Landslide disaster evolution analysis method and system based on coupling model

Abstract

The embodiment of the application discloses a landslide disaster evolution analysis method and system based on a coupling model, the method first integrates geological structure monitoring data, terrain and landform distribution data and hydrological environment monitoring data of a target earthquake area to generate a comprehensive monitoring data set; then, based on the data set, an interaction correlation of a target equation set is established, and a geological-landform-hydrological coupling model is constructed; according to a preset scenario parameter combination, the coupling model is used to dynamically simulate and quantitatively evaluate the instability probability and landslide scale of a potential landslide body, and a picture-text annotated quantitative evaluation report is generated; finally, based on the report, vulnerability analysis and risk evaluation are performed on a disaster-bearing body, and a comprehensive guidance report containing a disaster-bearing body vulnerability spatial distribution map, landslide risk prevention and control measure suggestions and regional space planning optimization scheme is generated, thereby providing a scientific basis for landslide disaster prevention and control and regional planning.

Description

TECHNICAL FIELD

[0001] The embodiment of the present application relates to the technical field of geological disaster analysis, in particular to a landslide disaster evolution analysis method and system based on a coupling model. BACKGROUND

[0002] High-intensity earthquake zones are located in unique geological structure belts, and their special geological background makes this region an extremely active and dangerous zone of landslide disasters. Frequent high-intensity seismic activity is the main driving factor for triggering landslides, which can cause large-scale and chain rock-soil instability in an instant. At the same time, the abundant rainfall in the region and the active surface water and groundwater jointly soften the rock-soil, raise the groundwater level and generate dynamic water pressure, continuously reducing the stability of the slope.

[0003] Landslide disasters can cause serious impact, not only threatening the safety of local residents, but also causing great damage to urban construction, road traffic, lifeline engineering, etc. Therefore, accurate and scientific landslide disaster risk assessment is imminent.

[0004] However, the existing technology lacks effective integration of multi-source data in landslide disaster analysis, which cannot fully and accurately grasp the synergistic effect between factors, and most of the existing technology only considers the influence of a single or a few factors, which cannot fully reflect the complex interaction of geological, geomorphic and hydrological factors, making the prediction and assessment of landslide disasters not accurate and comprehensive enough. SUMMARY

[0005] The embodiment of the present application provides a landslide disaster evolution analysis method and system based on a coupling model.

[0006] In a first aspect, the embodiment of the present application provides a landslide disaster evolution analysis method based on a coupling model, applied to a landslide disaster evolution analysis system based on a coupling model, and the method comprises:

[0007] Multi-source data integration processing is performed on the geological structure monitoring data, topographic and geomorphic distribution data and hydrological environment monitoring data of the target earthquake zone to generate a comprehensive monitoring data set;

[0008] Based on the comprehensive monitoring data set, an interaction correlation of a target equation set is established to construct a geological-geomorphic-hydrological coupling model reflecting the synergistic effect of multiple factors; the target equation set includes a geological action equation, a geomorphic evolution equation and a hydrological dynamic equation;

[0009] According to a preset scenario parameter combination, the instability probability and landslide scale of the potential landslide body in the target earthquake area are dynamically simulated and quantitatively evaluated by using the geology-landform-hydrology coupling model, and a quantitative evaluation report with graphic annotation containing the spatial distribution characteristics, time development trend and influence range of the landslide is generated.

[0010] Based on the quantitative evaluation report with graphic annotation, the vulnerability analysis and risk assessment of the hazard-affected body in the target earthquake area are performed, and a comprehensive guidance report containing the spatial distribution map of the vulnerability of the hazard-affected body, the suggestion of landslide risk prevention and control measures and the optimization scheme of regional spatial planning is generated.

[0011] In a second aspect, an embodiment of the present application provides a landslide disaster evolution analysis system based on a coupling model, comprising:

[0012] a processor;

[0013] a storage device having a computer program stored thereon,

[0014] When the computer program is executed by the processor, the processor implements any of the landslide disaster evolution analysis methods based on a coupling model.

[0015] An embodiment of the present application provides a readable storage medium having a program or instruction stored thereon, and the program or instruction is executed by a processor to implement the steps of the landslide disaster evolution analysis method based on a coupling model.

[0016] The embodiment of the present application integrates the geological structure monitoring data, the topographic and geomorphic distribution data and the hydrological environment monitoring data of the target earthquake area to generate a comprehensive monitoring data set. On this basis, a geology-landform-hydrology coupling model reflecting the synergistic effect of multiple factors is constructed, which fully considers the interaction between geology, topography and hydrology. Compared with traditional models, the coupling model can more realistically simulate the occurrence and development process of landslide disasters. The coupling model is used to dynamically simulate and quantitatively evaluate the potential landslide body according to a preset scenario parameter combination, and a quantitative evaluation report with graphic annotation is generated, which contains key information such as the spatial distribution characteristics, time development trend and influence range of the landslide, so that the prediction of landslide disasters is more accurate and comprehensive. Based on the report, the vulnerability analysis and risk assessment of the hazard-affected body are performed, and a comprehensive guidance report is generated, which provides a scientific and systematic decision basis for the prevention and control of landslide disasters and regional spatial planning, reduces the loss of the hazard-affected body caused by landslide disasters, and improves the disaster resistance of the region. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A flowchart of a landslide disaster evolution analysis method based on a coupling model provided by an embodiment of the present application.

[0018] Figure 2A module block diagram of the landslide disaster evolution analysis device based on a coupling model provided by the embodiment of the present application.

[0019] Figure 3 A basic structure schematic diagram of a landslide disaster evolution analysis system based on a coupling model provided by the embodiment of the present application. DETAILED DESCRIPTION

[0020] Referring to Figure 1 As shown in the figure, it is a flow chart of a landslide disaster evolution analysis method based on a coupling model provided by the embodiment of the present application, which can be applied to a landslide disaster evolution analysis system based on a coupling model. As shown in the figure, Figure 1 As shown in the figure, the method can include steps 110-140.

[0021] Step 110: Multi-source data integration processing is performed on the geological structure monitoring data, topographic and geomorphic distribution data and hydrological environment monitoring data of the target seismic area to generate a comprehensive monitoring data set.

[0022] In the landslide disaster evolution analysis scene, the target seismic area is in a special geological structure belt, with strong geological structure activity, complex topography and geomorphology, and variable hydrological environment. These factors interact to cause frequent landslide disasters. In order to accurately assess the landslide disaster risk of the region, comprehensive and accurate data must be obtained first. Geological structure monitoring data can be obtained through geological mapping, geophysical exploration and other means. Geological mapping can identify the distribution and characteristics of faults, folds and other structures in detail, and geophysical exploration such as electrical method, seismic method, gravity method, etc. can detect underground geological structure, rock and soil mechanical properties and underground water level changes and other information. Topographic and geomorphic distribution data can be obtained by remote sensing interpretation technology, using satellite, aerial and other remote sensing images to quickly obtain large-area topographic relief, slope, slope direction and other information, and can also identify potential landslide bodies and water system distribution. Hydrological environment monitoring data is collected through water level monitoring points, flow monitoring stations and other monitoring points set in the target seismic area, including underground water level changes, surface water flow and other information.

[0023] Because the data from different sources may have differences in format, accuracy, coordinate system, etc., multi-source data integration processing is needed. First, the data is quality controlled to check the integrity, accuracy and consistency of the data, and to eliminate erroneous or abnormal data. Then, standardization processing is performed to unify the format, accuracy and coordinate system of the data. For example, the fault strike data in the geological structure monitoring data and the elevation data in the topographic and geomorphic distribution data are converted to the same coordinate system. Through data integration, these multi-source data are fused into a unified data platform to generate a comprehensive monitoring data set, which contains detailed information of geology, topography, hydrology and other aspects of the target seismic area.

[0024] Step 120: based on the comprehensive monitoring data set, by establishing the interaction correlation of the target equation set, a geological-geomorphological-hydrological coupling model reflecting the synergistic effect of multiple factors is constructed; the target equation set includes geological action equation, geomorphic evolution equation and hydrological dynamic equation.

[0025] As a first preferred embodiment, step 120 includes:

[0026] Step 121: extracting geological structure deformation rate data, geomorphic form change data and hydrological flow change data from the comprehensive monitoring data set.

[0027] In the comprehensive monitoring data set, the geological structure deformation rate data reflects the activity degree of geological structure activity. It can be extracted from the geological structure monitoring data, for example, calculated by analyzing and calculating the fault displacement monitoring data. Specifically, the fault displacement data at different time points are differentially processed to obtain the displacement change amount of the fault in each time period, and then divided by the time interval to obtain the geological structure deformation rate. The geomorphic form change data reflects the dynamic change of the topography and geomorphology, which can be obtained by comparing the topography and geomorphology distribution data of different periods. For example, by comparing and analyzing different time remote sensing images, the change amount of topographic and geomorphic parameters such as slope, slope direction and elevation is calculated through image recognition and processing technology, so as to obtain the geomorphic form change data. The hydrological flow change data reflects the dynamic characteristics of the hydrological environment, which can be extracted from the hydrological environment monitoring data. For example, by analyzing the groundwater level monitoring data and surface water flow monitoring data, the groundwater level change rate and surface water flow change rate are calculated to obtain the hydrological flow change data.

[0028] Step 122: inputting the geological structure deformation rate data into the geological action equation to calculate the geological stress accumulation degree; inputting the geomorphic form change data into the geomorphic evolution equation to calculate the geomorphic stability index; inputting the hydrological flow change data into the hydrological dynamic equation to calculate the hydraulic erosion intensity.

[0029] The geological structure deformation rate data is inputted into the geological action equation, which will comprehensively consider the type of geological structure, deformation rate and mechanical properties of rock-soil mass and other factors to calculate the geological stress accumulation degree. The deformation of geological structure will cause the stress inside the rock-soil mass to change, and the geological stress will gradually increase with the accumulation of time. The geological action equation calculates the accumulation amount of geological stress by integral operation of the geological structure deformation rate combined with the mechanical parameters such as elastic modulus of rock-soil mass.

[0030] The geomorphological change data is input into the geomorphological evolution equation, which combines factors such as terrain slope, slope direction, rock-soil properties, and vegetation coverage to calculate a geomorphological stability index. Slope and slope direction affect the gravitational potential energy and sliding force of rock-soil, rock-soil properties determine its anti-sliding ability, and vegetation coverage plays a reinforcing role on the slope. The geomorphological evolution equation calculates the geomorphological stability index through comprehensive analysis of these factors using fuzzy evaluation and other methods. The higher the index, the more stable the geomorphology, and the lower the possibility of landslides; conversely, the higher the risk of landslides.

[0031] The hydrological flow change data is input into the hydrological dynamic equation, which considers factors such as rainfall intensity, rainfall duration, and groundwater permeability coefficient to calculate the hydraulic erosion intensity. Rainfall infiltration increases the water content of rock-soil, reducing its shear strength, while the flow of groundwater produces dynamic water pressure, causing erosion of rock-soil. The hydrological dynamic equation calculates rainfall infiltration and dynamic water pressure by analyzing the hydrological flow change data and combining relevant parameters of rainfall and groundwater, and then obtains the hydraulic erosion intensity.

[0032] Step 123: Establish a first interaction correlation between the geological stress accumulation degree and the geomorphological stability index, represented by a geomorphic deformation modification effect function of geological structure; establish a second interaction correlation between the geomorphological stability index and the hydraulic erosion intensity, represented by an influence effect function of geomorphic form on hydrological path; and establish a third interaction correlation between the hydraulic erosion intensity and the geological stress accumulation degree, represented by a weakening effect function of hydrological erosion on geological structure integrity.

[0033] When establishing the first interaction correlation between the geological stress accumulation degree and the geomorphological stability index, geological structure deformation changes the terrain and geomorphology, which in turn affects the geomorphological stability. For example, fault dislocation and fold deformation caused by geological structure activity can change the slope and slope direction of the mountain, increase the sliding force of rock-soil, and reduce the geomorphological stability. This relationship is described by a geomorphic deformation modification effect function of geological structure, which takes the geological stress accumulation degree as input, considers the mode and intensity of geological structure deformation, and outputs the change amount of geomorphic form, which in turn affects the geomorphological stability index.

[0034] When establishing the second interaction correlation between the geomorphic stability index and the hydraulic erosion intensity, the geomorphic form will affect the hydrological path. Factors such as the relief, slope, and aspect of the terrain determine the direction and speed of surface water flow and the infiltration path of groundwater. For example, steep slopes can cause surface water to flow rapidly, increasing hydraulic erosion, while gentle terrain can cause surface water to stay longer, affecting groundwater recharge. This relationship is described by an effect function of the influence of geomorphic form on the hydrological path, which takes the geomorphic stability index as input, considers the characteristics of the geomorphic form, and outputs the changes in the hydrological path, which in turn affects the hydraulic erosion intensity.

[0035] When establishing the third interaction correlation between the hydraulic erosion intensity and the geological stress accumulation degree, hydrological erosion can damage the geological structure and affect the distribution of geological stress. Rainfall infiltration and groundwater flow can increase the water content of rock-soil bodies, reducing their shear strength, while the dynamic water pressure can cause erosion of rock-soil bodies, leading to a weakening of the integrity of the geological structure. This relationship is described by a weakening effect function of the influence of hydrological erosion on the integrity of the geological structure, which takes the hydraulic erosion intensity as input, considers the mode and intensity of hydrological erosion, and outputs the degree of damage to the geological structure, which in turn affects the geological stress accumulation degree.

[0036] Step 124: Fuse the first interaction correlation, the second interaction correlation, and the third interaction correlation to construct a geological-geomorphic-hydrological coupling model reflecting the synergistic action of multiple factors.

[0037] The first interaction correlation, the second interaction correlation, and the third interaction correlation are fused to construct a geological-geomorphic-hydrological coupling model. During the fusion process, the geological action equation, the geomorphic evolution equation, and the hydrological dynamic equation need to be integrated so that they can be correlated and influenced by each other. Specifically, the geological stress accumulation degree, the geomorphic stability index, and the hydraulic erosion intensity are taken as coupling variables between the three equations, and they are connected through the three interaction correlations.

[0038] For example, in the geological action equation, the influence of the geomorphic stability index and the hydraulic erosion intensity on the geological stress accumulation degree is considered; in the geomorphic evolution equation, the influence of the geological stress accumulation degree and the hydraulic erosion intensity on the geomorphic stability index is considered; in the hydrological dynamic equation, the influence of the geological stress accumulation degree and the geomorphic stability index on the hydraulic erosion intensity is considered. In this way, the three equations form an organic whole, which can more comprehensively and accurately reflect the synergistic action between geological, geomorphic, and hydrological factors.

[0039] When building the model, the boundary conditions and initial conditions of the model also need to be considered. The boundary conditions include the boundaries of geological structures, topography, and hydrological environment, and the initial conditions include the initial state of geological stress, the initial state of topography, and the initial state of hydrological flow. By reasonably setting the boundary conditions and initial conditions, it is ensured that the model can accurately simulate the actual situation of the target earthquake zone.

[0040] Step 130: According to the preset scenario parameter combination, the instability probability and landslide scale of the potential landslide body in the target earthquake zone are dynamically simulated and quantitatively evaluated by using the geological-topography-hydrology coupling model, and a graphically annotated quantitative evaluation report containing landslide spatial distribution characteristics, time development trend and influence range is generated.

[0041] In an optional embodiment, step 130 includes:

[0042] Step 131: Obtain a preset scenario parameter combination, which includes earthquake activity intensity variation scenarios, precipitation intensity variation scenarios, and human engineering activity intensity variation scenarios.

[0043] The preset scenario parameter combination is set according to the actual situation of the target earthquake zone and the possible disaster scenarios. The earthquake activity intensity variation scenario considers the influence of different intensity earthquakes on landslides. In the target earthquake zone, earthquakes are frequent and uncertain, and earthquakes of different magnitude, focal depth and duration will cause different degrees of vibration and damage to the mountain, increasing the instability of the mountain and the possibility of triggering landslides. The precipitation intensity variation scenario considers the triggering effect of rainfall on landslides. The region has abundant rainfall, and different precipitation intensity and rainfall duration will cause the water content of rock-soil body to increase, the groundwater level to rise, and the stability of the slope to decrease. The human engineering activity intensity variation scenario considers the changes of human activities such as road construction, house building and mining to the topography and geological structure. These activities may destroy the original balance of the mountain, change the stress state of the rock-soil body, and increase the risk of landslides.

[0044] After obtaining the preset scenario parameter combination, it is used as input data for subsequent simulation calculation of the geological-topography-hydrology coupling model. These scenario parameter combinations provide different input conditions for the model, which can more comprehensively evaluate the instability probability and landslide scale of the potential landslide body under various conditions.

[0045] Step 132: inputting the seismic activity intensity change scenario into the geology-geomorphology-hydrology coupling model, calculating geological stress redistribution data under different seismic activity intensities through the geological action equation; inputting the precipitation intensity change scenario into the geology-geomorphology-hydrology coupling model, calculating groundwater penetration depth data under different precipitation intensities through the hydrodynamic equation; inputting the human engineering activity intensity change scenario into the geology-geomorphology-hydrology coupling model, calculating surface vegetation coverage change data under different human engineering activity intensities through the geomorphological evolution equation.

[0046] The seismic activity intensity change scenario is input into the geological action equation of the geology-geomorphology-hydrology coupling model. The geological action equation calculates the redistribution of geological stress under different seismic activity intensities according to factors such as the intensity, frequency, and focal depth of the earthquake. Earthquakes can cause mountain vibration and deformation, leading to redistribution of stress within the rock-soil body. The geological action equation simulates the impact of earthquakes on geological stress by considering factors such as the propagation characteristics of seismic waves, the mechanical properties of rock-soil bodies, and the characteristics of geological structures. For example, when seismic waves propagate in rock-soil bodies, they can cause elastic and plastic deformation of the rock-soil body, thereby changing the size and direction of geological stress. Through simulation of the seismic activity intensity change scenario, we obtain geological stress redistribution data under different seismic activity intensities.

[0047] The precipitation intensity change scenario is input into the hydrodynamic equation. The hydrodynamic equation considers factors such as rainfall intensity, rainfall duration, and rock-soil body permeability to calculate groundwater penetration depth data under different precipitation intensities. Rainfall infiltration is one of the important factors affecting slope stability. Different precipitation intensities and rainfall durations can lead to different groundwater penetration depths in rock-soil bodies. The hydrodynamic equation simulates the rainfall infiltration process by considering factors such as rock-soil body porosity and saturation to calculate the penetration depth of groundwater at different times and locations. For example, in the case of high rainfall intensity and long rainfall duration, the penetration depth of groundwater will increase, which may cause the groundwater level to rise, thereby increasing the sliding force of the slope.

[0048] The human engineering activity intensity change scenario is input into the geomorphological evolution equation. The geomorphological evolution equation calculates surface vegetation coverage change data under different human engineering activity intensities according to the type and intensity of human engineering activities. Human engineering activities such as road construction, building construction, and mining may damage surface vegetation, leading to a decrease in vegetation coverage. Vegetation has a slope-stabilizing effect and can increase the stability of the slope. The geomorphological evolution equation simulates human engineering activities by considering factors such as the scope, method, and time of engineering activities to calculate the changes in surface vegetation coverage. For example, large-scale road construction projects may damage a large amount of vegetation, leading to a significant decrease in vegetation coverage and thus reducing the stability of the slope.

[0049] Step 133: Based on the geologic stress redistribution data, groundwater penetration depth data, and surface vegetation coverage change data, use the geology-geomorphology-hydrology coupling model to calculate the instability probability of potential landslide bodies, and generate instability probability dynamic curve data of instability probability changing over time.

[0050] In the following steps, step 133 includes:

[0051] Step 1331: Extract the maximum principal stress value and the minimum principal stress value from the geologic stress redistribution data; extract the penetration depth rate of penetration depth changing over time from the groundwater penetration depth data; extract the vegetation root slope stabilization capacity index from the surface vegetation coverage change data; input the maximum principal stress value, the minimum principal stress value, the penetration depth rate, and the vegetation root slope stabilization capacity index into the instability probability calculation module of the geology-geomorphology-hydrology coupling model; calculate the initial time instability probability base value through the instability probability calculation module.

[0052] The maximum principal stress value and the minimum principal stress value are extracted from the geologic stress redistribution data. The maximum principal stress value and the minimum principal stress value reflect the stress state inside the rock-soil mass, which has an important influence on the occurrence of landslides. In the geologic stress redistribution data, by analyzing the stress data at different positions and different times, the maximum and minimum principal stress values are found. Specifically, the stress tensor at each time point can be subjected to eigenvalue decomposition to obtain the principal stress values, and then the maximum and minimum principal stress values are found among all time points.

[0053] The penetration depth rate of penetration depth changing over time is extracted from the groundwater penetration depth data. The penetration depth rate reflects the change of the effect of groundwater on rock-soil mass over time. The groundwater penetration depth data is subjected to time difference processing, and the penetration depth change amount of each time period is calculated, and then divided by the time interval to obtain the penetration depth rate. For example, the penetration depth data of adjacent two time points is subtracted, and then divided by the time interval to obtain the penetration depth rate of that time period.

[0054] The vegetation root slope stabilization capacity index is extracted from the surface vegetation coverage change data. The vegetation root slope stabilization capacity index reflects the enhancement of vegetation on the stability of the mountain. The vegetation root system can anchor the rock-soil mass and increase the anti-slide capacity of the slope. Through the analysis of the surface vegetation coverage change data, considering the factors such as the type, density, depth and distribution of the vegetation root system, the vegetation root slope stabilization capacity index is calculated. For example, according to the biomass of the vegetation, the tensile strength of the root system and other indicators, a calculation model of the vegetation root slope stabilization capacity index can be established.

[0055] The maximum principal stress value, the minimum principal stress value, the seepage depth rate, and the vegetation root system slope stabilization capacity index are input into a failure probability calculation module of the geology-landform-hydrology coupling model. The failure probability calculation module comprehensively considers these parameters, as well as the mechanical properties of the rock-soil body, the geometric shape of the slope, and other factors to calculate a failure probability basic value at the initial time, which is the failure probability of the potential landslide body under the initial conditions.

[0056] Step 1332: Set a time step increment, and calculate the geological stress cumulative change amount, the groundwater seepage depth change amount, and the vegetation root system slope stabilization capacity decay amount of each time node according to the time step increment.

[0057] A time step increment is set, for example, in units of days, weeks, or months. According to this time step, the relevant change amounts of each time node are calculated. The geological stress cumulative change amount refers to the increase or decrease of the geological stress within each time step. According to the geological stress redistribution data, the stress change within each time step is calculated. For example, by subtracting the geological stress data of the adjacent two time points, the geological stress change amount within the time step is obtained. At the same time, considering the continuous influence of geological structure activity, the geological stress change amount is accumulated to obtain the geological stress cumulative change amount.

[0058] The groundwater seepage depth change amount reflects the change of the groundwater seepage depth within each time step. It is related to factors such as rainfall intensity and geological structure. According to the groundwater seepage depth data, the seepage depth change within each time step is calculated. For example, by subtracting the groundwater seepage depth data of the adjacent two time points, the groundwater seepage depth change amount within the time step is obtained. In the calculation process, the influence of factors such as rainfall infiltration and groundwater discharge needs to be considered.

[0059] The vegetation root system slope stabilization capacity decay amount reflects the degree of weakening of the vegetation root system slope stabilization capacity over time. It may be caused by vegetation destruction due to human activities, natural factors, etc. According to the surface vegetation coverage change data, the vegetation root system slope stabilization capacity change within each time step is calculated. For example, by subtracting the vegetation root system slope stabilization capacity index of the adjacent two time points, the vegetation root system slope stabilization capacity decay amount within the time step is obtained. In the calculation process, the influence of factors such as the growth, death, and destruction of vegetation needs to be considered.

[0060] Step 1333: Based on the geological stress cumulative change amount, the groundwater seepage depth change amount, and the vegetation root system slope stabilization capacity decay amount, the failure probability basic value is dynamically corrected to obtain the failure probability correction value of each time node.

[0061] The increase in the accumulated change amount of geological stress may increase the instability probability, because the increase in geological stress makes the stress state inside the rock-soil body more unstable, increasing the possibility of landslides. The increase in the change amount of groundwater penetration depth also affects the instability probability. The increase in groundwater penetration depth increases the saturation of the rock-soil body, reducing its shear strength and thus increasing the sliding force of the slope. The increase in the attenuation amount of the vegetation root reinforcement capacity weakens the reinforcement of the mountain by vegetation, reduces the stability of the slope, and thus increases the instability probability.

[0062] During the dynamic correction process, a instability probability correction model is established, which takes the accumulated change amount of geological stress, the change amount of groundwater penetration depth, and the attenuation amount of the vegetation root reinforcement capacity as input parameters, considers factors such as the mechanical properties of the rock-soil body and the geometric shape of the slope, and obtains a correction coefficient of the instability probability through a certain calculation method. For example, a multiple linear regression model or a nonlinear regression model can be used to determine the parameters of the model based on historical data and empirical formulas. Then, the instability probability basic value is multiplied by the correction coefficient to obtain the instability probability correction value at each time node.

[0063] Step 1334: Arrange the instability probability correction values in time node order to generate instability probability dynamic curve data of the instability probability over time.

[0064] Arrange the instability probability correction values at each time node in time order. Through this arrangement, the trend of the instability probability over time can be clearly shown. Plot these data as a curve to obtain the instability probability dynamic curve data. When plotting the curve, interpolation and fitting methods can be used to make the curve smoother and more accurate. For example, use spline interpolation to interpolate the instability probability correction values to obtain more dense instability probability values at time points, and then use the least squares method to fit these data to obtain the instability probability dynamic curve. This curve intuitively reflects the dynamic changes of the instability probability of the potential landslide body. By observing the trend of the curve, the time and trend of the possible landslide can be predicted. For example, if the curve shows an upward trend and approaches a certain critical value, it means that the possibility of landslide is increasing, and monitoring and early warning need to be strengthened.

[0065] Step 134: Determine the critical instability time point of the potential landslide body based on the instability probability dynamic curve data. At the time corresponding to the critical instability time point, calculate the sliding distance data and impact area data of the landslide body using the geology-geomorphology-hydrology coupling model. Based on the sliding distance data and impact area data, determine the landslide scale grade.

[0066] The critical instability time point is determined according to the instability probability dynamic curve data. The critical instability time point refers to the time when the instability probability reaches a certain preset critical value. When the instability probability exceeds the critical value, the potential landslide body is likely to slide. A threshold of instability probability can be set to find the first time point exceeding the threshold in the instability probability dynamic curve, and the time point is determined as the critical instability time point. For example, a threshold of instability probability of 0.5 is set, and the first time point when the instability probability is greater than 0.5 is found in the instability probability dynamic curve, which is the critical instability time point.

[0067] At the moment corresponding to the critical instability time point, the sliding distance data and the influence area data of the landslide body are calculated by using the geological-geomorphological-hydrological coupling model. The model simulates the movement process of the landslide body after instability according to the interaction of geological, geomorphological and hydrological factors. In calculating the sliding distance data, the initial speed, acceleration, topography and other factors of the landslide body are considered, and the sliding distance is obtained by solving the motion equation of the landslide body. In calculating the influence area data, the volume, motion trajectory, terrain undulation and other factors of the landslide body are considered, and the influence area is obtained by simulating the accumulation range of the landslide body.

[0068] Based on the sliding distance data and the influence area data, the landslide scale grade is determined. The landslide scale can be divided into different grades according to the pre-set scale grade division standard, such as small, medium, large and extra-large. Different grades of landslides have different influences on the hazard-affected body, which provides an important basis for subsequent risk assessment and prevention and control. For example, when the sliding distance is short and the influence area is small, it is determined as a small landslide; when the sliding distance is long and the influence area is large, it is determined as a large landslide.

[0069] Step 135: integrating the instability probability dynamic curve data, the critical instability time point and the landslide scale grade, to generate a graphic annotation quantitative evaluation report containing the spatial distribution characteristics, time development trend and influence range of the landslide.

[0070] In an optional design idea, step 135 includes:

[0071] Step 1351: feature extraction is performed on the instability probability dynamic curve data to obtain the slope change rate data and the peak probability value of the curve.

[0072] Feature extraction is performed on the instability probability dynamic curve data to analyze the change law of the instability probability in depth. The slope change rate data reflects the speed of the change of the instability probability, which can help determine whether the growth or decline trend of the instability probability of the landslide is sharp. The slope change rate data is obtained by performing numerical derivation on the instability probability dynamic curve. Specifically, for discrete instability probability data points, the difference method can be used to calculate the slope between adjacent two data points, and then the slopes are differentiated to obtain the slope change rate. For example, for the instability probability data sequence P(t), the difference value ΔP = P(t+1)-P(t) of the instability probability of adjacent two time points is calculated, and the difference value Δ(ΔP) of the adjacent two slopes is calculated, and the slope change rate is obtained.

[0073] The peak probability value is the maximum value in the instability probability dynamic curve, which represents the maximum instability probability of the potential landslide at a certain time point. By traversing and searching the instability probability dynamic curve data, the maximum value of each data point is compared to find the maximum value, which is the peak probability value. The peak probability value can help determine the maximum possibility of landslide occurrence.

[0074] Step 1352: associate the critical instability time point with the peak probability value to determine the deterministic time interval of landslide occurrence.

[0075] Associating the critical instability time point with the peak probability value can more accurately predict the time of landslide occurrence. The critical instability time point is the time when the instability probability reaches the preset critical value, and the peak probability value represents the maximum instability probability. If the peak probability value appears near the critical instability time point, it can be considered that the landslide is more likely to occur in this time interval.

[0076] When determining the deterministic time interval of landslide occurrence, a time range can be set according to the actual situation. For example, if the time corresponding to the peak probability value is within a certain time period before and after the critical instability time point, this time period is determined as the deterministic time interval of landslide occurrence. When determining the time range, factors such as the change trend of the instability probability and the stability of the geological conditions need to be considered. If the instability probability remains at a high level after the critical instability time point and the slope change rate is small, it means that the possibility of landslide occurrence is large in a period of time, and the deterministic time interval can be appropriately expanded.

[0077] Step 1353: based on the landslide scale grade, divide the severity zoning of the landslide influence area, including extremely severe impact area, severe impact area, moderate impact area and light impact area.

[0078] The severity zoning of landslide influence area based on landslide scale classification is to better understand the impact of landslides on different areas. Different scale landslides have different damage to the surrounding area. According to the sliding distance, influence area and other factors of landslide, the landslide scale is divided into different grades, and then the severity zoning is correspondingly divided.

[0079] The extremely severe impact area is usually the area directly impacted by the landslide body. This area may suffer strong impact, burial and other damages from the landslide body, and the infrastructure such as buildings and roads may be completely destroyed, resulting in serious casualties and property losses. The severe impact area is also greatly affected, but relatively less than the extremely severe impact area. This area may be affected by the landslide, such as impact wave, flying stone, etc. Buildings may be damaged to varying degrees. The moderate impact area has moderate impact, which may be affected by ground vibration, dust flying, etc. caused by landslides, and some infrastructure may be damaged to some extent. The light impact area is less affected, which may only feel slight vibration or be affected by a small amount of dust.

[0080] When dividing the severity zoning, factors such as topography, building distribution, population density, etc. need to be considered. For example, in the area on the landslide movement path and with relatively flat terrain, the influence range of the landslide may be wider, and the range of the severity zoning may also be correspondingly expanded; while in the area with large topography or obstacles, the influence range of the landslide may be limited, and the range of the severity zoning may also be correspondingly reduced.

[0081] Step 1354: superimpose the certainty time interval, severity zoning and geographic spatial data of the target seismic area to generate a landslide spatial distribution feature layer; draw a landslide time development trend broken line graph according to the instability probability dynamic curve data and certainty time interval; convert the severity zoning into an influence range vector boundary.

[0082] Superimposing the certainty time interval and the severity zoning with the geographic spatial data of the target seismic area is to combine the time and space information of the landslide to more intuitively show the distribution of the landslide. The geographic spatial data of the target seismic area includes topography, landform, administrative division, building distribution and other information. Through geographic information system (GIS) technology, the certainty time interval and the severity zoning information are fused with the geographic spatial data to generate a landslide spatial distribution feature layer.

[0083] In the superimposition process, the deterministic time interval is added to the geospatial data in the time dimension, and the severity zoning is represented on the map with different colors or symbols. For example, red represents the extremely severe impact area, orange represents the severe impact area, yellow represents the moderate impact area, and green represents the light impact area. In this way, the spatial distribution feature layer of landslides can clearly show the landslide risk situation in different areas at different times.

[0084] According to the instability probability dynamic curve data and the deterministic time interval, a landslide time development trend line chart is drawn. The horizontal axis of the line chart represents time, and the vertical axis represents instability probability. The part of the instability probability dynamic curve data within the deterministic time interval is drawn on the line chart, and the critical instability time point and the peak probability value are marked. Through the line chart, the change trend of landslide instability probability with time and the risk change within the deterministic time interval can be observed intuitively.

[0085] The severity zoning is converted into impact range vector boundary. The severity zoning is usually represented in the form of area, in order to describe its range more accurately, it needs to be converted into vector boundary. Through digitizing the boundary of the severity zoning, it is converted into vector data. Specifically, polygon vector data can be used to represent the boundary of the severity zoning, and the vertex coordinates of each polygon correspond to the actual geographic location on the boundary. In this way, the impact range vector boundary can be more accurately displayed on the map, and further analysis and processing can be performed.

[0086] Step 1355: Fusion of the landslide spatial distribution feature layer, time development trend line chart and impact range vector boundary, to generate a picture-text annotated quantitative evaluation report containing landslide spatial distribution features, time development trend and impact range.

[0087] Fusion of landslide spatial distribution feature layer, time development trend line chart and impact range vector boundary, to generate a picture-text annotated quantitative evaluation report, is to integrate the information of landslide space, time and impact range, etc., to form a comprehensive and intuitive report.

[0088] In the fusion process, first, the landslide spatial distribution feature layer, time development trend line chart and impact range vector boundary are laid out and typeset. The landslide spatial distribution feature layer can be used as the main graph to show the spatial distribution of landslides; the time development trend line chart can be used as the auxiliary graph to show the change trend of landslide instability probability with time; and the impact range vector boundary can be marked on the main graph to clearly show the impact range of landslides.

[0089] Then, the charts are annotated with text. On the landslide spatial distribution characteristic layer, the names, ranges, and corresponding risk levels of different severity partitions are labeled. On the time development trend line chart, key information such as critical instability time points, peak probability values, and deterministic time intervals is labeled. At the same time, add textual explanations in the report to explain the meaning of the charts, analysis results and conclusions. For example, explain the characteristics and possible impacts of different severity partitions, analyze the trend of landslide instability probability and the prediction of future risks, etc.

[0090] Finally, the annotated charts and text are integrated to generate a quantitative evaluation report containing landslide spatial distribution characteristics, time development trends, and impact ranges. This report can provide reference for landslide disaster warning, prevention and control, and decision-making.

[0091] Step 140: Based on the annotated quantitative evaluation report, perform vulnerability analysis and risk assessment on the target seismic zone, and generate a comprehensive guidance report containing vulnerability spatial distribution of the target seismic zone, landslide risk prevention measures and regional spatial planning optimization scheme.

[0092] In a preferred embodiment, step 140 includes:

[0093] Step 141: Obtain the target seismic zone's hazard-bearing body basic data, including population distribution data, building distribution data, traffic network distribution data, and infrastructure distribution data.

[0094] In the landslide disaster evolution analysis scenario, obtaining the target seismic zone's hazard-bearing body basic data is the basis for vulnerability analysis and risk assessment. Population distribution data can be obtained through population census, statistical department data, etc. It reflects the population distribution in the target seismic zone, including population density, population age structure, etc. For example, through population census data, the population number and distribution of each community and village can be understood, providing a basis for calculating the population exposure in different landslide impact partitions.

[0095] Building distribution data can be obtained from city planning departments, property registration departments, etc. It covers building types, quantities, locations, etc. Building types include residential, commercial, industrial, etc. Different types of buildings have different vulnerability to landslides. For example, residential buildings may be more vulnerable to landslide impact and burial, while industrial buildings may have relatively strong resistance to landslides due to their special structure and purpose. Through building distribution data, building damage probability in different landslide impact partitions can be calculated.

[0096] The traffic network distribution data includes the distribution of traffic facilities such as highways, railways, and bridges, which can be obtained from the traffic management department. The traffic network is an important link connecting various regions, and landslide disasters may cause traffic line interruptions, affecting the transportation of personnel and materials. Through the traffic network distribution data, the length of the traffic line interruption can be calculated to assess the impact of landslides on the transportation system.

[0097] The infrastructure distribution data includes the distribution information of water supply, power supply, communication, and other facilities, which can be obtained from the relevant infrastructure management department. Infrastructure is an important support for people's life and production, and landslide disasters may cause infrastructure function loss, affecting people's normal life. Through the infrastructure distribution data, the infrastructure function loss degree can be calculated to understand the damage of landslides to infrastructure.

[0098] Step 142: Superimpose the landslide spatial distribution characteristics with the population distribution data to calculate the number of population exposure in different landslide impact zones; superimpose the landslide spatial distribution characteristics with the building distribution data to calculate the building damage probability in different landslide impact zones; superimpose the landslide spatial distribution characteristics with the traffic network distribution data to calculate the length of traffic line interruption; superimpose the landslide spatial distribution characteristics with the infrastructure distribution data to calculate the infrastructure function loss degree.

[0099] Superimpose the landslide spatial distribution characteristics with the population distribution data to calculate the number of population exposure in different landslide impact zones. Through geographic information system (GIS) technology, the severity of the landslide is divided into zones and the population distribution data is spatially analyzed. Specifically, the population distribution data is grid processed according to the geographical location, and each grid corresponds to a population number. Then, each grid is superimposed with the landslide impact zone, and the population number of the grid falling in different landslide impact zones is counted to obtain the population exposure number in different zones. For example, if an extremely severe impact zone contains multiple grids, the population numbers of these grids are added to obtain the population exposure number in the extremely severe impact zone.

[0100] Superimpose the landslide spatial distribution characteristics with the building distribution data to calculate the building damage probability in different landslide impact zones. Considering the impact force, damage range, and other factors of the landslide, combined with the type, structure, and seismic performance of the building, the damage of the building is evaluated. A building damage probability model can be established, which takes the intensity of the landslide, the characteristics of the building, and other parameters as input parameters, and outputs the damage probability of the building. For example, for a building located in an extremely severe impact zone, according to its structure type and seismic grade, combined with the impact force and damage range of the landslide, the damage probability of the building is calculated. Then, the buildings in each landslide impact zone are counted to obtain the building damage probability in the zone.

[0101] The landslide spatial distribution characteristics are superimposed with the traffic network distribution data to calculate the traffic route interruption length. Through spatial analysis of the traffic network data and the landslide influence partition, the intersection of the traffic route and the landslide influence partition is determined. For the traffic route intersecting with the landslide influence partition, the length of the route within the partition is calculated, and these lengths are added to obtain the traffic route interruption length. For example, if a road passes through a severe influence zone and a moderate influence zone, the lengths of the road within the two partitions are calculated respectively, and then added to obtain the interruption length of the road. The same calculation is performed for all traffic routes intersecting with the landslide influence partition, and finally the interruption lengths of all routes are added to obtain the total traffic route interruption length.

[0102] The landslide spatial distribution characteristics are superimposed with the infrastructure distribution data to calculate the infrastructure function loss degree. The damage mode and degree of the landslide to the infrastructure are considered, such as the landslide may damage water supply pipelines, power lines, communication base stations, etc. For each infrastructure, according to its type and position within the landslide influence partition, the degree of function loss is evaluated. For example, for water supply pipelines, according to the damaged length and degree within the landslide influence partition, the proportion of water supply function loss is calculated. All infrastructures are evaluated, and then their importance and influence range are considered comprehensively to calculate the infrastructure function loss degree.

[0103] Step 143: Based on the population exposure quantity, building damage probability, traffic route interruption length and infrastructure function loss degree, the vulnerability level of the hazard-affected body is classified to generate the hazard-affected body vulnerability level.

[0104] In one embodiment, step 143 includes:

[0105] Step 1431: Set the population exposure quantity threshold, building damage probability threshold, traffic route interruption length threshold and infrastructure function loss degree threshold.

[0106] The population exposure quantity threshold, building damage probability threshold, traffic route interruption length threshold and infrastructure function loss degree threshold are set to quantitatively evaluate the vulnerability of the hazard-affected body, and these thresholds are determined according to historical landslide disaster data, actual conditions of the target seismic area and relevant standards and specifications.

[0107] The population exposure quantity threshold refers to the number of people exposed to a landslide in a certain impact area. When the number of people exposed exceeds the threshold, the area is considered to have high population vulnerability. The population exposure quantity threshold can be determined based on factors such as population density, building load capacity, and other factors in the target earthquake area. For example, if a region's buildings are mainly multi-story residential buildings and the population density is high, the population exposure quantity threshold can be relatively low; if the region is mainly an industrial park and the population density is small, the population exposure quantity threshold can be relatively high.

[0108] The building damage probability threshold is a standard for determining the vulnerability of buildings in landslide disasters. According to factors such as the type, structure, and seismic performance of buildings, the damage probability threshold for different types of buildings is determined. For example, for old buildings with poor seismic performance, the damage probability threshold can be relatively low; for newly built buildings with good seismic performance, the damage probability threshold can be relatively high.

[0109] The traffic line interruption length threshold is used to assess the vulnerability of the transportation network. Considering factors such as the importance of the traffic line, the availability of alternative lines, and other factors, the traffic line interruption length threshold is determined. If a traffic line is the main channel connecting important areas and there is no alternative line, the interruption length threshold of the line can be relatively low; if there are other alternative lines available, the interruption length threshold can be relatively high.

[0110] The infrastructure function loss degree threshold is used to measure the vulnerability of infrastructure. According to factors such as the type, importance, and recovery difficulty of infrastructure, the infrastructure function loss degree threshold is determined. For example, water supply, power supply, and other infrastructure are essential to people's lives, and their function loss degree threshold can be relatively low; while some secondary communication facilities, their function loss degree threshold can be relatively high.

[0111] Step 1432: Compare the population exposure quantity with the population exposure quantity threshold to obtain a population exposure comparison result; compare the building damage probability with the building damage probability threshold to obtain a building damage comparison result; compare the traffic line interruption length with the traffic line interruption length threshold to obtain a traffic interruption comparison result; compare the infrastructure function loss degree with the infrastructure function loss degree threshold to obtain an infrastructure comparison result.

[0112] The population exposure quantity is compared with the population exposure quantity threshold value to determine whether the population exposure quantity exceeds the threshold value. If the population exposure quantity exceeds the threshold value, it indicates that the population vulnerability in the region is relatively high, and the population exposure comparison result is that the threshold value is exceeded; if the population exposure quantity does not exceed the threshold value, it indicates that the population vulnerability in the region is relatively low, and the population exposure comparison result is that the threshold value is not exceeded. For example, in a severe impact area, the calculated population exposure quantity is 1000 people, and the set population exposure quantity threshold value is 800 people, so the population exposure comparison result is that the threshold value is exceeded.

[0113] The building damage probability is compared with the building damage probability threshold value to determine the vulnerability of the building in the landslide disaster. If the building damage probability exceeds the threshold value, it indicates that the building vulnerability in the region is relatively high, and the building damage comparison result is that the threshold value is exceeded; if the building damage probability does not exceed the threshold value, it indicates that the building vulnerability in the region is relatively low, and the building damage comparison result is that the threshold value is not exceeded. For example, for a building in a moderate impact area, the calculated building damage probability is 0.6, and the set building damage probability threshold value is 0.5, so the building damage comparison result is that the threshold value is exceeded.

[0114] The traffic line interruption length is compared with the traffic line interruption length threshold value to evaluate the vulnerability of the traffic network. If the traffic line interruption length exceeds the threshold value, it indicates that the traffic network vulnerability in the region is relatively high, and the traffic interruption comparison result is that the threshold value is exceeded; if the traffic line interruption length does not exceed the threshold value, it indicates that the traffic network vulnerability in the region is relatively low, and the traffic interruption comparison result is that the threshold value is not exceeded. For example, in a light impact area, the calculated traffic line interruption length is 5 kilometers, and the set traffic line interruption length threshold value is 6 kilometers, so the traffic interruption comparison result is that the threshold value is not exceeded.

[0115] The infrastructure function loss degree is compared with the infrastructure function loss degree threshold value to measure the vulnerability of the infrastructure. If the infrastructure function loss degree exceeds the threshold value, it indicates that the infrastructure vulnerability in the region is relatively high, and the infrastructure comparison result is that the threshold value is exceeded; if the infrastructure function loss degree does not exceed the threshold value, it indicates that the infrastructure vulnerability in the region is relatively low, and the infrastructure comparison result is that the threshold value is not exceeded. For example, for a water supply infrastructure in a very severe impact area, the calculated function loss degree is 0.8, and the set infrastructure function loss degree threshold value is 0.7, so the infrastructure comparison result is that the threshold value is exceeded.

[0116] Step 1433: count the number of items in the population exposure comparison result, the building damage comparison result, the traffic interruption comparison result, and the infrastructure comparison result that exceed the corresponding threshold value; according to the number of items that exceed the corresponding threshold value, classify the hazard-affected body into a vulnerability level, and generate a hazard-affected body vulnerability level, which includes an extremely high vulnerability level, a high vulnerability level, a medium vulnerability level, and a low vulnerability level.

[0117] The number of items that exceed the corresponding threshold value is counted in the population exposure comparison result, the building damage comparison result, the traffic interruption comparison result, and the infrastructure comparison result. The four comparison results are summarized, and the number of items that exceed the threshold value is counted. For example, if the population exposure comparison result exceeds the threshold value, the building damage comparison result exceeds the threshold value, the traffic interruption comparison result does not exceed the threshold value, and the infrastructure comparison result exceeds the threshold value, the number of items that exceed the corresponding threshold value is 3.

[0118] According to the number of items that exceed the corresponding threshold value, the hazard-affected body is classified into a vulnerability level. When the number of items that exceed the threshold value is large, it indicates that the hazard-affected body has a higher vulnerability in landslide disasters. The specific classification criteria can be set according to actual conditions. For example, when the number of items that exceed the corresponding threshold value is 4, the hazard-affected body is classified into an extremely high vulnerability level; when the number of items is 3, it is classified into a high vulnerability level; when the number of items is 2, it is classified into a medium vulnerability level; and when the number of items is 1 or 0, it is classified into a low vulnerability level. Such classification can clearly indicate the vulnerability of different hazard-affected bodies.

[0119] Step 144: according to the hazard-affected body vulnerability level and the time development trend, evaluate the landslide risk level at different time stages; based on the landslide risk level and the influence range, develop landslide risk prevention and control measures suggestions; combine the landslide risk prevention and control measures suggestions and the historical spatial planning data of the target seismic area to generate a regional spatial planning optimization scheme.

[0120] In the following steps, the evaluation of the landslide risk level at different time stages according to the hazard-affected body vulnerability level and the time development trend includes:

[0121] Step 1441: extract a plurality of key time nodes from the time development trend, including pre-warning period data before landslide occurrence, initial stage data of landslide occurrence, mid-stage data of landslide development, and stable period data of landslide.

[0122] In the evolution process of landslide disaster, the characteristics and risk levels of different stages are different. Extracting key time nodes from the time development trend can more accurately assess the landslide risk at different stages. The pre-warning period data before the landslide occurs is usually the data in the period when the landslide instability probability starts to rise significantly but has not yet reached the critical value. This stage can be identified through monitoring data and model prediction, and its characteristics are that the potential risk of landslide is gradually increasing, but there is still some time to take preventive measures. For example, through monitoring and analysis of parameters such as geological stress accumulation degree, geomorphic stability index, and hydraulic erosion intensity, it is found that the trend of these parameters indicates that a landslide may occur in the future. At this time, the corresponding time period is the pre-warning period before the landslide occurs.

[0123] The initial stage data of landslide refers to the data in the period when the landslide just starts to occur. In this stage, the landslide body begins to show obvious displacement and damage, and the instability probability rises rapidly. The start time of this stage can be determined by real-time monitoring data such as displacement monitoring equipment and seismic monitoring instruments. For example, when the displacement monitoring instrument detects that the displacement of the landslide body suddenly increases and the instability probability exceeds the critical value, it marks the beginning of the initial stage of the landslide.

[0124] The mid-stage data of landslide reflects the state of the landslide in the development process. In this stage, the movement speed of the landslide body increases, the influence range gradually expands, and the damage degree to the surrounding environment and hazard-bearing bodies also increases. The data of this stage can be obtained by monitoring and analyzing parameters such as the movement trajectory of the landslide body and the area of the influence area. For example, through remote sensing image and geographic information system technology, the movement of the landslide body and the change of the influence range can be monitored in real time.

[0125] The stable period data of landslide refers to the relevant data after the landslide ends. In this stage, the movement of the landslide body stops, the instability probability returns to a low level, and the geological, geomorphic, and hydrological conditions gradually tend to be stable. The start time of this stage can be determined by long-term monitoring of parameters such as the displacement and deformation of the landslide body. For example, when the displacement monitoring instrument detects that the displacement of the landslide body no longer changes for a continuous period of time, and all monitoring parameters are stable within the normal range, it marks the beginning of the stable period of the landslide.

[0126] Step 1442: Time-dimensionally divide the hazard-bearing body vulnerability level according to the key time nodes to obtain the hazard-bearing body vulnerability level corresponding to each key time node.

[0127] The vulnerability level of the hazard-affected body is divided according to the key time nodes, because the impact of landslides is different at different time stages, and the vulnerability of the hazard-affected body will also change. During the pre-warning period before the landslide occurs, although the physical properties and structure of the hazard-affected body do not change, some preventive measures can be taken due to the relatively sufficient time, such as evacuating personnel, reinforcing buildings, etc., so the actual vulnerability of the hazard-affected body may decrease. For example, if the population in a high-risk area is evacuated in time during the pre-warning period, the vulnerability of the population in that area will decrease significantly.

[0128] At the initial stage of the landslide, the sudden movement and destruction of the landslide body will directly expose the hazard-affected body to danger, and at this time the vulnerability of the hazard-affected body will increase rapidly. For example, buildings may collapse instantly under the impact of the landslide, traffic lines may be cut off, and infrastructure may be damaged.

[0129] At the middle stage of the landslide development, as the influence range of the landslide expands, more hazard-affected bodies are affected, and the vulnerability of the hazard-affected bodies further increases. Moreover, due to the continuous destruction of the landslide, some hazard-affected bodies that originally had some disaster resistance may also be severely affected. For example, some reinforced buildings may also be damaged under the long-term action of the landslide.

[0130] In the stable period of the landslide, although the direct impact of the landslide has ended, the hazard-affected body may be affected by subsequent secondary disasters such as debris flow, mountain collapse, etc. At the same time, some damaged hazard-affected bodies need to be repaired and rebuilt, and their vulnerability still increases to some extent. For example, damaged buildings may be affected by other natural disasters during the repair process, causing the repair work to be hindered.

[0131] By dividing the vulnerability level of the hazard-affected body according to the key time nodes, the risk situation of the hazard-affected body at different time stages can be more accurately evaluated.

[0132] Step 1443: Based on the vulnerability level of the hazard-affected body corresponding to each key time node, calculate the landslide risk index, which is obtained by multiplying the vulnerability level of the hazard-affected body and the time urgency coefficient data.

[0133] The landslide risk index is an important indicator that comprehensively considers the vulnerability of the hazard-affected body and the time factor, which can more accurately evaluate the landslide risk at different time stages. The vulnerability level of the hazard-affected body corresponding to each key time node reflects the possibility and degree of the hazard-affected body being destroyed by the landslide at that time point. For example, an extremely high vulnerability level indicates that the hazard-affected body is likely to be severely damaged in the landslide disaster, while a low vulnerability level indicates that the hazard-affected body is relatively safe.

[0134] The time urgency coefficient data reflects the urgency of landslide occurrence at different time nodes. During the pre-warning period before landslide occurrence, there is sufficient time to take preventive measures, so the time urgency coefficient is relatively low. For example, measures such as evacuating personnel and reinforcing buildings can be taken to reduce the vulnerability of hazard-affected bodies. In the early stage of landslide occurrence, the landslide mass has already started to move, and the situation is urgent, so the time urgency coefficient is relatively high. At this time, emergency measures such as organizing rescue and relief need to be taken immediately. In the middle stage of landslide development, the influence range of landslide is expanding and the damage degree is increasing, so the time urgency coefficient is still relatively high. Measures need to be taken quickly to control the development of landslide and reduce losses. In the stable stage of landslide, although the landslide has stopped moving, follow-up processing work such as repairing damaged hazard-affected bodies and assessing disaster losses still needs to be done, so the time urgency coefficient is relatively low.

[0135] The landslide risk index is obtained by multiplying the vulnerability level of hazard-affected bodies and the time urgency coefficient data. For example, in the early stage of landslide occurrence, the vulnerability level of hazard-affected bodies is extremely high, corresponding to a higher numerical value, and the time urgency coefficient is also higher, so the landslide risk index obtained by multiplying the two is very large, indicating that the landslide risk at this time stage is very high. By calculating the landslide risk index, the landslide risk at different time stages can be quantitatively evaluated.

[0136] Step 1444: Set the landslide risk index threshold, compare the landslide risk index with the landslide risk index threshold, and output the landslide risk level at different time stages according to the comparison result, wherein the landslide risk level includes extremely dangerous level, high dangerous level, moderate dangerous level and low dangerous level.

[0137] The landslide risk index threshold is set to classify the landslide risk into different levels, so that the degree of landslide risk at different time stages can be more intuitively understood. The landslide risk index threshold can be determined according to historical landslide disaster data, actual conditions of the target seismic area, and relevant standards and specifications. For example, by analyzing similar past landslide disasters, statistics of landslide risk index ranges corresponding to different risk levels, and combining factors such as the geological, geomorphic and hydrological conditions of the target seismic area, the landslide risk index threshold suitable for the region can be determined.

[0138] The calculated landslide risk index is compared with the threshold value. If the landslide risk index exceeds the higher threshold value, it indicates that the landslide risk at this time stage is very high, and the corresponding time stage is classified as extremely dangerous level. In the extremely dangerous level, landslide may cause serious damage to hazard-affected bodies, and emergency measures such as large-scale evacuation of personnel and suspension of all possible affected production activities need to be taken immediately.

[0139] If the landslide risk index exceeds the medium threshold but does not exceed the higher threshold, the corresponding time period is classified as a high-risk level. Under the high-risk level, the landslide has a high probability of occurrence and may cause significant damage to the hazard-affected body, requiring enhanced monitoring and early warning, as well as more proactive prevention and control measures, such as reinforcing important buildings and strengthening traffic control.

[0140] If the landslide risk index exceeds the lower threshold but does not exceed the medium threshold, the corresponding time period is classified as a moderate-risk level. Under the moderate-risk level, the landslide has a certain probability of occurrence, but the damage is relatively small, and some preventive measures can be taken, such as strengthening the inspection and maintenance of the hazard-affected body and developing emergency plans.

[0141] If the landslide risk index does not exceed the lower threshold, the corresponding time period is classified as a low-risk level. Under the low-risk level, the landslide has a small probability of occurrence and has a small impact on the hazard-affected body, and routine monitoring and management can be carried out.

[0142] By comparing the landslide risk index with the threshold and classifying the risk level, clear guidance can be provided for landslide disaster prevention and control in different time periods, and resources can be allocated reasonably to improve prevention and control efficiency.

[0143] Further, based on the landslide risk level and the impact range, landslide risk prevention and control measures are proposed, including:

[0144] Step 1445: Superimpose the landslide risk level and the impact range to determine the risk prevention and control area and the non-risk prevention and control area.

[0145] Superimposing the landslide risk level and the impact range is to more accurately determine the areas that need to be focused on for prevention and control and the areas that can take relatively relaxed prevention and control measures. In the target seismic area, the landslide risk level and the impact range are different in different areas. Through geographic information system (GIS) technology, the landslide risk level map and the impact range map are spatially superimposed and analyzed. For example, the areas corresponding to the extremely dangerous level and the high-risk level are superimposed with the landslide impact range to determine the specific location and range of these high-risk areas in the actual geographic space. These areas are the risk prevention and control areas that need to be focused on and strict prevention and control measures need to be taken.

[0146] For the areas corresponding to the moderate-risk level and the low-risk level, the same superimposition analysis is performed with the landslide impact range. If these areas are less affected by the landslide or other factors can reduce the risk of landslide, such as the obstruction of topography and vegetation protection, these areas can be classified as non-risk prevention and control areas. In the non-risk prevention and control area, relatively relaxed prevention and control measures can be taken, but some monitoring and early warning still need to be carried out.

[0147] Through this superimposed analysis, the boundaries and ranges of the risk prevention and control area and the non-risk prevention and control area can be determined.

[0148] Step 1446: For the risk prevention and control area, engineering governance measure suggestions are made, including landslide body reinforcement measures, drainage system construction measures, and slope protection measures; for the non-risk prevention and control area, non-engineering prevention and control measure suggestions are made, including monitoring and early warning system construction measures and emergency plan development measures; according to the time development trend, the implementation priority of different prevention and control measures is determined.

[0149] For the risk prevention and control area, engineering governance measure suggestions are made. Landslide body reinforcement measures can enhance the stability of the landslide body and reduce the possibility of landslide occurrence. Common landslide body reinforcement measures include anchor rod reinforcement, retaining wall reinforcement, etc. Anchor rod reinforcement is to drill holes in the landslide body, insert anchor rods and grout cement mortar, connect the landslide body with stable rock or soil body, and improve the anti-slide capacity of the landslide body. Retaining wall reinforcement is to build retaining walls in the downhill direction of the landslide body to block the sliding force of the landslide body.

[0150] Drainage system construction measures can reduce the impact of groundwater on the landslide body. By building drainage ditches, drainage wells and other facilities, surface water and groundwater are timely discharged, the water content of rock-soil body is reduced, and the stability of the slope is improved. For example, a ring-shaped drainage ditch is built around the landslide body to intercept surface water and prevent it from flowing into the landslide body; drainage wells are set up in the landslide body to lower the groundwater level.

[0151] Slope protection measures can improve the erosion resistance of the slope. Common slope protection measures include vegetation slope protection, hanging net shotcrete, etc. Vegetation slope protection is to plant vegetation on the slope, use the root system of vegetation to fix soil, reduce soil and water loss, and at the same time, vegetation can also play a role in buffering rainwater erosion. Hanging net shotcrete is to lay steel mesh on the surface of the slope, and then spray concrete to form a protective layer, preventing the weathering and peeling of the rock-soil body of the slope.

[0152] For the non-risk prevention and control area, non-engineering prevention and control measure suggestions are made. Monitoring and early warning system construction measures can monitor the changes of landslide in real time and discover potential risks of landslide in time. Displacement monitoring instruments, water level monitoring instruments, inclinometers and other equipment can be installed to monitor the displacement, groundwater level, inclination and other parameters of the landslide body in real time. When the monitoring data shows abnormal changes, an early warning signal is sent in time to remind relevant personnel to take measures.

[0153] The emergency plan development measures are to quickly and effectively respond to the landslide when it occurs. The development of emergency plan needs to clarify the emergency organization, emergency response process, emergency rescue measures and other contents. For example, clarify the responsibilities and tasks of each department when the landslide occurs, develop personnel evacuation plan, material allocation plan, etc.

[0154] According to the time development trend, the implementation priority of different prevention and control measures is determined. In the early warning period before the landslide occurs, the monitoring and early warning system should be constructed in priority to master the dynamic changes of the landslide in time and provide basis for subsequent prevention and control decision. At the same time, some important disaster-bearing bodies can be prevented and reinforced. In the early stage of landslide, landslide body reinforcement measures and drainage system construction measures should be implemented as soon as possible to control the development of landslide and reduce the loss. In the middle stage of landslide development, the implementation of engineering governance measures should be continued, and at the same time, emergency plan should be started to organize rescue and emergency work. In the stable period of landslide, the main work is to repair and rebuild the damaged disaster-bearing bodies, and to evaluate and summarize the disaster loss.

[0155] Step 1447: integrate the engineering governance measure suggestion, non-engineering prevention and control measure suggestion and implementation priority to generate landslide risk prevention and control measure suggestion.

[0156] The integration of engineering governance measure suggestion, non-engineering prevention and control measure suggestion and implementation priority is to form a comprehensive and systematic landslide risk prevention and control measure suggestion. First of all, the engineering governance measure suggestion and non-engineering prevention and control measure suggestion are combed in detail to clarify the specific content, implementation location and implementation requirement of each measure. For example, for landslide body reinforcement measures, the specifications, spacing and of anchor rod, the size and structure of retaining wall, etc. are clarified; for monitoring and early warning system construction measures, the type, installation location and monitoring frequency of monitoring equipment, etc. are clarified.

[0157] Then, according to the implementation priority, each measure is sorted. The measures that need to be implemented in priority in different time stages are placed in the front to ensure that effective prevention and control measures can be taken in time in critical period. For example, in the early warning period before the landslide occurs, the monitoring and early warning system construction measures are placed in the first place; in the early stage of landslide, landslide body reinforcement measures and drainage system construction measures are placed in the front.

[0158] Finally, the combed measure suggestion and sorting result are integrated to form a complete landslide risk prevention and control measure suggestion, which should include the name, content, implementation location, implementation time, implementation responsibility unit and other detailed information of the measures to facilitate the execution of relevant departments and personnel. At the same time, the budget and resource demand of each measure can be attached to the suggestion to provide economic and resource guarantee for the implementation of prevention and control measures.

[0159] Further, the landslide risk prevention and control measure suggestion is combined with the historical spatial planning data of the target earthquake zone to generate a regional spatial planning optimization scheme, including:

[0160] Step 1448: Obtain the historical spatial planning data of the target earthquake zone, including land use planning data, urban construction planning data, and industrial layout planning data; superimpose the landslide risk prevention and control measure suggestion on the land use planning data to identify the land use types that need to be adjusted; superimpose the landslide risk prevention and control measure suggestion on the urban construction planning data to identify the construction areas that need to be avoided; superimpose the landslide risk prevention and control measure suggestion on the industrial layout planning data to identify the industrial parks that need to be relocated.

[0161] Obtaining the historical spatial planning data of the target earthquake zone is the basis for generating the regional spatial planning optimization scheme. Land use planning data reflects the purpose and distribution of land in the target earthquake zone, including agricultural land, construction land, and ecological land. Urban construction planning data includes the layout of cities and towns, the planning of buildings, etc., such as the distribution of residential areas, commercial areas, and industrial areas. Industrial layout planning data shows the distribution and development planning of industrial parks, such as the location and scale of industrial parks and logistics parks.

[0162] Superimposing the landslide risk prevention and control measure suggestion on the land use planning data analyzes the adaptability of different land use types under landslide risk. If some land use types are located in high-risk areas and are difficult to reduce risk through prevention and control measures, these land use types need to be adjusted. For example, adjusting construction land in the extremely dangerous level area to ecological land, such as building parks and forest land, can reduce landslide risk and improve the ecological environment.

[0163] Superimposing the landslide risk prevention and control measure suggestion on the urban construction planning data identifies the construction areas that need to be avoided. For high-risk areas, new urban construction projects should be avoided in these areas. For example, if a region is classified as an extremely dangerous level and cannot effectively reduce risk through engineering control measures, important buildings such as residences, schools, and hospitals should be explicitly prohibited in the urban construction planning in that area.

[0164] Superimposing the landslide risk prevention and control measure suggestion on the industrial layout planning data identifies the industrial parks that need to be relocated. Some industrial parks may be located in high-risk areas and may cause serious economic losses and environmental pollution once a landslide disaster occurs. For these industrial parks, relocation to a safe area should be considered. For example, relocating a chemical industry park located in a high-risk area to a place far from the landslide risk area to ensure the safe development of the industry.

[0165] Step 1449: 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, develop a spatial planning adjustment scheme; conduct a feasibility analysis on the spatial planning adjustment scheme to generate a feasibility evaluation result; according to the feasibility evaluation result, optimize the spatial planning adjustment scheme to generate a regional spatial planning optimization scheme.

[0166] 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, develop a spatial planning adjustment scheme. The scheme should clearly specify the specific content of the adjustment, the implementation steps, and the time arrangement. For example, for the land use types that need to be adjusted, specify the adjustment range, target use, and implementation method; for the construction areas that need to be avoided, demarcate the boundary and range of prohibited construction; for the industrial parks that need to be relocated, develop a relocation plan, including relocation time, new site selection, and relocation cost, etc.

[0167] A feasibility analysis of the spatial planning adjustment scheme needs to consider economic, social, environmental, and other factors. In terms of economy, analyze the implementation cost and benefit of the adjustment scheme, including land adjustment cost, industrial relocation cost, infrastructure construction cost, etc., and the impact on local economic development after adjustment. For example, evaluate the impact on land value and tax revenue after adjusting construction land to ecological land; evaluate the impact on industrial development and employment after relocating industrial parks.

[0168] In terms of society, consider the impact of the adjustment scheme on residents' lives and social stability. For example, analyze the impact of land use type adjustment on farmers' livelihoods, and the inconvenience brought by industrial park relocation to employees' employment and life. At the same time, fully solicit the opinions of local residents and relevant stakeholders to ensure that the implementation of the scheme is supported by society.

[0169] In terms of environment, evaluate the impact of the adjustment scheme on the ecological environment. For example, analyze the improvement of the ecological system after adjusting construction land to ecological land; evaluate the impact of industrial park relocation on the environment of the new site.

[0170] According to the feasibility evaluation result, optimize the spatial planning adjustment scheme. If the feasibility evaluation result shows that the scheme has some problems or deficiencies, the scheme needs to be modified and improved. For example, if the economic cost is too high, the adjustment range and method can be adjusted to reduce the cost; if the social impact is large, appropriate compensation and resettlement measures can be added to reduce the impact on residents. Through continuous optimization of the scheme, ensure that the regional spatial planning optimization scheme can not only reduce the landslide disaster risk, but also achieve the coordinated development of economy, society, and environment.

[0171] Step 145: Integrate the vulnerability level of the hazard-affected body, landslide risk prevention and control measures, and regional spatial planning optimization scheme to generate a comprehensive guidance report containing the spatial distribution map of the vulnerability level of the hazard-affected body, landslide risk prevention and control measures, and regional spatial planning optimization scheme.

[0172] Integrate the vulnerability level of the hazard-affected body, landslide risk prevention and control measures, and regional spatial planning optimization scheme to generate a comprehensive guidance report. First, present the vulnerability level of the hazard-affected body in the form of a spatial distribution map to intuitively show the vulnerability level of the hazard-affected body in different regions. Use geographic information system (GIS) technology to mark the vulnerability level of the hazard-affected body on the map with different colors or symbols, such as red for extremely high vulnerability level areas, yellow for high vulnerability level areas, green for medium vulnerability level areas, and blue for low vulnerability level areas.

[0173] Then, write the landslide risk prevention and control measures in detail in the report. Include engineering control measures and non-engineering prevention measures for different risk level areas, as well as the implementation priority and specific requirements of each measure. At the same time, attach relevant charts and explanations to make the prevention and control measures more clear and understandable.

[0174] Finally, include the regional spatial planning optimization scheme in the report. Introduce the content, implementation steps and time schedule of spatial planning adjustment, as well as the feasibility evaluation results and optimization process. At the same time, analyze the effect of regional spatial planning optimization scheme on reducing landslide disaster risk and promoting regional sustainable development.

[0175] In the report, you can also add some case analysis and experience summary to provide reference for relevant departments and personnel. For example, introduce the successful experience of other regions in landslide disaster prevention and control and spatial planning optimization, analyze the similarities and differences between this region and other regions, and put forward suggestions and measures suitable for this region. Through the generation of a comprehensive guidance report, provide comprehensive and systematic guidance for landslide disaster prevention and control and regional planning in the target earthquake area.

[0176] In one non-limiting embodiment, further comprising: extracting a model verification feature parameter set from the text annotation quantitative evaluation report, and extracting a prevention and control measure implementation effect data set from the comprehensive guidance report; obtaining new geological structure monitoring data, new topographic and geomorphic distribution data, and new hydrological environment monitoring data of a target seismic area, performing difference analysis on the new geological structure monitoring data, the new topographic and geomorphic distribution data, and the new hydrological environment monitoring data and the comprehensive monitoring data set to generate a data difference degree feature matrix; inputting the model verification feature parameter set, the prevention and control measure implementation effect data set, and the data difference degree feature matrix into the geological-geomorphic-hydrological coupling model, dynamically correcting the interaction and correlation relationship of the geological action equation, the geomorphic evolution equation, and the hydrological dynamic equation to generate a corrected geological-geomorphic-hydrological coupling model; verifying the corrected geological-geomorphic-hydrological coupling model using the new geological structure monitoring data, the new topographic and geomorphic distribution data, and the new hydrological environment monitoring data to generate a model prediction accuracy evaluation index; if the model prediction accuracy evaluation index meets a preset index condition, determining the corrected geological-geomorphic-hydrological coupling model as an updated geological-geomorphic-hydrological coupling model; if not, returning to the step of inputting the model verification feature parameter set, the prevention and control measure implementation effect data set, and the data difference degree feature matrix into the geological-geomorphic-hydrological coupling model to perform dynamic correction again.

[0177] In the above embodiments, the preset index condition is determined according to actual needs and application scenarios of the model. For example, the preset index condition can specify that the prediction error of the model is within a certain range, or the prediction accuracy of the model reaches a certain percentage. Comparing the model prediction accuracy evaluation index with the preset index condition, if the model prediction accuracy evaluation index meets the preset index condition, it means that the performance of the corrected model meets the requirements, and the corrected geological-geomorphic-hydrological coupling model can be determined as the updated geological-geomorphic-hydrological coupling model. The updated model can be used for subsequent landslide disaster prediction and evaluation work. If the model prediction accuracy evaluation index does not meet the preset index condition, it means that the corrected model still has problems and needs to be further corrected. Return to the step of inputting the model verification feature parameter set, the prevention and control measure implementation effect data set, and the data difference degree feature matrix into the geological-geomorphic-hydrological coupling model to perform dynamic correction again. In the re-correction process, the reasons for the inaccurate prediction of the model need to be analyzed in depth, and the parameters and interaction and correlation relationship in the model need to be adjusted until the prediction accuracy of the model meets the preset index condition.

[0178] In a non-limiting embodiment, further comprising: generating a landslide spatiotemporal feature dataset according to the graphic-text annotated quantitative evaluation report; generating a three-dimensional geological grid model of the target seismic area based on the landslide spatiotemporal feature dataset, the node coordinates of the three-dimensional geological grid model corresponding one-to-one to the geographic spatial coordinates in the landslide spatial distribution feature; mapping the instability probability dynamic curve data, critical instability time point, and landslide scale grade in the landslide spatiotemporal feature dataset as risk feature parameters to the corresponding nodes of the three-dimensional geological grid model to generate a three-dimensional grid model carrying risk feature parameters; dynamically evolving the three-dimensional grid model carrying risk feature parameters according to the time nodes to generate a three-dimensional risk evolution model sequence according to the time development trend; performing risk feature annotation on each model in the three-dimensional risk evolution model sequence to generate a three-dimensional dynamic evolution model with annotated risk features; wherein the content of the risk feature annotation includes node color gradient corresponding to the instability probability dynamic curve data, time axis mark corresponding to the critical instability time point, and impact range boundary line corresponding to the landslide scale grade.

[0179] In this way, by performing risk feature annotation on each model in the three-dimensional risk evolution model sequence, a three-dimensional dynamic evolution model with annotated risk features is generated, which can more intuitively and in more detail show the landslide risk situation of the target seismic area, providing a powerful tool for the research and prevention and control of landslide disasters. For example, by observing the three-dimensional dynamic evolution model with annotated risk features, a high-risk area can be quickly determined, and prevention and control measures can be taken in a timely manner; the development trend of the landslide can be analyzed, the range that the landslide can affect can be predicted, and an emergency plan can be prepared in advance.

[0180] The embodiment of the present application generates a comprehensive monitoring dataset by integrating multi-source data of geological structure monitoring data, topographic and geomorphic distribution data, and hydrological environment monitoring data of the target seismic area; on this basis, a geological-geomorphic-hydrological coupling model reflecting the synergistic effect of multiple factors is constructed, fully considering the interaction between geological, geomorphic, and hydrological factors, and compared with traditional models, the coupling model can more realistically simulate the occurrence and development process of landslide disasters; the coupling model is used to dynamically simulate and quantitatively evaluate potential landslides according to a combination of preset scenario parameters, and the generated graphic-text annotated quantitative evaluation report contains key information such as the spatial distribution feature, time development trend, and impact range of the landslide, making the prediction of landslide disasters more accurate and comprehensive; based on this report, vulnerability analysis and risk assessment of the hazard-affected body are performed, and a comprehensive guidance report is generated, providing a scientific and systematic decision basis for the prevention and control of landslide disasters and regional spatial planning, which can reduce the loss of the hazard-affected body caused by landslide disasters and improve the disaster resistance of the region.

[0181] Please refer to Figure 2, provide a module block diagram of a landslide disaster evolution analysis device based on a coupling model, the landslide disaster evolution analysis device based on the coupling model comprises:

[0182] A data integration module is configured to perform multi-source data integration processing on geological structure monitoring data, topographic distribution data and hydrological environment monitoring data of a target seismic area, and generate a comprehensive monitoring data set.

[0183] A model construction module is configured to construct a geological-geomorphological-hydrological coupling model reflecting the synergistic effect of multiple factors by establishing an interaction correlation of a target equation set based on the comprehensive monitoring data set; the target equation set comprises a geological action equation, a geomorphological evolution equation and a hydrological dynamic equation.

[0184] A quantitative evaluation module is configured to perform dynamic simulation and quantitative evaluation processing on the instability probability and landslide scale of a potential landslide body in the target seismic area by using the geological-geomorphological-hydrological coupling model according to a preset scenario parameter combination, and generate a graphic and text annotated quantitative evaluation report containing landslide spatial distribution characteristics, time development trend and influence range.

[0185] A seismic area analysis module is configured to perform vulnerability analysis and risk assessment processing on hazard-affected bodies in the target seismic area based on the graphic and text annotated quantitative evaluation report, and generate a comprehensive guidance report containing a hazard-affected body vulnerability spatial distribution map, landslide risk prevention and control measure suggestions and regional spatial planning optimization scheme.

[0186] Referring to Figure 3 The figure is a schematic diagram of the basic structure of a landslide disaster evolution analysis system 200 based on a coupling model provided by an embodiment of the application, and the landslide disaster evolution analysis system 200 based on the coupling model comprises:

[0187] A processor 201;

[0188] A storage device 202 having a computer program 2020 stored thereon;

[0189] When the computer program 2020 is executed by the processor 201, the processor 201 implements any of the landslide disaster evolution analysis methods based on the coupling model.

[0190] On the basis described above, a readable storage medium is provided, and the readable storage medium has a program or instruction stored thereon, and the program or instruction is executed by a processor to implement the steps of the method described above.

[0191] It should be noted that the various embodiments described in the specification are intended to be exemplary only and that the scope of the application is not intended to be limited to the embodiments described in the specification.

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 coupling model reflecting the synergistic effects of multiple factors is constructed by establishing the interaction relationships of the target equation set: Geological structural deformation rate data, geomorphological change data, and hydrological flow change data are extracted from the comprehensive monitoring dataset; the geological structural deformation rate data are input into the geological process equation to calculate the degree of geological stress accumulation; the geomorphological change data are input into the geomorphological evolution equation to calculate the geomorphological stability index; the hydrological flow change data are input into the hydrological dynamic equation to calculate the hydraulic erosion intensity; a first interaction relationship is established between the degree of geological stress accumulation and the geomorphological stability index, represented by the effect function of geological structural deformation on geomorphological transformation; a second interaction relationship is established between the geomorphological stability index and the hydraulic erosion intensity, represented by the influence effect of geomorphology on hydrological pathways. The function represents the third interaction relationship between the intensity of hydraulic erosion and the degree of geological stress accumulation. This third interaction relationship is represented by a function showing the weakening effect of hydrological erosion on the integrity of the geological structure. The first, second, and third interaction relationships are integrated to construct a geological-geomorphological-hydrological coupling model reflecting the synergistic effects of multiple factors. The target equation set includes geological equations, geomorphological evolution equations, and hydrodynamic equations. The geological equations are used to calculate the redistribution of geological stress under different seismic activity intensities based on factors such as earthquake intensity, frequency, and focal depth. The hydrodynamic equations are used to calculate groundwater infiltration depth data under different rainfall intensities based on factors such as rainfall intensity, rainfall duration, and the permeability coefficient of soil and rock. The geomorphological evolution equations are used to calculate surface vegetation cover changes under different intensities of human engineering activities based on the type and intensity of these activities. 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 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.

3. The landslide disaster evolution analysis method based on a coupled model according to claim 2, 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 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.

4. The landslide disaster evolution analysis method based on a coupled model according to claim 2, 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.

5. 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.

6. The landslide hazard evolution analysis method based on a coupled model according to claim 5, 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.

7. The landslide disaster evolution analysis method based on a coupled model according to claim 5, 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.

8. The landslide disaster evolution analysis method based on a coupled model according to claim 5, 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.

9. A landslide hazard 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 1-8.

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