A method and system for real-time identification of rainfall-induced landslide failure modes

Abstract

The present application relates to the field of landslide geological disaster monitoring, and particularly relates to a method and system for real-time identification of rainfall-type landslide failure mode, comprising: S1: selecting a monitoring key area; S2: arranging a reference station and a monitoring station in the landslide key area; S3: collecting original observation data, performing data solving at an edge end of each integrated GNSS receiver to obtain coordinate information; S4: transmitting the coordinate information to the cloud to establish a landslide cumulative displacement-time curve library; S5: extracting the cumulative displacement-time curve at the time of rainfall from the cloud, and calculating and comparing the deformation time sequence characteristics of different parts of the landslide; S6: identifying the landslide failure mode, and outputting a three-dimensional visual result of the failure through the cloud GIS software. The method adopts the integrated GNSS receiver to perform data solving at the edge end, thereby reducing the data transmission pressure, improving the solving efficiency, and achieving the effect of real-time monitoring; through the data transmitted to the cloud, the result visualization of the landslide failure mode judgment is realized.

Description

Technical Field

[0001] This invention relates to the field of landslide geological hazard monitoring, specifically to a method and system for real-time identification of rainfall-induced landslide failure modes. Background Technology

[0002] Based on their different failure modes, landslides can be divided into two categories: traction-type and shove-type. The former mainly involves bottom-up deformation, exhibiting a backward, progressive failure; while the latter mainly involves top-down deformation, exhibiting a forward, progressive failure. Landslides with different failure modes differ significantly in terms of hazard severity and treatment methods. A thorough understanding of landslide deformation characteristics and modes can lead to effective early warning of landslide disasters.

[0003] Currently, in engineering applications, the identification of landslide deformation and failure modes under rainfall conditions is mainly based on on-site investigation results, including the geological and geomorphological characteristics of the slope, the properties and structure of the soil and rock mass, and deformation and macroscopic damage signs of some surfaces and buildings. The most commonly used methods for investigation and analysis are the expert experience method and the on-site monitoring method. The expert experience method involves experts combining their experience in existing landslide engineering in the area to conduct on-site investigations and observe the macroscopic landslide deformation phenomena that have already occurred to determine the failure mode of the landslide. However, this method is subjective, inaccurate, and cannot provide quantitative judgments. The on-site monitoring method involves geological mapping, marking the surface, and installing monitoring equipment to observe changes in the surface displacement of the landslide to obtain the failure mode of the landslide. However, on-site monitoring requires a lot of manpower and resources, is complex, and commonly used monitoring instruments cannot achieve high-precision real-time calculations, resulting in data lag and failing to achieve the desired effect of determining the failure mode. Summary of the Invention

[0004] To address the aforementioned problems in existing technologies, this invention provides a method and system for real-time identification of rainfall-induced landslide failure modes, thereby solving the problems of insufficient quantitative accuracy in landslide failure mode judgment, monitoring lag, and inability to achieve timely identification in existing technologies.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for real-time identification of rainfall-induced landslide failure modes, characterized by comprising the following steps:

[0006] S1: Taking into account the landslide situation, it is divided into front, middle and rear parts. The lower side of the tension joint, the inner side of the shear joint, the bulging area and the geometric center of the landslide boundary are selected as the key monitoring areas.

[0007] S2: Deploy multiple integrated GNSS receivers as reference stations and monitoring stations in key landslide areas;

[0008] S3: Base stations and monitoring stations collect raw observation data and transmit it through the internal communication network to the edge of their respective integrated GNSS receivers for data processing to obtain coordinate information;

[0009] S4: The integrated GNSS receiver transmits coordinate information to the cloud via the communication network, stores the coordinate information, and establishes a library of cumulative landslide displacement-time curves.

[0010] S5: Extract the cumulative displacement-time curve during rainfall from the cloud, and calculate and compare the deformation time sequence characteristics of different parts of the landslide: the start time of sliding, the improved tangent angle α, the displacement azimuth angle ∠a, and the displacement vector angle ∠b;

[0011] S6: Identify the landslide failure mode, calculate the displacement and velocity components in the xyz directions, and output the three-dimensional visualization results of the failure through cloud-based GIS software.

[0012] In one alternative approach, the step of deploying the integrated GNSS receiver specifically includes, considering the scale of the landslide, geological and topographical conditions, and instrument and equipment conditions, deploying an integrated GNSS receiver as a reference station in the stable area outside the landslide, and deploying integrated GNSS receivers as monitoring stations along the front, middle, and rear of the landslide in key deformation areas to collect raw observation data from the monitoring points.

[0013] In one optional approach, the edge-end calculation process specifically includes transmitting raw observation data to the GNSS receiver's calculation module, and calculating coordinates based on RTK positioning technology using the RTKLIB software built into the calculation module. The raw observation data includes carrier wave, pseudorange, broadcast ephemeris, Doppler, and signal-to-noise ratio. The basic idea is differential positioning technology, which involves establishing a reference station and differentially combining the carrier phase observations from the monitoring station receiver and the reference station receiver to eliminate errors and output differential correction data in real time, thereby obtaining high-precision positioning.

[0014] In one optional approach, the RTK positioning technology specifically includes: single-point positioning based on the input raw observation data, error elimination using a double-difference model of carrier phase / pseudorange from two receivers, extended Kalman filter estimation, fixing some ambiguities, and back-substituting to obtain the positioning result. Specifically, the Kalman filter estimation uses extended Kalman filter floating-point calculation, and the fixing of some ambiguities uses least squares ambiguity correlation adjustment.

[0015] In one alternative approach, the multipath error in each error correction is mitigated using the Empirical Mode Decomposition (EMD) algorithm, and the signal decomposition mode is as follows:

[0016]

[0017] Where: n is the decomposition scale; rn (t) is the residual sequence; f s (t) represents the s-th mode function; t represents a specific moment in the time series.

[0018] The EMD algorithm decomposes the input signal into corresponding high-frequency and low-frequency components. Multipath error primarily manifests as low-frequency characteristics. By selecting an appropriate order to decompose and reconstruct the signal, the effects of multipath error and noise can be obtained. The signal decomposition steps are as follows:

[0019] (1) Find all the extreme points of x(t);

[0020] (2) Use interpolation to form two envelope functions, emin(t) and emax(t);

[0021] (3) Calculate the mean m(t) = [emin(t) + emax(t)] / 2;

[0022] (4) Extract detailed information d(t) = x(t) - m(t);

[0023] (5) Repeat steps (1) to (4) for the remaining mean.

[0024] In one optional embodiment, step S5 specifically includes: extracting the curve with a faster acquisition frequency from the rainfall data; calculating the improved tangent angle α, displacement azimuth angle ∠a, and displacement vector angle ∠b for each deformation stage curve; comparing the start time of sliding and the improved tangent angle α for different parts of the landslide in the same stage; and obtaining the landslide deformation direction based on ∠a and ∠b; wherein the formulas for α, ∠a, and ∠b are:

[0025]

[0026] Where: T(j) is the transformed ordinate value with the same dimension as time, t j For a certain monitoring time, Δt is the unit time corresponding to the calculation of cumulative displacement, and ΔT is the change of T(j) within a unit time period;

[0027]

[0028] Where: Δy is the change in coordinate in the y direction, and Δx is the change in coordinate in the x direction;

[0029]

[0030] Where: Δz is the change in coordinate in the z-direction.

[0031] In one alternative approach, step S6 specifically includes:

[0032] When the front part of the slope changes displacement before the rear part, and the improvement tangent angle at each stage is greater than that at the rear, the landslide is determined to be a traction landslide.

[0033] When the rear part of the slope undergoes displacement changes before the front part, and the improved tangent angle at each stage is greater than that at the rear, the landslide is determined to be a traction landslide.

[0034] When two parts of the slope undergo displacement changes almost simultaneously, and the difference in the improved tangent angle at each stage is less than 1 degree, the landslide is determined to be a traction-pushing composite landslide.

[0035] The displacement and velocity components in the xyz directions are calculated, and the three-dimensional failure results are output through cloud-based GIS software. The results include displacement components, velocity components, and slip direction. The landslide failure mode and three-dimensional visualization results are displayed on the cloud interface.

[0036] The present invention provides a system scheme for real-time identification of rainfall-induced landslide failure modes, comprising a data acquisition module, an edge computing module, a communication module, a data analysis module, and a result output module.

[0037] The data acquisition module includes: a base station GNSS receiver and multiple monitoring station GNSS receivers, which collect the number of base points and the observation data of the monitoring points according to the acquisition frequency set by the receiver's adaptive frequency conversion function;

[0038] The communication module connects the transmission module, edge computing module, and data analysis module in the form of an internal communication network, and uses TCP protocol and serial port for bidirectional communication.

[0039] The edge resolution module uses the built-in RTKLIB software and RTK positioning technology to calculate the coordinates of the monitoring points based on the observation data from the data acquisition module, thereby obtaining the coordinate information of the monitoring points.

[0040] The data analysis module stores the calculated coordinate information, establishes the cumulative displacement-time curve of the landslide, analyzes the temporal characteristics of landslide deformation during rainfall, identifies the landslide failure mode, and calculates the displacement and velocity components in the xyz directions.

[0041] The result output module will determine the landslide failure mode and display the three-dimensional landslide failure results output by GIS on the cloud platform interface.

[0042] In one alternative approach, the data acquisition module, edge computing module, and communication module are integrated into the GNSS receiver; the data analysis module and result output module are integrated into the cloud platform.

[0043] In one alternative, the integrated GNSS receiver is connected to a solar battery via a cable to ensure a long-term stable power supply for the entire monitoring equipment.

[0044] Compared with the prior art, the present invention has the following advantages:

[0045] This invention discloses a method and system for real-time identification of rainfall-induced landslide failure modes. It deploys multiple GNSS monitoring devices to monitor the displacement and deformation of shallow soil and rock masses, extracts temporal characteristics of deformation at different locations after each rainfall event, and analyzes these characteristics to determine the landslide failure mode. The method employs an integrated GNSS receiver, performing data processing at the edge, reducing data transmission pressure, improving processing efficiency, and achieving real-time monitoring. The receiver also features adaptive frequency conversion, collecting data normally under normal conditions and encrypting data collection during rainfall or when changes occur. The mode identification process is conducted on a cloud platform. By transmitting data to the cloud, the landslide failure mode identification results are visualized, providing a scientific basis for timely early warning of landslide disasters and the implementation of appropriate mitigation measures. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating a method for real-time identification of rainfall-induced landslide failure modes according to an embodiment of the present invention; Detailed implementation method:

[0047] 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.

[0048] The present invention will now be described in further detail:

[0049] Example 1:

[0050] This invention provides a method for real-time identification of rainfall-induced landslide failure modes, as shown in the appendix. Figure 1 As shown, it includes the following steps:

[0051] S1: Taking into account the landslide situation, it is divided into front, middle and rear parts. The lower side of the tension joint, the inner side of the shear joint, the bulging area and the geometric center of the landslide boundary are selected as the key monitoring areas.

[0052] S2: Deploy multiple integrated GNSS receivers as reference stations and monitoring stations in key landslide areas;

[0053] The GNSS receiver mentioned above integrates data acquisition, communication, self-decomposition, and adaptive frequency conversion functions.

[0054] The specific steps for deploying integrated GNSS receivers include, considering the scale of the landslide, geological and topographical conditions, and instrument and equipment conditions, deploying an integrated GNSS receiver as a reference station in the stable area outside the landslide, and deploying integrated GNSS receivers as monitoring stations in key deformation areas along the front, middle, and rear sections to collect raw observation data from the monitoring points.

[0055] S3: The base station and monitoring station collect raw observation data and transmit it through the internal communication network to the edge of their respective integrated GNSS receivers for data processing to obtain coordinate information;

[0056] The edge-end calculation process specifically includes transmitting raw observation data to the GNSS receiver's calculation module, and calculating coordinates based on RTK positioning technology using the RTKLIB software built into the calculation module. The raw observation data includes carrier wave, pseudorange, broadcast ephemeris, Doppler, and signal-to-noise ratio. The basic idea is differential positioning technology, which involves establishing a reference station and differentially combining the carrier phase observations from the monitoring station receiver and the reference station receiver to eliminate errors and output differential correction data in real time, thereby obtaining high-precision positioning.

[0057] In practice, during measurement, the base station GNSS receiver continuously observes all visible satellites, modulates the collected carrier phase observations onto the carrier of the base station radio, and then transmits them through the base station radio. Simultaneously, the monitoring station GNSS receiver observes the satellites and collects carrier phase observations, while also receiving the signals transmitted by the base station radio. These signals are then demodulated to obtain the base station carrier phase observations. The monitoring station GNSS receiver then utilizes a motion-based integer ambiguity resolution technique. Once the integer ambiguity is accurately resolved, it can be back-substituted to precisely determine each position.

[0058] The RTK positioning technology specifically includes: single-point positioning based on the input raw observation data; error elimination using a double-difference model of carrier phase / pseudorange from two receivers; extended Kalman filter estimation; partial ambiguity fixing; and back-substitution to obtain the positioning result. Specifically, extended Kalman filter floating-point calculation is used for Kalman filter estimation, and least squares ambiguity correlation adjustment is used to fix the partial ambiguity.

[0059] In practice, pseudorange and carrier phase double-difference observations are used. The double-difference observation equation is as follows:

[0060]

[0061] Where: p is the pseudorange observation value (m), r is the true satellite-to-ground distance (m), φ is the carrier phase observation value (rad), λ is the carrier wavelength (m), subscript i represents the monitoring station, subscript j represents the reference station, superscript p represents satellite p, and superscript q represents satellite q;

[0062] The Kalman filter measurement model is constructed using the above formula. The single-difference ambiguity parameter is selected as the estimator. Assuming N satellites are observed, and the first satellite is used as the double-difference reference satellite, the model is constructed as follows:

[0063] Z(t)=H(t)ΔX(t)+V(t)

[0064]

[0065]

[0066] Wherein: V(t) is the error of the double-difference observation, which is generally obtained by changing the pseudorange error (a pre-set fixed value) and the carrier phase error (a pre-set fixed value) through double-difference.

[0067] In the error corrections described above, the Empirical Mode Decomposition (EDM) algorithm is used to reduce the impact of multipath errors. The signal decomposition mode is as follows:

[0068]

[0069] Where: n is the decomposition scale; r n (t) is the residual sequence; f s (t) represents the s-th mode function; t represents a specific moment in the time series.

[0070] Specifically, the EMD algorithm can decompose the input signal into corresponding high-frequency and low-frequency components. Multipath error mainly manifests as low-frequency characteristics. By selecting an appropriate order to decompose and reconstruct the signal, the effects of multipath error and noise can be obtained. The signal decomposition steps are as follows:

[0071] (1) Find all the extreme points of x(t);

[0072] (2) Use interpolation to form two envelope functions, emin(t) and emax(t);

[0073] (3) Calculate the mean m(t) = [emin(t) + emax(t)] / 2;

[0074] (4) Extract detailed information d(t) = x(t) - m(t);

[0075] (5) Repeat steps (1) to (4) for the remaining mean.

[0076] S4: The integrated GNSS receiver transmits coordinate information to the cloud via the communication network, stores the coordinate information, and establishes a library of cumulative landslide displacement-time curves.

[0077] S5: Extract the cumulative displacement-time curve during rainfall from the cloud, and calculate and compare the deformation time sequence characteristics of different parts of the landslide: the start time of sliding, the improved tangent angle α, the displacement azimuth angle ∠a, and the displacement vector angle ∠b;

[0078] The S5 step specifically includes: extracting the curve with a faster acquisition frequency from the rainfall; calculating the improved tangent angle α, displacement azimuth angle ∠a, and displacement vector angle ∠b for each deformation stage; comparing the start time of sliding and the improved tangent angle α for different parts of the landslide in the same stage; and obtaining the landslide deformation direction based on ∠a and ∠b; the formulas for α, ∠a, and ∠b are:

[0079]

[0080] Where: T(j) is the transformed ordinate value with the same dimension as time, t j For a certain monitoring time, Δt is the unit time corresponding to the calculation of cumulative displacement, and ΔT is the change of T(j) within a unit time period;

[0081]

[0082] Where: Δy is the change in coordinate in the y direction, and Δx is the change in coordinate in the x direction;

[0083]

[0084] Where: Δz is the change in coordinate in the z-direction.

[0085] S6: Identify the landslide failure mode, calculate the displacement and velocity components in the xyz directions, and output the three-dimensional visualization results of the failure through cloud-based GIS software;

[0086] The S6 step specifically includes:

[0087] When the front part of the slope changes displacement before the rear part, and the improvement tangent angle at each stage is greater than that at the rear, the landslide is determined to be a traction landslide.

[0088] When the rear part of the slope undergoes displacement changes before the front part, and the improved tangent angle at each stage is greater than that at the rear, the landslide is determined to be a traction landslide.

[0089] When two parts of the slope undergo displacement changes almost simultaneously, and the difference in the improved tangent angle at each stage is less than 1 degree, the landslide is determined to be a traction-pushing composite landslide.

[0090] The displacement and velocity components in the xyz directions are calculated, and the three-dimensional failure results are output through cloud-based GIS software. The results include displacement components, velocity components, and slip direction. The landslide failure mode and three-dimensional visualization results are displayed on the cloud interface.

[0091] Example 2:

[0092] This invention also proposes a system for real-time identification of rainfall-induced landslide failure modes, including a data acquisition module, an edge computing module, a communication module, a data analysis module, and a result output module;

[0093] The data acquisition module includes: a base station GNSS receiver and multiple monitoring station GNSS receivers, which collect the number of base points and the observation data of the monitoring points according to the acquisition frequency set by the receiver's adaptive frequency conversion function;

[0094] The communication module connects the transmission module, edge computing module, and data analysis module in the form of an internal communication network, and uses TCP protocol and serial port for bidirectional communication.

[0095] The edge resolution module uses the built-in RTKLIB software and RTK positioning technology to calculate the coordinates of the monitoring points based on the observation data from the data acquisition module, thereby obtaining the coordinate information of the monitoring points.

[0096] The data analysis module stores the calculated coordinate information, establishes the cumulative displacement-time curve of the landslide, analyzes the temporal characteristics of landslide deformation during rainfall, identifies the landslide failure mode, and calculates the displacement and velocity components in the xyz directions.

[0097] The result output module will determine the landslide failure mode and display the three-dimensional landslide failure results output by GIS on the cloud platform interface.

[0098] The data acquisition module, edge computing module, and communication module are integrated into the GNSS receiver; the data analysis module and result output module are integrated into the cloud platform.

[0099] The integrated GNSS receiver is connected to a solar battery via a cable to ensure a long-term stable power supply for the entire monitoring equipment.

[0100] This invention discloses a method and system for real-time identification of rainfall-induced landslide failure modes. It deploys multiple GNSS monitoring devices to monitor the displacement and deformation of shallow soil and rock masses, extracts temporal characteristics of deformation at different locations after each rainfall event, and analyzes these characteristics to determine the landslide failure mode. The method employs an integrated GNSS receiver, performing data processing at the edge, reducing data transmission pressure, improving processing efficiency, and achieving real-time monitoring. The receiver also features adaptive frequency conversion, collecting data normally under normal conditions and encrypting data collection during rainfall or when changes occur. The mode identification process is conducted on a cloud platform. By transmitting data to the cloud, the landslide failure mode identification results are visualized, providing a scientific basis for timely early warning of landslide disasters and the implementation of appropriate mitigation measures.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for real-time identification of rainfall-induced landslide failure modes, characterized in that, Includes the following steps: S1: Divide the landslide into front, middle and rear sections, and select the lower side of the tension joint, the inner side of the shear joint, the bulging zone, and the geometric center of the landslide boundary as key monitoring areas; S2: Deploy multiple integrated GNSS receivers as reference stations and monitoring stations in key landslide areas; S3: Base stations and monitoring stations collect raw observation data and transmit it through the internal communication network to the edge of their respective integrated GNSS receivers for data processing to obtain coordinate information; S4: The integrated GNSS receiver transmits coordinate information to the cloud via the communication network, stores the coordinate information, and establishes a library of cumulative landslide displacement-time curves. S5: Extract the cumulative displacement-time curve during rainfall from the cloud, and calculate and compare the deformation time sequence characteristics of different parts of the landslide: the start time of sliding, the improved tangent angle α, the displacement azimuth angle ∠a, and the displacement vector angle ∠b; S6: Identify the landslide failure mode, calculate the displacement and velocity components in the xyz directions, and output the three-dimensional visualization results of the failure through cloud-based GIS software; The formulas for α, ∠a, and ∠b are: Where: T(j) is the transformed ordinate value with the same dimension as time, t j For a certain monitoring time, Δt is the unit time corresponding to the calculation of cumulative displacement, and ΔT is the change of T(j) within a unit time period; in: The change in coordinate in the y-direction. This represents the change in coordinate along the x-direction; in: This represents the change in coordinate along the z-direction; The S5 step includes: extracting the curve with a faster acquisition frequency from the rainfall; calculating the improved tangent angle α, displacement azimuth angle ∠a, and displacement vector angle ∠b of the curves for each deformation stage; comparing the start time of sliding and the improved tangent angle α of different parts of the landslide in the same stage; and obtaining the landslide sliding direction based on ∠a and ∠b. Step S6 includes: When the front part of the slope changes displacement before the rear part, and the improvement tangent angle at each stage is greater than that at the rear, the landslide is determined to be a traction landslide. When the rear part of the slope undergoes displacement changes before the front part, and the improved tangent angle at each stage is greater than that at the front, the landslide is determined to be a push-type landslide. When two parts of the slope undergo displacement changes almost simultaneously, and the difference in the improved tangent angle at each stage is less than 1 degree, the landslide is determined to be a traction-pushing composite landslide. The displacement and velocity components in the xyz directions are calculated, and the three-dimensional failure results are output through cloud-based GIS software. The results include displacement components, velocity components, and slip direction. The landslide failure mode and three-dimensional visualization results are displayed on the cloud interface.

2. The method for real-time identification of rainfall-induced landslide failure modes as described in claim 1, characterized in that: The deployment of the integrated GNSS receiver includes the following steps: deploying an integrated GNSS receiver as a reference station in the stable area outside the landslide, and deploying integrated GNSS receivers as monitoring stations in the key deformation areas at each location along the front, middle, and rear sections to collect raw observation data from the monitoring points.

3. The method for real-time identification of rainfall-induced landslide failure modes as described in claim 1, characterized in that: The edge-end calculation process includes transmitting raw observation data to the GNSS receiver calculation module, and calculating coordinates based on RTK positioning using the RTKLIB software built into the calculation module; the raw observation data includes carrier wave, pseudorange, broadcast ephemeris, Doppler, and signal-to-noise ratio.

4. The method for real-time identification of rainfall-induced landslide failure modes as described in claim 3, characterized in that: The RTK positioning coordinate calculation specifically includes: single-point positioning based on the input raw observation data, eliminating errors using a double-difference model of carrier phase / pseudorange from two receivers, extended Kalman filter estimation, fixing some ambiguities, and back-substituting to obtain the positioning result.

5. The method for real-time identification of rainfall-induced landslide failure modes as described in claim 4, characterized in that: In the error correction, multipath error is addressed by employing an empirical mode decomposition algorithm to reduce its impact. The signal decomposition mode is as follows: Where: n is the decomposition scale; It is a residual sequence; Let be the s-th mode function; t is a certain moment in the time series.

6. A system for real-time identification of rainfall-induced landslide failure modes using the method described in any one of claims 1-5, characterized in that: It includes a data acquisition module, an edge computing module, a communication module, a data analysis module, and a result output module; The data acquisition module includes: a base station GNSS receiver and multiple monitoring station GNSS receivers, which collect the number of base points and the observation data of the monitoring points according to the acquisition frequency set by the receiver's adaptive frequency conversion function; The communication module connects the transmission module, edge computing module, and data analysis module in the form of an internal communication network, and uses TCP protocol and serial port for bidirectional communication. The edge resolution module uses the built-in RTKLIB software and RTK positioning technology to calculate the coordinates of the monitoring points based on the observation data from the data acquisition module, thereby obtaining the coordinate information of the monitoring points. The data analysis module stores the calculated coordinate information, establishes the cumulative displacement-time curve of the landslide, analyzes the temporal characteristics of landslide deformation during rainfall, identifies the landslide failure mode, and calculates the displacement and velocity components in the xyz directions. The result output module will determine the landslide failure mode and display the three-dimensional landslide failure results output by GIS on the cloud platform interface.

7. The system for real-time identification of rainfall-induced landslide failure modes as described in claim 6, characterized in that: The data acquisition module, edge computing module, and communication module are integrated into the GNSS receiver; the data analysis module and result output module are integrated into the cloud platform.

8. The system for real-time identification of rainfall-induced landslide failure modes as described in claim 6, characterized in that: The GNSS receiver is connected to a solar battery via a cable.

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