Method and device for cross-time scale joint ancient landslide reactivation risk classification assessment

Abstract

The application provides a kind of ancient landslide revival risk grading evaluation method and device across time scale. The method comprises obtaining historical observation data of ancient landslide area, calculating space-time probability, dividing long time scale susceptibility grade;Calculate the ground deformation rate along the radar line-of-sight direction;Two-dimensional deformation conversion is carried out, the ground deformation rate along the slope direction is obtained, the ground deformation rate grade is divided, the susceptibility grade is updated, and the risk grade is obtained;Analysis of dynamic trigger factor, update risk grade according to short time scale ancient landslide revival dynamics equation, obtain ancient landslide revival risk grading evaluation model;The data to be analyzed is input into the ancient landslide revival risk grading evaluation model, and the ancient landslide revival risk grading evaluation result is output. In this way, the dynamic correlation of the development and evolution mechanism of ancient landslide and the dynamic trigger factor in the time domain can be strengthened, and the ancient landslide revival risk grading evaluation of the coupling correlation constraint of internal and external factors in the time scale can be realized.

Description

Technical Field

[0001] The present invention generally relates to the field of geospatial data processing, and more particularly, to a method and apparatus for cross-time-scale integrated risk grading assessment of ancient landslide reactivation. Background Art

[0002] Paleolandslides are the product of complex, long-term slope evolution processes. They have complex material composition and unique geotechnical properties, making them highly hidden and sensitive to disturbances. A paleolandslide generally refers to a slope that has experienced one or more sliding events and will slide again or have a tendency to slide when disturbed by external factors (such as heavy rainfall or changes in reservoir water levels). Unstable paleolandslides are potential hazards for human engineering activities. Southwest my country, due to its unique geographical location, topographical features, and extreme climatic conditions, is experiencing increasingly frequent geological disasters caused by the instability and reactivation of paleolandslides. The instability and reactivation of paleolandslides, induced by the coupling of the region's geographical and geological environment (i.e., long-term factors) with internal and external factors such as reservoir water and rainfall, are influenced by numerous factors influencing their development and deformation, and their deformation and failure mechanisms are complex. With the construction of a large amount of infrastructure and the influence of extreme external factors (such as heavy rainfall and changes in reservoir water levels), landslide geological hazards are becoming increasingly severe.

[0003] Due to the widespread, threatening, and devastating impacts of reactivated ancient landslides, accurate quantitative assessment of their instability and reactivation risks has become a necessary and urgent requirement for pre-disaster scientific early warning and proactive disaster prevention and mitigation. Field surveys are no longer sufficient to quantitatively assess the risks of reactivated ancient landslide hazards, and the use of data-driven models for landslide susceptibility analysis has become the mainstream. Among them, landslide susceptibility analysis based on machine learning methods has strong data processing capabilities for nonlinear relationships. Combined with surface deformation rate data obtained by SBAS-InSAR technology, it can track detailed deformation and damage information throughout the life cycle of ancient landslide instability and reactivation, identify critical trigger points for secondary hazards and disaster chains, and then conduct local microscopic analysis of short-time-scale dynamic models to improve the accuracy and reliability of risk assessments of reactivated ancient landslide instability and hazard risks, as well as scientific monitoring and early warning capabilities.

[0004] The use of traditional machine learning methods relies on a large number of representative data samples. Processing small sample data can easily lead to underfitting and overfitting problems in risk assessment analysis results. At the same time, it is easy to ignore the impact of external extreme factors (such as heavy rainfall, earthquakes, etc.) on the resurrection of ancient landslides. The assessment has not established a dynamic correlation with the short-term dynamic triggering process, and cannot cope with situations such as the suddenness of disaster-inducing dynamic triggering factors and the dynamic randomness of short-term triggering processes. In addition, the amount of information on the spatiotemporal evolution of ancient landslide resurrection disasters is relatively one-sided and lagging, and lacks timeliness, which makes it difficult to avoid the randomness, bias and blindness of the assessment results.

[0005] While further research is underway to understand the mechanisms underlying the reactivation of potential instabilities over long timescales during the evolution of ancient landslides, reactivation also faces challenges such as the sudden nature of triggering factors and the dynamic randomness of short-term triggering processes. Currently, little research has focused on the triggering processes, particularly the dynamics, of ancient landslide reactivation. Risk assessments struggle to dynamically correlate with short-term dynamic triggering processes, hindering machine learning from accurately assessing reactivation risk levels. This, coupled with the inability to accurately assess landslide hazards and implement scientific monitoring and early warning measures for the vast number of potential hazards awaiting prediction, ultimately leads to ineffective disaster prevention, mitigation, and emergency response, resulting in significant losses. Summary of the Invention

[0006] According to an embodiment of the present invention, a cross-timescale integrated risk grading and assessment scheme for ancient landslide reactivation is provided. This scheme strengthens the dynamic temporal correlation between ancient landslide development and evolution mechanisms and dynamic triggering factors, addressing the inaccuracy and reliability of single-scale assessments. It enables a grading and assessment of ancient landslide reactivation risk in complex disaster-prone scenarios and across timescales, coupled with internal and external factors.

[0007] In a first aspect of the present invention, a cross-timescale integrated ancient landslide reactivation risk grading assessment method is provided. The method comprises:

[0008] Obtain historical observation data of ancient landslide areas, construct a characteristic evaluation factor system for ancient landslide reactivation disasters, calculate the spatiotemporal probability of reactivation disasters during the long-term development and evolution of ancient landslides, and classify the susceptibility level of ancient landslide reactivation disasters on long time scales based on the spatiotemporal probability;

[0009] Acquire a Sentinel-1 image dataset and calculate the surface deformation rate along the radar line of sight using the SBAS-InSAR method; perform a two-dimensional deformation transformation on the surface deformation rate along the radar line of sight to obtain the surface deformation rate along the slope direction, and classify the surface deformation rate into different levels; update the long-term susceptibility level of ancient landslide reactivation disasters based on the surface deformation rate level to obtain a long-term ancient landslide reactivation risk level; the long-term ancient landslide reactivation risk level includes a high risk level;

[0010] The areas corresponding to the high-risk levels are taken as key nodes in the development and evolution of ancient landslides. The dynamic triggering factors that induce the resurgence of ancient landslides are analyzed at the key nodes. The dynamic equations of the short-time-scale resurgence of ancient landslides are constructed. The residual shear strength of the sliding zone soil under secondary disasters is calculated based on the dynamic equations of the short-time-scale resurgence of ancient landslides. The long-time-scale resurgence risk level of ancient landslides is updated to obtain a graded assessment model for the resurgence of ancient landslides.

[0011] The historical observation data of the ancient landslide area to be analyzed and the Sentinel-1 image dataset are input into the ancient landslide reactivation risk grading assessment model, and the ancient landslide reactivation risk grading assessment results are output.

[0012] Furthermore, the construction of the ancient landslide reactivation disaster characteristic evaluation factor system includes:

[0013] Extracting characteristic factors of deep geological conditions and topographic and geomorphic conditions from historical observation data of the ancient landslide area;

[0014] Normalizing the deep geological condition characteristic factors and the topographic and geomorphic condition characteristic factors;

[0015] Calculate the landslide frequency ratio and information entropy weight of the normalized characteristic factors.

[0016] Furthermore, the calculation of the surface deformation rate along the radar line of sight using the SBAS-InSAR method includes:

[0017] Pairing interferometric image pairs on the Sentinel-1 image dataset to obtain a plurality of paired interferometric image pairs;

[0018] generating an interference pattern using the plurality of paired interference image pairs, performing a flattening process on the interference pattern, performing adaptive filtering on the flattened interference pattern, and generating a coherence coefficient to obtain an optimized interference pattern;

[0019] The optimized interferogram is subjected to a first SBAS inversion to obtain a first estimated deformation rate and residual topography; the first estimated deformation rate and residual topography are then subjected to a second SBAS inversion to obtain displacement in a time series; and the displacement in the time series is then geocoded to obtain the surface deformation rate along the radar line of sight.

[0020] Furthermore, performing a two-dimensional deformation conversion on the surface deformation rate in the radar line of sight direction to obtain the surface deformation rate along the slope direction includes:

[0021] Among them, V Slope is the surface deformation rate along the slope direction; V Los is the surface deformation rate along the radar line of sight; α s is the angle between the azimuth and the true north direction; α is the slope direction; β is the angle between the line of sight and the slope; θ is the angle of incidence; The slope gradient.

[0022] Among them, V Slope is the surface deformation rate along the slope direction; V Losis the surface deformation rate along the radar line of sight; α s is the angle between the azimuth and the true north direction; α is the slope direction; β is the angle between the line of sight and the slope; θ is the angle of incidence; The slope gradient.

[0023] Furthermore, the updating of the long-time scale susceptibility level of ancient landslide reactivation disasters corresponding to the surface deformation rate level includes:

[0024] If the level of the surface deformation rate grade corresponding to the target ancient landslide area is higher than the level of the susceptibility grade of the ancient landslide resurrection disaster corresponding to the target ancient landslide area, then the level of the susceptibility grade of the ancient landslide resurrection disaster corresponding to the target ancient landslide area is updated to the level of the surface deformation rate grade corresponding to the target ancient landslide area; the updated susceptibility grade of the ancient landslide resurrection disaster is used as the long-term ancient landslide resurrection risk level;

[0025] The surface deformation rate grade is the same as the susceptibility grade of ancient landslide reactivation disaster.

[0026] Furthermore, the calculation of the residual shear strength of the sliding zone soil under secondary disasters based on the dynamic equation of the resurrection of the ancient landslide on a short time scale includes:

[0027] During the instability process of the ancient landslide, when the sliding body is in the critical triggering state between the intermittent period and the sliding period, the shear strength of the sliding zone soil is calculated as follows:

[0028] τ f =c′ Rec +{σ-[u0+p(z m ,t diff )]}tanφ′ Rec

[0029] Among them, τ f is the shear strength of sliding zone soil; c' Rec is the effective cohesion of the sliding zone soil after strength recovery; σ is the total stress; u0 is the initial pore water pressure of the sliding zone soil; p(z m ,t diff ) is the water pressure increment in the pores of the sliding zone soil caused by the head pressure generated by rainfall or reservoir water level fluctuations diffusing to the sliding surface, z m is the thickness of the landslide; t diff The time it takes for the head pressure generated by rainfall or reservoir water level changes to diffuse to the sliding surface; φ' Rec is the effective residual internal friction angle after strength recovery;

[0030] The landslide block of unit width in the sliding body is selected as the research object. During the instability process of the ancient landslide, the landslide block does not undergo relative displacement and the movement rate and direction are the same. When the landslide block is in the critical triggering state between the intermittent period and the sliding period, the sliding force τ is calculated:

[0031]

[0032] When the landslide block enters the sliding period and begins to slide along the sliding surface, the initial acceleration a0 is obtained and the residual shear strength of the sliding zone soil during the sliding period is calculated:

[0033]

[0034] Where, τ is the sliding force; is the average weight of the landslide block; g is the acceleration of gravity; a0 is the initial acceleration of the landslide block after breaking through the critical state; a is the acceleration of the landslide block during the sliding period; τ v is the residual shear strength of the sliding zone soil during the sliding period.

[0035] Furthermore, the updating of the long-term ancient landslide reactivation risk level includes:

[0036] If the shear rate effect of the residual shear strength of the sliding zone soil is a positive rate effect, then the level of the long-time scale ancient landslide reactivation risk level corresponding to the key node is positively correlated with the shear rate effect of the residual shear strength of the sliding zone soil, and the level of the long-time scale ancient landslide reactivation risk level corresponding to the key node is updated in grades according to the degree of positive correlation;

[0037] Among them, the shear rate effect of the residual shear strength of the sliding zone soil is a positive rate effect, then:

[0038] h=τ v -τ r >0

[0039] h is the shear rate effect coefficient, which indicates the degree of positive correlation. The larger h is, the higher the degree of positive correlation is. τ v is the residual shear strength of the sliding zone soil during the sliding period; τ r is the residual shear strength of sliding zone soil during the intermittent period.

[0040] In a second aspect of the present invention, a device for grading and assessing the risk of ancient landslide reactivation across time scales is provided. The device comprises:

[0041] The first classification module is used to obtain historical observation data of the ancient landslide area, construct a characteristic evaluation factor system for ancient landslide reactivation disasters, calculate the spatiotemporal probability of reactivation disasters during the long-term development and evolution of ancient landslides, and classify the susceptibility level of ancient landslide reactivation disasters on a long time scale based on the spatiotemporal probability;

[0042] The second grading module is used to obtain the Sentinel-1 image data set and calculate the surface deformation rate along the radar line of sight using the SBAS-InSAR method; perform a two-dimensional deformation transformation on the surface deformation rate along the radar line of sight to obtain the surface deformation rate along the slope direction, and classify the surface deformation rate into grades; update the long-term scale paleo-landslide reactivation hazard susceptibility grade corresponding to the surface deformation rate grade to obtain the long-term scale paleo-landslide reactivation risk grade; the long-term scale paleo-landslide reactivation risk grade includes a high risk grade;

[0043] A level update module is used to take the area corresponding to the high-risk level as the key node of the development and evolution of the ancient landslide, analyze the dynamic triggering factors that induce the resurgence of the ancient landslide at the key node, construct the dynamic equation of the short-time scale ancient landslide resurgence, and calculate the residual shear strength of the sliding zone soil under the secondary disaster based on the dynamic equation of the short-time scale ancient landslide resurgence, update the long-time scale ancient landslide resurgence risk level, and obtain the ancient landslide resurgence risk grading assessment model;

[0044] The evaluation module is used to input the historical observation data of the ancient landslide area to be analyzed and the Sentinel-1 image data set into the ancient landslide resurrection risk classification evaluation model, and output the ancient landslide resurrection risk classification evaluation result.

[0045] In a third aspect of the present invention, an electronic device is provided. The electronic device comprises at least one processor; and a memory communicatively connected to the at least one processor; the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of the first aspect of the present invention.

[0046] In a fourth aspect of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method of the first aspect of the present invention.

[0047] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The above and other features, advantages and aspects of the embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0049] Figure 1 A flowchart of a cross-time-scale combined ancient landslide reactivation risk classification assessment method according to an embodiment of the present invention is shown;

[0050] Figure 2 A block diagram of a device for grading and assessing the risk of ancient landslide reactivation combined across time scales according to an embodiment of the present invention is shown;

[0051] Figure 3 shows a block diagram of an exemplary electronic device capable of implementing embodiments of the present invention;

[0052] Among them, 300 is an electronic device, 301 is a CPU, 302 is a ROM, 303 is a RAM, 304 is a bus, 305 is an I / O interface, 306 is an input unit, 307 is an output unit, 308 is a storage unit, and 309 is a communication unit. DETAILED DESCRIPTION

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0054] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0055] The present invention can strengthen the dynamic correlation between the development and evolution mechanism of ancient landslides and dynamic triggering factors in the time domain, solve the problem of insufficient accuracy and reliability of single-scale assessment, and realize the graded assessment of the resurrection risk of ancient landslides with complex disaster-prone scenarios and coupled correlation constraints of internal and external factors across time scales.

[0056] Figure 1 A flow chart of a cross-time-scale combined ancient landslide reactivation risk grading assessment method according to an embodiment of the present invention is shown.

[0057] The method includes:

[0058] S101. Obtain historical observation data of the ancient landslide area, construct a characteristic evaluation factor system for ancient landslide reactivation disasters, calculate the spatiotemporal probability of reactivation disasters during the long-term development and evolution of ancient landslides, and divide the susceptibility level of ancient landslide reactivation disasters on a long time scale according to the spatiotemporal probability.

[0059] As an embodiment of the present invention, ancient landslide is a geological environment that has experienced one or more sliding and has a sliding trend when subjected to external factors, such as heavy rainfall, reservoir water level changes, etc. The landslide is caused to slide again or has a sliding trend. The southwest region of my country, due to its special geographical location, topographical features and extreme climatic conditions, causes the geological disaster caused by the instability and revival of ancient landslides in the region to become increasingly frequent. The instability and revival of the ancient landslide induced by the coupling of the geographical geological environment (i.e., long-term factors) and the internal and external factors such as reservoir water and rainfall in the southwest region, its development, deformation influencing factors are numerous, the deformation damage mechanism is complicated, along with the construction of a large amount of infrastructure and the influence of external extreme factors (such as heavy rainfall, reservoir water level changes, etc.), the landslide geological disaster presents an increasingly serious trend, as the research object of the present embodiment.

[0060] As an embodiment of the present invention, the construction of a characteristic evaluation factor system for ancient landslide resurrection disasters includes:

[0061] First, deep geological condition characteristic factors and topographic and geomorphic condition characteristic factors are extracted from historical observation data of the ancient landslide area. The deep geological condition characteristic factors include faults, lithology, soil erosion, and crack development. The topographic and geomorphic condition characteristic factors include slope and aspect, profile curvature, and vegetation cover.

[0062] Secondly, the deep geological condition characteristic factors and the topographic and geomorphic condition characteristic factors are normalized.

[0063] The characteristic evaluation factors of ancient landslide reactivation disaster are multi-source and heterogeneous, including continuous and discrete types. Spatial preprocessing is required to classify and quantify the discrete evaluation factors, and to adopt hierarchical discretization processing for the continuous evaluation factors to unify the model input.

[0064] In this embodiment, the normalization process is as follows:

[0065]

[0066] Among them, Y * is the sample data before normalization; Y is the sample data before normalization; max is the maximum value of the sample data, and min is the minimum value of the sample data.

[0067] Comprehensive extraction forms the graded quantification of each evaluation factor, and generates thematic maps of each landslide evaluation factor.

[0068] Finally, the landslide frequency ratio and information entropy weight of the normalized characteristic factors are calculated.

[0069]

[0070] Among them, N i 、S i Respectively represent the distribution of evaluation factors u i The number of ancient landslide units in a certain classification category and the area of ​​the classification region; N and S are the total number of ancient landslide units and the total area of ​​the study area; FR ij 、p ij are the landslide frequency ratio and distribution probability of the jth classification category under the i-th evaluation factor index, FR ij >1 indicates that the state conditions of this category of factors are conducive to the resurgence of ancient landslides; FR ij <1 indicates that the state condition is not conducive to the resurgence of ancient landslides; K is a constant term, which is determined by the number of classification categories K of each ancient landslide disaster environmental evaluation factor. i Value determination; WE i is the entropy weight, E i The entropy is the information entropy. The entropy weight is inversely proportional to the information entropy. The larger the entropy weight is, the smaller the uncertainty of the information in the characteristic evaluation factor data is, and the greater the contribution of this evaluation factor to the development of ancient landslide resurrection disasters.

[0071] By calculating the landslide frequency ratio and information entropy weight, the influence of the ancient landslide resurrection disaster characteristic evaluation factor on the ancient landslide disaster environmental characteristic law and contribution degree is quantified. Combining the ancient landslide knowledge mechanism law with the basic geographical overview of the study area, the mechanism and spatial statistical law of the regional ancient landslide development and evolution are comprehensively summarized.

[0072] As an embodiment of the present invention, the calculation of the spatiotemporal probability of the resurrection disaster of the ancient landslide during the long-term development and evolution process, and the classification of the susceptibility level of the long-term ancient landslide resurrection disaster according to the spatiotemporal probability, include:

[0073] Using the historical data of the study area, which is characterized by normalized and hierarchically quantified characteristic evaluation factors, as input, a random forest model was constructed by integrating multiple decision trees using bagging and random selection features. This model hierarchically characterizes the long-term development and evolution of ancient landslides and the spatiotemporal probability of resurrection disasters. The random forest model is a classifier that uses multiple trees to train and predict samples. In machine learning, a random forest is a classifier that contains multiple decision trees, and its output category is determined by the mode of the categories output by the individual trees.

[0074] In this embodiment, the susceptibility level of ancient landslide reactivation disasters on a long time scale can be divided into multiple levels according to the spatiotemporal probability, for example, divided into five levels, namely extremely low susceptibility, low susceptibility, medium susceptibility, high susceptibility, and extremely high susceptibility.

[0075] As an embodiment of the present invention, after the susceptibility levels are divided, the continuous ancient landslide susceptibility layer is reclassified into five discrete ancient landslide susceptibility level zoning maps using the natural breakpoint method.

[0076] S102. Acquire a Sentinel-1 image dataset and calculate the surface deformation rate along the radar line of sight using the SBAS-InSAR method; perform a two-dimensional deformation transformation on the surface deformation rate along the radar line of sight to obtain the surface deformation rate along the slope direction, and classify the surface deformation rate into levels; update the long-time scale susceptibility level of ancient landslide reactivation disasters according to the surface deformation rate level to obtain a long-time scale ancient landslide reactivation risk level; the long-time scale ancient landslide reactivation risk level includes a high risk level.

[0077] The Sentinel-1 image dataset is image data collected by the Sentinel-1 satellite. The Sentinel-1 satellite is an Earth observation satellite in the European Space Agency's Copernicus program (GMES). It consists of two satellites and carries a C-band synthetic aperture radar that can provide continuous imagery (day, night, and all weather conditions).

[0078] As an embodiment of the present invention, the calculation of the surface deformation rate along the radar line of sight using the SBAS-InSAR method includes:

[0079] First, interferometric image pairs are paired on the Sentinel-1 image dataset to obtain a plurality of paired interferometric image pairs.

[0080] In this example, interferometric image pair matching is performed on the Sentinel-1 image dataset, and the image pair results are output as a graph. Given N input scenes, the maximum number of possible pairs is (N*(N-1)) / 2. The connection diagram generation tool selects the optimal pairing combination. The program automatically selects a super master image, which serves as the reference image throughout the entire process, and all image pairs are registered to it.

[0081] Secondly, an interference pattern is generated by using the paired interference image pairs, the interference pattern is flattened, and the flattened interference pattern is adaptively filtered to obtain an optimized interference pattern.

[0082] Specifically, to generate an interferogram by interferometrically processing all paired interferogram pairs, we need to set the multi-look ratio, calculated from the sensor's original sampling interval and incident angle, to ensure that the final result has the same resolution in both azimuth and range directions as possible. Note: The ratio of multi-look ratios must be greater than 1:1.

[0083] Specifically, the deflating process removes interference fringes with the same interval caused by the flat ground effect, while retaining interference fringes caused by terrain undulations and elevation fluctuations.

[0084] Specifically, adaptive filtering filters the interferogram after flattening in the previous step, removing phase noise caused by flat-ground interference and making the interference fringes smoother. Simultaneously, it generates an interferogram coherence map (describing phase quality) and a filtered main image intensity map, increasing the signal-to-noise ratio of the interferogram and providing more reliable coherence, preparing data for SBAS inversion.

[0085] Finally, the optimized interferogram undergoes a first SBAS inversion to obtain the first estimated deformation rate and residual topography. This first SBAS inversion uses a linear model. Clear subsidence and uplift are visible in the first estimated rate map. The next step in the SBAS inversion yields an optimized rate estimate, determining the location and spatial extent of the deformation. The first estimated deformation rate and residual topography are then subjected to a second SBAS inversion to obtain the time series displacement. The second SBAS inversion follows the first estimated deformation rate. In this step, filtering is used to remove the atmospheric phase to obtain the final time series deformation. Atmospheric filtering smoothes the time series deformation. This removal of atmospheric pressure is achieved through low-pass and high-pass filtering. At the end of this step, the final deformation rate is calculated based on the deformation model selected in the first inversion. This is a clean time series deformation calculated using a polynomial. The squared value of the model calculation is also generated. The time series displacement is then geocoded to obtain the surface deformation rate along the radar line of sight. The deformation result and deformation rate are reprojected to the user-defined direction, which is directly projected along the radar line of sight.

[0086] As an embodiment of the present invention, since landslides mostly slide along the slope surface, the deformation information in the radar line of sight direction cannot accurately reflect the actual deformation of the slope surface. Considering the geometric relationship between the radar line of sight direction and the slope direction, assuming that the movement occurs along the direction specified by the unit vector, the following formula is used to convert the deformation rate in the line of sight direction into the deformation rate in the slope direction.

[0087]

[0088] V Slope =V Los / cosβ

[0089] In the above formula, cosβ is expressed as:

[0090]

[0091] wherein V Slope is the ground deformation rate along the slope direction; V Los is the ground deformation rate along the radar line-of-sight direction; α s is the angle between the azimuth direction and the north direction; α is the slope aspect; β is the angle between the line of sight and the slope; θ is the incidence angle; is the slope gradient.

[0092] The ground deformation rate along the radar line-of-sight direction is calculated by the SBAS-InSAR method; the ground deformation rate along the slope direction is obtained by two-dimensional deformation conversion on the ground deformation rate along the radar line-of-sight direction, aiming to reduce the false negative number that may occur by using a single machine learning model, accurately update the susceptibility level, and further establish a deep and accurate reactivation risk grading standard, so as to macroscopically determine the danger level and the danger area range of the ancient landslide.

[0093] In this embodiment, the actual ground deformation rate data is obtained by the SBAS-InSAR method, and according to the ground deformation rate threshold range, the susceptibility level of the long-time-scale ancient landslide reactivation disaster is divided into the same level, i.e., five rate levels.

[0094] As an embodiment of the present application, the susceptibility level of the long-time-scale ancient landslide reactivation disaster is updated according to the ground deformation rate level, and a long-time-scale ancient landslide reactivation risk level is obtained, which specifically includes:

[0095] If the level of the ground deformation rate level corresponding to the target ancient landslide area is higher than the level of the susceptibility level of the ancient landslide reactivation disaster corresponding to the target ancient landslide area, the level of the susceptibility level of the ancient landslide reactivation disaster corresponding to the target ancient landslide area is updated to the level of the ground deformation rate level corresponding to the target ancient landslide area; and the updated susceptibility level of the ancient landslide reactivation disaster is used as the long-time-scale ancient landslide reactivation risk level.

[0096] In this embodiment, as shown in Table 1, the susceptibility level of the ancient landslide reactivation disaster is updated to the long-time-scale ancient landslide reactivation risk level:

[0097]

[0098] Table 1

[0099] In Table 1, the columns represent the five-level classification of surface deformation rate, the rows represent the five-level classification of susceptibility, and the elements in the table represent the updated long-term paleo-landslide reactivation risk levels. For example, if the first row, second column, indicates a susceptibility level of 1 and a surface deformation rate level of 2, since the surface deformation rate level is higher than the susceptibility level, the susceptibility level needs to be increased by 1 level to correspond to the risk level.

[0100] The long-term ancient landslide reactivation risk level has the same number of levels as the surface deformation rate level and the susceptibility level of ancient landslide reactivation disasters. For example, in this embodiment, the long-term ancient landslide reactivation risk level is divided into five levels, namely, very low risk level, low risk level, medium risk level, high risk level, and very high risk level. The high risk level and the very high risk level are considered high risk levels. That is, the high risk level is a level with a higher risk of ancient landslide reactivation.

[0101] S103. The area corresponding to the high-risk level is regarded as the key node of the development and evolution of the ancient landslide. The dynamic triggering factors that induce the resurgence of the ancient landslide are analyzed at the key node. The dynamic equation of the short-time scale ancient landslide resurgence is constructed. The residual shear strength of the sliding zone soil under the secondary disaster is calculated based on the dynamic equation of the short-time scale ancient landslide resurgence. The long-time scale ancient landslide resurgence risk level is updated to obtain the ancient landslide resurgence risk grading assessment model.

[0102] In some embodiments, the dynamic triggering factor that induces the reactivation of the ancient landslide is, for example, periodic changes in reservoir water levels caused by rainfall infiltration due to seasonal heavy rainfall.

[0103] In some embodiments, a dynamic equation for the reactivation of ancient landslides on a short timescale is constructed. The effects of rainfall and reservoir water level fluctuations on the landslide boundary can be simplified to a pressure with a certain head, which affects the pore water pressure in various parts of the landslide in the form of hydraulic diffusion. Assuming that the landslide body is a homogeneous soil mass, based on the characteristics of one-dimensional vertical instantaneous Darcy flow in a uniform porous medium, the diffusion process of the head pressure generated by rainfall or reservoir water level fluctuations in the landslide body can be expressed as follows:

[0104]

[0105] Where p(z,t) is the pore water pressure increment at the depth z of the landslide block at time t; D is the hydraulic diffusion coefficient of the landslide body.

[0106]

[0107] Among them, τ r is the residual shear strength of the sliding zone soil during the intermittent period; c' ris the effective residual cohesion of the sliding zone soil; σ is the total stress; u0 is the initial pore water pressure of the sliding zone soil; is the effective residual internal friction angle.

[0108] When the sliding body is in the critical state between the intermittent period and the sliding period, the shear strength of the sliding zone soil τ f It can be expressed as:

[0109] τ f =c′ Rec +{σ-[u0+p(z m ,t diff )]}tanφ′ Rec

[0110] Among them, τ f is the shear strength of sliding zone soil; c' Rec is the effective cohesion of the sliding zone soil after strength recovery; σ is the total stress; u0 is the initial pore water pressure of the sliding zone soil; p(z m ,t diff ) is the water pressure increment in the pores of the sliding zone soil caused by the head pressure generated by rainfall or reservoir water level fluctuations diffusing to the sliding surface, z m is the thickness of the landslide; t diff The time it takes for the head pressure generated by rainfall or reservoir water level changes to diffuse to the sliding surface; φ' Rec is the effective residual internal friction angle after strength recovery.

[0111] Taking a unit-width landslide block as the research object, during the intermittent reactivation of slow sliding, the block itself does not undergo relative displacement, so the movement rate at any point on the landslide block (including the sliding surface) is the same in magnitude and direction. Assuming that the landslide block is a rigid body, the thrust from the upper block and the resistance from the lower block are equal in magnitude and opposite in direction. Therefore, when the landslide block is in the critical state between the intermittent period and the sliding period, we have:

[0112]

[0113] Where, τ is the sliding force; is the average weight of the landslide block; g is the acceleration of gravity; a0 is the initial acceleration of the landslide block after breaking through the critical state; a is the acceleration of the landslide block during the sliding period; τ v is the residual shear strength of the sliding zone soil during the sliding period; m is the mass of the landslide block per unit width; S is the bottom area of ​​the landslide block per unit width.

[0114] After obtaining the initial acceleration a0, the landslide body entering the sliding period begins to slide along the sliding surface. At this time,

[0115]

[0116] Where f(v) is the function of the landslide movement velocity v.

[0117] In some embodiments, updating the long-term ancient landslide reactivation risk level includes:

[0118] If the shear rate effect of the residual shear strength of the sliding zone soil is a positive rate effect, then the level of the long-time scale ancient landslide resurrection risk level corresponding to the key node is positively correlated with the shear rate effect of the residual shear strength of the sliding zone soil, and the level of the long-time scale ancient landslide resurrection risk level corresponding to the key node is updated according to the degree of positive correlation.

[0119] Among them, the shear rate effect of the residual shear strength of the sliding zone soil is a positive rate effect, then:

[0120] h=τ v -τ r >0

[0121] h is the shear rate effect coefficient, which indicates the degree of positive correlation. The larger h is, the higher the degree of positive correlation is. τ v is the residual shear strength of the sliding zone soil during the sliding period; τ r is the residual shear strength of sliding zone soil during the intermittent period.

[0122] Specifically, when τ v >τ r When τ v <τ r When τ v =τ r When , there is no effect.

[0123] The updating of the long-time scale ancient landslide reactivation risk level corresponding to the key node according to the degree of positive correlation specifically includes:

[0124] If the shear rate effect coefficient is higher than the high risk coefficient threshold range, the long-time scale ancient landslide resurrection risk level corresponding to the key node will be raised to the corresponding highest risk level.

[0125] In summary, this is the dynamic equation that describes the short-time-scale triggering and reactivation of ancient landslides on the reservoir bank.

[0126] S104 , inputting the historical observation data of the ancient landslide area to be analyzed and the Sentinel-1 image dataset into the ancient landslide reactivation risk grading assessment model, and outputting the ancient landslide reactivation risk grading assessment result.

[0127] According to the embodiment of the present application, the joint mechanism of the short-time scale dynamic trigger factor and the long-time scale ancient landslide development evolution mechanism is quantitatively associated, the single scale analysis of the ancient landslide revival risk is overcome, on the basis of the systematic analysis of the implicit cause mechanism and the explicit representation of the ancient landslide, the typical spatio-temporal correlation characteristics existing in the short-term dynamic trigger factor and the long-term landslide deformation displacement evolution process are deeply analyzed, the critical trigger point of the secondary disaster and the disaster chain is found, and the joint response mechanism of the short-time scale dynamic trigger factor and the long-time scale ancient landslide development evolution mechanism is established. Under the framework of machine learning, the high-level mapping between the ancient landslide evolution mechanism model and the dynamic trigger model is established, the multi-scale and multi-stage real-time dynamic risk tracking is realized, and the cross-scale joint ancient landslide instability revival risk quantitative evaluation is realized on the basis of the susceptibility analysis. Finally, the ancient landslide revival risk quantitative measurement of the disaster-pregnant scene complexity and the cross-scale internal and external factor coupling correlation constraint can be realized, the dynamic quantitative correlation of the ancient landslide development evolution mechanism and the dynamic trigger factor in the time domain can be strengthened, the problems of the single scale evaluation accuracy and reliability can be solved, the risk quantitative measurement of the multi-scale and multi-stage internal and external factor coupling correlation constraint can be supported, and the ancient landslide revival hidden danger can be accurately evaluated in advance and the scientific and effective monitoring and early warning can be implemented.

[0128] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the action sequence described, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily necessary for the present application.

[0129] The above is the introduction of the method embodiment, and the scheme described in the present application will be further described through the device embodiment.

[0130] As shown in Figure 2 , the device 200 comprises:

[0131] The first hierarchical module 210 is configured to acquire historical observation data of the ancient landslide area, construct an ancient landslide revival disaster feature evaluation factor system, calculate the spatio-temporal probability of the ancient landslide long-term development evolution process revival disaster occurrence, and divide the long-time scale ancient landslide revival disaster susceptibility level according to the spatio-temporal probability.

[0132] The second grading module 220 is configured to obtain a Sentinel-1 image data set, calculate a ground surface deformation rate along a radar line-of-sight direction by using an SBAS-InSAR method, perform two-dimensional deformation conversion on the ground surface deformation rate along the radar line-of-sight direction to obtain a ground surface deformation rate along a slope direction, and divide the ground surface deformation rate into grades; and update the long-time-scale ancient landslide reactivation disaster susceptibility grade corresponding to the ground surface deformation rate grade to obtain a long-time-scale ancient landslide reactivation risk grade; the long-time-scale ancient landslide reactivation risk grade comprises a high risk grade.

[0133] The grade updating module 230 is configured to take a region corresponding to the high risk grade as a key node of ancient landslide development and evolution, analyze a dynamic triggering factor of inducing ancient landslide reactivation at the key node, construct a dynamic equation of short-time-scale ancient landslide reactivation, calculate a residual shear strength of a landslide zone under secondary disasters according to the dynamic equation of short-time-scale ancient landslide reactivation, and update the long-time-scale ancient landslide reactivation risk grade to obtain an ancient landslide reactivation risk grading and evaluation model.

[0134] The evaluation module 240 is configured to input historical observation data and the Sentinel-1 image data set of an ancient landslide region to be analyzed into the ancient landslide reactivation risk grading and evaluation model, and output an ancient landslide reactivation risk grading and evaluation result.

[0135] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described modules can refer to the corresponding process in the foregoing method embodiments, which will not be described herein again.

[0136] In the technical solution of the present application, the acquisition, storage and application of user personal information comply with relevant laws and regulations and do not violate public order and good customs.

[0137] According to the embodiments of the present application, the present application further provides an electronic device and a readable storage medium.

[0138] Figure 3 A schematic block diagram of an electronic device 300 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application described and / or claimed in this document.

[0139] The electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. Various programs and data required for the operation of the electronic device 300 can also be stored in the RAM 303. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0140] Multiple components in the electronic device 300 are connected to the I / O interface 305, including an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a magnetic disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0141] The computing unit 301 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as methods S101 to S104. For example, in some embodiments, methods S101 to S104 can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the methods S101 to S104 described above can be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to execute methods S101 to S104 in any other appropriate manner (for example, by means of firmware).

[0142] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0143] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0144] In the context of the present invention, machine-readable medium can be a tangible medium that can contain or store a program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0145] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0146] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0147] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0148] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0149] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A cross-timescale joint ancient landslide reactivation risk grading assessment method, characterized by: include: Obtain historical observation data of ancient landslide areas, construct a characteristic evaluation factor system for ancient landslide reactivation disasters, calculate the spatiotemporal probability of reactivation disasters during the long-term development and evolution of ancient landslides, and classify the susceptibility level of ancient landslide reactivation disasters on long time scales based on the spatiotemporal probability; Acquire Sentinel-1 image datasets and calculate the surface deformation rate along the radar line of sight using the SBAS-InSAR method; Performing a two-dimensional deformation transformation on the surface deformation rate along the radar line of sight to obtain the surface deformation rate along the slope direction, and classifying the surface deformation rate into grades; Updating the long-time-scale susceptibility level of ancient landslide reactivation disasters according to the surface deformation rate level to obtain the long-time-scale ancient landslide reactivation risk level; The risk level of the reactivation of ancient landslides over a long time scale includes a high risk level; The areas corresponding to the high-risk levels are taken as key nodes in the development and evolution of ancient landslides. The dynamic triggering factors that induce the resurgence of ancient landslides are analyzed at the key nodes. The dynamic equations of the short-time-scale resurgence of ancient landslides are constructed. The residual shear strength of the sliding zone soil under secondary disasters is calculated based on the dynamic equations of the short-time-scale resurgence of ancient landslides. The long-time-scale resurgence risk level of ancient landslides is updated to obtain a graded assessment model for the resurgence of ancient landslides. Inputting historical observation data of the ancient landslide area to be analyzed and the Sentinel-1 image dataset into the ancient landslide reactivation risk grading assessment model, and outputting the ancient landslide reactivation risk grading assessment results; The method of calculating the surface deformation rate along the radar line of sight by the SBAS-InSAR method includes: Pairing interferometric image pairs on the Sentinel-1 image dataset to obtain a plurality of paired interferometric image pairs; generating an interferogram using the plurality of paired interferometric image pairs; flattening the interferogram; adaptively filtering the flattened interferogram; and generating a coherence coefficient to obtain an optimized interferogram; performing a first SBAS inversion on the optimized interferogram to obtain a first estimated deformation rate and residual terrain; performing a second SBAS inversion on the first estimated deformation rate and residual terrain to obtain a displacement in a time series; and geocoding the displacement in the time series to obtain a surface deformation rate along a radar line of sight. The residual shear strength of the sliding zone soil under secondary disasters is calculated based on the dynamic equation of the resurrection of the ancient landslide on a short time scale, including: During the instability process of the ancient landslide, when the sliding body is in the critical triggering state between the intermittent period and the sliding period, the shear strength of the sliding zone soil is calculated as follows: t f =c′ Rec +{σ-[u0+p(z m ,t diff )]}tanφ′ Rec Among them, τ f is the shear strength of sliding zone soil; c' Rec is the effective cohesion of the sliding zone soil after strength recovery; σ is the total stress; u0 is the initial pore water pressure of the sliding zone soil; p(z m ,t diff ) is the water pressure increment in the pores of the sliding zone soil caused by the head pressure generated by rainfall or reservoir water level fluctuations diffusing to the sliding surface, z m is the thickness of the landslide; t diff The time it takes for the head pressure generated by rainfall or reservoir water level changes to diffuse to the sliding surface; φ' Rec is the effective residual internal friction angle after strength recovery; The landslide block of unit width in the sliding body is selected as the research object. During the instability process of the ancient landslide, the landslide block does not undergo relative displacement and the movement rate and direction are the same. When the landslide block is in the critical triggering state between the intermittent period and the sliding period, the sliding force τ is calculated: When the landslide block enters the sliding period and begins to slide along the sliding surface, the initial acceleration a0 is obtained and the residual shear strength of the sliding zone soil during the sliding period is calculated: Where, τ is the sliding force; is the average weight of the landslide block; g is the acceleration of gravity; a0 is the initial acceleration of the landslide block after breaking through the critical state; a is the acceleration of the landslide block during the sliding period; τ v is the residual shear strength of the sliding zone soil during the sliding period.

2. The method according to claim 1, characterized in that The aforementioned construction of a characteristic evaluation factor system for ancient landslide reactivation disasters includes: Extracting characteristic factors of deep geological conditions and topographic and geomorphic conditions from historical observation data of the ancient landslide area; Normalizing the deep geological condition characteristic factors and the topographic and geomorphic condition characteristic factors; Calculate the landslide frequency ratio and information entropy weight of the normalized characteristic factors.

3. The method according to claim 1, characterized in that The performing two-dimensional deformation conversion on the surface deformation rate in the radar line of sight direction to obtain the surface deformation rate along the slope direction includes: Among them, V Slope is the surface deformation rate along the slope direction; V Los is the surface deformation rate along the radar line of sight; α s is the angle between the azimuth and due north; α is the slope direction; α is the angle between the line of sight and the slope; θ is the angle of incidence; The slope gradient.

4. The method according to claim 1, wherein The updating of the susceptibility level of the long-term ancient landslide resurrection disaster corresponding to the surface deformation rate level includes: If the level of the surface deformation rate grade corresponding to the target ancient landslide area is higher than the level of the susceptibility grade of the ancient landslide resurrection disaster corresponding to the target ancient landslide area, then the level of the susceptibility grade of the ancient landslide resurrection disaster corresponding to the target ancient landslide area is updated to the level of the surface deformation rate grade corresponding to the target ancient landslide area; the updated susceptibility grade of the ancient landslide resurrection disaster is used as the long-term ancient landslide resurrection risk level; The surface deformation rate grade is the same as the susceptibility grade of ancient landslide reactivation disaster.

5. The method according to claim 1, wherein The updated long-term ancient landslide reactivation risk level includes: If the shear rate effect of the residual shear strength of the sliding zone soil is a positive rate effect, then the level of the long-time scale ancient landslide reactivation risk level corresponding to the key node is positively correlated with the shear rate effect of the residual shear strength of the sliding zone soil, and the level of the long-time scale ancient landslide reactivation risk level corresponding to the key node is updated in grades according to the degree of positive correlation; Among them, the shear rate effect of the residual shear strength of the sliding zone soil is a positive rate effect, then: h=τ v -t r >0 h is the shear rate effect coefficient, which indicates the degree of positive correlation. The larger h is, the higher the degree of positive correlation is. τ v is the residual shear strength of the sliding zone soil during the sliding period; τ r is the residual shear strength of sliding zone soil during the intermittent period.

6. A cross-timescale combined ancient landslide instability reactivation risk grading assessment device, characterized by: include: The first classification module is used to obtain historical observation data of the ancient landslide area, construct a characteristic evaluation factor system for ancient landslide reactivation disasters, calculate the spatiotemporal probability of reactivation disasters during the long-term development and evolution of ancient landslides, and classify the susceptibility level of ancient landslide reactivation disasters on a long time scale based on the spatiotemporal probability; The second hierarchical module is used to obtain the Sentinel-1 image dataset and calculate the surface deformation rate along the radar line of sight using the SBAS-InSAR method; Performing a two-dimensional deformation transformation on the surface deformation rate along the radar line of sight to obtain the surface deformation rate along the slope direction, and classifying the surface deformation rate into grades; Updating the long-time-scale susceptibility level of ancient landslide reactivation disasters according to the surface deformation rate level to obtain the long-time-scale ancient landslide reactivation risk level; The risk level of the reactivation of ancient landslides over a long time scale includes a high risk level; A level update module is used to take the area corresponding to the high-risk level as the key node of the development and evolution of the ancient landslide, analyze the dynamic triggering factors that induce the resurgence of the ancient landslide at the key node, construct the dynamic equation of the short-time scale ancient landslide resurgence, and calculate the residual shear strength of the sliding zone soil under the secondary disaster based on the dynamic equation of the short-time scale ancient landslide resurgence, update the long-time scale ancient landslide resurgence risk level, and obtain the ancient landslide resurgence risk grading assessment model; An assessment module, configured to input historical observation data of the ancient landslide area to be analyzed and a Sentinel-1 image dataset into the ancient landslide reactivation risk grading assessment model, and output an ancient landslide reactivation risk grading assessment result; The method of calculating the surface deformation rate along the radar line of sight by the SBAS-InSAR method includes: Pairing interferometric image pairs on the Sentinel-1 image dataset to obtain a plurality of paired interferometric image pairs; generating an interferogram using the plurality of paired interferometric image pairs; flattening the interferogram; adaptively filtering the flattened interferogram; and generating a coherence coefficient to obtain an optimized interferogram; performing a first SBAS inversion on the optimized interferogram to obtain a first estimated deformation rate and residual terrain; performing a second SBAS inversion on the first estimated deformation rate and residual terrain to obtain a displacement in a time series; and geocoding the displacement in the time series to obtain a surface deformation rate along a radar line of sight. The residual shear strength of the sliding zone soil under secondary disasters is calculated based on the dynamic equation of the resurrection of the ancient landslide on a short time scale, including: During the instability process of the ancient landslide, when the sliding body is in the critical triggering state between the intermittent period and the sliding period, the shear strength of the sliding zone soil is calculated as follows: t f =c′ Rec +{σ-[u0+p(z m ,t diff )]}tanφ′ Rec Among them, τ f is the shear strength of sliding zone soil; c' Rec is the effective cohesion of the sliding zone soil after strength recovery; σ is the total stress; u0 is the initial pore water pressure of the sliding zone soil; p(z m ,t diff ) is the water pressure increment in the pores of the sliding zone soil caused by the head pressure generated by rainfall or reservoir water level fluctuations diffusing to the sliding surface, z m is the thickness of the landslide; t diff The time it takes for the head pressure generated by rainfall or reservoir water level changes to diffuse to the sliding surface; φ' Rec is the effective residual internal friction angle after strength recovery; The landslide block of unit width in the sliding body is selected as the research object. During the instability process of the ancient landslide, the landslide block does not undergo relative displacement and the movement rate and direction are the same. When the landslide block is in the critical triggering state between the intermittent period and the sliding period, the sliding force τ is calculated: When the landslide block enters the sliding period and begins to slide along the sliding surface, the initial acceleration a0 is obtained and the residual shear strength of the sliding zone soil during the sliding period is calculated: Where, τ is the sliding force; is the average weight of the landslide block; g is the acceleration of gravity; a0 is the initial acceleration of the landslide block after breaking through the critical state; a is the acceleration of the landslide block during the sliding period; τ v is the residual shear strength of the sliding zone soil during the sliding period.

7. An electronic device comprising at least one processor; and A memory communicatively connected to the at least one processor; characterized in that The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 5.

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