High-speed slope slippage disaster monitoring and early warning method

Abstract

The invention belongs to the technical field of slope slippage disaster monitoring and early warning, and relates to a high-speed slope slippage disaster monitoring and early warning method. According to the method, a trend term, a periodic term and a high-frequency disturbance term are reconstructed and separated by constructing a displacement time sequence matrix and performing empirical mode decomposition, a high-frequency disturbance term space abnormal region is identified, and a rainfall driving type slippage region is marked in combination with phase lag stability of the periodic term and rainfall data and acceleration characteristics of the trend term; and calculating a trend term space gradient to judge the run-through of the slip plane, constructing a comprehensive instability index, and matching historical samples through dynamic time warping to output graded early warning. The method solves the problems that in the prior art, multi-source mixed signals are difficult to peel off, rainfall response time lag is neglected, space deformation evolution excavation is lacked, so that slip plane penetration recognition is difficult, and quantitative grading of early warning is lacked. According to the invention, fine separation of multi-source signals and accurate identification of deep slippage risks are realized, and the accuracy, timeliness and emergency decision support capability of early warning are improved.

Description

Technical Field

[0001] This invention belongs to the field of slope slip disaster monitoring and early warning technology, and relates to a method for monitoring and early warning of high-speed slope slip disaster. Background Technology

[0002] Highway slope slippage is a common geological hazard that seriously threatens transportation and the safety of people's lives and property. Currently, monitoring and early warning technologies for such hazards mainly rely on monitoring equipment deployed on the slope surface to acquire deformation data and analyze data changes to determine slope stability. However, existing monitoring and early warning methods have the following specific problems in practical applications: First, existing analysis methods often employ simple smoothing filters or threshold discrimination, making it difficult to effectively extract the dominant components reflecting the long-term evolution of the slope, the fluctuation components affected by seasonal environmental factors, and random disturbance components from the mixed original monitoring sequences. Because environmental noise and short-term fluctuations cannot be accurately eliminated, subtle structural anomalies are easily masked, thus reducing the accuracy of early warnings.

[0003] Secondly, for rainfall-induced landslides, existing technologies often directly establish statistical correlations between rainfall and displacement, neglecting the time-delayed response of the slope's internal hydrogeological structure to rainfall infiltration. Furthermore, they fail to further differentiate whether this delayed response is sustained over time, making it difficult to distinguish between transient hydrological effects and genuine precursors to deep structural instability, easily leading to missed or false alarms.

[0004] Furthermore, existing monitoring and early warning models mostly focus on single-point time-series data analysis and lack the ability to explore the evolution characteristics of the overall spatial deformation field in the monitoring area. This makes it difficult to accurately identify whether potential slip surfaces have formed a connection in space and to capture the critical characteristics of the transformation from local deformation to overall instability.

[0005] Finally, existing early warning mechanisms are mostly based on static threshold triggering and lack dynamic comparative analysis of the entire disaster evolution process. As a result, the output early warning information lacks quantitative level and scale prediction support, making it difficult to meet the actual needs of graded emergency response. Summary of the Invention

[0006] In view of this, in order to solve the problems mentioned in the background technology, a method for monitoring and early warning of landslide disasters on high-speed slopes is proposed.

[0007] The objective of this invention can be achieved through the following technical solution: a method for monitoring and early warning of high-speed slope landslide disasters, comprising: constructing a displacement time series matrix based on surface displacement monitoring data, performing empirical mode decomposition on it, and reconstructing and separating trend terms, periodic terms, and high-frequency disturbance terms.

[0008] High-value connected regions in the spatial distribution of high-frequency disturbance terms are identified as deformation anomaly areas. The phase lag between the periodic terms of monitoring points in the area and the rainfall data of the same period is analyzed. When a stable lag relationship exists and the trend term shows acceleration characteristics, it is marked as a rainfall-driven slip zone.

[0009] Calculate the spatial gradient of the trend term in the rainfall-driven slip zone. When the gradient direction changes abruptly and the gradient value increases exponentially, the slip surface is determined to be connected, and the spatial gradient magnitude is used as the slip intensity index.

[0010] For slip zones with continuous slip surfaces, a comprehensive instability index is constructed based on the deformation acceleration of the slip intensity index and trend term. By matching with the dynamic time warping of historical instability samples, a graded early warning instruction is output, which includes the slip urgency index level and the damage scale prediction.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention constructs a displacement time series matrix and performs empirical mode decomposition, and reconstructs and separates the trend term, periodic term and high-frequency disturbance term according to the frequency order. It solves the problem that the multi-source components in the original monitoring sequence are mixed and difficult to separate, which makes it easy for small structural abnormal signals to be masked by environmental noise and short-term fluctuations. It realizes the fine separation of long-term cumulative deformation, environmental load fluctuation and local random disturbance, effectively eliminates background noise interference, and improves the sensitivity and early warning accuracy of early abnormal signal identification.

[0012] (2) This invention identifies rainfall-driven slip zones by analyzing the phase lag stability of the periodic term and rainfall data, combined with the acceleration characteristics of the second derivative of the trend term. This overcomes the shortcomings of existing technologies that neglect the time delay characteristics and persistence of rainfall response, making it difficult to distinguish between transient hydrological effects and precursors of deep structural instability. It identifies the risk of rainfall-induced deep slip, eliminates interference from random hydrological fluctuations, reduces the false alarm and missed alarm rates during the rainy season, and enhances the adaptability and reliability of the early warning mechanism for rainfall conditions.

[0013] (3) This invention calculates the spatial gradient of the trend term and determines when the gradient direction changes abruptly and the value increases exponentially, thus obtaining the slip surface connectivity index. This changes the current situation where the focus is on single-point time series analysis, which lacks spatial deformation field evolution mining and cannot accurately identify the potential spatial connectivity state of the slip surface. It captures the critical spatial characteristics of the transformation from local deformation to overall instability, clarifies the geometric criteria for continuous slip zone connectivity, and promptly locks in the overall instability risk, improving the timeliness and spatial identification of the disaster critical state determination.

[0014] (4) This invention constructs a comprehensive instability index and uses dynamic time warping to match historical samples to output the urgency level and damage scale prediction. This avoids the shortcomings of static threshold triggering without dynamic comparison throughout the entire process, which leads to a lack of quantitative level and scale prediction support for early warning information. It realizes the morphological similarity comparison with historical instability evolution paths, provides quantitative urgency level classification and potential damage volume prediction, and improves the scientificity and practicality of emergency decision support. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a method for monitoring and early warning of landslide disasters on high-speed slopes in this invention.

[0017] Figure 2 This is a flowchart of the marking method for rainfall-driven slip zones in this invention.

[0018] Figure 3 This is a flowchart of the method for obtaining the slip strength index in this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] The specific scheme of the high-speed slope slip disaster monitoring and early warning method provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0022] Please see Figure 1As shown, the implementation of this invention includes S1 to S4: First, S1 constructs a displacement time series matrix and performs empirical mode decomposition to reconstruct the original monitoring signal into trend terms, periodic terms, and high-frequency disturbance terms, thereby extracting effective features and removing noise from complex multi-source signals; then, S2, based on this, uses the spatial distribution of high-frequency disturbance terms to lock the deformation anomaly area, and combines the phase lag stability of the periodic terms and rainfall data with the acceleration characteristics of the trend terms to identify rainfall-driven slip zones; further, S3 introduces spatial gradient analysis for the locked slip zones, and determines the slip surface connectivity by capturing gradient direction abrupt changes and magnitude exponential growth characteristics, and calculates slip intensity indicators to quantify the degree of danger; finally, S4 integrates the trend term acceleration and slip intensity indicators to construct a comprehensive instability index, matches historical instability samples using a dynamic time warping algorithm, and outputs a graded early warning instruction including urgency level and damage scale prediction.

[0023] S1. Construct a displacement time series matrix based on surface displacement monitoring data, perform empirical mode decomposition on it, and reconstruct and separate the trend term, periodic term, and high-frequency disturbance term.

[0024] Because surface displacement monitoring data is essentially a nonlinear superposition of multiple components such as geological structural evolution, environmental load fluctuations, and random disturbances, direct analysis of it can easily mask the trend components reflecting long-term slope creep with high-frequency environmental noise and short-term seasonal fluctuations, making it difficult to identify subtle early signs of instability.

[0025] Therefore, the surface displacement monitoring data of each monitoring point on the slope surface is collected first within a preset time window, for example, the surface displacement monitoring data is collected hourly using the past 72 hours as the time window; then, a two-dimensional displacement time series matrix is ​​constructed with time as the row and the spatial location of the monitoring point as the column. Each row of the matrix represents the displacement value of all monitoring points at the same sampling time, and each column represents the displacement time series of a single monitoring point at different times.

[0026] Furthermore, empirical mode decomposition is performed on each column of data in the displacement time series matrix to obtain several intrinsic mode components and an intrinsic residual arranged in descending order of frequency.

[0027] As an example, during the decomposition process, the standard deviation is used as the screening stopping condition. The standard deviation of two adjacent screening results is calculated. When the standard deviation is less than a preset threshold, such as between 0.2 and 0.3, the screening stops to determine the intrinsic modal components. Each component is extracted in turn until the remaining part is a monotonic function or has only one extreme point, thus obtaining the final intrinsic residual.

[0028] Subsequently, the average period of each intrinsic mode component obtained from the decomposition is calculated. Typically, the average period of the noise component is extremely short, while the average period of the trend component is extremely long. Therefore, based on the sampling frequency of the monitoring data, a first threshold is set to twice the sampling time interval, and a second threshold is set to 1 / 10 of the total monitoring duration.

[0029] Components with an average period less than twice the sampling time interval are identified as high-frequency noise components and used to construct high-frequency disturbance terms that reflect non-stationary disturbance characteristics such as local soil and rock loosening or micro-fractures. Components with an average period greater than 1 / 10 of the total monitoring time and intrinsic residuals are identified as low-frequency trend components and used to construct trend terms that characterize the long-term cumulative deformation of the slope under gravity. The remaining components are identified as periodic components affected by the environment and used to construct periodic terms.

[0030] S2. Identify high-value connected regions in the spatial distribution of high-frequency disturbance terms as deformation anomaly areas. Analyze the phase lag between the periodic terms of monitoring points in the area and the rainfall data of the same period. When there is a stable lag relationship and the trend term shows acceleration characteristics, it is marked as a rainfall-driven slip zone.

[0031] Given that high-frequency disturbance terms characterize local non-stationary disturbances, when local damage or micro-fractures occur within the slope, they will manifest as an abnormally high amplitude in the spatial distribution of these high-frequency disturbance terms. Analyzing only the disturbance amplitude at a single monitoring point makes it difficult to distinguish isolated noise from spatially correlated real anomalies. Therefore, it is necessary to construct a continuous disturbance field through spatial interpolation and identify high-value regions with spatial connectivity to pinpoint potential deformation anomalies.

[0032] In one specific embodiment, based on the spatial coordinates of each monitoring point and the current amplitude of the corresponding high-frequency disturbance term, the Kriging interpolation method is used to spatially interpolate the amplitude of the high-frequency disturbance term at each monitoring point to generate a disturbance amplitude distribution field covering the entire slope surface; then, the spatial average amplitude of the disturbance amplitude distribution field is calculated, and grid cells with amplitudes higher than the spatial average amplitude are identified by a connected component analysis algorithm, and adjacent grid cells are merged into connected regions.

[0033] Due to the potential for small-area pseudo-anomalies caused by noise, connected regions exceeding a preset minimum area threshold are marked as deformation anomaly zones. The preset minimum area threshold can be set according to the slope size; for example, for a 100-meter-long slope, the minimum area can be set to 10 square meters. This area represents a potentially dangerous location within the slope where localized stress concentration or micro-fractures have occurred.

[0034] Furthermore, considering that the formation of deformation anomaly zones may be caused by various factors such as rainfall-driven events or slope instability, if rainfall infiltration is the main driving factor, then the periodic term should have a time lag relationship with rainfall data, and this lag relationship should be persistent; if it is a precursor to instability, then the trend term should show a continuous acceleration characteristic. Therefore, it is necessary to identify rainfall-driven landslide zones by analyzing the lag response characteristics of the periodic term and rainfall, combined with the evolution pattern of the trend term.

[0035] In one specific embodiment, please refer to Figure 2 As shown, the marking method for the rainfall-driven slip zone has the following process: S201. First, in the deformation anomaly zone, calculate the cross-correlation function between the periodic term of each monitoring point and the rainfall data of the same period, identify the maximum value in the cross-correlation function sequence, multiply the corresponding lag time step by the data acquisition time interval to obtain the phase lag time, and form a phase lag time sequence.

[0036] S202. Calculate the variance of the phase lag time series within the sliding time window. When the variance of the current window is less than the variance of the previous window, and the absolute value of the rate of change of the variance is less than the preset convergence threshold, such as between 0.05 and 0.1, it indicates that the rainfall infiltration path has formed a stable channel, the delay time of the deformation response relative to the rainfall event tends to be fixed, the influence of random hydrological fluctuations is excluded, and a stable lag relationship is determined to exist.

[0037] S203. Calculate the second derivative of the trend term with respect to time for each monitoring point in the deformation anomaly zone. When the sign of the second derivative is continuously positive, it indicates that the slope deformation rate is constantly increasing and is in the accelerated creep stage. The corresponding trend term is then judged to have accelerated characteristics.

[0038] S204 Finally, the deformation anomaly area that simultaneously satisfies the existence of a stable hysteresis relationship and the corresponding trend term shows acceleration characteristics is marked as a rainfall-driven slip zone. This area represents a dangerous slip zone that is dominated by rainfall infiltration and has entered the stage of accelerated deformation.

[0039] S3. Calculate the spatial gradient of the trend term in the rainfall-driven slip zone. When the gradient direction changes abruptly and the gradient value increases exponentially, the slip surface is determined to be connected, and the spatial gradient magnitude is used as the slip intensity index.

[0040] Considering that the connection of the slip surface is a critical geometric feature that marks the transformation of a slope from local deformation to overall instability, its physical essence is that the fracture surfaces within the slope have connected to form a continuous sliding channel. When the slip surface connects, a sudden displacement occurs between the slip body and the stable rock mass, the spatial transmission direction of deformation changes, and the deformation rate changes from linear growth to exponential growth. Therefore, this critical physical process can be captured by analyzing the direction and magnitude variation characteristics of the spatial gradient of the trend term.

[0041] In one specific embodiment, please refer to Figure 3 As shown, the method for obtaining the slip intensity index is as follows: S301. First, within the rainfall-driven slip zone, based on the coordinates of the monitoring points, an irregular triangular mesh is constructed using the Delaunay triangulation algorithm as a spatial grid model. The nodes of this grid model represent the locations of the monitoring points, and the grid cells cover the entire slip zone. Based on this, the first derivatives of the trend term in each direction of the spatial coordinates are calculated using the finite difference method, and the spatial gradient vector of the trend term is synthesized.

[0042] S302. Next, calculate the direction angle of the spatial gradient vector. When the absolute value of the difference between the direction angle at the current time step and the previous time step exceeds the standard deviation of the direction angle sequence within a preset time window, such as 10 days, it indicates that the main direction of deformation has changed beyond the normal fluctuation range. At this time, it is determined that the spatial gradient direction has abruptly changed. This phenomenon indicates that the spatial distribution direction of the slip surface has been reconstructed, and the motion trend of the slip body has reversed.

[0043] S303. Time series analysis is performed on the magnitude of the spatial gradient vector. Exponential and linear growth models are constructed and fitted. When the sum of squared residuals of the exponential growth model is less than that of the linear growth model, and the coefficient of the exponential term in the exponential growth model is positive, it indicates that the growth pattern of the gradient magnitude is more exponential than nonlinear. In this case, the gradient value is determined to be growing exponentially. This phenomenon indicates that the stress concentration on the slip surface is rapidly increasing, and the spatial inhomogeneity of deformation is accelerating.

[0044] S304. When the spatial gradient direction changes abruptly and the gradient value increases exponentially, it indicates that the slip body has formed a through surface in the spatial structure and has entered the accelerated development stage before instability. At this time, the slip surface of the slip region is determined to be through, and the magnitude of the current spatial gradient vector is defined as the slip intensity index. This index is used to quantify the development degree of the slip surface and the overall activity intensity of the slip body.

[0045] S4. For slip zones where the slip surface is continuous, a comprehensive instability index is constructed based on the deformation acceleration of the slip strength index and the trend term. By matching the dynamic time regularization with historical instability samples, a graded early warning instruction containing the slip urgency index level and the damage scale prediction is output.

[0046] Because the evolution path of slope instability is diverse and uncertain, using only the slip strength index is insufficient to fully reflect the complex state of slope instability. Furthermore, the evolution timescales differ among different slopes, and directly comparing absolute values ​​can easily lead to misjudgments. At the same time, the lack of comparison with the evolution paths of historical disasters results in a lack of predictability in early warnings and makes it difficult to quantify the scale of damage.

[0047] Therefore, a comprehensive instability index is constructed and a dynamic time warping algorithm is introduced for evolution path matching to predict the urgency of slippage and the scale of damage.

[0048] In one specific embodiment, the second derivative of the trend term within the slip zone through which the slip surface is connected is calculated as the deformation acceleration. This index reflects the rate of change of the deformation rate and characterizes the intensity of the accelerated motion of the slip body.

[0049] Considering the differences in dimensions and magnitudes between the slip strength index and the deformation acceleration, the range standardization method is used to normalize both, mapping them to the [0, 1] interval. Subsequently, the information entropy value of each index is calculated to determine the difference coefficient, and then the weight coefficient of each index is calculated. The normalized values ​​are summed with their corresponding weights to construct a comprehensive instability index, which comprehensively reflects the strength and acceleration characteristics of slope deformation.

[0050] Furthermore, a historical instability sample library containing the entire evolution data of typical historical landslide cases is established. The dynamic time-warped distance between the current comprehensive instability index curve and each sample curve in the historical instability sample library is calculated. The calculation method for the dynamic time-warped distance is as follows: the current comprehensive instability index curve is used as the sequence to be matched, and the sample curves in the historical instability sample library are used as the reference sequence; the Euclidean distance between each data point in the sequence to be matched and each data point in the reference sequence is calculated, and a distance matrix is ​​constructed.

[0051] Based on the distance matrix, a dynamic programming algorithm is used to recursively calculate the minimum cumulative distance among all possible matching paths from the start point of the sequence to the current position, and a cumulative cost matrix is ​​constructed. The optimal matching path connecting the start point and the end point is found by backtracking the cumulative cost matrix, and the values ​​of the end point of the sequence to be matched and the end point of the comparison sequence in the cumulative cost matrix are determined as the dynamic time warping distance.

[0052] Subsequently, the historical instability sample with the smallest distance was selected as a reference benchmark to characterize the most likely evolution pattern of the current slope. For the damage scale prediction, the actual slip volume and its corresponding slip area recorded in the reference benchmark were extracted. The surface projection area of ​​the slip zone connected by the current slip surface was calculated, i.e., the grid areas within the slip zone were summed. Based on the ratio of the surface projection area to the slip area area corresponding to the reference benchmark, the actual slip volume was proportionally corrected, and the corrected volume value was used as the damage scale prediction.

[0053] Furthermore, based on the optimal matching path of the dynamic time warping algorithm, the matching time point corresponding to the end time series point of the current comprehensive instability index curve on the reference baseline curve is determined, thereby locating the position of the current evolution stage in the historical disaster cycle.

[0054] Then, calculate the slope of the tangent at the end of the current comprehensive instability index curve and the slope of the tangent at the matching time point of the time series curve corresponding to the reference benchmark. Specifically, the difference quotient of several time series points at the end can be used as an approximation of the slope. The ratio of the current slope to the slope of the reference benchmark is used as the slip urgency index, which reflects the acceleration of the current deformation rate relative to similar historical disasters.

[0055] Finally, the slip urgency index is normalized. Specifically, the Sigmoid function is used to normalize it to the zero-to-one interval, with the zero-to-one-quarter range corresponding to a blue alert level, the one-quarter-to-half range to a yellow alert level, the one-half-to-three-quarters range to an orange alert level, and the three-quarters-to-one range to a red alert level. The final output is a graded warning instruction that includes the slip urgency index level and the estimated damage scale.

[0056] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0057] Those skilled in the art will recognize that the algorithmic steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0058] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0059] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0060] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring and early warning of landslide disasters on highway slopes, characterized in that, include: Based on surface displacement monitoring data, a displacement time series matrix is ​​constructed, and empirical mode decomposition and reconstruction are performed to separate the trend term, periodic term and high-frequency disturbance term. High-value connected regions in the spatial distribution of high-frequency disturbance terms are identified as deformation anomaly areas. The phase lag between the periodic terms and the concurrent rainfall data of the monitoring points in the area is analyzed. When a stable lag relationship exists and the trend term shows acceleration characteristics, it is marked as a rainfall-driven slip zone. Calculate the spatial gradient of the trend term in rainfall-driven slip zone. When the gradient direction changes abruptly and the gradient value increases exponentially, the slip surface is determined to be connected, and the spatial gradient magnitude is used as the slip intensity index. For slip zones with continuous slip surfaces, a comprehensive instability index is constructed based on the deformation acceleration of the slip intensity index and trend term. By matching with the dynamic time warping of historical instability samples, a graded early warning instruction is output, which includes the slip urgency index level and the damage scale prediction.

2. The method for monitoring and early warning of landslide disasters on highway slopes as described in claim 1, characterized in that, The method for constructing the displacement time series matrix is ​​as follows: Surface displacement monitoring data of each monitoring point on the slope surface are collected within a preset time window, and a displacement time series matrix is ​​constructed with time as the row and the spatial location of the monitoring point as the column.

3. The method for monitoring and early warning of landslide disasters on highway slopes as described in claim 1, characterized in that, The method for obtaining the trend term, periodic term, and high-frequency disturbance term is as follows: Empirical mode decomposition is performed on each column of data in the displacement time series matrix to obtain several intrinsic mode components and an intrinsic residual arranged in descending order of frequency. Calculate the average period of each intrinsic mode component, and superimpose and reconstruct the intrinsic mode components with an average period less than the first threshold into a high-frequency disturbance term; The intrinsic modal components with an average period greater than the second threshold are superimposed with the intrinsic residuals to reconstruct the trend term; the remaining intrinsic modal components with an average period between or equal to the first and second thresholds are superimposed to reconstruct the periodic term.

4. The method for monitoring and early warning of landslide disasters on highway slopes as described in claim 1, characterized in that, The method for identifying the abnormal deformation zone is as follows: Spatial interpolation is performed on the high-frequency disturbance term to generate a disturbance amplitude distribution field. High-value connected regions with amplitudes higher than the spatial average amplitude are identified in the disturbance amplitude distribution field, and these regions are classified as deformation anomaly areas.

5. The method for monitoring and early warning of landslide disasters on highway slopes as described in claim 1, characterized in that, The method for marking the rainfall-driven slip zone is as follows: Within the deformation anomaly zone, the cross-correlation function between the periodic term of each monitoring point and the concurrent rainfall data is calculated. The maximum value in the cross-correlation function sequence is identified, and its corresponding lag time step is multiplied by the data acquisition time interval to obtain the phase lag time, thus forming a phase lag time series. Calculate the variance of the phase lag time series within the sliding time window. When the variance of the current window is less than the variance of the previous window, and the absolute value of the rate of change of the variance is less than the preset convergence threshold, it is determined that there is a stable lag relationship. Calculate the second derivative of the trend term with respect to time for each monitoring point in the deformation anomaly zone. When the sign of the second derivative is consistently positive, it is determined that the corresponding trend term exhibits acceleration characteristics. Deformation anomaly regions that simultaneously satisfy the conditions of having a stable hysteresis relationship and exhibiting acceleration characteristics in the corresponding trend terms are marked as rainfall-driven slip regions.

6. The method for monitoring and early warning of landslide disasters on highway slopes as described in claim 1, characterized in that, The method for obtaining the slip strength index is as follows: Within the rainfall-driven slip zone, a spatial grid model is constructed based on the coordinates of monitoring points. The first derivative of the trend term in each direction of the spatial coordinates is calculated using the finite difference method, and the spatial gradient vector of the trend term is synthesized. Calculate the direction angle of the spatial gradient vector. When the absolute value of the difference between the direction angle at the current time step and the previous time step exceeds the standard deviation of the direction angle sequence within the preset time window, it is determined that the spatial gradient direction has abruptly changed. Time series analysis was performed on the magnitude of the spatial gradient vector. An exponential growth model and a linear growth model were constructed and fitted respectively. When the sum of squared residuals of the exponential growth model was less than that of the linear growth model, and the coefficient of the exponential term of the exponential growth model was positive, the gradient value was determined to be exponentially growing. When the spatial gradient direction changes abruptly and the gradient value increases exponentially, the slip surface of the slip zone is determined to be connected, and the magnitude of the current spatial gradient vector is defined as the slip strength index.

7. The method for monitoring and early warning of landslide disasters on highway slopes as described in claim 1, characterized in that, The method for constructing the comprehensive instability index is as follows: Calculate the second derivative of the trend term within the slip zone through which the slip surface is connected as the deformation acceleration; The slip strength index and deformation acceleration are normalized, and the weights of the two are calculated using the entropy weight method and then summed to construct a comprehensive instability index.

8. The method for monitoring and early warning of landslide disasters on highway slopes as described in claim 1, characterized in that, The method for obtaining the tiered early warning instructions is as follows: Establish a historical instability sample library, calculate the dynamic time warp distance between the current comprehensive instability index curve and the curves of each sample in the historical instability sample library, and select the historical instability sample with the smallest distance as the reference benchmark. Based on the optimal matching path of the dynamic time warping algorithm, the matching time point on the reference curve corresponding to the end time series point of the current comprehensive instability index curve is determined. Calculate the slope of the tangent at the end of the current comprehensive instability index curve and the slope of the tangent at the matching time point of the time series curve corresponding to the reference benchmark, and use the ratio of the two as the slip urgency index. The slip urgency index is normalized to the range of zero to one. The range of zero to one-quarter corresponds to the blue warning level, the range of one-quarter to one-half corresponds to the yellow warning level, the range of one-half to three-quarters corresponds to the orange warning level, and the range of three-quarters to one corresponds to the red warning level. The output includes a graded early warning command that includes the slip urgency index level and the estimated damage scale.

9. The method for monitoring and early warning of landslide disasters on highway slopes as described in claim 8, characterized in that, The method for calculating the dynamic time warp distance is as follows: The current comprehensive instability index curve is used as the sequence to be matched, and the sample curves in the historical instability sample library are used as the reference sequence. Calculate the distance between each data point in the sequence to be matched and each data point in the reference sequence, and construct a distance matrix; Based on the distance matrix, a dynamic programming algorithm is used to recursively calculate the minimum cumulative distance among all possible matching paths from the start of the sequence to the current position, and a cumulative cost matrix is ​​constructed. The optimal matching path connecting the starting point and the ending point is found by backtracking the cumulative cost matrix, and the values ​​of the ending point of the sequence to be matched and the ending point of the comparison sequence in the cumulative cost matrix are determined as the dynamic time warping distance.

10. The method for monitoring and early warning of landslide disasters on highway slopes as described in claim 8, characterized in that, The method for obtaining the estimated scale of damage is as follows: Extract the actual slip volume and its corresponding slip region area recorded in the reference datum; Calculate the surface projection area of ​​the slip zone currently penetrated by the slip surface; Based on the ratio of the surface projection area to the area of ​​the slip zone corresponding to the reference benchmark, the actual slip volume is proportionally corrected, and the corrected volume value is used as the estimated scale of damage.

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