intensity of geological structural deformation, and outputs the change in geomorphology, which in turn affects the geomorphological stability index.

[0026] When establishing the second interaction relationship between the geomorphological stability index and the intensity of hydraulic erosion, geomorphology influences hydrological pathways. Factors such as topographic relief, slope, and aspect determine the direction and velocity of surface water flow, as well as the infiltration pathways of groundwater. For example, steep slopes cause surface water to flow rapidly, increasing hydraulic erosion; while gentle terrain may cause surface water to remain for a longer period, affecting groundwater recharge. This relationship is described by an effect function on the influence of geomorphology on hydrological pathways. This function takes the geomorphological stability index as input, considers the characteristics of the geomorphology, and outputs the changes in hydrological pathways, thus affecting the intensity of hydraulic erosion.

[0027] When establishing a third interaction relationship between hydraulic erosion intensity and the degree of geological stress accumulation, hydrological erosion damages geological structures and affects the distribution of geological stress. Rainfall infiltration and groundwater flow increase the water content of soil and rock masses, reducing their shear strength. Simultaneously, dynamic water pressure exerts scouring effects on the soil and rock masses, weakening the integrity of the geological structure. This relationship is described by a function of the weakening effect of hydrological erosion on the integrity of geological structures. This function takes hydraulic erosion intensity as input, considers the mode and intensity of hydrological erosion, and outputs the degree of damage to the geological structure, thus affecting the degree of geological stress accumulation.

[0028] Step 124: Integrate the first interaction relationship, the second interaction relationship, and the third interaction relationship to construct a geological-geomorphological-hydrological coupling model that reflects the synergistic effect of multiple factors.

[0029] The first, second, and third interaction relationships are integrated to construct a coupled geological-geomorphological-hydrological model. This integration process requires consolidating the geological equations, geomorphological evolution equations, and hydrodynamic equations to ensure their interrelationship and mutual influence. Specifically, the degree of geological stress accumulation, geomorphological stability index, and hydraulic erosion intensity are used as coupling variables among the three equations, connected through the three interaction relationships.

[0030] For example, the geological process equation considers the influence of geomorphic stability index and hydraulic erosion intensity on the degree of geological stress accumulation; the geomorphic evolution equation considers the influence of geological stress accumulation and hydraulic erosion intensity on the geomorphic stability index; and the hydrodynamic equation considers the influence of geological stress accumulation and geomorphic stability index on the hydraulic erosion intensity. In this way, the three equations form an organic whole, enabling a more comprehensive and accurate reflection of the synergistic effects between geological, geomorphic, and hydrological factors.

[0031] When constructing a model, it is also necessary to consider its boundary conditions and initial conditions. Boundary conditions include the boundaries of geological structures, topography, and hydrological environment, while initial conditions include the initial state of geological stress, the initial state of landform, and the initial state of hydrological flow. By setting appropriate boundary and initial conditions, it is ensured that the model can accurately simulate the actual conditions of the target seismic zone.

[0032] Step 130: Based on the preset combination of scenario parameters, use the geological-geomorphological-hydrological coupling model to dynamically simulate and quantitatively evaluate the instability probability and scale of potential landslides in the target seismic zone, and generate a graphic and text-annotated quantitative evaluation report containing the spatial distribution characteristics, temporal development trend and impact range of the landslides.

[0033] In an optional embodiment, step 130 includes: Step 131: Obtain a preset combination of scenario parameters, which includes scenarios of changes in earthquake activity intensity, changes in precipitation intensity, and changes in the intensity of human engineering activities.

[0034] The preset scenario parameter combinations are set based on the actual conditions and possible disaster scenarios in the target seismic zone. The seismic activity intensity variation scenario considers the impact of earthquakes of different intensities on landslides. In the target seismic zone, seismic activity is frequent and uncertain; earthquakes of different magnitudes, focal depths, and durations will cause varying degrees of vibration and damage to the mountains, thereby increasing mountain instability and the likelihood of landslides. The precipitation intensity variation scenario considers the triggering effect of rainfall on landslides. This region has abundant rainfall; different rainfall intensities and durations will lead to increased water content in the soil and rock, rising groundwater levels, and reduced slope stability. The human engineering activity intensity variation scenario considers the alteration of topography and geological structure caused by human activities such as road construction, building construction, and mining. These activities may disrupt the original equilibrium of the mountains, change the stress state of the soil and rock, and increase the risk of landslides.

[0035] After obtaining the preset combination of scenario parameters, they are used as input data for subsequent simulation calculations of the geological-geomorphological-hydrological coupled model. These combination of scenario parameters provide different input conditions for the model, enabling a more comprehensive assessment of the instability probability and scale of potential landslides under various conditions.

[0036] Step 132: Input the earthquake activity intensity change scenario into the geological-geomorphological-hydrological coupled model, and calculate the geological stress redistribution data under different earthquake activity intensities using the geological action equation; input the precipitation intensity change scenario into the geological-geomorphological-hydrological coupled model, and calculate the groundwater infiltration depth data under different precipitation intensities using the hydrodynamic equation; input the human engineering activity intensity change scenario into the geological-geomorphological-hydrological coupled model, and calculate the surface vegetation cover change data under different human engineering activity intensities using the geomorphological evolution equation.

[0037] By inputting scenarios of varying seismic activity intensity into the geological-geomorphological-hydrological coupled model's geological action equations, the equations calculate the redistribution of geological stress under different seismic intensities, taking into account factors such as earthquake intensity, frequency, and focal depth. Earthquakes cause vibrations and deformations in mountains, leading to a redistribution of stress within the rock and soil mass. The geological action equations simulate the impact of earthquakes on geological stress by considering factors such as the propagation characteristics of seismic waves, the mechanical properties of the rock and soil mass, and the characteristics of geological structures. For example, when seismic waves propagate within the rock and soil mass, they induce elastic and plastic deformations, thereby altering the magnitude and direction of geological stress. Through simulations of varying seismic activity intensity scenarios, geological stress redistribution data under different seismic intensities are obtained.

[0038] By inputting the scenario of varying precipitation intensity into the hydrodynamic equation, which considers factors such as rainfall intensity, rainfall duration, and the permeability coefficient of the soil and rock mass, the hydrodynamic equation calculates groundwater infiltration depth data under different rainfall intensities. Rainfall infiltration is one of the important factors affecting slope stability; different rainfall intensities and durations lead to different groundwater infiltration depths in the soil and rock mass. The hydrodynamic equation, through simulation of the rainfall infiltration process, considers factors such as the porosity and saturation of the soil and rock mass to calculate the groundwater infiltration depth at different times and locations. For example, under conditions of high rainfall intensity and long rainfall duration, the groundwater infiltration depth will increase, potentially leading to a rise in the groundwater level and thus increasing the slope's sliding force.

[0039] By inputting the scenario of changes in the intensity of human engineering activities into the geomorphological evolution equation, the equation calculates the changes in surface vegetation cover under different intensities of human engineering activities. Human engineering activities such as road construction, building construction, and mining can damage surface vegetation, leading to a decrease in vegetation cover. Vegetation has a slope stabilizing effect, increasing slope stability. The geomorphological evolution equation calculates changes in surface vegetation cover by simulating human engineering activities, considering factors such as the scope, method, and time of these activities. For example, large-scale road construction projects may destroy a large amount of vegetation, leading to a significant decrease in vegetation cover, thereby reducing slope stability.

[0040] Step 133: Based on the geological stress redistribution data, groundwater infiltration depth data, and surface vegetation cover change data, the instability probability of potential landslide bodies is calculated using the geological-geomorphological-hydrological coupling model, and dynamic curve data of instability probability changing over time is generated.

[0041] In the following steps, step 133 includes: Step 1331: Extract the maximum principal stress value and the minimum principal stress value from the geological stress redistribution data; extract the seepage depth rate as a function of time from the groundwater seepage depth data; extract the vegetation root slope stabilization capacity index from the surface vegetation cover change data; input the maximum principal stress value, the minimum principal stress value, the seepage depth rate, and the vegetation root slope stabilization capacity index into the instability probability calculation module of the geological-geomorphological-hydrological coupled model; calculate the basic instability probability value at the initial moment through the instability probability calculation module.

[0042] The maximum and minimum principal stress values ​​are extracted from geological stress redistribution data. These values ​​reflect the stress state within the soil and rock mass and significantly influence landslide occurrence. The maximum and minimum principal stress values ​​are identified by analyzing stress data at different locations and times within the geological stress redistribution data. Specifically, eigenvalue decomposition can be performed on the stress tensor at each time point to obtain the principal stress values, and then the maximum and minimum principal stress values ​​can be found across all time points.

[0043] The seepage rate, which measures the change in seepage depth over time, is extracted from groundwater seepage depth data. The seepage rate reflects the changing effect of groundwater on soil and rock over time. By performing time-difference processing on the groundwater seepage depth data, the change in seepage depth for each time period is calculated, and then divided by the time interval, the seepage rate is obtained. For example, subtracting the seepage depth data from two adjacent time points and then dividing by the time interval yields the seepage rate for that time period.

[0044] The vegetation root system slope stabilization capacity index is extracted from surface vegetation cover change data. This index reflects the enhancing effect of vegetation on mountain stability. Vegetation roots can anchor soil and rock, increasing the slope's resistance to sliding. By analyzing surface vegetation cover change data and considering factors such as vegetation type, density, root depth, and distribution, the vegetation root system slope stabilization capacity index can be calculated. For example, a calculation model for the vegetation root system slope stabilization capacity index can be established based on indicators such as vegetation biomass and root tensile strength.

[0045] The maximum principal stress, minimum principal stress, seepage depth rate, and vegetation root system slope stabilization capacity index are input into the instability probability calculation module of the geological-geomorphological-hydrological coupled model. The instability probability calculation module comprehensively considers these parameters, as well as the mechanical properties of the soil and rock mass, the geometry of the slope, and other factors to calculate the basic instability probability value at the initial moment. This basic value represents the instability probability of the potential landslide body under the initial conditions.

[0046] Step 1332: Set the time step increment, and calculate the cumulative change of geological stress, the change of groundwater infiltration depth, and the attenuation of vegetation root slope stabilization capacity at each time node according to the time step increment.

[0047] Define a time step increment, such as in days, weeks, or months. Calculate the relevant changes at each time point according to this time step. The cumulative change in geological stress refers to the increase or decrease in geological stress within each time step. Calculate the stress changes within each time step based on geological stress redistribution data. For example, the change in geological stress within a time step can be obtained by subtracting the geological stress data from two adjacent time points. Simultaneously, considering the continuous impact of tectonic activity, the changes in geological stress are accumulated to obtain the cumulative change in geological stress.

[0048] The change in groundwater infiltration depth reflects the alteration of groundwater infiltration depth within each time step. It is related to factors such as rainfall intensity and geological structure. Based on groundwater infiltration depth data, the change in infiltration depth within each time step is calculated. For example, the change in groundwater infiltration depth within a time step is obtained by subtracting the groundwater infiltration depth data from two adjacent time points. The calculation process needs to consider the influence of factors such as rainfall infiltration and groundwater discharge.

[0049] The decay of vegetation root system slope stabilization capacity reflects the degree to which this capacity weakens over time. This decay may be caused by vegetation destruction due to human activities, natural factors, or other reasons. Based on data on changes in surface vegetation cover, the change in vegetation root system slope stabilization capacity is calculated for each time step. For example, the decay of vegetation root system slope stabilization capacity within a given time step is obtained by subtracting the vegetation root system slope stabilization capacity indices from those at two adjacent time points. The calculation process must consider the influence of factors such as vegetation growth, death, and damage.

[0050] Step 1333: Based on the cumulative change in geological stress, the change in groundwater infiltration depth, and the attenuation of the slope stabilization capacity of vegetation roots, the basic value of instability probability is dynamically corrected to obtain the corrected value of instability probability at each time point.

[0051] An increase in the cumulative change of geological stress may increase the probability of instability because increased geological stress makes the stress state within the soil and rock mass more unstable, increasing the likelihood of landslides. An increase in the change of groundwater infiltration depth also affects the probability of instability; increased groundwater infiltration depth increases the saturation of the soil and rock mass, reducing its shear strength and thus increasing the slope's sliding force. An increase in the attenuation of the slope-stabilizing capacity of vegetation roots weakens the reinforcing effect of vegetation on the mountain, reducing slope stability and further increasing the probability of instability.

[0052] In the dynamic correction process, it is necessary to establish an instability probability correction model. This model uses the cumulative change in geological stress, the change in groundwater infiltration depth, and the attenuation of vegetation root system slope stabilization capacity as input parameters, considering factors such as the mechanical properties of the soil and rock mass and the geometry of the slope. The correction coefficient for the instability probability is obtained through a specific calculation method. For example, a multiple linear regression model or a nonlinear regression model can be used, and the model parameters can be determined based on historical data and empirical formulas. Then, the base value of the instability probability is multiplied by the correction coefficient to obtain the corrected instability probability value at each time point.

[0053] Step 1334: Arrange the instability probability correction values ​​in chronological order to generate dynamic curve data of instability probability changing over time.

[0054] The instability probability correction values ​​for each time point are arranged chronologically. This arrangement clearly shows the trend of instability probability changes over time. These data are then plotted as a curve to obtain the dynamic instability probability curve. Interpolation and fitting methods can be used to make the curve smoother and more accurate. For example, spline interpolation can be used to interpolate the instability probability correction values, resulting in a more densely packed set of instability probability values ​​at different time points. Then, the least squares method is used to fit these data, yielding the dynamic instability probability curve. This curve visually reflects the dynamic changes in the instability probability of a potential landslide. By observing the curve's trend, the timing and trend of a potential landslide can be predicted. For example, if the curve shows an upward trend and approaches a certain critical value, it indicates an increasing likelihood of a landslide, requiring strengthened monitoring and early warning.

[0055] Step 134: Based on the instability probability dynamic curve data, determine the critical instability time point of the potential landslide body; at the time corresponding to the critical instability time point, use the geological-geomorphological-hydrological coupling model to calculate the sliding distance data and the area data of the affected area of ​​the landslide body; based on the sliding distance data and the area data of the affected area, determine the landslide scale level.

[0056] The critical instability time point is determined based on the dynamic curve data of the instability probability. The critical instability time point refers to the time when the instability probability reaches a certain preset threshold. When the instability probability exceeds this threshold, the potential landslide body is very likely to experience a landslide. This can be achieved by setting an instability probability threshold and finding the first time point in the dynamic curve that exceeds this threshold; this is then identified as the critical instability time point. For example, if the instability probability threshold is set to 0.5, the first time point in the dynamic curve where the instability probability is greater than 0.5 is the critical instability time point.

[0057] At the critical instability point, a coupled geological-geomorphological-hydrological model is used to calculate the landslide distance and affected area. The model simulates the landslide's movement after instability based on the interactions of geological, geomorphological, and hydrological factors. When calculating the sliding distance, factors such as the landslide's initial velocity, acceleration, and topography are considered; the sliding distance is obtained by solving the landslide's motion equations. When calculating the affected area, factors such as the landslide's volume, trajectory, and topographic relief are considered; the affected area is obtained by simulating the landslide's deposition range.

[0058] Based on sliding distance data and affected area data, the scale of a landslide is determined. Landslides can be classified into different levels, such as small, medium, large, and extra-large, according to pre-defined scale classification standards. Different levels of landslides have varying degrees of impact on the affected body, providing important data for subsequent risk assessment and prevention. For example, a short sliding distance and a small affected area are classified as a small landslide; a long sliding distance and a large affected area are classified as a large landslide.

[0059] Step 135: Integrate the dynamic curve data of instability probability, critical instability time point, and landslide scale level to generate a quantitative assessment report with graphic annotations that includes the spatial distribution characteristics, temporal development trend, and impact range of the landslide.

[0060] In one alternative design approach, step 135 includes: Step 1351: Perform feature extraction on the instability probability dynamic curve data to obtain the slope change rate data and peak probability value of the curve.

[0061] Feature extraction from the dynamic curve data of instability probability is performed to analyze the changing patterns of instability probability in greater depth. The rate of change of slope reflects the speed of change in instability probability, helping to determine whether the increase or decrease in landslide instability probability is drastic. The rate of change of slope data is obtained by numerically differentiating the dynamic curve of instability probability. Specifically, for discrete instability probability data points, the slope between two adjacent data points can be calculated using the difference method, and then these slopes are further differencing to obtain the rate of change of slope. For example, for an instability probability data sequence P(t), the difference in instability probability between two adjacent time points ΔP = P(t+1) - P(t), and then the difference in slope between two adjacent points Δ(ΔP), can be calculated to obtain the rate of change of slope.

[0062] The peak probability value is the maximum value in the instability probability dynamic curve, representing the maximum probability of instability of a potential landslide body at a certain point in time. By traversing the instability probability dynamic curve data and comparing the instability probability values ​​of each data point, the maximum value is found, which is the peak probability value. The peak probability value can help determine the greatest likelihood of a landslide occurring.

[0063] Step 1352: Associate the critical instability time point with the peak probability value to determine the deterministic time interval for landslide occurrence.

[0064] Correlating the critical instability time point with the peak probability value allows for more accurate prediction of when a landslide will occur. The critical instability time point is the time when the instability probability reaches a preset critical value, while the peak probability value represents the highest probability of instability. If the peak probability value appears near the critical instability time point, it can be considered that a landslide is more likely to occur within this time interval.

[0065] When determining the definitive time interval for landslide occurrence, a time range can be set based on the actual situation. For example, if the peak probability value corresponds to a period before or after the critical instability time point, this period can be defined as the definitive time interval for landslide occurrence. When determining the time range, factors such as the changing trend of the instability probability and the stability of geological conditions need to be considered. If the instability probability remains high after the critical instability time point, and the rate of change of the slope is small, it indicates that the probability of landslide occurrence is high for a period of time, and the definitive time interval can be appropriately expanded.

[0066] Step 1353: Based on the landslide scale level, divide the landslide impact area into severity zones, including extremely severe impact zone, severe impact zone, moderate impact zone, and slight impact zone.

[0067] The purpose of classifying landslide-affected areas into severity zones based on landslide size is to better understand the extent of landslide impact on different regions. Landslides of different sizes cause varying degrees of damage to the surrounding areas. Based on factors such as the landslide's sliding distance and the area of ​​the affected region, landslide sizes are classified into different levels, and then corresponding severity zones are defined.

[0068] Extremely severe impact zones are typically areas directly impacted by landslides. These areas may suffer severe damage from the impact and burial caused by the landslide, potentially resulting in the complete destruction of buildings, roads, and other infrastructure, leading to significant casualties and property losses. Severely impact zones also experience considerable impact, but less so than extremely severe zones. These areas may be affected by the landslide's ripples, such as shockwaves and flying rocks, causing varying degrees of damage to buildings. Moderately impact zones experience a moderate level of impact, potentially including ground vibrations and dust storms, with some infrastructure possibly suffering damage. Mildly impact zones experience minimal impact, possibly only feeling slight vibrations or experiencing a small amount of dust.

[0069] When classifying severity zones, factors such as topography, building distribution, and population density need to be considered. For example, in areas along the landslide path and with relatively flat terrain, the impact range of the landslide may be wider, and the range of severity zones may be correspondingly larger; while in areas with large topographic relief or obstacles, the impact range of the landslide may be limited, and the range of severity zones will be correspondingly smaller.

[0070] Step 1354: Overlay the deterministic time interval, severity zoning, and geospatial data of the target earthquake zone to generate a landslide spatial distribution feature layer; draw a landslide time development trend line graph based on the instability probability dynamic curve data and the deterministic time interval; convert the severity zoning into an impact range vector boundary.

[0071] Overlaying deterministic time intervals and severity zoning with geospatial data of the target seismic zone combines the temporal and spatial information of landslides, providing a more intuitive view of their distribution. The geospatial data of the target seismic zone includes information on topography, landforms, administrative divisions, and building distribution. Using Geographic Information System (GIS) technology, the deterministic time intervals and severity zoning information are fused with the geospatial data to generate a landslide spatial distribution feature layer.

[0072] During the overlay process, deterministic time intervals are added to the geospatial data as information in the time dimension, and severity zones are represented on the map using different colors or symbols. For example, red represents extremely severe impact areas, orange represents severely impact areas, yellow represents moderately impact areas, and green represents lightly impacted areas. In this way, the landslide spatial distribution feature layer can clearly show the landslide risk situation in different areas at different times.

[0073] Based on the dynamic curve data of instability probability and the deterministic time interval, a line graph of the landslide's time development trend is plotted. The horizontal axis of the line graph represents time, and the vertical axis represents the instability probability. The portion of the dynamic curve data of instability probability within the deterministic time interval is plotted on the line graph, and the critical instability time point and peak probability value are marked. The line graph allows for a visual observation of the changing trend of landslide instability probability over time, as well as the changes in risk within the deterministic time interval.

[0074] Severity zones are converted into vector boundaries of influence. Severity zones are typically represented as regions; to more accurately describe their extent, they need to be converted into vector boundaries. This is done by digitizing the boundaries of severity zones, converting them into vector data. Specifically, polygonal vector data can be used to represent the boundaries of severity zones, with the vertex coordinates of each polygon corresponding to the actual geographical location on the zone boundary. This allows the vector boundaries of influence to be displayed more accurately on the map and enables further analysis and processing.

[0075] Step 1355: Integrate the landslide spatial distribution feature layer, the time development trend line graph, and the influence range vector boundary to generate a graphic and text-annotated quantitative assessment report containing the landslide spatial distribution features, time development trend, and influence range.

[0076] By integrating landslide spatial distribution feature layers, time development trend line graphs, and impact range vector boundaries to generate a graphically labeled quantitative assessment report, the aim is to consolidate information on the space, time, and impact range of landslides into a comprehensive and intuitive report.

[0077] During the integration process, the spatial distribution feature layer of landslides, the time-based trend line graph, and the vector boundary of the impact range are first laid out and arranged. The spatial distribution feature layer of landslides can be used as the main image to show the spatial distribution of landslides; the time-based trend line graph can be used as a supplementary image to show the changing trend of landslide instability probability over time; and the vector boundary of the impact range can be marked on the main image to clarify the impact range of the landslides.

[0078] Next, the charts and graphs are annotated. On the landslide spatial distribution characteristic layer, the names, extents, and corresponding risk levels of different severity zones are marked. On the time trend line graph, key information such as the critical instability time point, peak probability value, and deterministic time interval are marked. Simultaneously, textual explanations are added to the report to clarify the meaning of the charts and graphs, the analysis results, and the conclusions. For example, the characteristics of different severity zones and their potential impacts are explained, the changing trend of landslide instability probability is analyzed, and predictions of future risks are provided.

[0079] Finally, the charts and text descriptions with annotations are integrated to generate a quantitative assessment report with annotations that includes the spatial distribution characteristics, temporal development trends, and impact range of landslides. This report can provide a reference for early warning, prevention and control, and decision-making regarding landslide disasters.

[0080] Step 140: Based on the above-mentioned graphic and textual annotation quantitative assessment report, perform vulnerability analysis and risk assessment on the disaster-bearing bodies in the target seismic zone, and generate a comprehensive guidance report that includes a spatial distribution map of the vulnerability of disaster-bearing bodies, suggestions for landslide risk prevention and control measures, and regional spatial planning optimization schemes.

[0081] In a preferred embodiment, step 140 includes: Step 141: Obtain basic data on the disaster-bearing bodies in the target earthquake zone. The basic data on the disaster-bearing bodies includes population distribution data, building distribution data, transportation network distribution data, and infrastructure distribution data.

[0082] In landslide hazard evolution analysis scenarios, obtaining basic data on the disaster-bearing bodies in the target seismic zone is fundamental for vulnerability analysis and risk assessment. Population distribution data, which can be obtained through censuses and statistical departments, reflects the distribution of the population within the target seismic zone, including information such as population density and age structure in different areas. For example, census data can provide information on the population size and distribution of each community and village, providing a basis for subsequent calculations of population exposure levels within different landslide impact zones.

[0083] Building distribution data can be obtained from urban planning departments, property registration departments, etc., and covers information such as building type, quantity, and location. Building types include residential, commercial, and industrial buildings, and different types of buildings have different vulnerabilities to landslides. For example, residential buildings may be more susceptible to landslide impact and burial, while industrial buildings may have relatively stronger resistance to landslides due to their specific structure and use. Building distribution data can be used to calculate the probability of building damage within different landslide impact zones.

[0084] Transportation network distribution data includes the distribution of transportation facilities such as highways, railways, and bridges, and can be obtained from transportation management departments. Transportation networks are vital links connecting various regions; landslides can disrupt transportation routes, affecting the transport of people and goods. By using transportation network distribution data, the length of transportation route disruptions can be calculated, and the impact of landslides on the transportation system can be assessed.

[0085] Infrastructure distribution data includes information on the distribution of facilities such as water supply, power supply, and communications, which can be obtained from relevant infrastructure management departments. Infrastructure is a vital support for people's lives and production; landslides can cause infrastructure dysfunction, affecting people's normal lives. Infrastructure distribution data can be used to calculate the degree of infrastructure dysfunction and understand the extent of landslide damage to infrastructure.

[0086] Step 142: Overlay the landslide spatial distribution characteristics with population distribution data to calculate the number of people exposed in different landslide impact zones; overlay the landslide spatial distribution characteristics with building distribution data to calculate the probability of building damage in different landslide impact zones; overlay the landslide spatial distribution characteristics with transportation network distribution data to calculate the length of transportation line interruption; overlay the landslide spatial distribution characteristics with infrastructure distribution data to calculate the degree of infrastructure function loss.

[0087] By overlaying landslide spatial distribution characteristics with population distribution data, the population exposure numbers within different landslide impact zones are calculated. Geographic Information System (GIS) technology is used to perform spatial analysis of landslide severity zones and population distribution data. Specifically, the population distribution data is gridded according to geographical location, with each grid corresponding to a population count. Then, each grid is overlaid with the landslide impact zones, and the population count of grids falling within different landslide impact zones is counted to obtain the population exposure numbers within each zone. For example, if an extremely severe impact zone contains multiple grids, the population counts of these grids are added together to obtain the population exposure numbers within that extremely severe impact zone.

[0088] By overlaying landslide spatial distribution characteristics with building distribution data, the probability of building damage within different landslide impact zones is calculated. Considering factors such as landslide impact force and damage extent, and combining information such as building type, structure, and seismic performance, the damage situation of buildings is assessed. A building damage probability model can be established, which takes landslide intensity and building characteristics as input parameters and outputs the building damage probability. For example, for buildings located in extremely severe impact zones, the probability of damage is calculated based on their structural type and seismic resistance level, combined with the landslide impact force and damage extent. Then, statistics are collected on buildings within each landslide impact zone to obtain the building damage probability for that zone.

[0089] The spatial distribution characteristics of landslides are overlaid with traffic network distribution data to calculate the length of traffic line interruptions. Spatial analysis of traffic network data and landslide impact zones is performed to determine the intersection of traffic lines with these zones. For traffic lines intersecting with landslide impact zones, their lengths within each zone are calculated, and these lengths are summed to obtain the total length of traffic line interruptions. For example, if a highway passes through both a severely affected zone and a moderately affected zone, the lengths of the highway within each zone are calculated separately and then summed to obtain the interruption length of the highway. The same calculation is performed for all traffic lines intersecting with landslide impact zones, and finally, the interruption lengths of all lines are summed to obtain the total length of traffic line interruptions.

[0090] By overlaying landslide spatial distribution characteristics with infrastructure distribution data, the degree of infrastructure functional loss is calculated. The manner and extent of landslide damage to infrastructure are considered; for example, landslides may damage water supply pipelines, power lines, and communication base stations. For each infrastructure, the degree of functional loss is assessed based on its type and location within the landslide impact zone. For instance, for water supply pipelines, the proportion of water supply function loss is calculated based on the damaged length and extent of damage within the landslide impact zone. All infrastructure is assessed, and then, considering their importance and impact range, the degree of infrastructure functional loss is calculated.

[0091] Step 143: Based on the population exposure number, building damage probability, transportation line interruption length and infrastructure function loss degree, classify the vulnerability level of the disaster-bearing body and generate the vulnerability level of the disaster-bearing body.

[0092] In one embodiment, step 143 includes: Step 1431: Set thresholds for population exposure, building damage probability, transportation line interruption length, and infrastructure function loss.

[0093] Setting thresholds for population exposure, building damage probability, transportation line interruption length, and infrastructure function loss is to quantify the vulnerability of disaster-bearing bodies. These thresholds are determined based on historical landslide disaster data, the actual situation of the target seismic zone, and relevant standards and specifications.

[0094] The population exposure threshold refers to the threshold at which the exposed population level exceeds a certain threshold within a landslide-affected area, indicating a high level of vulnerability in that region. This threshold can be determined based on factors such as population density and building load-bearing capacity in the target seismic zone. For example, if an area primarily consists of multi-story residential buildings with a high population density, the population exposure threshold can be relatively low; conversely, if the area is mainly an industrial park with a low population density, the threshold can be relatively high.

[0095] The building damage probability threshold is a standard for judging the vulnerability of buildings in landslide disasters. Different types of buildings have different damage probability thresholds based on factors such as their type, structure, and seismic performance. For example, older buildings with poor seismic performance may have a relatively low damage probability threshold, while newly built buildings with good seismic performance may have a relatively high damage probability threshold.

[0096] The interruption length threshold for transportation routes is used to assess the vulnerability of transportation networks. The threshold is determined by considering factors such as the importance of the route and the availability of alternative routes. If a route is a major thoroughfare connecting important areas and has no alternative routes, its interruption length threshold can be relatively low; conversely, if alternative routes are available, the threshold can be relatively high.

[0097] Infrastructure failure thresholds are used to measure the vulnerability of infrastructure. These thresholds are determined based on factors such as the type of infrastructure, its importance, and the difficulty of restoration. For example, infrastructure such as water and electricity supply is crucial to people's lives, and its failure threshold can be relatively low; while some less important communication facilities may have relatively high failure thresholds.

[0098] Step 1432: Compare the number of people exposed to the threshold for the number of people exposed to the population to obtain the population exposure comparison result; compare the probability of building damage to the threshold for the probability of building damage to obtain the building damage comparison result; compare the length of traffic line interruption to the threshold for the length of traffic line interruption to obtain the traffic interruption comparison result; compare the degree of infrastructure function loss to the threshold for the degree of infrastructure function loss to obtain the infrastructure comparison result.

[0099] The number of exposed individuals is compared to a threshold to determine if the number exceeds the threshold. If the number exceeds the threshold, it indicates that the population vulnerability in the area is high, and the comparison result is "exceeds the threshold." If the number does not exceed the threshold, it indicates that the population vulnerability in the area is relatively low, and the comparison result is "does not exceed the threshold." For example, in a severely affected area, if the calculated number of exposed individuals is 1000, and the set threshold is 800, then the comparison result is "exceeds the threshold."

[0100] The probability of building damage is compared with a building damage probability threshold to determine the vulnerability of buildings in landslide disasters. If the probability of building damage exceeds the threshold, it indicates that the buildings in the area are highly vulnerable, and the building damage comparison result exceeds the threshold. If the probability of building damage does not exceed the threshold, it indicates that the buildings in the area are relatively vulnerable, and the building damage comparison result does not exceed the threshold. For example, for a building in a moderately affected area, the calculated probability of building damage is 0.6, while the set building damage probability threshold is 0.5; therefore, the building damage comparison result exceeds the threshold.

[0101] The vulnerability of the transportation network is assessed by comparing the length of traffic line interruptions with a traffic line interruption length threshold. If the length of traffic line interruptions exceeds the threshold, it indicates that the transportation network in that area is highly vulnerable, and the traffic interruption comparison result is "exceeds the threshold." If the length of traffic line interruptions does not exceed the threshold, it indicates that the transportation network in that area is relatively vulnerable, and the traffic interruption comparison result is "does not exceed the threshold." For example, in a slightly affected area, if the calculated length of traffic line interruptions is 5 kilometers, and the set threshold for traffic line interruption length is 6 kilometers, then the traffic interruption comparison result is "does not exceed the threshold."

[0102] The vulnerability of infrastructure is measured by comparing the degree of infrastructure function loss with a threshold. If the degree of infrastructure function loss exceeds the threshold, it indicates that the infrastructure vulnerability in the area is high, and the infrastructure comparison result is "exceeds the threshold." If the degree of infrastructure function loss does not exceed the threshold, it indicates that the infrastructure vulnerability in the area is relatively low, and the infrastructure comparison result is "not exceeding the threshold." For example, for water supply infrastructure in a severely affected area, if the calculated degree of function loss is 0.8, and the set threshold for infrastructure function loss is 0.7, then the infrastructure comparison result is "exceeds the threshold."

[0103] Step 1433: Count the number of items exceeding the corresponding threshold in the population exposure comparison results, building damage comparison results, traffic disruption comparison results, and infrastructure comparison results; based on the number of items exceeding the corresponding threshold, classify the vulnerability level of the disaster-bearing body and generate the vulnerability level of the disaster-bearing body, which includes extremely high vulnerability level, high vulnerability level, medium vulnerability level, and low vulnerability level.

[0104] The number of items exceeding the corresponding thresholds in the comparison results of population exposure, building damage, traffic disruption, and infrastructure is calculated. The four comparison results are then aggregated to count the number of items exceeding the threshold. For example, if the population exposure comparison result exceeds the threshold, the building damage comparison result exceeds the threshold, the traffic disruption comparison result does not exceed the threshold, and the infrastructure comparison result exceeds the threshold, then the number of items exceeding the corresponding threshold is 3.

[0105] The vulnerability level of a landslide-bearing body is classified based on the number of items exceeding a corresponding threshold. A higher number of items exceeding the threshold indicates greater vulnerability of the body to landslides. Specific classification criteria can be set according to actual circumstances. For example, if four items exceed the threshold, the body is classified as extremely vulnerable; three items as highly vulnerable; two items as moderately vulnerable; and one or zero items as low vulnerable. This classification clearly defines the vulnerability level of different landslide-bearing bodies.

[0106] Step 144: Assess the landslide risk level at different time stages based on the vulnerability level and time trend of the disaster-bearing body; formulate landslide risk prevention and control measures based on the landslide risk level and impact range; and generate a regional spatial planning optimization scheme by combining the landslide risk prevention and control measures and historical spatial planning data of the target seismic zone.

[0107] In the following steps, the assessment of landslide risk levels at different time stages, based on the vulnerability level of the affected body and its temporal development trend, includes: Step 1441: Extract multiple key time nodes from the time development trend. The key time nodes include data during the early warning period before the landslide, data during the initial stage of the landslide, data during the middle stage of the landslide development, and data during the stabilization period of the landslide.

[0108] The characteristics and risk levels differ at different stages of landslide evolution. Extracting key time points from the temporal development trend allows for a more accurate assessment of landslide risk at different stages. Data from the pre-landslide warning period typically falls within the timeframe where the probability of landslide instability begins to rise significantly but has not yet reached a critical value. This stage can be identified through monitoring data and model predictions. Its characteristic is that the potential risk of landslides gradually increases, but there is still some time to take preventative measures. For example, by monitoring and analyzing parameters such as the degree of geological stress accumulation, geomorphic stability index, and hydraulic erosion intensity, the changing trends of these parameters can indicate that a landslide may occur in the near future; the corresponding time period is the pre-landslide warning period.

[0109] Data from the initial stage of a landslide refers to the period immediately following the onset of the landslide. During this stage, the landslide body begins to exhibit significant displacement and fracturing, and the probability of instability rises rapidly. The start time of this stage can be determined through real-time monitoring data from displacement monitoring equipment, seismic monitoring instruments, etc. For example, when a displacement monitoring instrument detects a sudden increase in the displacement of the landslide body, and the probability of instability exceeds a critical value, it signifies that the landslide has entered its initial stage.

[0110] Mid-stage landslide development data reflects the state of a landslide during its development. At this stage, the landslide's movement speed accelerates, its impact area gradually expands, and the damage to the surrounding environment and affected structures increases. Data for this stage can be obtained by monitoring and analyzing parameters such as the landslide's trajectory and the area of ​​its impact. For example, remote sensing imagery and geographic information system (GIS) technology can be used to monitor the landslide's movement and changes in its impact area in real time.

[0111] Landslide stabilization period data refers to data related to the aftermath of a landslide. During this stage, landslide movement ceases, the probability of instability returns to a low level, and geological, geomorphological, and hydrological conditions gradually stabilize. The start time of this stage can be determined through long-term monitoring of parameters such as landslide displacement and deformation. For example, when displacement monitoring instruments continuously detect that the landslide displacement no longer changes for a period of time, and all monitoring parameters remain stable within the normal range, it signifies that the landslide has entered the stabilization period.

[0112] Step 1442: Divide the vulnerability level of the disaster-bearing body according to the time dimension based on the key time nodes to obtain the vulnerability level of the disaster-bearing body corresponding to each key time node.

[0113] The vulnerability level of landslide-bearing bodies is classified according to key time points because the impact of landslides varies at different time stages, and the vulnerability of the bodies also changes. During the warning period before a landslide occurs, although the physical properties and structure of the landslide-bearing body itself remain unchanged, there is relatively ample time to take preventative measures, such as evacuating people and reinforcing buildings. Therefore, the actual vulnerability of the landslide-bearing body may be reduced. For example, if the population in high-risk areas is evacuated in a timely manner during the warning period, the vulnerability of the population in that area will be significantly reduced.

[0114] In the early stages of a landslide, the sudden movement and collapse of the landslide mass directly exposes the affected area to danger, rapidly increasing its vulnerability. For example, buildings may collapse instantly under the impact of a landslide, transportation routes may be cut off, and infrastructure may be damaged.

[0115] In the middle stages of landslide development, as the affected area expands, more disaster-bearing structures are impacted, further increasing their vulnerability. Moreover, due to the continued damage caused by the landslide, some disaster-bearing structures that originally possessed a certain degree of resilience may also be severely affected. For example, some reinforced buildings may also suffer damage under the long-term effects of the landslide.

[0116] During the stabilization phase of a landslide, although the direct impact of the landslide has ended, the affected areas may still be susceptible to secondary disasters such as mudslides and landslides. Simultaneously, some damaged affected areas require repair and reconstruction, and their vulnerability remains somewhat increased. For example, damaged buildings may be affected by other natural disasters during the repair process, hindering the repair work.

[0117] By classifying the vulnerability level of disaster-bearing bodies according to key time nodes, the risk situation of disaster-bearing bodies at different time stages can be assessed more accurately.

[0118] Step 1443: Calculate the landslide risk index based on the vulnerability level of the disaster-bearing body corresponding to each key time node. The landslide risk index is obtained by multiplying the vulnerability level of the disaster-bearing body by the time urgency coefficient data.

[0119] The landslide risk index is an important indicator that comprehensively considers the vulnerability of the affected body and the time factor, enabling a more accurate assessment of landslide risk at different time stages. The vulnerability level of the affected body corresponding to each key time point reflects the likelihood and extent to which the affected body will suffer landslide damage at that time. For example, an extremely high vulnerability level indicates that the affected body is very likely to suffer severe damage in a landslide disaster, while a low vulnerability level indicates that the affected body is relatively safe.

[0120] The time urgency coefficient reflects the urgency of landslides at different time points. During the pre-landslide warning period, there is relatively ample time to take preventative measures, resulting in a lower time urgency coefficient. For example, measures such as evacuating people and reinforcing buildings can reduce the vulnerability of affected structures. In the early stages of a landslide, the landslide body has already begun to move, and the situation is urgent, leading to a higher time urgency coefficient. At this time, immediate emergency measures are needed, such as organizing rescue and relief efforts. In the middle stages of landslide development, the impact area continues to expand, and the degree of damage intensifies, maintaining a relatively high time urgency coefficient. Rapid measures are required to control the landslide's development and minimize losses. In the landslide stabilization period, although the landslide has stopped moving, follow-up work is still necessary, such as repairing damaged affected structures and assessing disaster losses; therefore, the time urgency coefficient is relatively low.

[0121] The landslide risk index is obtained by multiplying the vulnerability level of the affected body by the time urgency coefficient. For example, in the early stages of a landslide, the vulnerability level of the affected body is extremely high, corresponding to a high numerical value, and the time urgency coefficient is also high. Therefore, the resulting landslide risk index will be very large, indicating that the landslide risk at that time stage is extremely high. By calculating the landslide risk index, the landslide risk at different time stages can be quantitatively assessed.

[0122] Step 1444: Set a landslide risk index threshold, compare the landslide risk index with the landslide risk index threshold, and output the landslide risk level for different time periods based on the comparison results. The landslide risk level includes extremely dangerous level, highly dangerous level, moderately dangerous level and low dangerous level.

[0123] Setting landslide risk index thresholds is to classify landslide risks into levels, allowing for a more intuitive understanding of the degree of landslide risk at different times. Landslide risk index thresholds can be determined based on historical landslide disaster data, the actual conditions of the target seismic zone, and relevant standards and specifications. For example, by analyzing similar past landslide disasters, statistically analyzing the range of landslide risk indices corresponding to different risk levels, and combining this with factors such as the geological, geomorphological, and hydrological conditions of the target seismic zone, a suitable landslide risk index threshold for that region can be determined.

[0124] The calculated landslide risk index is compared with a threshold. If the landslide risk index exceeds a higher threshold, it indicates that the landslide risk during that time period is very high, and the corresponding time period is classified as extremely dangerous. Under the extremely dangerous level, landslides may cause severe damage to the affected area, requiring immediate emergency response measures, such as large-scale evacuation of people and cessation of all potentially affected production activities.

[0125] If the landslide risk index exceeds the medium threshold but does not exceed the high threshold, the corresponding time period is classified as high-risk. At the high-risk level, the likelihood of a landslide is high, and it may cause significant damage to the affected area. Strengthened monitoring and early warning systems are necessary, along with proactive prevention and control measures such as reinforcing important buildings and strengthening traffic control.

[0126] If the landslide risk index exceeds the lower threshold but not the middle threshold, the corresponding time period is classified as moderate hazard. Under the moderate hazard level, landslides have a certain probability of occurring, but the degree of damage is relatively small. Some preventive measures can be taken, such as strengthening the inspection and maintenance of the disaster-bearing body and developing emergency plans.

[0127] If the landslide risk index does not exceed a lower threshold, the corresponding time period is classified as a low-risk level. Under the low-risk level, the probability of a landslide is relatively small, and the impact on the affected area is also relatively small, allowing for routine monitoring and management.

[0128] By comparing the landslide risk index with a threshold and classifying the risk level, clear guidance can be provided for landslide disaster prevention and control at different time stages, so as to rationally allocate prevention and control resources and improve prevention and control efficiency.

[0129] Furthermore, the recommendations for landslide risk prevention and control measures based on the landslide risk level and impact range include: Step 1445: Overlay the landslide risk level with the impact range to determine the risk prevention and control area and the non-risk prevention and control area.

[0130] Overlaying landslide risk levels with their impact ranges helps to more accurately identify areas requiring focused prevention and control measures and areas where relatively lenient prevention and control measures can be implemented. Within a target seismic zone, different areas have varying landslide risk levels and impact ranges. Geographic Information System (GIS) technology is used to spatially overlay landslide risk level maps and impact range maps. For example, overlaying areas corresponding to extremely high and highly dangerous risk levels with the landslide impact ranges determines the specific location and extent of these high-risk areas in actual geographic space. These areas are designated as risk prevention and control zones, requiring focused attention and strict prevention and control measures.

[0131] For areas corresponding to moderate and low risk levels, an overlay analysis is also performed on the landslide impact range. If these areas are less affected by landslides, or if other factors can reduce the risk of landslides, such as topographical barriers or vegetation protection, then these areas can be classified as non-risk control zones. In non-risk control zones, relatively lenient control measures can be adopted, but some monitoring and early warning are still necessary.

[0132] This overlay analysis clarifies the boundaries and scope of risk control areas and non-risk control areas.

[0133] Step 1446: For the risk prevention and control area, formulate engineering mitigation measures recommendations, including landslide reinforcement measures, drainage system construction measures, and slope protection measures; for the non-risk prevention and control area, formulate non-engineering prevention and control measures recommendations, including monitoring and early warning system construction measures and emergency plan development measures; and determine the implementation priority of different prevention and control measures based on the time development trend.

[0134] For risk prevention and control areas, engineering mitigation measures are recommended. Landslide reinforcement measures can enhance the stability of landslides and reduce the likelihood of landslides. Common landslide reinforcement measures include anchor bolt reinforcement and retaining wall reinforcement. Anchor bolt reinforcement involves drilling holes in the landslide body, inserting anchor bolts, and injecting cement mortar to connect the landslide body to stable rock or soil, thereby improving the landslide's resistance to sliding. Retaining wall reinforcement involves constructing retaining walls on the downslope direction of the landslide to block the downward force of the landslide.

[0135] Drainage system construction measures can reduce the impact of groundwater on landslides. By constructing drainage ditches, wells, and other facilities, surface water and groundwater can be removed in a timely manner, reducing the water content of the soil and rock and improving slope stability. For example, a ring-shaped drainage ditch can be built around the landslide to intercept surface water and prevent it from flowing into the landslide; drainage wells can be installed within the landslide to lower the groundwater level.

[0136] Slope protection measures can improve the erosion resistance of slopes. Common slope protection measures include vegetation slope protection and shotcrete with wire mesh. Vegetation slope protection involves planting vegetation on the slope, using the root system to stabilize the soil and reduce soil erosion. The vegetation also acts as a buffer against rainwater runoff. Shotcrete with wire mesh involves laying a steel mesh on the slope surface and then spraying concrete to form a protective layer that prevents weathering and erosion of the slope's soil and rock.

[0137] For non-risk control areas, recommendations for non-engineering control measures should be formulated. The construction of a monitoring and early warning system can monitor landslide changes in real time and promptly identify potential landslide risks. Equipment such as displacement monitors, water level monitors, and inclinometers can be installed to monitor parameters such as landslide displacement, groundwater level, and inclination in real time. When abnormal changes occur in the monitoring data, early warning signals should be issued promptly to remind relevant personnel to take measures.

[0138] Emergency response plans are designed to enable a rapid and effective response when landslides occur. Developing an emergency response plan requires clearly defining the emergency organization, emergency response procedures, and emergency rescue measures. For example, it should clarify the responsibilities and tasks of each department during a landslide, and develop personnel evacuation plans and resource allocation plans.

[0139] Based on the timeline of development, the priority of implementing different prevention and control measures should be determined. During the early warning period before a landslide occurs, priority should be given to constructing a monitoring and early warning system to promptly grasp the dynamic changes of the landslide and provide a basis for subsequent prevention and control decisions. Simultaneously, preventative reinforcement can be carried out on some important disaster-bearing bodies. In the initial stage of a landslide, landslide reinforcement measures and drainage system construction should be implemented as soon as possible to control the landslide's development and reduce losses. In the middle stage of landslide development, the implementation of engineering control measures should continue to be strengthened, while emergency plans should be activated to organize rescue and relief work. During the landslide stabilization period, the main focus should be on the repair and reconstruction of damaged disaster-bearing bodies, as well as the assessment and summary of disaster losses.

[0140] Step 1447: Integrate the engineering mitigation measures recommendations, non-engineering prevention and control measures recommendations, and implementation priorities to generate landslide risk prevention and control measures recommendations.

[0141] Integrating recommendations for engineering mitigation measures, non-engineering prevention and control measures, and implementation priorities aims to create a comprehensive and systematic set of recommendations for landslide risk prevention and control. First, a detailed review of both engineering and non-engineering prevention and control measures is conducted, clarifying the specific content, implementation location, and requirements for each measure. For example, for landslide reinforcement measures, the specifications and spacing of anchor bolts, and the dimensions and structure of retaining walls are specified; for monitoring and early warning system construction measures, the types of monitoring equipment, installation locations, and monitoring frequencies are defined.

[0142] Then, the measures are prioritized according to their implementation priorities. Measures that need to be implemented first at different time stages are placed first to ensure that effective prevention and control measures can be taken in a timely manner during critical periods. For example, during the early warning period before a landslide occurs, the construction of a monitoring and early warning system is prioritized; in the early stage of a landslide, landslide reinforcement measures and drainage system construction measures are prioritized.

[0143] Finally, the compiled recommendations and ranking results will be integrated to form a complete set of landslide risk prevention and control measures. This set of recommendations should include detailed information such as the name, content, implementation location, implementation time, and responsible unit of each measure to facilitate implementation by relevant departments and personnel. Additionally, the recommendations can include the budget and resource requirements for each measure to provide economic and resource guarantees for the implementation of prevention and control measures.

[0144] Furthermore, the process of combining the landslide risk prevention and control measures recommendations with historical spatial planning data of the target seismic zone to generate a regional spatial planning optimization scheme includes: Step 1448: Obtain historical spatial planning data for the target earthquake zone, including land use planning data, urban construction planning data, and industrial layout planning data; overlay the landslide risk prevention and control measures recommendations with the land use planning data to identify the land use types that need to be adjusted; overlay the landslide risk prevention and control measures recommendations with the urban construction planning data to identify the construction areas that need to be avoided; overlay the landslide risk prevention and control measures recommendations with the industrial layout planning data to identify the industrial parks that need to be relocated.

[0145] Obtaining historical spatial planning data for the target seismic zone is fundamental to generating optimized regional spatial planning schemes. Land use planning data reflects the land use and distribution within the target seismic zone, including agricultural land, construction land, and ecological land. Urban construction planning data includes the layout of towns and the planning of buildings, such as the distribution of residential areas, commercial districts, and industrial zones. Industrial layout planning data showcases the distribution and development plans of industrial parks, such as the location and scale of industrial parks and logistics parks.

[0146] By overlaying landslide risk prevention and control measures with land use planning data, the adaptability of different land use types under landslide risk can be analyzed. If certain land use types are located in high-risk areas and the risk cannot be reduced through prevention and control measures, it is necessary to consider adjusting these land use types. For example, converting construction land located in extremely dangerous areas into ecological land, such as building parks or woodlands, can both reduce landslide risk and improve the ecological environment.

[0147] By overlaying landslide risk prevention and control recommendations with urban construction planning data, areas that need to be avoided can be identified. For high-risk areas, new urban construction projects should be avoided. For example, if an area is designated as extremely dangerous and the risk cannot be effectively reduced through engineering measures, the construction of important buildings such as residences, schools, and hospitals in that area should be explicitly prohibited in the urban construction plan.

[0148] By overlaying landslide risk prevention and control recommendations with industrial layout planning data, industrial parks that need to be relocated can be identified. Some industrial parks may be located in high-risk areas, where landslides could cause severe economic losses and environmental pollution. For these industrial parks, relocation to safer areas should be considered. For example, chemical industrial parks located in highly hazardous areas should be relocated to places far away from landslide risk areas to ensure the safe development of the industry.

[0149] Step 1449: Based on the land use type that needs to be adjusted, the construction area that needs to be avoided, and the industrial park that needs to be relocated, formulate a spatial planning adjustment scheme; conduct a feasibility analysis on the spatial planning adjustment scheme and generate a feasibility assessment result; optimize the spatial planning adjustment scheme according to the feasibility assessment result and generate a regional spatial planning optimization scheme.

[0150] Based on the land use types requiring adjustment, the construction areas to be avoided, and the industrial parks to be relocated, a spatial planning adjustment plan should be formulated. The plan should clearly define the specific content of the adjustment, implementation steps, and timeline. For example, for land use types requiring adjustment, the scope, target use, and implementation methods should be detailed; for construction areas to be avoided, the boundaries and areas where construction is prohibited should be delineated; for industrial parks to be relocated, a relocation plan should be formulated, including the relocation timeline, new site selection, and relocation costs.

[0151] Feasibility analysis of spatial planning adjustment schemes requires consideration of multiple factors, including economic, social, and environmental aspects. Economically, it involves analyzing the implementation costs and benefits of the adjustment scheme, including land relocation expenses, industrial relocation costs, infrastructure construction costs, and the impact on local economic development. For example, it may assess the impact of converting construction land to ecological land on land value and tax revenue; or evaluate the impact of relocating industrial parks on industrial development and employment.

[0152] In terms of social aspects, the impact of the adjustment plan on residents' lives and social stability should be considered. For example, the impact of land use type adjustments on farmers' livelihoods should be analyzed, as well as the inconvenience caused to employees' employment and lives by industrial park relocation. At the same time, the opinions of local residents and relevant stakeholders should be fully solicited to ensure that the implementation of the plan has social support.

[0153] In terms of the environment, the impact of the proposed adjustments on the ecological environment is assessed. For example, the improvement effect on the ecosystem after converting construction land into ecological land is analyzed; the impact of relocating the industrial park on the environment of the new site is evaluated.

[0154] Based on the feasibility assessment results, the spatial planning adjustment scheme will be optimized. If the feasibility assessment results indicate that the scheme has certain problems or deficiencies, it needs to be modified and improved. For example, if the economic costs are too high, the scope and method of adjustment can be adjusted to reduce costs; if the social impact is significant, corresponding compensation and resettlement measures can be added to reduce the impact on residents. Through continuous optimization, the optimized regional spatial planning scheme will ensure that it can both reduce the risk of landslide disasters and achieve coordinated economic, social, and environmental development.

[0155] Step 145: Integrate the vulnerability levels of the disaster-bearing bodies, the landslide risk prevention and control measures recommendations, and the regional spatial planning optimization scheme to generate a comprehensive guidance report that includes a spatial distribution map of the vulnerability of disaster-bearing bodies, landslide risk prevention and control measures recommendations, and the regional spatial planning optimization scheme.

[0156] A comprehensive guidance report is generated by integrating the vulnerability levels of disaster-bearing bodies, recommendations for landslide risk prevention and control measures, and regional spatial planning optimization schemes. First, the vulnerability levels of disaster-bearing bodies are presented in the form of a spatial distribution map, visually demonstrating the vulnerability degree of disaster-bearing bodies in different areas. Geographic Information System (GIS) technology can be used to mark the vulnerability levels of disaster-bearing bodies on the map using different colors or symbols, such as red for areas with extremely high vulnerability levels, yellow for areas with high vulnerability levels, green for areas with medium vulnerability levels, and blue for areas with low vulnerability levels.

[0157] Then, detailed recommendations for landslide risk prevention and control measures should be included in the report. This includes engineering and non-engineering control measures for areas with different risk levels, as well as the implementation priority and specific requirements for each measure. Relevant charts and explanations should also be attached to make the recommendations clearer and easier to understand.

[0158] Finally, the report includes an optimized regional spatial planning scheme. It details the content, implementation steps, and timeline of the spatial planning adjustments, as well as the feasibility assessment results and optimization process. Furthermore, it analyzes the role of the optimized regional spatial planning scheme in reducing landslide risk and promoting regional sustainable development.

[0159] The report can also include case studies and summaries of experiences to provide reference for relevant departments and personnel. For example, it could introduce successful experiences from other regions in landslide disaster prevention and spatial planning optimization, analyze the similarities and differences between this region and other regions, and propose suggestions and measures suitable for this region. By generating a comprehensive guidance report, it can provide comprehensive and systematic guidance for landslide disaster prevention and regional planning in the target seismic zone.

[0160] In a non-limiting embodiment, the method further includes: extracting a model validation feature parameter set from the text and image annotation quantitative evaluation report, and extracting a dataset of prevention and control measure implementation effects from the comprehensive guidance report; acquiring newly added geological structure monitoring data, newly added topographic and geomorphic distribution data, and newly added hydrological environment monitoring data of the target seismic zone; performing a difference analysis between the newly added geological structure monitoring data, newly added topographic and geomorphic distribution data, and newly added hydrological environment monitoring data and the comprehensive monitoring dataset to generate a data difference feature matrix; inputting the model validation feature parameter set, the dataset of prevention and control measure implementation effects, and the data difference feature matrix into the geological-geomorphic-hydrological coupling model, and performing a difference analysis on the geological process equation, the geomorphic evolution equation, and the hydrological process equation. The interaction relationships of the dynamic equations are dynamically corrected to generate a corrected geological-geomorphological-hydrological coupled model. The corrected geological-geomorphological-hydrological coupled model is then validated using the newly added geological structure monitoring data, newly added topographic and geomorphological distribution data, and newly added hydrological environment monitoring data to generate a model prediction accuracy evaluation index. If the model prediction accuracy evaluation index meets the preset index conditions, the corrected geological-geomorphological-hydrological coupled model is determined as the updated geological-geomorphological-hydrological coupled model. If it does not meet the conditions, the process returns to the step of inputting the model validation feature parameter set, the prevention and control measure implementation effect dataset, and the data difference feature matrix into the geological-geomorphological-hydrological coupled model for dynamic correction again.

[0161] In the above embodiments, the preset index conditions are determined based on actual needs and the application scenario of the model. For example, the preset index conditions may specify that the model's prediction error is within a certain range, or that the model's prediction accuracy reaches a certain percentage. The model prediction accuracy evaluation index is compared with the preset index conditions. If the model prediction accuracy evaluation index meets the preset index conditions, it indicates that the performance of the corrected model has met the requirements, and the corrected geological-geomorphological-hydrological coupling model can be identified as the updated geological-geomorphological-hydrological coupling model. The updated model can be used for subsequent landslide disaster prediction and assessment work. If the model prediction accuracy evaluation index does not meet the preset index conditions, it indicates that the corrected model still has problems and needs further correction. The process returns to the step of inputting the model validation feature parameter set, the dataset of prevention and control measures implementation effects, and the data difference feature matrix into the geological-geomorphological-hydrological coupling model for dynamic correction again. During the re-correction process, it is necessary to deeply analyze the reasons for the model's inaccurate predictions and adjust the parameters and interaction relationships in the model until the model's prediction accuracy meets the preset index conditions.

[0162] In a non-limiting embodiment, the method further includes: generating a landslide spatiotemporal feature dataset based on the graphic and textual annotation quantitative assessment report; generating a three-dimensional geological grid model of the target seismic zone based on the landslide spatiotemporal feature dataset, wherein the node coordinates of the three-dimensional geological grid model correspond one-to-one with the geographic spatial coordinates in the landslide spatial distribution features; mapping the instability probability dynamic curve data, critical instability time points, and landslide scale levels in the landslide spatiotemporal feature dataset to the corresponding nodes of the three-dimensional geological grid model as risk feature parameters, thereby generating a three-dimensional grid model carrying risk feature parameters; performing dynamic evolution simulation of the three-dimensional grid model carrying risk feature parameters according to the time development trend, thereby generating a three-dimensional risk evolution model sequence; and annotating each model in the three-dimensional risk evolution model sequence with risk features, thereby generating a three-dimensional dynamic evolution model with annotated risk features; wherein the content of the risk feature annotation includes the node color gradient corresponding to the instability probability dynamic curve data, the time axis marker corresponding to the critical instability time point, and the boundary line of the influence range corresponding to the landslide scale level.

[0163] Thus, by labeling each model in the three-dimensional risk evolution model sequence with risk features, a three-dimensional dynamic evolution model with labeled risk features is generated. This model can more intuitively and in more detail display the landslide risk situation in the target seismic zone, providing a powerful tool for landslide disaster research and prevention. For example, by observing the three-dimensional dynamic evolution model with labeled risk features, high-risk areas can be quickly identified, and prevention and control measures can be taken in a timely manner; the development trend of landslides can be analyzed, the potential impact range of landslides can be predicted, and emergency plans can be prepared in advance.

[0164] This invention integrates multi-source data, including geological structure monitoring data, topographic distribution data, and hydrological environment monitoring data, in the target seismic zone to generate a comprehensive monitoring dataset. Based on this dataset, a geological-geomorphological-hydrological coupled model reflecting the synergistic effects of multiple factors is constructed. This model fully considers the interactions between geological, geomorphological, and hydrological factors, and compared to traditional models, it can more realistically simulate the occurrence and development of landslide disasters. Using this coupled model, potential landslide bodies are dynamically simulated and quantitatively assessed based on preset scenario parameter combinations. The generated graphic and text-annotated quantitative assessment report includes key information such as the spatial distribution characteristics, temporal development trend, and impact range of the landslide, making landslide disaster prediction more accurate and comprehensive. Based on this report, vulnerability analysis and risk assessment are conducted on the affected bodies, and a comprehensive guidance report is generated. This provides a scientific and systematic decision-making basis for landslide disaster prevention and control and regional spatial planning, reducing the losses to affected bodies caused by landslide disasters and improving the region's disaster resistance capacity.

[0165] Please see Figure 2A block diagram of a landslide hazard evolution analysis device based on a coupled model is provided. The landslide hazard evolution analysis device based on the coupled model includes: The data integration module is used to integrate and process multi-source data such as geological structure monitoring data, topographic distribution data and hydrological environment monitoring data of the target seismic zone to generate a comprehensive monitoring dataset. The model building module is used to construct a geological-geomorphological-hydrological coupled model that reflects the synergistic effects of multiple factors by establishing the interaction and correlation relationships of the target equation set based on the comprehensive monitoring dataset; the target equation set includes geological process equations, geomorphological evolution equations, and hydrodynamic equations. The quantitative assessment module is used to dynamically simulate and quantitatively assess the instability probability and scale of potential landslides in the target seismic zone based on a preset combination of scenario parameters and the geological-geomorphological-hydrological coupling model, and generate a quantitative assessment report with graphic annotations that includes the spatial distribution characteristics, temporal development trend and impact range of the landslides. The seismic zone analysis module is used to perform vulnerability analysis and risk assessment on the disaster-bearing bodies in the target seismic zone based on the graphic and text-annotated quantitative assessment report, and generate a comprehensive guidance report that includes a spatial distribution map of the vulnerability of disaster-bearing bodies, suggestions for landslide risk prevention and control measures, and regional spatial planning optimization schemes.

[0166] See Figure 3 As shown in the figure, this is a schematic diagram of the basic structure of a landslide disaster evolution analysis system 200 based on a coupled model provided in an embodiment of the present invention. The landslide disaster evolution analysis system 200 based on a coupled model includes: Processor 201; Storage device 202, on which computer program 2020 is stored; When the computer program 2020 is executed by the processor 201, the processor 201 implements any of the aforementioned methods for landslide disaster evolution analysis based on the coupled model.

[0167] Based on the above, a readable storage medium is provided, on which a program or instructions are stored, and when the program or instructions are executed by a processor, the steps of the above method are implemented.

[0168] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems or apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and relevant parts can be referred to the method section.

Claims

1. A method for landslide hazard evolution analysis based on a coupled model, characterized in that, The method includes: Multi-source data integration and processing are performed on geological structure monitoring data, topographic distribution data, and hydrological environment monitoring data of the target seismic zone to generate a comprehensive monitoring dataset. Based on the comprehensive monitoring dataset, a geological-geomorphological-hydrological coupled model reflecting the synergistic effects of multiple factors is constructed by establishing the interaction and correlation relationships of the target equation set; the target equation set includes geological process equations, geomorphological evolution equations, and hydrodynamic equations. Based on the preset combination of scenario parameters, the geological-geomorphological-hydrological coupling model is used to dynamically simulate and quantitatively evaluate the instability probability and scale of potential landslides in the target seismic zone, and generate a graphic and text-annotated quantitative evaluation report containing the spatial distribution characteristics, temporal development trend and impact range of the landslides. Based on the aforementioned graphic and textual annotation quantitative assessment report, vulnerability analysis and risk assessment are performed on the disaster-bearing bodies within the target seismic zone, generating a comprehensive guidance report that includes a spatial distribution map of the vulnerability of disaster-bearing bodies, suggestions for landslide risk prevention and control measures, and regional spatial planning optimization schemes.

2. The landslide disaster evolution analysis method based on a coupled model according to claim 1, characterized in that, Based on the comprehensive monitoring dataset, a geological-geomorphological-hydrological coupled model reflecting the synergistic effects of multiple factors is constructed by establishing the interaction relationships of the target equation set, including: Geological deformation rate data, geomorphological change data, and hydrological flow change data are extracted from the comprehensive monitoring dataset. Input the geological structure deformation rate data into the geological process equation to calculate the degree of geological stress accumulation; The landform change data is input into the landform evolution equation to calculate the landform stability index; Input the hydrological flow change data into the hydrodynamic equation to calculate the hydraulic erosion intensity; A first interaction relationship between the degree of geological stress accumulation and the geomorphological stability index is established, and the first interaction relationship is represented by the effect function of geological structural deformation on geomorphological transformation. A second interaction relationship between the geomorphological stability index and the intensity of hydraulic erosion is established, and the second interaction relationship is represented by the effect function of geomorphological morphology on hydrological path. A third interaction relationship between the intensity of hydraulic erosion and the degree of geological stress accumulation is established, and the third interaction relationship is represented by the weakening effect function of hydrological erosion on the integrity of geological structure. By integrating the first interaction relationship, the second interaction relationship, and the third interaction relationship, a geological-geomorphological-hydrological coupling model reflecting the synergistic effect of multiple factors is constructed.

3. The landslide disaster evolution analysis method based on a coupled model according to claim 1, characterized in that, Based on a preset combination of scenario parameters, the geological-geomorphological-hydrological coupled model is used to dynamically simulate and quantitatively assess the instability probability and scale of potential landslides in the target seismic zone, generating a graphic and text-annotated quantitative assessment report containing landslide spatial distribution characteristics, temporal development trends, and impact range, including: Obtain a preset combination of scenario parameters, which includes scenarios of changes in earthquake activity intensity, changes in precipitation intensity, and changes in the intensity of human engineering activities. The earthquake activity intensity change scenario is input into the geological-geomorphological-hydrological coupled model, and the geological stress redistribution data under different earthquake activity intensities are calculated through the geological process equation. The precipitation intensity variation scenario is input into the geological-geomorphological-hydrological coupled model, and the groundwater infiltration depth data under different precipitation intensities are calculated through the hydrodynamic equation. The scenario of changes in the intensity of human engineering activities is input into the geological-geomorphological-hydrological coupled model, and the change data of surface vegetation cover under different intensities of human engineering activities are calculated through the geomorphological evolution equation. Based on the geological stress redistribution data, groundwater infiltration depth data, and surface vegetation cover change data, the instability probability of potential landslide bodies is calculated using the geological-geomorphological-hydrological coupling model, generating dynamic curve data of instability probability changing over time. Based on the instability probability dynamic curve data, the critical instability time point of the potential landslide body is determined; At the critical instability time point, the landslide distance and affected area data are calculated using the geological-geomorphological-hydrological coupling model. Based on the sliding distance data and the area of ​​the affected region, the scale level of the landslide is determined; By integrating the dynamic curve data of instability probability, critical instability time points, and landslide scale levels, a quantitative assessment report with graphic annotations is generated, which includes the spatial distribution characteristics, temporal development trend, and impact range of the landslide.

4. The landslide disaster evolution analysis method based on a coupled model according to claim 3, characterized in that, Based on the geological stress redistribution data, groundwater infiltration depth data, and surface vegetation cover change data, the geological-geomorphological-hydrological coupled model is used to calculate the instability probability of potential landslides, generating dynamic instability probability curve data that varies with time, including: Extract the maximum and minimum principal stress values ​​from the geological stress redistribution data; Extract the rate of change of seepage depth over time from the groundwater seepage depth data; Extract the vegetation root system slope stabilization capacity index from the aforementioned surface vegetation cover change data; The maximum principal stress value, minimum principal stress value, seepage depth rate, and vegetation root system slope stabilization capacity index are input into the instability probability calculation module of the geological-geomorphological-hydrological coupling model. The basic value of the instability probability at the initial moment is calculated by the instability probability calculation module. Set the time step increment, and calculate the cumulative change of geological stress, the change of groundwater infiltration depth, and the attenuation of vegetation root slope stabilization capacity at each time node in sequence according to the time step increment. Based on the cumulative change in geological stress, the change in groundwater infiltration depth, and the attenuation of the slope stabilization capacity of vegetation roots, the basic value of instability probability is dynamically corrected to obtain the corrected value of instability probability at each time point. The instability probability correction values ​​are arranged in chronological order according to time nodes to generate dynamic curve data of instability probability changing over time.

5. The landslide disaster evolution analysis method based on a coupled model according to claim 3, characterized in that, The process integrates the dynamic curve data of instability probability, critical instability time points, and landslide scale levels to generate a quantitative assessment report with graphic annotations, including landslide spatial distribution characteristics, temporal development trends, and impact range. Feature extraction is performed on the instability probability dynamic curve data to obtain the slope change rate data and peak probability value of the curve; By associating the critical instability time point with the peak probability value, a deterministic time interval for landslide occurrence is determined; Based on the landslide scale level, the landslide impact area is divided into severity zones, which include extremely severe impact zone, severe impact zone, moderate impact zone, and slight impact zone. The deterministic time intervals and severity zones are overlaid with the geospatial data of the target earthquake zone to generate a landslide spatial distribution feature layer; a landslide time development trend line graph is drawn based on the instability probability dynamic curve data and the deterministic time intervals; and the severity zones are converted into influence range vector boundaries. By integrating the landslide spatial distribution feature layer, the time development trend line graph, and the influence range vector boundary, a quantitative assessment report with graphic annotations is generated, which includes the landslide spatial distribution features, time development trend, and influence range.

6. The landslide disaster evolution analysis method based on a coupled model according to claim 1, characterized in that, The quantitative assessment report based on the graphic annotations performs vulnerability analysis and risk assessment on the disaster-bearing bodies within the target seismic zone, generating a comprehensive guidance report that includes a spatial distribution map of the vulnerability of disaster-bearing bodies, suggestions for landslide risk prevention and control measures, and an optimized regional spatial planning scheme. Acquire basic data on disaster-bearing bodies in the target earthquake zone, including population distribution data, building distribution data, transportation network distribution data, and infrastructure distribution data; By overlaying the spatial distribution characteristics of landslides with population distribution data, the number of people exposed in different landslide-affected zones can be calculated. By overlaying the spatial distribution characteristics of landslides with building distribution data, the probability of building damage in different landslide-affected zones is calculated. The spatial distribution characteristics of the landslides are superimposed with the traffic network distribution data to calculate the length of traffic line interruption. The spatial distribution characteristics of landslides are overlaid with infrastructure distribution data to calculate the degree of infrastructure functional loss. Based on the population exposure, building damage probability, transportation line interruption length, and infrastructure function loss, the vulnerability level of the disaster-bearing body is classified, and a disaster-bearing body vulnerability level is generated. Based on the vulnerability level of the disaster-bearing body and the time development trend, assess the landslide risk level at different time stages; Based on the aforementioned landslide risk level and impact range, recommendations for landslide risk prevention and control measures are formulated. Based on the landslide risk prevention and control measures recommendations and historical spatial planning data of the target earthquake zone, an optimized regional spatial planning scheme is generated. By integrating the vulnerability levels of the disaster-bearing bodies, the recommendations for landslide risk prevention and control measures, and the regional spatial planning optimization scheme, a comprehensive guidance report is generated, which includes a spatial distribution map of the vulnerability of disaster-bearing bodies, recommendations for landslide risk prevention and control measures, and the regional spatial planning optimization scheme.

7. The landslide disaster evolution analysis method based on a coupled model according to claim 6, characterized in that, The vulnerability level of a disaster-bearing body is classified based on the population exposure number, building damage probability, transportation line interruption length, and infrastructure function loss, generating a vulnerability level for the disaster-bearing body, including: Set thresholds for population exposure, building damage probability, transportation line disruption length, and infrastructure function loss. The population exposure quantity is compared with the population exposure quantity threshold to obtain the population exposure comparison result; The probability of building damage is compared with a building damage probability threshold to obtain the building damage comparison result; The traffic interruption length is compared with the traffic interruption length threshold to obtain the traffic interruption comparison result; The degree of infrastructure function loss is compared with the threshold for the degree of infrastructure function loss to obtain the infrastructure comparison result; The number of items exceeding the corresponding thresholds in the population exposure comparison results, building damage comparison results, traffic disruption comparison results, and infrastructure comparison results is counted. Based on the number of items exceeding the corresponding threshold, the vulnerability level of the disaster-bearing body is classified, and a vulnerability level of the disaster-bearing body is generated. The vulnerability level of the disaster-bearing body includes extremely high vulnerability level, high vulnerability level, medium vulnerability level and low vulnerability level.

8. The landslide disaster evolution analysis method based on a coupled model according to claim 6, characterized in that, The assessment of landslide risk levels at different time stages based on the vulnerability level of the affected body and its temporal development trend includes: Several key time nodes are extracted from the time development trend, including data from the early warning period before landslides occur, data from the initial stage of landslides, data from the middle stage of landslide development, and data from the stabilization period of landslides. The vulnerability level of the disaster-bearing body is divided into time dimensions according to key time nodes to obtain the vulnerability level of the disaster-bearing body corresponding to each key time node; Based on the vulnerability level of the disaster-bearing body corresponding to each key time node, a landslide risk index is calculated. The landslide risk index is obtained by multiplying the vulnerability level of the disaster-bearing body by the time urgency coefficient data. A landslide risk index threshold is set, and the landslide risk index is compared with the landslide risk index threshold. Based on the comparison results, the landslide risk level for different time periods is output, including extremely dangerous level, highly dangerous level, moderately dangerous level and low dangerous level.

9. The landslide hazard evolution analysis method based on a coupled model according to claim 6, characterized in that, Based on the landslide risk level and impact range, the proposed landslide risk prevention and control measures include: By overlaying the landslide risk level with the impact range, risk prevention and control areas and non-risk prevention and control areas are determined. For the aforementioned risk prevention and control area, engineering mitigation measures are proposed, including landslide reinforcement measures, drainage system construction measures, and slope protection measures. For the aforementioned non-risk prevention and control areas, recommendations for non-engineering prevention and control measures are proposed, including measures for the construction of monitoring and early warning systems and measures for the development of emergency response plans. Based on the aforementioned time trend, the implementation priority of different prevention and control measures is determined; The engineering mitigation measures, non-engineering prevention and control measures, and implementation priorities are integrated to generate landslide risk prevention and control measures. The process of combining the landslide risk prevention and control measures recommendations with historical spatial planning data of the target seismic zone to generate a regional spatial planning optimization scheme includes: Obtain historical spatial planning data for the target seismic zone, including land use planning data, urban construction planning data, and industrial layout planning data; By overlaying the landslide risk prevention and control measures recommendations with land use planning data, the land use types that need to be adjusted can be identified. By overlaying the landslide risk prevention and control measures recommendations with urban construction planning data, construction areas that need to be avoided can be identified. By overlaying the landslide risk prevention and control measures recommendations with industrial layout planning data, industrial parks that need to be relocated can be identified. Based on the land use types that need to be adjusted, the construction areas that need to be avoided, and the industrial parks that need to be relocated, a spatial planning adjustment scheme shall be formulated. A feasibility analysis was conducted on the proposed spatial planning adjustment scheme, and a feasibility assessment result was generated. Based on the feasibility assessment results, the spatial planning adjustment scheme is optimized to generate an optimized regional spatial planning scheme.

10. A landslide disaster evolution analysis system based on a coupled model, characterized in that, include: processor; A storage device storing a computer program, which, when executed by the processor, causes the processor to implement the landslide disaster evolution analysis method based on a coupled model as described in any one of claims 1-9.

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