Slope radar system and method based on digital dechirp technology

Abstract

The invention relates to the technical field of slope detection, and discloses a slope radar system and method based on a digital dechirp technology, the system integrates radar transmitting and receiving, digital dechirp processing, data storage, data analysis and a user interface module, and high-precision measurement is realized by transmitting broadband or ultra-broadband linear frequency modulation signals and receiving reflection echoes. And the data analysis module adopts a machine learning algorithm to carry out preprocessing, feature extraction and model construction on the collected historical slope displacement and deformation mode data so as to form a slope stability prediction model. One-dimensional range profile data obtained in real time is analyzed through a model, the slope stability state is predicted and compared with a preset threshold value, and an alarm mechanism is triggered in time. The intelligent level of slope monitoring is improved, deep excavation and accurate prediction of the slope deformation mode are achieved, and a powerful guarantee is provided for geological disaster prevention and public safety.

Description

Technical Field

[0001] The present invention relates to the technical field of slope detection, and in particular to a slope radar system and method based on digital deslope technology. Background Art

[0002] Slope stability monitoring is a key link in preventing geological disasters and ensuring public safety. With the rapid development of radar technology, slope radar systems have gradually become the preferred solution in the field of slope monitoring due to their long-distance monitoring, large-scale coverage, high-precision measurement and strong real-time performance. However, despite the significant progress made in the hardware level of radar monitoring systems, existing technologies still face many challenges and shortcomings in data analysis, especially in slope stability assessment and prediction.

[0003] Existing slope radar systems often rely on empirical models to analyze slope deformation data. These methods are unable to cope with complex and variable slope deformation patterns and are difficult to dig deep into the underlying information behind the data, which limits the accuracy of slope stability assessment and leads to inaccurate prediction results when faced with new and unseen slope deformation patterns. Summary of the invention

[0004] The object of the present invention is to provide a slope radar system and method based on digital de-slope technology to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a slope radar system based on digital deslope technology, comprising: Radar transmitting and receiving module, responsible for transmitting broadband or ultra-wideband linear frequency modulation signals and receiving reflected echo signals from the slope; The digital de-skewing processing module is located inside the FPGA and processes the echo sampling digital signal converted by the A / D converter ADC, and further processes the processed data through FFT to obtain the one-dimensional range image data of the target; A data storage module is responsible for storing the one-dimensional range image data in a high-speed memory and transmitting the data to the data analysis module in real time through a communication device; The data analysis module receives data from the data storage module and uses machine learning algorithms to analyze the stability of the slope. This module is used to identify slope deformation patterns, predict potential landslide risks, and provide early warning information. The user interface module provides a human-computer interaction interface to display real-time monitoring data, analysis results and warning information.

[0006] Preferably, the data analysis module includes: A data collection unit collects historical slope displacement data and deformation pattern data monitored by the slope radar system; The data preprocessing unit preprocesses the collected data, including data cleaning and standardization, to generate a basic data set; The feature extraction unit, based on the preprocessed basic data set, uses a feature selection algorithm to screen out feature variables that are highly correlated with slope stability and generate a feature data set; The model building unit combines the slope feature vector and the corresponding slope stability label and uses a machine learning algorithm to build a slope stability prediction model, which is used to predict the future stability state of the slope; The data analysis unit obtains the one-dimensional range image data monitored by the slope radar system in real time, obtains the displacement data and deformation mode data of the current slope based on the one-dimensional range image data, further inputs the data into the trained slope stability prediction model, receives the predicted slope stability state output by the model; and compares the slope stability state predicted by the model with the preset threshold to evaluate the current stability status of the slope; if the evaluation result shows that the slope has an unstable landslide trend, the alarm mechanism is triggered.

[0007] Preferably, the displacement data includes the displacement of the slope at different time points; and the deformation pattern data includes the crack distribution of the slope.

[0008] Preferably, the model building unit selects a linear regression algorithm to build a slope stability prediction model, and the algorithm formula of the model is: , in, is the target variable, representing the stability index of the slope; is the input variable, representing the slope displacement data and deformation pattern data that affect the slope stability; is the intercept term, which represents the basic value of the slope stability index when there is no influence of any environmental parameters and geological parameters; is the regression coefficient, which is determined by model training; ε is the error term.

[0009] Preferably, before the prediction model training begins, for each feature in the linear regression model Corresponding parameters Assign an initial value, which is set to a random number close to 0 and is also the intercept term An initial value is also given, and these initial parameter values ​​will serve as the starting point for model training.

[0010] Preferably, the slope stability prediction model continuously iterates and updates the parameters of the linear regression model through a gradient descent algorithm. , in order to minimize the loss function value between the true slope stability index and the predicted value; the calculation formula of the gradient descent algorithm is: , in, Represents the model parameters, including all weights and biases that the model needs to learn; Represents the learning rate, which is used to control the step size of parameter update; Represents the loss function About parameters The gradient of is a vector pointing to the direction where the loss function grows fastest; Indicates an assignment operation, that is, updating parameters The value of .

[0011] Preferably, the mean square error (MSE) algorithm is used as the loss function for model evaluation. The MSE algorithm is used to calculate the average value of the square of the error between the model prediction value and the actual value. The calculation formula is: , in, is the number of samples in the test set, is the actual stability index of the ith sample, It is the stability index of the i-th sample predicted by the model; The prediction performance of the LSTM model is evaluated based on the calculated MSE value. The smaller the MSE value, the more accurate the model's prediction is and the smaller the deviation between the predicted value and the actual value is.

[0012] Preferably, the radar transmitting and receiving module sends the data of the linear frequency modulation signal to the high-speed D / A converter DAC for processing through FPGA to obtain an intermediate frequency broadband signal, which is then mixed with the spot frequency local oscillator to obtain a radio frequency signal for transmission; in the receiving part, the radio frequency echo signal received by the antenna is filtered and amplified, mixed with the spot frequency local oscillator to obtain an intermediate frequency broadband signal, and then subjected to analog-to-digital conversion processing by a high-speed A / D converter ADC.

[0013] Preferably, the data preprocessing unit uses the Z-score method to standardize the data, subtracting the mean of each numerical data and dividing it by its standard deviation, so that the processed data conforms to the standard normal distribution. The formula for standardization is: ;in, is the original data, is the mean of the original data, is the standard deviation of the original data, is the standardized data.

[0014] Preferably, a method for using a slope radar system based on digital deslope technology comprises: Transmitting a broadband or ultra-wideband linear frequency modulation signal to the slope through a radar transmitting and receiving module, and receiving a reflected echo signal from the slope; The reflected echo signal is converted by an A / D converter ADC and then transmitted to a digital de-skewing processing module; the echo sampling digital signal is processed by the digital de-skewing processing module, and the processed data is processed by FFT to obtain the one-dimensional range image data of the target; The one-dimensional range image data is stored in the data storage module, and the data is transmitted to the data analysis module in real time through the communication device; The data analysis module uses machine learning algorithms to analyze the received data, identify slope deformation patterns, predict potential landslide risks, and provide early warning information; Real-time monitoring data, analysis results and warning information are displayed through the user interface module.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The data analysis module uses machine learning algorithms to conduct in-depth analysis of slope deformation data, and can automatically identify and extract features that are highly related to slope stability, and build a more accurate slope stability prediction model. Compared with traditional empirical models, the present invention has higher accuracy and reliability in predicting the future stability state of the slope. The present invention constructs a slope stability prediction model through a machine learning algorithm, and realizes intelligent analysis and decision support for slope deformation data. The model can automatically learn and adapt to different slope deformation modes, which improves the generalization ability and intelligence level of the system.

[0016] The data analysis unit can evaluate the current stability of the slope in real time and trigger the alarm mechanism in time according to the prediction results, which helps to timely discover potential landslide risks, provide timely warning information to relevant departments, and effectively prevent the occurrence of geological disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a working principle diagram of a slope radar system based on digital deslope technology according to the present invention; Figure 2 Training flow chart of slope stability prediction model. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1-2The present invention provides a technical solution: a slope radar system based on digital deslope technology, which can efficiently and accurately monitor the stability of the slope, predict potential landslide risks, and provide timely warning information. The specific implementation of the system will be described in detail below.

[0020] The system includes a radar transmitting and receiving module. This module is responsible for transmitting broadband or ultra-wideband linear frequency modulation signals and receiving reflected echo signals from the slope. In the transmitting part, the FPGA sends the data of the linear frequency modulation signal to the high-speed D / A converter DAC for processing to obtain an intermediate frequency broadband signal. This intermediate frequency broadband signal is then mixed with the point frequency local oscillator to generate a radio frequency signal and transmitted through the antenna. In the receiving part, the radio frequency echo signal received by the antenna is first filtered and amplified, and then mixed with the same point frequency local oscillator to restore it to an intermediate frequency broadband signal. This intermediate frequency broadband signal is then converted to digital by a high-speed A / D converter ADC to obtain a digital echo signal for subsequent processing.

[0021] The digital de-skewing processing module further processes the echo sampling digital signal converted by the A / D converter ADC. This module is located inside the FPGA and processes the digital signal through a specific de-skewing algorithm to eliminate the slope component in the signal. The processed data is then processed by FFT (Fast Fourier Transform) to obtain the one-dimensional range image data of the target.

[0022] The data storage module is responsible for storing the processed data in high-speed memory. This module ensures the integrity and security of the data and transmits the data to the data processing center in real time through the communication device.

[0023] The data analysis module receives the data from the data storage module and uses a machine learning algorithm to analyze the stability of the slope.

[0024] The user interface module provides a human-computer interaction interface for displaying real-time monitoring data, analysis results and warning information. Users can intuitively understand the stability of the slope through this interface and take corresponding preventive measures based on the warning information.

[0025] The present invention will be further described below in conjunction with Examples 1 to 3: Embodiment 1:

[0026] The data analysis module further includes: Data collection unit: This unit is responsible for collecting historical slope displacement data and deformation pattern data monitored by the slope radar system over a long period of time. These data are the basis for subsequent analysis. Through continuous accumulation, a huge data set can be formed to provide rich data support for the assessment of slope stability.

[0027] Data preprocessing unit: The collected raw data often contains noise, outliers or missing values, so preprocessing is required. This unit first cleans the data to remove invalid or erroneous data points, and then standardizes the data to eliminate the differences between different dimensions and magnitudes to make the data comparable. The data preprocessing unit uses the Z-score method to standardize the data, subtracting the mean from each numerical data and dividing it by its standard deviation, so that the processed data conforms to the standard normal distribution. The formula for standardization is: ;in, is the original data, is the mean of the original data, is the standard deviation of the original data, is the standardized data.

[0028] After preprocessing, the basic data set is generated.

[0029] Feature extraction unit: Based on the basic data set, this unit uses feature selection algorithm to filter out feature variables that are highly correlated with slope stability. These feature variables can reflect the key information of slope deformation and are of great significance for predicting slope stability. The filtering method is used for feature extraction, and the Pearson correlation coefficient is used to measure the linear correlation between each feature and the corresponding label. The calculation formula of the Pearson correlation coefficient is: , Among them, r represents the correlation coefficient between the feature and the corresponding label, n represents the number of data points, x represents the feature value, and y represents the corresponding label.

[0030] According to the calculated correlation coefficient, the features whose absolute values ​​are greater than the set threshold are extracted as useful features to form a feature data set.

[0031] Model building unit: Combining the slope feature vector and the corresponding slope stability label (such as stable, unstable, etc.), this unit uses machine learning algorithms to build a slope stability prediction model. During the model training process, the prediction accuracy and generalization ability of the model are improved by continuously adjusting parameters and optimizing algorithms. After the training is completed, a prediction model that can predict the future stability state of the slope is obtained.

[0032] Data analysis unit: This unit obtains the one-dimensional range image data monitored by the slope radar system in real time, and obtains the displacement data and deformation mode data of the current slope by processing this data. These data are input into the trained slope stability prediction model, and the model outputs the predicted slope stability state. Then, this unit compares the slope stability state predicted by the model with the preset threshold to evaluate the current stability of the slope. If the evaluation result shows that the slope has an unstable landslide trend (that is, the prediction result exceeds the threshold), the alarm mechanism is immediately triggered and early warning information is sent to relevant personnel so that preventive measures can be taken in time. Embodiment 2:

[0033] In the data analysis module of the slope radar system, the model building unit uses a linear regression algorithm to build a slope stability prediction model. The model aims to predict the stability index of the slope through the displacement data and deformation mode data of the slope. The specific implementation method of the model construction is described in detail below.

[0034] First, the algorithm formula for defining the linear regression model is as follows: ,

[0035] in, is the target variable, representing the stability index of the slope, which can be a numerical value or a classification label reflecting the stability of the slope; are input variables, representing various slope displacement data and deformation mode data that affect slope stability; is the intercept term, which represents the basic value of the slope stability index when there is no influence of any environmental parameters and geological parameters; are regression coefficients, which are determined through model training and reflect the influence of each input variable on the slope stability index; ε is the error term, which represents the deviation between the model prediction value and the actual value.

[0036] Before the prediction model training begins, it is necessary to assign an initial value to the parameter (i.e., regression coefficient) corresponding to each feature (i.e., input variable) in the linear regression model. These initial values ​​are usually set to random numbers close to 0 to ensure that the starting point of the model training is neutral and will not be biased towards a specific result due to the selection of initial parameters. At the same time, the intercept term Assign an initial value, which can also be a random number close to 0.

[0037] Taking the specific slope monitoring data as an example, suppose there are m slope samples, each sample contains n features (such as displacement, deformation speed, etc.) and a stability index. These data are organized into a matrix form, where the dimension of the input matrix X is m×(n+1) (including the intercept term) and the dimension of the target vector Y is m×1. The goal of model training is to find a set of optimal regression coefficients. , so that the error between the predicted value and the actual value is minimized.

[0038] During the training process, the gradient descent method can be used to iteratively update the value of the regression coefficient until the predetermined convergence condition or the upper limit of the number of iterations is reached. In each iteration, the predicted value is calculated based on the current regression coefficient and compared with the actual value to calculate the error term ε. Then, the value of the regression coefficient is adjusted according to the error term and a certain learning rate to reduce the error. The calculation formula of the gradient descent algorithm is: ,

[0039] in, Represents the model parameters, including all weights and biases that the model needs to learn; Represents the learning rate, which is used to control the step size of parameter update; Represents the loss function About parameters The gradient of is a vector pointing to the direction where the loss function grows fastest; Indicates an assignment operation, that is, updating parameters The value of .

[0040] In the data analysis module of the slope radar system, in order to evaluate the performance of the constructed slope stability prediction model, the mean squared error (MSE) algorithm is used as the loss function. The MSE algorithm is used to calculate the average value of the square of the error between the model prediction value and the actual value, which is an important indicator for measuring the model prediction accuracy. The following describes in detail the specific implementation of the MSE algorithm in model evaluation.

[0041] First, define the calculation formula of the MSE algorithm as follows: ,

[0042] in, is the number of test set samples, that is, the size of the dataset used to evaluate the model performance; is the actual stability index of the i-th sample, that is, the true stability state or value of the slope; It is the stability indicator of the i-th sample predicted by the model, that is, the predicted value given by the model based on the input features.

[0043] In the process of model evaluation, the data set needs to be divided into a training set and a test set. The training set is used to train the model, that is, to adjust the model parameters to minimize the MSE value on the training set; while the test set is used to evaluate the prediction performance of the model, that is, to calculate the MSE value of the model on the test set.

[0044] Taking slope stability prediction as an example, assume that a slope stability prediction model has been trained through linear regression algorithm or other machine learning algorithm. In order to evaluate the prediction performance of the model, a part of the slope monitoring data is selected as a test set. For each sample in the test set, its features are input into the model to obtain the stability index predicted by the model. At the same time, the actual stability index of the sample is also obtained .

[0045] Then, according to the calculation formula of the MSE algorithm, the average of the squared errors between the predicted values ​​and the actual values ​​of all samples in the test set is calculated to obtain the MSE value. This MSE value reflects the prediction performance of the model on the test set: the smaller the MSE value, the more accurate the model's prediction and the smaller the deviation between the predicted value and the actual value; conversely, the larger the MSE value, the worse the model's prediction performance.

[0046] Finally, the prediction performance of the model can be evaluated based on the calculated MSE value. If the MSE value is small and meets the predetermined performance requirements, it can be considered that the model has good prediction performance and can be used for actual slope stability monitoring and early warning. If the MSE value is large, it is necessary to further adjust the model parameters or select other more appropriate algorithms to improve the prediction performance of the model. Embodiment 3:

[0047] A method for using a slope radar system based on digital deslope technology is intended to achieve real-time monitoring and early warning of slope stability. The specific implementation steps of the method are as follows: The radar transmitting and receiving modules transmit broadband or ultra-wideband linear frequency modulation signals to the slope. This signal has high resolution and anti-interference ability, and can accurately reflect the slight deformation of the slope surface. After the signal is transmitted, the module will continue to receive reflected echo signals from the slope, which carry the deformation information of the slope surface.

[0048] The reflected echo signal is sent to the A / D converter (ADC) for analog to digital conversion. The converted digital signal is transmitted to the digital de-skewing processing module. In this module, the echo sampling digital signal is de-skewed to eliminate the phase offset caused by the difference in signal propagation path. The processed data is then processed by the fast Fourier transform (FFT) to obtain the one-dimensional range image data of the target. These data represent the distance between each point on the slope surface and the target radar in digital form, thereby reflecting the deformation of the slope.

[0049] The one-dimensional range image data is stored in the data storage module for subsequent data analysis and processing. At the same time, these data are also transmitted to the data analysis module in real time through the communication device. The communication device can be wired or wireless to ensure timely transmission and reception of data.

[0050] In the data analysis module, the machine learning algorithm is used to analyze the received one-dimensional range image data. Through the trained model, the deformation mode of the slope is identified, including key parameters such as displacement and deformation speed. Combined with historical data and geological information, the model can predict potential landslide risks and provide early warning information. The early warning information can include the possibility of landslides, the expected time of occurrence, the scope of impact, etc.

[0051] Finally, the real-time monitoring data, analysis results and warning information are displayed through the user interface module. The user interface can be graphical, showing the stability of the slope in an intuitive way. Users can view the real-time deformation of the slope, historical data comparison, warning information and other contents through the interface, so as to take necessary preventive measures in time.

[0052] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0053] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A slope radar system based on digital deslope technology, characterized in that: The system comprises: Radar transmitting and receiving module, responsible for transmitting broadband or ultra-wideband linear frequency modulation signals and receiving reflected echo signals from the slope; The digital de-skewing processing module is located inside the FPGA and processes the echo sampling digital signal converted by the A / D converter ADC, and further processes the processed data through FFT to obtain the one-dimensional range image data of the target; A data storage module is responsible for storing the one-dimensional range image data in a high-speed memory and transmitting the data to the data analysis module in real time through a communication device; The data analysis module receives data from the data storage module and uses machine learning algorithms to analyze the stability of the slope. This module is used to identify slope deformation patterns, predict potential landslide risks, and provide early warning information. The user interface module provides a human-computer interaction interface to display real-time monitoring data, analysis results and warning information.

2. A slope radar system based on digital deslope technology according to claim 1, characterized in that: The data analysis module includes: A data collection unit collects historical slope displacement data and deformation pattern data monitored by the slope radar system; The data preprocessing unit preprocesses the collected data, including data cleaning and standardization, to generate a basic data set; The feature extraction unit, based on the preprocessed basic data set, uses a feature selection algorithm to screen out feature variables that are highly correlated with slope stability and generate a feature data set; The model building unit combines the slope feature vector and the corresponding slope stability label and uses a machine learning algorithm to build a slope stability prediction model, which is used to predict the future stability state of the slope; The data analysis unit obtains the one-dimensional range image data monitored by the slope radar system in real time, obtains the displacement data and deformation mode data of the current slope based on the one-dimensional range image data, further inputs the data into the trained slope stability prediction model, receives the predicted slope stability state output by the model; and compares the slope stability state predicted by the model with the preset threshold to evaluate the current stability status of the slope; if the evaluation result shows that the slope has an unstable landslide trend, the alarm mechanism is triggered.

3. The slope radar system based on digital deslope technology according to claim 2, characterized in that: The displacement data includes the displacement of the slope at different time points; the deformation mode data includes the crack distribution of the slope.

4. The slope radar system based on digital deslope technology according to claim 2, characterized in that: The model building unit selects a linear regression algorithm to build a slope stability prediction model, and the algorithm formula of the model is: ， in, is the target variable, representing the stability index of the slope; is the input variable, representing the slope displacement data and deformation pattern data that affect the slope stability; is the intercept term, which represents the basic value of the slope stability index when there is no influence of any environmental parameters and geological parameters; is the regression coefficient, which is determined by model training; ε is the error term.

5. The slope radar system based on digital deslope technology according to claim 4, characterized in that: Before the prediction model training starts, for each feature in the linear regression model Corresponding parameters Assign an initial value, which is set to a random number close to 0 and is also the intercept term An initial value is also given, and these initial parameter values ​​will serve as the starting point for model training.

6. The slope radar system based on digital deslope technology according to claim 5, characterized in that: The slope stability prediction model uses a gradient descent algorithm to continuously iterate and update the parameters of the linear regression model. , in order to minimize the loss function value between the true slope stability index and the predicted value; the calculation formula of the gradient descent algorithm is: ， in, Represents the model parameters, including all weights and biases that the model needs to learn; Represents the learning rate, which is used to control the step size of parameter update; Represents the loss function About parameters The gradient of is a vector pointing to the direction where the loss function grows fastest; Indicates an assignment operation, that is, updating parameters The value of .

7. The slope radar system based on digital deslope technology according to claim 6, characterized in that: The mean square error (MSE) algorithm is used as the loss function for model evaluation. The MSE algorithm is used to calculate the average value of the square of the error between the model prediction value and the actual value. The calculation formula is: ， in, is the number of test set samples, is the actual stability index of the ith sample, It is the stability index of the i-th sample predicted by the model; The prediction performance of the LSTM model is evaluated based on the calculated MSE value. The smaller the MSE value, the more accurate the model's prediction is and the smaller the deviation between the predicted value and the actual value is.

8. The slope radar system based on digital deslope technology according to claim 1, characterized in that: The radar transmitting and receiving module sends the data of the linear frequency modulation signal to the high-speed D / A converter DAC through the FPGA for processing, obtains the intermediate frequency broadband signal, and then mixes it with the point frequency local oscillator to obtain the radio frequency signal for transmission; In the receiving part, the RF echo signal received by the antenna is filtered and amplified, mixed with the point-frequency local oscillator to obtain an intermediate frequency broadband signal, and then converted into a digital signal by a high-speed A / D converter ADC.

9. The slope radar system based on digital deslope technology according to claim 2, characterized in that: The data preprocessing unit uses the Z-score method to standardize the data, subtracting the mean value from each numerical data and dividing it by its standard deviation, so that the processed data conforms to the standard normal distribution. The formula for standardization is: ;in, is the original data, is the mean of the original data, is the standard deviation of the original data, is the standardized data.

10. A method for using a slope radar system based on digital deslope technology, characterized in that: The method comprises: The radar transmitting and receiving modules transmit broadband or ultra-wideband linear frequency modulation signals to the slope, and receive reflected echo signals from the slope; The reflected echo signal is converted by an A / D converter ADC and then transmitted to a digital de-skewing processing module; the echo sampling digital signal is processed by the digital de-skewing processing module, and the processed data is processed by FFT to obtain the one-dimensional range image data of the target; The one-dimensional range image data is stored in the data storage module, and the data is transmitted to the data analysis module in real time through the communication device; The data analysis module uses machine learning algorithms to analyze the received data, identify slope deformation patterns, predict potential landslide risks, and provide early warning information; Real-time monitoring data, analysis results and warning information are displayed through the user interface module.

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