Slope stability analysis method and system based on data analysis

Abstract

The invention discloses a slope stability analysis method and system based on data analysis, belongs to the technical field of slope safety analysis, and aims to solve the problems of dependence on hypothesis simplified model and neglect of real-time dynamic change in the prior art. S2, dynamic environment factor analysis; s3, slope stability analysis; s4, establishing an intelligent decision-making and early warning system; s5, system integration and user interaction; s6, performing system optimization and continuous learning; by comprehensively considering complex soil body characteristics such as heterogeneity and anisotropy of the slope and dynamic changes of environmental factors such as external rainfall, meteorology and earthquakes, the system can provide a more real and accurate slope stability analysis result, an intelligent algorithm and a data analysis technology are adopted, and the stability of the slope is improved. The dependence on a simplified physical model and a static hypothesis is reduced, so that the scientificity and the practicability of an analysis result are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of slope safety analysis, and in particular relates to a slope stability analysis method and system based on data analysis. Background Art

[0002] Slope stability analysis is a very important research topic in civil engineering, geological engineering, transportation engineering and other fields. Especially in mountain highway, railway construction, mining and water conservancy projects, slope stability analysis is directly related to engineering safety, environmental protection and the safety of life and property. The purpose of slope stability analysis is to assess the risk of slope instability such as landslide, collapse, and sliding under external forces such as precipitation, earthquakes, and human activities, and then take corresponding reinforcement or protection measures.

[0003] Traditional slope stability analysis methods mainly rely on geological surveys and field tests. With the development of information technology and sensing technology, slope stability analysis has gradually entered the data analysis method, but it still has the following defects and shortcomings:

[0004] First, traditional slope stability analysis methods often rely on assumptions and simplified physical models. For example, the limit equilibrium method assumes that the slope is an ideal sliding body and ignores complex characteristics such as soil heterogeneity and anisotropy. This makes it difficult to fully and accurately reflect the actual slope conditions, significantly limiting the accuracy and reliability of the analysis results.

[0005] Second, slope stability is affected by many factors, such as precipitation, earthquakes, and climate change, and these factors are dynamic in reality. Traditional methods are usually based on static assumptions for analysis and fail to fully consider the changes in slope stability at different times and under different environmental conditions. This static analysis method often cannot respond to changes in the external environment in a timely manner, resulting in the slope stability assessment being unable to reflect actual real-time risks.

[0006] Therefore, a slope stability analysis method and system based on data analysis is needed to solve the problems existing in the existing technology of relying on simplified models with assumptions and ignoring real-time dynamic changes. Summary of the Invention

[0007] The purpose of the present invention is to provide a slope stability analysis method and system based on data analysis to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a slope stability analysis method and system based on data analysis, comprising the following steps:

[0009] S1. Data acquisition and preprocessing: Real-time data on slope deformation, stress, groundwater level, precipitation, temperature, air pressure, and seismic activity are collected at the slope monitoring site. The collected data are denoised, standardized, and interpolated to eliminate sensor errors and data loss. At the same time, the real-time data is time-synchronized.

[0010] S2. Dynamic environmental factor analysis: By acquiring real-time data on slope deformation, stress, groundwater level, precipitation, temperature, air pressure, and seismic activity, key factors that affect slope stability are identified. Using regression analysis or neural networks, a quantitative relationship model between key factors and slope stability is established to identify the impact of changes in key factors on slope stability.

[0011] S3. Slope stability analysis: Based on the geological survey data of the slope, a physical model of the slope is established. Taking into account the heterogeneity and anisotropy of the soil, combined with the changes in key factors, an intelligent algorithm model is applied to conduct real-time slope stability analysis. By training the algorithm model, the slope stability prediction results are dynamically adjusted to reflect the changing trend of slope stability with changes in the external environment in real time.

[0012] S4. Establish an intelligent decision-making and early warning system: Based on real-time monitoring data and slope stability prediction results, establish an automated early warning mechanism. By analyzing the trend of slope stability changes, combining historical data and environmental factors, provide intelligent decision support;

[0013] S5. System integration and user interaction: By integrating slope monitoring, data collection, data analysis, early warning, and decision support, a slope stability analysis platform is formed. This platform provides a user interaction interface and enables engineering personnel to view slope stability analysis results, risk assessment reports, and early warning information in real time.

[0014] S6. System optimization and continuous learning: The system continuously updates training data and models based on changes in the external environment and slope stability performance, improving its adaptability to different geological conditions and environmental factors.

[0015] It should be noted that in the data collection and preprocessing steps, the multidimensional data processing model for processing the collected data is:

[0016] D proccssed =Standardize(Interp(D raw ))-Denoise(D raw )

[0017] Where D proccssed is the processed data;

[0018] D raw It is the raw data collected from the sensor;

[0019] Interp(·) represents the interpolation function, which is used to fill in missing data;

[0020] Standardize(·) represents the standardization function, which makes the data satisfy the distribution with mean 0 and variance 1;

[0021] Denoise(·) is a denoising function used to eliminate sensor noise.

[0022] It is worth mentioning that in the dynamic environmental factor analysis step, the quantitative relationship model between key factors and slope stability is:

[0023] S stability (t) = f(X CNV (t),θ)

[0024] Where S stability (t) is the change of slope stability with time;

[0025] X CNV (t) is the value of the critical factor at time t;

[0026] θ is the model parameter to be learned, obtained through regression analysis or neural network training,

[0027] f(·) is a mathematical mapping function between dynamic environmental factors and slope stability, which converts environmental data into stability indicators to support real-time analysis and early warning.

[0028] It should be further explained that in the slope stability analysis step, the slope stability S stability (t1) is:

[0029] S stability (t1)=Alg AI (M geology, X cnv (t))

[0030] Where M geology It is a physical model of the slope, including geological survey data;

[0031] X cnv (t) is the time series data of key factors;

[0032] Alg AI (·) is an intelligent algorithm model that is trained based on historical data and provides dynamic predictions.

[0033] As a preferred embodiment, in the step of establishing an intelligent decision-making and early warning system, the automated early warning model is:

[0034] P alert(t)=H(Alg AI (S stability (t), X CNV (t)))

[0035] Where, P alert (t) is the early warning signal, which is triggered if the slope stability is lower than the critical value;

[0036] H(·) is a heuristic function or threshold judgment, which is used to decide whether to issue an early warning.

[0037] As a preferred embodiment, the system integration and user interaction step includes a real-time feedback model:

[0038] UI feedback =G(D monitor , S stability (t), P alert (t))

[0039] In the formula, UI feedback It is the information fed back by the user interface, showing slope stability, risk assessment reports and early warning information;

[0040] D monitor It is sensor data collected in real time.

[0041] As a preferred embodiment, in the system optimization and continuous learning steps, the updated model is:

[0042] θ new =θ old +Δθ

[0043] Where θ new are the optimized model parameters;

[0044] θ old are the current model parameters;

[0045] Δθ is a parameter adjustment based on new real-time data updates.

[0046] As a preferred embodiment, the method for implementing the above-mentioned slope stability analysis method based on data analysis includes:

[0047] Data acquisition module: used to collect various slope data from multiple sources in real time to provide data support for subsequent analysis;

[0048] Data preprocessing module: used to clean, process and integrate the collected data to ensure data quality and consistency, and provide clean, structured and consistent data for analysis models;

[0049] Feature extraction and selection module: used to extract key features from raw data, reduce data dimensions and improve the analysis efficiency of the model by extracting and selecting important features;

[0050] Slope stability analysis model module: used to build and train analysis models, evaluate slope stability, evaluate slope stability through data-driven models, and predict slope change trends and potential risks under different conditions;

[0051] Real-time monitoring and dynamic update module: used to monitor slope changes in real time and dynamically adjust stability assessments based on new monitoring data;

[0052] Risk assessment and early warning module: used to evaluate potential risks and issue early warnings based on the slope stability analysis results output by the model, timely identify slope stability problems, provide effective early warning and decision support, and prevent disasters;

[0053] Visualization and decision support module: used to display analysis results in the form of charts, 3D models and / or heat maps to assist decision makers in making judgments;

[0054] System Feedback and Optimization Module: This module provides feedback and optimizes the model based on system operation and analysis results, ensuring the long-term reliability and accuracy of the system and continuously optimizing analysis results and system performance over time.

[0055] Compared with the prior art, the slope stability analysis method and system provided by the present invention based on data analysis have at least the following beneficial effects:

[0056] (1) By comprehensively considering the complex soil characteristics of the slope, such as heterogeneity and anisotropy, as well as the dynamic changes of external environmental factors such as precipitation, meteorology, and earthquakes, the system can provide more realistic and accurate slope stability analysis results. By using intelligent algorithms and data analysis techniques, it reduces the reliance on simplified physical models and static assumptions, thereby improving the scientificity and practicality of the analysis results.

[0057] (2) Through real-time monitoring and dynamic analysis, it can respond to changes in the external environment at any time and timely evaluate the stability of the slope. At the same time, the system's early warning mechanism can issue an alarm in time when the slope stability reaches a critical value, providing real-time risk information to engineering personnel and avoiding possible catastrophic consequences.

[0058] (3) Through automated data collection, preprocessing, and analysis, a large amount of manual intervention is reduced, and the analysis efficiency and accuracy are improved. The system is able to self-learn and optimize, and automatically update the training model based on the continuously accumulated monitoring data, ensuring that the system can cope with different geological conditions and environmental changes, and improving the intelligence level of slope stability analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a flow chart of the method of the present invention;

[0060] Figure 2 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0062] The present invention will be further described below with reference to the embodiments.

[0063] See also Figure 1-2 The present invention provides a slope stability analysis method and system based on data analysis, comprising the following steps:

[0064] S1. Data acquisition and preprocessing: Real-time data on slope deformation, stress, groundwater level, precipitation, temperature, air pressure, and seismic activity are collected at the slope monitoring site. The collected data are denoised, standardized, and interpolated to eliminate sensor errors and data loss. At the same time, the real-time data is time-synchronized.

[0065] S2. Dynamic environmental factor analysis: By acquiring real-time data on slope deformation, stress, groundwater level, precipitation, temperature, air pressure, and seismic activity, key factors that affect slope stability are identified. Using regression analysis or neural networks, a quantitative relationship model between key factors and slope stability is established to identify the impact of changes in key factors on slope stability.

[0066] S3. Slope stability analysis: Based on the geological survey data of the slope, a physical model of the slope is established. Taking into account the heterogeneity and anisotropy of the soil, combined with the changes in key factors, an intelligent algorithm model is applied to conduct real-time slope stability analysis. By training the algorithm model, the slope stability prediction results are dynamically adjusted to reflect the changing trend of slope stability with changes in the external environment in real time.

[0067] S4. Establish an intelligent decision-making and early warning system: Based on real-time monitoring data and slope stability prediction results, establish an automated early warning mechanism. By analyzing the trend of slope stability changes, combining historical data and environmental factors, provide intelligent decision support;

[0068] S5. System integration and user interaction: By integrating slope monitoring, data collection, data analysis, early warning, and decision support, a slope stability analysis platform is formed. This platform provides a user interaction interface and enables engineering personnel to view slope stability analysis results, risk assessment reports, and early warning information in real time.

[0069] S6. System optimization and continuous learning: The system continuously updates training data and models based on changes in the external environment and slope stability performance, improving its adaptability to different geological conditions and environmental factors.

[0070] Further as Figure 1 As shown, it is worth noting that the data collection and preprocessing steps include

[0071] Multidimensional data processing model:

[0072] D proccssed =Standardize(Interp(D raw ))-Denoise(D raw )

[0073] D proccssed is the processed data;

[0074] D raw It is the raw data collected from the sensor;

[0075] Interp(·) represents the interpolation function, which is used to fill in missing data;

[0076] Standardize(·) represents the standardization function, which makes the data satisfy the distribution with mean 0 and variance 1;

[0077] Denoise(·) is a denoising function used to eliminate sensor noise. It ensures that the denoised, normalized, and interpolated data provide clear and accurate information for subsequent analysis. The processed data eliminates anomalies and noise, enabling the model to more accurately reflect changes in slope stability. Interpolation and normalization can avoid the negative impact of data gaps and inconsistencies on model training, ensuring the model's stability and flexibility in changing environments.

[0078] Further as Figure 1 As shown, it is worth noting that the dynamic environmental factor analysis steps include

[0079] Relationship model between external environmental factors and slope stability:

[0080] S stability (t) = f(X CNV (t),θ)

[0081] Where Sstability (t) is the change of slope stability with time;

[0082] X CNV (t) is the value of the critical factor at time t;

[0083] θ is the model parameter to be learned, obtained through regression analysis or neural network training,

[0084] f(·) is a mathematical mapping function between dynamic environmental factors and slope stability. It converts environmental data into stability indicators, supporting real-time analysis and early warning. Using dynamic time series data combined with machine learning methods to capture the impact of environmental factors on slope stability, we can clearly capture the temporal trends of environmental factors (such as precipitation, temperature, air pressure, and seismic activity). Machine learning models can identify the patterns of these factors' impacts on slope stability at different time points and establish precise quantitative relationships, making predictions more realistic. Dynamic time series data can handle the interactions of multiple environmental factors at the same time, and machine learning models can identify complex correlations through learning multidimensional features.

[0085] Further as Figure 1 As shown, it is worth noting that in the slope stability analysis step, the slope stability S stability (t1) is:

[0086] S stability (t1)=Alg AI (M geology, X cnv (t))

[0087] Where M geology It is a physical model of the slope, including geological survey data;

[0088] X cnv (t) is the time series data of key factors;

[0089] Alg AI (·) is an intelligent algorithm model that is trained based on historical data and provides dynamic predictions. It combines the complex characteristics of soil with dynamic environmental data and uses AI algorithms to conduct real-time slope stability assessments. Slope soils often have complex characteristics such as heterogeneity and anisotropy, which are difficult to fully reflect with traditional physical models. However, by continuously learning from historical data, AI algorithms can effectively capture these complex characteristics and combine them with dynamic changes in environmental factors to achieve a more comprehensive stability analysis.

[0090] Further as Figure 1 As shown, it is worth mentioning that in the steps of establishing an intelligent decision-making and early warning system, the automated early warning model is:

[0091] P alert (t)=H(Alg AI (S stability (t), X CNV (t)))

[0092] Where, P alert (t) is the early warning signal, which is triggered if the slope stability is lower than the critical value;

[0093] H(·) is a heuristic function or threshold used to determine whether to issue an early warning. An intelligent algorithm analyzes slope stability in real time, combining historical data with environmental trends to automatically provide early warnings. By monitoring the slope's status in real time, it can rapidly respond to external environmental changes (such as precipitation and seismic activity), providing instant slope stability analysis and early warnings. Compared to traditional manual or periodic inspections, this method can detect potential hazards earlier and provide more efficient early warnings.

[0094] Further as Figure 1 As shown, it is worth noting that the system integration and user interaction steps include a real-time feedback model:

[0095] UI feedback =G(D monitor , S stability (t), P alert (t))

[0096] In the formula, UI feedback It is the information fed back by the user interface, showing slope stability, risk assessment reports and early warning information;

[0097] D monitor It is sensor data collected in real time; it integrates slope monitoring, analysis and decision support functions to provide users with real-time feedback and simplify the decision-making process. By displaying slope stability, risk assessment reports and early warning information in real time, engineering personnel can quickly understand the current status and potential risks of the slope, and thus respond quickly, avoiding delays and decision-making errors caused by information lags. The real-time feedback model enables all monitoring data, analysis results and early warning information to be displayed intuitively, enhancing the transparency of the system. Users can clearly see the relationship between monitoring data and analysis results, which helps to make more reasonable judgments on slope stability.

[0098] Further as Figure 1 As shown, it is worth noting that in the system optimization and continuous learning steps, the updated model is:

[0099] θ new =θ old +Δθ

[0100] Where θ new are the optimized model parameters;

[0101] θ old are the current model parameters;

[0102] Δθ is a parameter adjustment based on new real-time data updates; continuous learning and optimization enable the system to respond to changes in different geological conditions and dynamic environmental factors. Continuous optimization and learning enable the system to adjust as geological conditions change. For example, geological characteristics such as soil type, rock structure, and groundwater level will affect the stability of the slope. Through continuous learning, the system can automatically adjust the analysis model based on new geological exploration data to provide more accurate predictions. Over time, the system can accumulate more historical data and environmental change information. Through continuous optimization and updating, long-term reliability and efficiency are ensured. Continuous learning enables the system to better adapt to possible environmental changes in the future and ensure its long-term effective operation.

[0103] Further as Figure 2 As shown, it is worth mentioning that the slope stability analysis method based on data analysis mentioned above includes:

[0104] Data acquisition module: responsible for collecting various slope data from multiple sources in real time, ensuring the real-time, comprehensive and high accuracy of the data, and providing data support for subsequent analysis.

[0105] Data preprocessing module: cleans, processes and integrates the collected data to ensure data quality and consistency, provides clean, structured and consistent data for the analysis model, and avoids deviations in analysis results due to data problems.

[0106] Feature extraction and selection module: Extracts key features from raw data to help improve the efficiency and accuracy of model analysis. By extracting and selecting important features, it reduces data dimensions and improves the analysis efficiency of the model.

[0107] Slope stability analysis model module: Build and train analysis models to evaluate slope stability. Use data-driven models to evaluate slope stability and accurately predict slope change trends and potential risks under different conditions.

[0108] Real-time monitoring and dynamic update module: monitors slope changes in real time and dynamically adjusts stability assessments based on new monitoring data to ensure the timeliness of slope stability analysis results and can respond to changes in the external environment and dynamic monitoring data in real time.

[0109] Risk assessment and early warning module: Based on the slope stability analysis results output by the model, potential risks are assessed and early warnings are issued to promptly identify slope stability issues, provide effective early warning and decision support, and prevent disasters.

[0110] Visual display and decision support module: The analysis results are displayed in the form of charts, three-dimensional models, heat maps, etc. to assist decision makers in making judgments. Through intuitive display and real-time feedback, decision makers are helped to take timely response measures and improve emergency response efficiency.

[0111] System Feedback and Optimization Module: Provides feedback and optimizes the model based on system operation and analysis results to ensure the long-term reliability and accuracy of the system, and continuously optimizes analysis results and system performance over time.

[0112] This program has the following working process:

[0113] Step 1: Using various sensors such as displacement sensors, stress and strain sensors, and groundwater level sensors, various data such as slope deformation, stress, groundwater level, precipitation, temperature, air pressure, and seismic activity are collected in real time at the slope monitoring site. The collected data is then de-noised, standardized, and interpolated to eliminate sensor errors and data gaps. The real-time data is also synchronized.

[0114] Step 2: By acquiring real-time external environmental data such as precipitation, meteorological changes, and seismic activity, we identify key factors that affect slope stability. We then use regression analysis, neural networks, and other methods to establish a quantitative relationship model between external environmental factors and slope stability, and identify the impact of changes in environmental factors on slope stability.

[0115] Step 3: Based on the geological survey data of the slope, a physical model of the slope is established. This takes into account complex characteristics such as soil heterogeneity and anisotropy. Combined with changes in dynamic environmental factors, an intelligent algorithm is applied to perform real-time slope stability analysis. By training the algorithm model, the slope stability prediction results are dynamically adjusted to reflect the changing trend of slope stability as the external environment changes in real time.

[0116] Step 4: Establish an automated early warning mechanism based on real-time monitoring data and slope stability analysis results. The system analyzes slope stability trends, combines historical data and environmental factors, and provides intelligent decision support.

[0117] Step 5: By integrating the slope monitoring system, data acquisition system, data analysis system, early warning system, and decision support system, a comprehensive and automated slope stability analysis platform is formed. The system provides a user-friendly interactive interface, allowing engineering personnel to view slope stability analysis results, risk assessment reports, and early warning information in real time.

[0118] Step 6: The system continuously updates training data and models based on changes in the external environment and slope stability performance, improving its adaptability to different geological conditions and environmental factors.

[0119] In summary: By comprehensively considering the complex soil characteristics of the slope, such as heterogeneity and anisotropy, as well as the dynamic changes of external environmental factors such as precipitation, meteorology, and earthquakes, the system can provide more realistic and accurate slope stability analysis results. The use of intelligent algorithms and data analysis techniques reduces the reliance on simplified physical models and static assumptions, thereby improving the scientific nature and practicality of the analysis results. Through real-time monitoring and dynamic analysis, it can respond to changes in the external environment at any time and timely evaluate the stability status of the slope. At the same time, the system's early warning mechanism can issue an alarm in time when the slope stability reaches a critical value, providing real-time risk information to engineering personnel to avoid possible catastrophic consequences. Through automated data collection, preprocessing and analysis, a large amount of manual intervention is reduced, and the analysis efficiency and accuracy are improved. The system can self-learn and optimize, and automatically update the training model based on the continuously accumulated monitoring data to ensure that the system can cope with different geological conditions and environmental changes, and improve the intelligence level of slope stability analysis.

[0120] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A slope stability analysis method based on data analysis, characterized by: The following steps are involved: S1. Data acquisition and preprocessing: Real-time data on slope deformation, stress, groundwater level, precipitation, temperature, air pressure, and seismic activity are collected at the slope monitoring site. The collected data are denoised, standardized, and interpolated to eliminate sensor errors and data loss. At the same time, the real-time data is time-synchronized. S2. Dynamic environmental factor analysis: By acquiring real-time data on slope deformation, stress, groundwater level, precipitation, temperature, air pressure, and seismic activity, key factors that affect slope stability are identified. Using regression analysis or neural networks, a quantitative relationship model between key factors and slope stability is established to identify the impact of changes in key factors on slope stability. S3. Slope stability analysis: Based on the geological survey data of the slope, a physical model of the slope is established. Taking into account the heterogeneity and anisotropy of the soil, combined with the changes in key factors, an intelligent algorithm model is applied to conduct real-time slope stability analysis. By training the algorithm model, the slope stability prediction results are dynamically adjusted to reflect the changing trend of slope stability with changes in the external environment in real time. S4. Establish an intelligent decision-making and early warning system: Based on real-time monitoring data and slope stability prediction results, establish an automated early warning mechanism. By analyzing the trend of slope stability changes, combining historical data and environmental factors, provide intelligent decision support; S5. System integration and user interaction: By integrating slope monitoring, data collection, data analysis, early warning, and decision support, a slope stability analysis platform is formed. This platform provides a user interaction interface and enables engineering personnel to view slope stability analysis results, risk assessment reports, and early warning information in real time. S6. System optimization and continuous learning: The system continuously updates training data and models based on changes in the external environment and slope stability performance, improving its adaptability to different geological conditions and environmental factors.

2. The slope stability analysis method based on data analysis according to claim 1, characterized in that: In the data collection and preprocessing steps, a multidimensional data processing model is used to process the collected data: D proccssed =Standardize(Interp(D raw ))-Denoise(D raw ) Where D proccssed is the processed data; D raw It is the raw data collected from the sensor; Interp(·) represents the interpolation function, which is used to fill in missing data; Standardize(·) represents the standardization function, which makes the data satisfy the distribution with mean 0 and variance 1; Denoise(·) is a denoising function used to eliminate sensor noise.

3. The slope stability analysis method based on data analysis according to claim 2, characterized in that: In the dynamic environmental factor analysis step, the quantitative relationship model between key factors and slope stability is: S stability (t)=f(X CNV (t),θ) Where S stability (t) is the change of slope stability with time; X CNV (t) is the value of the critical factor at time t; θ is the model parameter to be learned, obtained through regression analysis or neural network training, f(·) is a mathematical mapping function between dynamic environmental factors and slope stability, which converts environmental data into stability indicators to support real-time analysis and early warning.

4. The slope stability analysis method based on data analysis according to claim 3 is characterized in that: In the slope stability analysis step, the slope stability S stability (t1) is: S stability (t1)=Alg AI (M geology, X cnv (t)) Where M geology It is a physical model of the slope, including geological survey data; X cnv (t) is the time series data of key factors; Alg AI (·) is an intelligent algorithm model that is trained based on historical data and provides dynamic predictions.

5. The slope stability analysis method based on data analysis according to claim 4 is characterized in that: In the step of establishing an intelligent decision-making and early warning system, the automated early warning model is: P alert (t)=H(Alg AI (S stability (t),X CNV (t))) Where, P alert (t) is the early warning signal, which is triggered if the slope stability is lower than the critical value; H(·) is a heuristic function or threshold judgment, which is used to decide whether to issue an early warning.

6. The slope stability analysis method based on data analysis according to claim 5, characterized in that: The system integration and user interaction steps include a real-time feedback model: UI feedback =G(D monitor ,S stability (t),P alert (t)) In the formula, UI feedback It is the information fed back by the user interface, showing slope stability, risk assessment reports and early warning information; D monitor It is sensor data collected in real time.

7. The slope stability analysis method based on data analysis according to claim 6, characterized in that: In the system optimization and continuous learning steps, the updated model is: i new =θ old +Δθ Where θ new are the optimized model parameters; θ old are the current model parameters; Δθ is a parameter adjustment based on new real-time data updates.

8. A slope stability analysis system based on data analysis, for implementing the slope stability analysis method based on data analysis according to any one of claims 1 to 7, characterized in that: include: Data acquisition module: used to collect various slope data from multiple sources in real time to provide data support for subsequent analysis; Data preprocessing module: used to clean, process and integrate the collected data to ensure data quality and consistency, and provide clean, structured and consistent data for analysis models; Feature extraction and selection module: used to extract key features from raw data, reduce data dimensions and improve the analysis efficiency of the model by extracting and selecting important features; Slope stability analysis model module: used to build and train analysis models, evaluate slope stability, evaluate slope stability through data-driven models, and predict slope change trends and potential risks under different conditions; Real-time monitoring and dynamic update module: used to monitor slope changes in real time and dynamically adjust stability assessments based on new monitoring data; Risk assessment and early warning module: used to evaluate potential risks and issue early warnings based on the slope stability analysis results output by the model, timely identify slope stability problems, provide effective early warning and decision support, and prevent disasters; Visualization and decision support module: used to display analysis results in the form of charts, 3D models and / or heat maps to assist decision makers in making judgments; System Feedback and Optimization Module: This module provides feedback and optimizes the model based on system operation and analysis results, ensuring the long-term reliability and accuracy of the system and continuously optimizing analysis results and system performance over time.

Images