Freeze-thaw landslide deformation monitoring system driven by SAR satellite data

Abstract

The invention relates to the technical field of remote sensing monitoring, in particular to an SAR satellite data driven freeze-thaw landslide deformation monitoring system which comprises a multi-band SAR data acquisition module, a data preprocessing module, a deformation detection module, a dynamic modeling module, a risk assessment early warning module and a report comprehensive display module. Wherein the multi-band SAR data acquisition module is used for acquiring multi-band radar image data; the data preprocessing module is used for carrying out denoising, correction and image registration processing; the deformation detection module is used for generating a high-precision earth surface deformation graph; the dynamic modeling module is used for constructing a dynamic geologic model of the freeze-thaw landslide; and the risk assessment early warning module is used for carrying out landslide risk assessment. According to the invention, through integration of SAR satellite data and dynamic geological modeling, the precision and real-time performance of freeze-thaw landslide monitoring are significantly improved, the accuracy and timeliness of disaster early warning are enhanced, and efficient scientific and technological support is provided for geological disaster management.

Description

Technical Field

[0001] The invention relates to the technical field of remote sensing monitoring, and in particular to a freeze-thaw landslide deformation monitoring system driven by SAR satellite data. Background Art

[0002] As global climate change intensifies, freeze-thaw cycles have a significant impact on the geological stability of mountainous and cold regions, especially in causing landslides and other geological disasters. In these areas, soil and rock layers undergo freezing and thawing processes, resulting in changes in material properties, hydrological conditions and their mechanical behavior, which in turn affect the stability of the surface and underground. Traditional geological monitoring technologies, such as ground observation stations and conventional remote sensing technologies, although able to monitor and predict these changes to a certain extent, are often limited by the coverage capabilities of the equipment and the real-time nature of monitoring, and are difficult to be widely used in remote and harsh geographical environments.

[0003] Existing monitoring systems often fail to capture the dynamic changes of geological deformation during freeze-thaw in real time, especially in areas with complex terrain or extreme climate. In addition, existing technologies have many limitations in integrating geological, meteorological and deformation data and building dynamic geological models, which limits the accuracy and timeliness of predictions of disasters such as landslides. Therefore, it is particularly important to develop a system that can monitor and accurately predict the risk of freeze-thaw landslides in real time. Summary of the invention

[0004] Based on the above purpose, the present invention provides a freeze-thaw landslide deformation monitoring system driven by SAR satellite data.

[0005] The freeze-thaw landslide deformation monitoring system driven by SAR satellite data includes a multi-band SAR data acquisition module, a data preprocessing module, a deformation detection module, a dynamic modeling module, a risk assessment and early warning module, and a comprehensive report display module; among which:

[0006] Multi-band SAR data acquisition module: Through the multi-band synthetic aperture radar satellite system, multi-band radar image data covering the freeze-thaw zone is obtained in real time to characterize the surface and underground deformation at different depths, as well as the deformation under different freeze-thaw states;

[0007] Data preprocessing module: receives radar image data from the multi-band SAR data acquisition module, combines the temperature and humidity data of the freeze-thaw zone, and performs denoising, correction and image registration processing;

[0008] Deformation detection module: Based on the pre-processed multi-band SAR data, deformation detection is performed using interferometry and differential interferometry techniques to generate high-precision surface deformation maps;

[0009] Dynamic modeling module: Based on the deformation map generated by the deformation detection module and combined with the geological and meteorological data of the freeze-thaw area, a dynamic geological model of freeze-thaw landslide is constructed to simulate the deformation behavior of the landslide during the freeze-thaw process;

[0010] Risk assessment and early warning module: Use the dynamic geological model and deformation data provided by the dynamic modeling module to conduct landslide risk assessment and set a landslide risk threshold based on real-time data. When the landslide risk predicted by the model exceeds the set threshold, an early warning signal is automatically generated;

[0011] Comprehensive report display module: receives the warning signals and deformation detection results generated by the risk assessment and early warning module, generates freeze-thaw landslide monitoring reports through data visualization technology, and displays the risk distribution map of the monitored area.

[0012] Optionally, the multi-band SAR data acquisition module includes a multi-band radar transmitting unit, a radar signal receiving unit and a data processing unit; wherein:

[0013] Multi-band radar transmitting unit: It includes radar wave transmitters of multiple frequency bands, which are used to transmit L-band, C-band and X-band radar signals respectively. L-band is used to detect deeper surface and underground structures, C-band is used for medium-depth surface layer monitoring, and X-band is used for high-resolution detection of shallow surface and surface freeze-thaw state. Radar wave signals of each frequency band are selected according to their penetration depth and resolution to obtain surface and underground echo signals at different depths.

[0014] Radar signal receiving unit: a multi-band radar receiver configured to receive in real time the radar signal echoes emitted by the multi-band radar transmitting unit, and to capture the echo signals reflecting the surface and underground deformation information at different depths in the freeze-thaw zone;

[0015] Data processing unit: decodes the received multi-band radar signals in real time, extracts the original echo data and deformation information carried in the radar signals of each band, and classifies the extracted original multi-band data according to depth and frequency.

[0016] Optionally, the data preprocessing module includes a denoising unit, a correction unit and an image registration unit; wherein:

[0017] De-noising unit: used to receive the original radar image data from the multi-band SAR data acquisition module and remove the noise in the radar image data in real time by applying adaptive filtering technology;

[0018] Correction unit: used to perform radiation correction and geometric correction on the denoised radar image data;

[0019] Image registration unit: used to accurately align the corrected multi-band radar image with the reference image. It is based on feature point-based image registration technology. By extracting feature points from the radar image and the reference image, the similarity between the feature points is calculated and matched.

[0020] Optionally, the deformation detection module includes an interference pattern generation unit, a differential interference processing unit, a phase unwrapping unit and a deformation pattern generation unit; wherein:

[0021] Interference pattern generation unit: used to receive multi-band SAR data from the data preprocessing module, and generate an interference pattern based on multi-temporal radar images through synthetic aperture radar interferometry technology. Specifically, two or more radar images acquired at different times are coherently processed, the phase difference is calculated, and an interference pattern is formed to capture surface deformation information;

[0022] Differential interferometry processing unit: Based on the interferogram generation unit, the interferogram is processed through differential interferometry technology to eliminate the influence of terrain and track errors;

[0023] Phase unwrapping unit: used to unwrap the phase ambiguity in the differential interferogram, specifically converting the phase ambiguity into continuous phase information through the least squares unwrapping algorithm;

[0024] Deformation map generation unit: It is used to combine the phase information after unwrapping processing and the deformation amount extracted by the differential interference processing unit, and use the interpolation algorithm to perform spatial interpolation and refinement processing on the deformation data to generate a high-resolution deformation map reflecting the details of the surface deformation.

[0025] Optionally, the phase unwrapping unit specifically includes:

[0026] Preliminary calculation of differential phase map: Receive the differential interferogram from the differential interferometry processing unit and represent it as a phase map ,in and are the horizontal and vertical coordinates of the image respectively. Each pixel value in the differential phase map is the phase of its corresponding position. , the phase value is arrive Within the range, the preliminary calculation formula of the phase diagram is: ,in, and are the complex pixel values ​​of the two-phase radar images, for The complex conjugate of represents the phase angle of a complex number, is the obtained phase difference;

[0027] Calculation of phase difference between adjacent pixels: Preliminary calculation of phase map Analyze and detect the phase jump point, specifically by calculating the phase difference between adjacent pixels , detect the jump point, the formula is: ,in, is the phase difference, and are the phase values ​​of adjacent pixels respectively. When it exceeds a predetermined threshold, it is judged as a phase jump point;

[0028] Establish phase difference equations: Establish phase difference equations for the detected jump points. is the true phase after unwrapping, then the system of equations is expressed as:

[0029] ,in, is the expected true phase after unwrapping, is an integer representing the ambiguity multiple of the phase;

[0030] Apply the least squares untangling algorithm: Convert the system of equations into a matrix form and apply the least squares untangling algorithm to solve it. The goal of the least squares untangling algorithm is to find the optimal integer value, so that the phase after unwrapping The error between the original phase and the unwrapped phase is minimized, and the phase calculation formula after unwrapping is: ,in, is a coefficient matrix, representing the relationship between pixels, It includes phase difference The vector of is the phase vector after unwrapping;

[0031] Smoothing of the phase image: Smoothing is performed to remove the existing small-scale noise. The smoothing algorithm is Gaussian filtering, and its formula is: ,in, is the smoothed phase diagram, is a Gaussian kernel function that defines the weights of pixels in the neighborhood. and is the offset of the Gaussian kernel function.

[0032] Optionally, the deformation map generating unit specifically includes:

[0033] Data gridding: First, the unwrapped phase data from the phase unwrapping unit Perform grid processing;

[0034] Selection of spatial interpolation algorithm: Select the spatial interpolation algorithm for the gridded phase data Perform interpolation processing, specifically select the inverse distance weighted interpolation algorithm as the spatial interpolation algorithm, and output after interpolation processing As the interpolated phase value;

[0035] Smoothing of interpolated data: Smoothing is performed to eliminate local anomalies caused by interpolation, and the phase value after smoothing is expressed as ;

[0036] Generation of high-resolution deformation map: Finally, the smoothed interpolated data Converted into a high-resolution surface deformation map by combining the smoothed phase data with the digital elevation model and using geographic coordinate projection technology to map the interpolated phase values ​​to the actual geographic coordinate system to generate a high-resolution deformation map .

[0037] Optionally, the dynamic modeling module includes a data collection and integration unit, a geological model building unit, a freeze-thaw dynamic simulation unit and a landslide deformation prediction unit; wherein:

[0038] Data collection and integration unit: first receives the high-resolution deformation map generated by the deformation detection module and geological data of the freeze-thaw zone and weather data ; Among them, the three-dimensional geological data includes the geological layer information on the surface and underground, and the meteorological data includes temperature , precipitation and humidity curves over time, the data collection and integration unit also includes the integration of deformation data with geological and meteorological data into a unified spatial reference frame;

[0039] Geological model building unit: based on data integration, based on integrated geological data Construct a three-dimensional geological model of the freeze-thaw zone, specifically using the finite element method to express the mechanical properties and stress state of the underground geological layer;

[0040] Freeze-thaw dynamic simulation unit: Based on the constructed three-dimensional geological model, the freeze-thaw dynamic simulation unit combines meteorological data Conduct dynamic simulation of the freeze-thaw process, and calculate the deformation of the geological layer during the freeze-thaw cycle by simulating the impact of the freeze-thaw process on the stress and deformation of the geological layer;

[0041] Landslide deformation prediction unit: used to calculate the stress and displacement data of the freeze-thaw dynamic simulation unit and the deformation map generated by the deformation detection module ,The landslide risk in the freeze-thaw zone is evaluated by a landslide prediction model. The prediction model is based on the critical instability conditions of landslide and predicts the landslide areas that will occur under freeze-thaw action.

[0042] Optionally, the geological model building unit includes:

[0043] Discretization of geological data: First, the received three-dimensional geological data is discretized, and the continuous geological layer information is divided into a limited number of units and nodes, where each unit represents a small volume in the geological layer and the node is the vertex of the unit;

[0044] Generation of material property matrix: For each discretized unit, material property parameters of the geological layer are extracted based on geological data, including elastic modulus , Poisson's ratio ,density , and generate the material property matrix for each element ;

[0045] Calculation of the element stiffness matrix: Based on the material property matrix of each element , calculate the stiffness matrix of the element ;

[0046] Assembly of the global stiffness matrix: Assemble the stiffness matrices of all elements Assemble into the overall stiffness matrix according to the connection relationship of the nodes , represents the stiffness characteristics of the entire 3D geological model;

[0047] Application of boundary conditions and loads: for the overall stiffness matrix Apply boundary conditions and loads. Boundary conditions define fixed boundaries of the geological model, while loads include temperature changes caused by freeze-thaw processes. and external pressure ;

[0048] Model solution and result analysis: Finally, the finite element model equations are solved using numerical solution methods. , get the node's displacement vector .

[0049] Optionally, the risk assessment and early warning module includes a real-time data receiving unit, a landslide risk calculation unit, a landslide risk threshold setting unit and an early warning signal generating unit; wherein:

[0050] Real-time data receiving unit: receives dynamic geological model data provided by the dynamic modeling module and deformation data ,in, Indicates that over time Changing geological conditions, including stress and displacement information; For time The surface deformation data at the time, including the deformation of the landslide area;

[0051] Landslide risk calculation unit: After receiving real-time data, it is used to calculate the landslide risk based on the geological model data. and deformation data Calculate the landslide risk factor ;

[0052] Landslide risk threshold setting unit: based on the calculated landslide risk coefficient , setting different levels of landslide risk thresholds , the landslide risk threshold is divided into several levels, including low risk , Medium Risk and high risk ;

[0053] Warning signal generation unit: used when the landslide risk calculation unit detects Less than the set landslide risk threshold When a warning signal is generated, the warning signal is automatically generated. The logic of generating the warning signal includes:

[0054] like , a high-risk warning signal is generated;

[0055] like and , a medium risk warning signal is generated;

[0056] like and , a low risk warning signal is generated.

[0057] Optionally, the report comprehensive display module includes a data integration unit, a risk distribution map generation unit, a deformation data visualization unit, a monitoring report generation unit and an early warning information display unit; wherein:

[0058] Data integration unit: receives warning signals from the risk assessment and warning module and deformation detection results from the deformation detection module, and standardizes data from different sources;

[0059] Risk distribution map generation unit: Based on geographic information system technology, the landslide risk level is combined with geographic spatial data to generate a landslide risk distribution map of the monitoring area. Specifically, the landslide risk level information provided by the risk assessment and early warning module is mapped to the actual geographic coordinates, and different colors or symbols are used to mark the risk levels to intuitively display the spatial distribution of landslide risks.

[0060] Deformation data visualization unit: used to visualize the deformation detection results provided by the deformation detection module and generate a three-dimensional view or contour map of the surface deformation;

[0061] Monitoring report generation unit: used to integrate the visualization results into a comprehensive monitoring report, which includes a landslide risk distribution map, a three-dimensional view of the surface deformation, analysis results of the landslide area, and risk predictions and recommended emergency response measures;

[0062] Warning information display unit: used to display the warning signals generated by the risk assessment and warning module in the form of graphics and text on the user interface, and highlight high-risk areas according to the landslide risk level and specific location.

[0063] Beneficial effects of the present invention:

[0064] The present invention significantly improves the accuracy and real-time performance of monitoring freeze-thaw landslide deformation by integrating multi-band SAR satellite data with advanced data analysis technology. The system utilizes synthetic aperture radar interferometry and differential interferometry technology to generate accurate surface deformation maps and capture dynamic surface changes in real time. In addition, the construction of a dynamic geological model combines geological and meteorological data, so that the system can not only monitor geological deformation in real time, but also predict its future trend, providing strong scientific support for early warning of landslides and other related geological disasters.

[0065] The present invention, through its risk assessment and early warning module and comprehensive report display module, provides an efficient decision support tool for geological disaster management. The risk assessment report and landslide risk threshold setting automatically generated by the system make disaster response more rapid and accurate. The timely generation of early warning signals and the intuitive display of risk distribution maps greatly help relevant departments and the public understand the immediate risk status and take corresponding prevention and disaster reduction measures, thereby reducing potential economic losses and protecting people's lives. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0067] Figure 1 A schematic diagram of a freeze-thaw landslide deformation monitoring system according to an embodiment of the present invention;

[0068] Figure 2 Schematic diagram of a deformation detection module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0070] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0071] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0072] like Figure 1-Figure 2 As shown in the figure, the freeze-thaw landslide deformation monitoring system driven by SAR satellite data includes a multi-band SAR data acquisition module, a data preprocessing module, a deformation detection module, a dynamic modeling module, a risk assessment and early warning module, and a comprehensive report display module; among which:

[0073] Multi-band SAR data acquisition module: Through the multi-band synthetic aperture radar (SAR) satellite system, multi-band radar image data covering the freeze-thaw zone is acquired in real time to characterize the surface and underground deformation at different depths, as well as the deformation under different freeze-thaw states, and the collected data is transmitted to the data preprocessing module;

[0074] Data preprocessing module: receives radar image data from the multi-band SAR data acquisition module, combines it with the temperature and humidity data of the freeze-thaw zone, performs denoising, correction and image registration processing to ensure the accuracy and consistency of the data, and transmits the processed data to the deformation detection module;

[0075] Deformation detection module: Based on the pre-processed multi-band SAR data, deformation detection is performed using interferometry technology (InSAR) and differential interferometry technology (DInSAR) to generate high-precision surface deformation maps;

[0076] Dynamic modeling module: Based on the deformation map generated by the deformation detection module and combined with the geological and meteorological data of the freeze-thaw zone, a dynamic geological model of freeze-thaw landslide is constructed to simulate the landslide deformation behavior during the freeze-thaw process, and the model data is transmitted to the risk assessment and early warning module;

[0077] Risk assessment and early warning module: Use the dynamic geological model and deformation data provided by the dynamic modeling module to conduct landslide risk assessment and set a landslide risk threshold based on real-time data. When the landslide risk predicted by the model exceeds the set threshold, an early warning signal is automatically generated and the early warning information is transmitted to the comprehensive report display module;

[0078] Report comprehensive display module: receives the warning signals and deformation detection results generated by the risk assessment and early warning module, generates freeze-thaw landslide monitoring reports through data visualization technology, and displays the risk distribution map of the monitored area to support the prediction and decision-making of future landslide risks.

[0079] The multi-band SAR data acquisition module includes a multi-band radar transmitting unit, a radar signal receiving unit and a data processing unit; wherein:

[0080] Multi-band radar transmitting unit: It includes radar wave transmitters of multiple frequency bands, which are used to transmit L-band, C-band and X-band radar signals respectively. L-band is used to detect deeper surface and underground structures, C-band is used for medium-depth surface layer monitoring, and X-band is used for high-resolution detection of shallow surface and surface freeze-thaw state. Radar wave signals of each frequency band are selected according to their penetration depth and resolution to obtain surface and underground echo signals at different depths.

[0081] Radar signal receiving unit: a multi-band radar receiver configured to receive in real time the radar signal echoes emitted by the multi-band radar transmitting unit, and to capture the echo signals reflecting the surface and underground deformation information at different depths in the freeze-thaw zone;

[0082] Data processing unit: decodes the received multi-band radar signals in real time, extracts the original echo data and deformation information carried in the radar signals of each band, and classifies the extracted original multi-band data according to depth and frequency, and transmits them to the data preprocessing module for more detailed analysis and fusion;

[0083] The data processing unit processes include:

[0084] Real-time decoding processing: Real-time decoding of received radar signals through fast Fourier transform (FFT) to extract frequency components , converted into frequency domain signal , for subsequent processing and analysis;

[0085] Extracting raw echo data and deformation information: Using the range Doppler algorithm, the frequency components of the radar signal The distance converted to the target and speed , which reflects the deformation of the surface and underground;

[0086] Data classification: Based on the round-trip time of radar signals and frequency , extract the data according to different depths and frequency Classification is performed to ensure that deformation information at different depths can be accurately distinguished and processed.

[0087] The specific steps are as follows:

[0088] First, the received radar signal Decoding and fast Fourier transform, the formula is: ,in, is the received radar signal over time The change function of For frequency Representation of radar signals in dimensions;

[0089] Then, the frequency domain signal is processed using the range Doppler algorithm to generate the range and speed Information, the formula is: and ,in, is the distance between the target and the radar, is the speed of light, is the frequency change of the radar signal, is the bandwidth of the radar signal, is the target speed, is the radar wavelength, is the Doppler shift;

[0090] Then, after extracting the deformation information, the data processing unit calculates the depth of penetration of the radar wave. and frequency Characteristics, the extracted original multi-band data is sorted by depth and frequency To classify;

[0091] Finally, the classified multi-band data will be transmitted to the data preprocessing module for further fusion and analysis.

[0092] Through the collaborative work of the above units, the multi-band SAR data acquisition module can obtain multi-band radar image data covering the freeze-thaw zone in real time and efficiently, and provide basic data input for subsequent deformation detection and analysis modules, thereby ensuring the accuracy and comprehensiveness of the monitoring system.

[0093] The data preprocessing module includes a denoising unit, a correction unit and an image registration unit; wherein:

[0094] De-noising unit: It is used to receive the original radar image data from the multi-band SAR data acquisition module and remove the noise in the radar image data in real time by applying adaptive filtering technology. The denoising unit is specifically based on the temperature of the freeze-thaw zone. and humidity The data adjusts the filtering parameters to adapt to the noise characteristics under different environmental conditions. The denoising process adopts the adaptive median filtering algorithm, which is achieved by the following formula: ,in, is the median filter function. Median filtering is a nonlinear digital filtering technique that is often used to remove image noise. is the pixel value after filtering, is the filter window The pixel value set within the filter window is based on the temperature and humidity Dynamic adjustment to ensure high signal-to-noise ratio under different environmental conditions;

[0095] Correction unit: used to perform radiation correction and geometric correction on the denoised radar image data; radiation correction specifically adjusts the brightness of the radar image through the following formula and contrast , eliminate radiation errors caused by environmental factors: and ,in, and is the corrected brightness and contrast, and Based on temperature and humidity Adjusted radiation correction factor, is the contrast adjustment coefficient, is the average contrast of the image; the geometric correction corrects the geometric distortion of the radar image through the coordinate information of the ground control point (GCP). The geometric correction formula is:

[0096] ,in, is the pixel coordinate of the original image, is the corrected pixel coordinate, matrix and vector It is the correction parameter calculated based on GCP data;

[0097] Image registration unit: used to accurately align the corrected multi-band radar image with the reference image. The specific image registration technology is based on feature points. By extracting feature points from the radar image and the reference image, the similarity between the feature points is calculated and matched. During the matching process, the normalized correlation coefficient (NCC) is used to calculate the similarity of the image blocks. The formula is: ,in, and are the pixel values ​​of the radar image and the reference image, respectively. and The registration unit dynamically adjusts the registration parameters according to the NCC value. , ensuring registration accuracy and stability;

[0098] Through the collaborative work of the above units, the data preprocessing module can efficiently receive and process the radar image data from the multi-band SAR data acquisition module, and combine it with the temperature and humidity data of the freeze-thaw zone to perform denoising, correction and image registration processing, providing high-quality preprocessing data for subsequent deformation detection and analysis, ensuring the accuracy and reliability of the entire monitoring system.

[0099] The deformation detection module includes an interference pattern generation unit, a differential interference processing unit, a phase unwrapping unit and a deformation pattern generation unit; wherein:

[0100] Interference pattern generation unit: used to receive multi-band SAR data from the data preprocessing module, and generate interference patterns based on multi-temporal radar images through synthetic aperture radar interferometry (InSAR) technology. Specifically, two or more radar images acquired at different times are coherently processed, and the phase difference is calculated to form an interference pattern to capture surface deformation information. The specific method is to obtain a phase difference image by pixel-by-pixel multiplication of the complex pixel values ​​of the two images, where the phase difference is used to reflect the slight deformation of the surface.

[0101] Differential interferometry processing unit: Based on the interferogram generation unit, the differential interferometry measurement technology (DInSAR) is used to process the interferogram to eliminate the influence of terrain and orbit errors, so as to focus on the relative deformation detection of the surface. Specifically, the multi-temporal interferogram is differentially calculated to remove terrain effects such as ground undulations, and the orbit error is corrected in combination with the external digital elevation model (DEM) data to extract the surface deformation.

[0102] Phase unwrapping unit: used to unwrap the phase ambiguity in the differential interferogram. Specifically, the phase ambiguity is converted into continuous phase information through the least squares unwrapping algorithm (LSM) to ensure the accuracy of the surface deformation. The specific operation includes smoothing the phase jump in the interferogram and restoring the continuous surface deformation information to generate a high-precision surface deformation map.

[0103] Deformation map generation unit: used to combine the unwrapped phase information and the deformation extracted by the differential interferometry processing unit, and use the interpolation algorithm to spatially interpolate and refine the deformation data to generate a high-resolution deformation map reflecting the details of the surface deformation; through the collaborative work of the above units, the deformation detection module can generate a high-precision surface deformation map based on the pre-processed multi-band SAR data using interferometry technology and differential interferometry technology, providing accurate deformation information for subsequent dynamic modeling and risk assessment, ensuring the accuracy and reliability of the entire monitoring system.

[0104] The phase unwrapping unit specifically includes:

[0105] Preliminary calculation of differential phase map: Receive the differential interferogram from the differential interferometry processing unit and represent it as a phase map ,in and are the horizontal and vertical coordinates of the image respectively. Each pixel value in the differential phase map is the phase of its corresponding position. , the phase value is arrive Within the range, the preliminary calculation formula of the phase diagram is: ,in, and are the complex pixel values ​​of the two-phase radar images, for The complex conjugate of represents the phase angle of a complex number, is the obtained phase difference;

[0106] Calculation of phase difference between adjacent pixels: Preliminary calculation of phase map Analyze and detect phase jump points, which are usually caused by phase ambiguity, that is, the phase value is and The break between the two pixels is calculated by , detect the jump point, the formula is: ,in, is the phase difference, and are the phase values ​​of adjacent pixels respectively. When it exceeds a predetermined threshold, it is judged as a phase jump point;

[0107] Establish phase difference equations: Establish phase difference equations for the detected jump points. is the true phase after unwrapping, then the system of equations is expressed as:

[0108] ,in, is the expected true phase after unwrapping, is an integer representing the ambiguity multiple of the phase;

[0109] Apply the least squares untangling algorithm: Convert the system of equations into a matrix form and apply the least squares untangling algorithm (LSM) to solve it. The goal of least squares untangling is to find the optimal integer value, so that the phase after unwrapping The error between the original phase and the unwrapped phase is minimized, and the phase calculation formula after unwrapping is: ,in, is a coefficient matrix, representing the relationship between pixels, It includes phase difference The vector of is the phase vector after unwrapping;

[0110] Smoothing of the phase image: Smoothing is performed to remove the existing small-scale noise and ensure the continuity and smoothness of the phase map. The smoothing algorithm is Gaussian filtering, and its formula is: ,in, is the smoothed phase diagram, is a Gaussian kernel function that defines the weights of pixels in the neighborhood. and is the offset of the Gaussian kernel function; through the above steps, the phase unwrapping unit can effectively process the phase ambiguity phenomenon in the differential interferogram, generate continuous phase information, and ensure the high accuracy and reliability of the final generated surface deformation map.

[0111] The deformation map generation unit specifically includes:

[0112] Data gridding: First, the unwrapped phase data from the phase unwrapping unit Grid processing is performed and mapped into a regular grid coordinate system. is the coordinate of the grid node, then the gridded phase data is expressed as , whose value is equal to the unwrapped phase value at the corresponding position;

[0113] Selection of spatial interpolation algorithm: Select the spatial interpolation algorithm for the gridded phase data Perform interpolation processing, specifically select the inverse distance weighted interpolation algorithm as the spatial interpolation algorithm, and output after interpolation processing As the interpolated phase value, the specific formula is: ,in, is the interpolated phase value, is the interpolation weight, defined as: ,in, is the point to be interpolated With known points The distance between is the interpolation power parameter, which is used to control the speed of weight decay;

[0114] Smoothing of interpolated data: Smoothing is performed to eliminate local anomalies caused by interpolation, and the phase value after smoothing is expressed as ; Commonly used smoothing methods include Gaussian filtering or bilateral filtering. The formula for Gaussian filtering is: ,in, is the phase value after smoothing, is a Gaussian kernel function, which is used to define the weights of pixels in the neighborhood. and is the offset of the filter window;

[0115] Generation of high-resolution deformation map: Finally, the smoothed interpolated data Converted into a high-resolution surface deformation map by combining the smoothed phase data with the digital elevation model and using geographic coordinate projection technology to map the interpolated phase values ​​to the actual geographic coordinate system to generate a high-resolution deformation map , whose expression is: ,in, is the final shape, is the proportional coefficient, which is used to convert the phase difference into the actual surface deformation. Through the above steps, the deformation map generation unit can effectively use the interpolation algorithm to perform spatial interpolation and refinement on the deformation data, generate a high-resolution deformation map reflecting the details of the surface deformation, and provide accurate data support for subsequent geological analysis and landslide risk assessment.

[0116] The dynamic modeling module includes data collection and integration unit, geological model construction unit, freeze-thaw dynamic simulation unit and landslide deformation prediction unit; among them:

[0117] Data collection and integration unit: first receives the high-resolution deformation map generated by the deformation detection module and geological data of the freeze-thaw zone and weather data ; Among them, the three-dimensional geological data includes the geological layer information on the surface and underground, and the meteorological data includes temperature , precipitation and humidity curves over time, the data collection and integration unit also includes the integration of deformation data with geological and meteorological data into a unified spatial reference frame for subsequent modeling;

[0118] Geological model building unit: based on data integration, based on integrated geological data Construct a three-dimensional geological model of the freeze-thaw zone, specifically using the finite element method (FEM) to express the mechanical properties and stress state of the underground geological layer;

[0119] Freeze-thaw dynamic simulation unit: Based on the constructed three-dimensional geological model, the freeze-thaw dynamic simulation unit combines meteorological data (including temperature , precipitation and humidity ) to conduct dynamic simulation of the freeze-thaw process. By simulating the influence of the freeze-thaw process on the stress and deformation of the geological layer, the deformation of the geological layer within the freeze-thaw cycle is calculated. The calculation formula is: ,in, For time The stress change at any time, is the material property matrix of geological layers affected by meteorological conditions, is the node displacement change;

[0120] Landslide deformation prediction unit: used to calculate the stress and displacement data of the freeze-thaw dynamic simulation unit and the deformation map generated by the deformation detection module The landslide risk in the freeze-thaw zone is evaluated by the landslide prediction model. The prediction model is based on the critical instability conditions of landslides and predicts the landslide area that will occur under the action of freeze-thaw. The expression of the landslide prediction model is: ,in, is the landslide safety factor, is the shear strength of the geological layer, is the surface deformation reflected in the deformation map, if When it is less than the set critical value, it is predicted that a landslide is about to occur; through the above units, the dynamic modeling module can construct a dynamic geological model of freeze-thaw landslide based on the deformation map generated by the deformation detection module and combined with the geological and meteorological data of the freeze-thaw zone. It is used to simulate the landslide deformation behavior during the freeze-thaw process and provide accurate landslide risk prediction, providing key data support for the subsequent risk assessment and early warning modules.

[0121] The geological model building unit includes:

[0122] Discretization of geological data: First, the received three-dimensional geological data is discretized, and the continuous geological layer information is divided into a limited number of units and nodes, where each unit represents a small volume in the geological layer and the node is the vertex of the unit; the discretized geological model is composed of node coordinates Connection relationship with the unit express;

[0123] Generation of material property matrix: For each discretized unit, material property parameters of the geological layer are extracted based on geological data, including elastic modulus , Poisson's ratio ,density , and generate the material property matrix for each element ; Material property matrix It represents the mechanical behavior of the unit during the freezing and thawing process, and its calculation formula is:

[0124] ,

[0125] in, is the elastic modulus, is Poisson's ratio;

[0126] Calculation of the element stiffness matrix: Based on the material property matrix of each element , calculate the stiffness matrix of the element ; Element stiffness matrix It represents the deformation response of the unit under the action of external force, and its calculation formula is: ,in, is the strain-displacement matrix, is the volume integration area of ​​the unit;

[0127] Assembly of the global stiffness matrix: Assemble the stiffness matrices of all elements According to the connection relationship of the nodes Assemble into the overall stiffness matrix , represents the stiffness characteristics of the entire three-dimensional geological model; the overall stiffness matrix Satisfies the following equation: ,in, is the node displacement vector, which indicates the displacement change of the geological layer during the freezing and thawing process. is the external load vector, which represents the force of the external environment on the geological layer;

[0128] Application of boundary conditions and loads: for the overall stiffness matrix Apply boundary conditions and loads. Boundary conditions define the fixed boundaries of the geological model (such as the condition that the bottom of the rock layer does not move), while loads include temperature changes caused by freeze-thaw processes. and external pressure , by applying these conditions, the complete finite element model equations are formed;

[0129] Model solution and result analysis: Finally, numerical solution methods (such as Gaussian elimination method or conjugate gradient method) are used to solve the finite element model equations. , get the node's displacement vector ; By analyzing the displacement results, the deformation influence of the freeze-thaw process on the geological layer can be obtained, and further used to simulate the deformation behavior of the landslide; through the above steps, the geological model construction unit can use the finite element method, based on the deformation map generated by the deformation detection module and the geological and meteorological data of the freeze-thaw zone, to construct a three-dimensional geological model of the freeze-thaw zone, which is used to simulate the deformation behavior of the landslide during the freeze-thaw process, providing a basis for dynamic modeling and landslide prediction.

[0130] The risk assessment and early warning module includes a real-time data receiving unit, a landslide risk calculation unit, a landslide risk threshold setting unit, and an early warning signal generating unit; wherein:

[0131] Real-time data receiving unit: receives dynamic geological model data provided by the dynamic modeling module and deformation data ,in, Indicates that over time Changing geological conditions, including stress and displacement information; For time The surface deformation data at the time, including the deformation of the landslide area;

[0132] Landslide risk calculation unit: After receiving real-time data, it is used to calculate the landslide risk based on the geological model data. and deformation data Calculate the landslide risk factor , the landslide risk factor is calculated based on the following formula: ,in, is the shear strength of the geological layer, indicating the shear strength of the geological layer in time Anti-slip ability at all times; is the shear stress, indicating the possibility of landslide; if Less than the preset threshold , it indicates that there is a landslide risk;

[0133] Landslide risk threshold setting unit: based on the calculated landslide risk coefficient , setting different levels of landslide risk thresholds , the landslide risk threshold is divided into several levels, including low risk , Medium Risk and high risk The specific threshold setting is optimized and adjusted based on historical data, geological conditions and real-time monitoring data to ensure that each landslide risk level corresponds to The scope is reasonable;

[0134] Warning signal generation unit: used when the landslide risk calculation unit detects Less than the set landslide risk threshold When a warning signal is generated, the warning signal is automatically generated. The logic of generating the warning signal includes:

[0135] like , a high-risk warning signal is generated;

[0136] like and , a medium risk warning signal is generated;

[0137] like and , a low-risk warning signal is generated; each warning signal contains the potential area of ​​landslide, risk level and recommended emergency response measures, and the warning information is transmitted to the report comprehensive display module for display and management; through these units, the risk assessment and warning module can use the dynamic geological model and deformation data provided by the dynamic modeling module to conduct accurate landslide risk assessment, and set a reasonable landslide risk threshold based on real-time data. When it is detected that the landslide risk coefficient exceeds the threshold, the corresponding warning signal is automatically generated to provide timely and reliable decision-making support for landslide prevention and control.

[0138] The report comprehensive display module includes a data integration unit, a risk distribution map generation unit, a deformation data visualization unit, a monitoring report generation unit and an early warning information display unit; among which:

[0139] Data integration unit: Receives warning signals from the risk assessment and warning module and deformation detection results from the deformation detection module, and standardizes data from different sources to ensure data consistency in time, space and content. The integrated data will serve as the basis for subsequent data visualization and report generation;

[0140] Risk distribution map generation unit: Based on geographic information system (GIS) technology, the landslide risk level is combined with geographic spatial data to generate a landslide risk distribution map of the monitoring area. Specifically, the landslide risk level information provided by the risk assessment and early warning module is mapped to the actual geographic coordinates, and different colors or symbols are used to mark the risk levels to intuitively display the spatial distribution of landslide risks. The risk distribution map will also be dynamically updated to reflect real-time risk changes;

[0141] Deformation data visualization unit: used to visualize the deformation detection results provided by the deformation detection module, generate a three-dimensional view or contour map of the surface deformation, and generate a three-dimensional deformation map by combining the surface deformation data with the geographic elevation data to show the changes of the surface during the freeze-thaw process. For larger landslide areas, this unit also supports zooming in on local areas for detailed analysis, and can overlay historical deformation data for comparative analysis;

[0142] Monitoring report generation unit: used to integrate the visualization results into a comprehensive monitoring report, which includes a landslide risk distribution map, a 3D view of the surface deformation, analysis results of the landslide area, as well as risk predictions and recommended emergency response measures. The monitoring report is also generated into documents in different formats, including PDF or HTML, for easy distribution and archiving;

[0143] Warning information display unit: used to display the warning signals generated by the risk assessment and warning module in the form of graphics and text on the user interface, and highlight high-risk areas according to the landslide risk level and specific location, and provide detailed risk descriptions and response suggestions. Users can also view historical warning information and corresponding deformation data through this unit to make timely decisions; through the collaborative work of these units, the report comprehensive display module can efficiently receive and process data from the risk assessment and warning module and the deformation detection module, use data visualization technology to generate clear and intuitive freeze-thaw landslide monitoring reports, and display the risk distribution map of the monitored area, providing users with comprehensive landslide monitoring and warning information, and supporting scientific decision-making and emergency response.

[0144] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0145] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. The freeze-thaw landslide deformation monitoring system driven by SAR satellite data is characterized by: It includes multi-band SAR data acquisition module, data preprocessing module, deformation detection module, dynamic modeling module, risk assessment and early warning module and comprehensive report display module; among which: Multi-band SAR data acquisition module: Through the multi-band synthetic aperture radar satellite system, multi-band radar image data covering the freeze-thaw zone is obtained in real time to characterize the surface and underground deformation at different depths, as well as the deformation under different freeze-thaw states; Data preprocessing module: receives radar image data from the multi-band SAR data acquisition module, combines the temperature and humidity data of the freeze-thaw zone, and performs denoising, correction and image registration processing; Deformation detection module: Based on the pre-processed multi-band SAR data, deformation detection is performed using interferometry and differential interferometry techniques to generate high-precision surface deformation maps; Dynamic modeling module: Based on the deformation map generated by the deformation detection module and combined with the geological and meteorological data of the freeze-thaw area, a dynamic geological model of freeze-thaw landslide is constructed to simulate the deformation behavior of the landslide during the freeze-thaw process; Risk assessment and early warning module: Use the dynamic geological model and deformation data provided by the dynamic modeling module to conduct landslide risk assessment and set a landslide risk threshold based on real-time data. When the landslide risk predicted by the model exceeds the set threshold, an early warning signal is automatically generated; Comprehensive report display module: receives the warning signals and deformation detection results generated by the risk assessment and early warning module, generates freeze-thaw landslide monitoring reports through data visualization technology, and displays the risk distribution map of the monitored area.

2. The freeze-thaw landslide deformation monitoring system driven by SAR satellite data according to claim 1 is characterized in that: The multi-band SAR data acquisition module includes a multi-band radar transmitting unit, a radar signal receiving unit and a data processing unit; wherein: Multi-band radar transmitting unit: It includes radar wave transmitters of multiple frequency bands, which are used to transmit L-band, C-band and X-band radar signals respectively. L-band is used to detect deeper surface and underground structures, C-band is used for medium-depth surface layer monitoring, and X-band is used for high-resolution detection of shallow surface and surface freeze-thaw state. Radar wave signals of each frequency band are selected according to their penetration depth and resolution to obtain surface and underground echo signals at different depths. Radar signal receiving unit: a multi-band radar receiver configured to receive in real time the radar signal echoes emitted by the multi-band radar transmitting unit, and to capture the echo signals reflecting the surface and underground deformation information at different depths in the freeze-thaw zone; Data processing unit: decodes the received multi-band radar signals in real time, extracts the original echo data and deformation information carried in the radar signals of each band, and classifies the extracted original multi-band data according to depth and frequency.

3. The freeze-thaw landslide deformation monitoring system driven by SAR satellite data according to claim 1 is characterized in that: The data preprocessing module includes a denoising unit, a correction unit and an image registration unit; wherein: De-noising unit: used to receive the original radar image data from the multi-band SAR data acquisition module and remove the noise in the radar image data in real time by applying adaptive filtering technology; Correction unit: used to perform radiation correction and geometric correction on the denoised radar image data; Image registration unit: used to accurately align the corrected multi-band radar image with the reference image. It is based on feature point-based image registration technology. By extracting feature points from the radar image and the reference image, the similarity between the feature points is calculated and matched.

4. The freeze-thaw landslide deformation monitoring system driven by SAR satellite data according to claim 1 is characterized in that: The deformation detection module includes an interference pattern generation unit, a differential interference processing unit, a phase unwrapping unit and a deformation pattern generation unit; wherein: Interference pattern generation unit: used to receive multi-band SAR data from the data preprocessing module, and generate an interference pattern based on multi-temporal radar images through synthetic aperture radar interferometry technology. Specifically, two or more radar images acquired at different times are coherently processed, the phase difference is calculated, and an interference pattern is formed to capture surface deformation information; Differential interferometry processing unit: Based on the interferogram generation unit, the interferogram is processed through differential interferometry technology to eliminate the influence of terrain and track errors; Phase unwrapping unit: used to unwrap the phase ambiguity in the differential interferogram, specifically converting the phase ambiguity into continuous phase information through the least squares unwrapping algorithm; Deformation map generation unit: It is used to combine the phase information after unwrapping processing and the deformation amount extracted by the differential interference processing unit, and use the interpolation algorithm to perform spatial interpolation and refinement processing on the deformation data to generate a high-resolution deformation map reflecting the details of the surface deformation.

5. The freeze-thaw landslide deformation monitoring system driven by SAR satellite data according to claim 4 is characterized in that: The phase unwrapping unit specifically includes: Preliminary calculation of differential phase map: Receive the differential interferogram from the differential interferometry processing unit and represent it as a phase map ,in and are the horizontal and vertical coordinates of the image respectively. Each pixel value in the differential phase map is the phase of its corresponding position. , the phase value is arrive Within the range, the preliminary calculation formula of the phase diagram is: ,in, and are the complex pixel values ​​of the two-phase radar images, for The complex conjugate of represents the phase angle of a complex number, is the obtained phase difference; Calculation of phase difference between adjacent pixels: Preliminary calculation of phase map Analyze and detect the phase jump point, specifically by calculating the phase difference between adjacent pixels , detect the jump point, the formula is: ,in, is the phase difference, and are the phase values ​​of adjacent pixels respectively. When it exceeds a predetermined threshold, it is judged as a phase jump point; Establish phase difference equations: Establish phase difference equations for the detected jump points. is the true phase after unwrapping, then the system of equations is expressed as: ,in, is the expected true phase after unwrapping, is an integer representing the ambiguity multiple of the phase; Apply the least squares untangling algorithm: Convert the system of equations into a matrix form and apply the least squares untangling algorithm to solve it. The goal of the least squares untangling algorithm is to find the optimal integer value, so that the unwrapped phase The error between the original phase and the unwrapped phase is minimized, and the phase calculation formula after unwrapping is: ,in, is a coefficient matrix, representing the relationship between pixels, It includes phase difference The vector of is the phase vector after unwrapping; Smoothing of the phase image: Smoothing is performed to remove the existing small-scale noise. The smoothing algorithm is Gaussian filtering, and its formula is: ,in, is the smoothed phase diagram, is a Gaussian kernel function that defines the weights of pixels in the neighborhood. and is the offset of the Gaussian kernel function.

6. The freeze-thaw landslide deformation monitoring system driven by SAR satellite data according to claim 5 is characterized in that: The deformation map generating unit specifically comprises: Data gridding: First, the unwrapped phase data from the phase unwrapping unit Perform grid processing; Selection of spatial interpolation algorithm: Select the spatial interpolation algorithm for the gridded phase data Perform interpolation processing, specifically select the inverse distance weighted interpolation algorithm as the spatial interpolation algorithm, and output after interpolation processing As the interpolated phase value; Smoothing of interpolated data: Smoothing is performed to eliminate local anomalies caused by interpolation, and the phase value after smoothing is expressed as ; Generation of high-resolution deformation map: Finally, the smoothed interpolated data Converted into a high-resolution surface deformation map by combining the smoothed phase data with the digital elevation model and using geographic coordinate projection technology to map the interpolated phase values ​​to the actual geographic coordinate system to generate a high-resolution deformation map .

7. The freeze-thaw landslide deformation monitoring system driven by SAR satellite data according to claim 6 is characterized in that: The dynamic modeling module includes a data collection and integration unit, a geological model construction unit, a freeze-thaw dynamic simulation unit and a landslide deformation prediction unit; wherein: Data collection and integration unit: first receives the high-resolution deformation map generated by the deformation detection module and geological data of the freeze-thaw zone and weather data ; Among them, the three-dimensional geological data includes the geological layer information on the surface and underground, and the meteorological data includes temperature , precipitation and humidity curves over time, the data collection and integration unit also includes the integration of deformation data with geological and meteorological data into a unified spatial reference frame; Geological model building unit: based on data integration, based on integrated geological data Construct a three-dimensional geological model of the freeze-thaw zone, specifically using the finite element method to express the mechanical properties and stress state of the underground geological layer; Freeze-thaw dynamic simulation unit: Based on the constructed three-dimensional geological model, the freeze-thaw dynamic simulation unit combines meteorological data Conduct dynamic simulation of the freeze-thaw process, and calculate the deformation of the geological layer during the freeze-thaw cycle by simulating the impact of the freeze-thaw process on the stress and deformation of the geological layer; Landslide deformation prediction unit: used to calculate the stress and displacement data of the freeze-thaw dynamic simulation unit and the deformation map generated by the deformation detection module ,The landslide risk in the freeze-thaw zone is evaluated by a landslide prediction model. The prediction model is based on the critical instability conditions of landslide and predicts the landslide areas that will occur under freeze-thaw action.

8. The freeze-thaw landslide deformation monitoring system driven by SAR satellite data according to claim 1 is characterized in that: The geological model building unit comprises: Discretization of geological data: First, the received three-dimensional geological data is discretized, and the continuous geological layer information is divided into a limited number of units and nodes, where each unit represents a small volume in the geological layer and the node is the vertex of the unit; Generation of material property matrix: For each discretized unit, material property parameters of the geological layer are extracted based on geological data, including elastic modulus , Poisson's ratio ,density , and generate the material property matrix for each element ; Calculation of the element stiffness matrix: Based on the material property matrix of each element , calculate the stiffness matrix of the element ; Assembly of the global stiffness matrix: Assemble the stiffness matrices of all elements Assemble into the overall stiffness matrix according to the connection relationship of the nodes , represents the stiffness characteristics of the entire 3D geological model; Application of boundary conditions and loads: for the overall stiffness matrix Apply boundary conditions and loads. Boundary conditions define fixed boundaries of the geological model, while loads include temperature changes caused by freeze-thaw processes. and external pressure ; Model solution and result analysis: Finally, the finite element model equations are solved using numerical solution methods. , get the node's displacement vector .

9. The freeze-thaw landslide deformation monitoring system driven by SAR satellite data according to claim 1, characterized in that: The risk assessment and early warning module includes a real-time data receiving unit, a landslide risk calculation unit, a landslide risk threshold setting unit and an early warning signal generating unit; wherein: Real-time data receiving unit: receives dynamic geological model data provided by the dynamic modeling module and deformation data ,in, Indicates that over time Changing geological conditions, including stress and displacement information; For time The surface deformation data at the time, including the deformation of the landslide area; Landslide risk calculation unit: After receiving real-time data, it is used to calculate the landslide risk based on the geological model data. and deformation data Calculate the landslide risk factor ; Landslide risk threshold setting unit: based on the calculated landslide risk coefficient , setting different levels of landslide risk thresholds , the landslide risk threshold is divided into several levels, including low risk , Medium Risk and high risk ; Warning signal generation unit: used when the landslide risk calculation unit detects Less than the set landslide risk threshold When a warning signal is generated, the warning signal is automatically generated. The logic of generating the warning signal includes: like , a high-risk warning signal is generated; like and , a medium risk warning signal is generated; like and , a low risk warning signal is generated.

10. The freeze-thaw landslide deformation monitoring system driven by SAR satellite data according to claim 1, characterized in that: The report comprehensive display module includes a data integration unit, a risk distribution map generation unit, a deformation data visualization unit, a monitoring report generation unit and an early warning information display unit; wherein: Data integration unit: receives warning signals from the risk assessment and warning module and deformation detection results from the deformation detection module, and standardizes data from different sources; Risk distribution map generation unit: Based on geographic information system technology, the landslide risk level is combined with geographic spatial data to generate a landslide risk distribution map of the monitoring area. Specifically, the landslide risk level information provided by the risk assessment and early warning module is mapped to the actual geographic coordinates, and different colors or symbols are used to mark the risk levels to intuitively display the spatial distribution of landslide risks. Deformation data visualization unit: used to visualize the deformation detection results provided by the deformation detection module and generate a three-dimensional view or contour map of the surface deformation; Monitoring report generation unit: used to integrate the visualization results into a comprehensive monitoring report, which includes a landslide risk distribution map, a three-dimensional view of the surface deformation, analysis results of the landslide area, and risk predictions and recommended emergency response measures; Warning information display unit: used to display the warning signals generated by the risk assessment and warning module in the form of graphics and text on the user interface, and highlight high-risk areas according to the landslide risk level and specific location.

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