A real-time calculation method and system for slope stability

Abstract

The present invention relates to the field of slope stability calculation, and specifically to a method and system for real-time calculation of slope stability. The method includes: constructing a three-dimensional geological model of the slope and dividing it into three-dimensional blocks; based on the monitoring results of the three-dimensional blocks and the three-dimensional geological model, extracting the spatial characteristics and monitoring time series data of the three-dimensional blocks; constructing a three-dimensional block stability prediction model, and combining the spatial characteristics and the monitoring time series data for prediction to obtain the three-dimensional block stability prediction value; correcting the three-dimensional block stability prediction value based on seismic data and rainfall data to obtain a corrected stability prediction value; constructing a prediction value fusion model, and performing fusion calculation using the prediction value fusion model according to the three-dimensional block stability prediction value and the corrected stability prediction value to obtain the real-time prediction value of the slope stability. The present invention solves the problems of inaccurate and untimely slope stability calculation in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of slope stability calculation, and specifically to a method and system for real-time calculation of slope stability. Background Art

[0002] Slope stability is crucial for engineering safety, life and property safety, and the ecological environment. However, there are many deficiencies in existing slope stability calculation methods.

[0003] Traditional two-dimensional analysis methods only consider the forces from a planar perspective, ignoring the three-dimensional characteristics of slopes. The actual geological structure of slopes is complex, including multiple layers of rock and soil masses, faults, and joints, etc. The deviation of two-dimensional calculation results is large and it is difficult to meet the engineering requirements.

[0004] Although numerical simulation methods can simulate complex geological conditions, they consume a large amount of computing resources and take a long time, and are not suitable for large-scale slopes or real-time assessment scenarios. Moreover, it is difficult to obtain the parameters on which it depends, and the uncertainty of the parameters affects the accuracy of the results.

[0005] In addition, it is difficult for existing methods to comprehensively and accurately consider the influence of external environmental factors. Earthquakes and rainfall will change the mechanical properties and stress states of slopes. Rainfall reduces the shear strength of rock and soil masses, and the inertial forces generated by earthquakes increase additional loads. The combined action of the two may cause a significant decline in slope stability, while traditional methods cannot accurately reflect these changes in real time.

[0006] With the development of engineering construction, the requirements for the accuracy, real-time performance, and comprehensiveness of slope stability assessment are getting higher and higher. Therefore, there is an urgent need for a calculation method for engineering construction under complex geological conditions that can comprehensively consider the three-dimensional geological structure of slopes, real-time monitoring data, and the dynamic influence of the external environment, realize accurate and dynamic monitoring and assessment, provide a reliable basis for engineering decision-making, and prevent slope instability disasters. Summary of the Invention

[0007] Aiming at the defects in the prior art, the present invention provides a method and system for real-time calculation of slope stability, which solves the problem in the prior art that the stable calculation of slopes is inaccurate and untimely due to insufficient consideration, so that the slope stability is in real-time monitoring, and geological disasters of slopes can be significantly reduced.

[0008] To achieve the above object, an aspect of the present invention provides a real-time calculation method for slope stability. The method includes: constructing a three-dimensional geological model of the slope, and dividing the three-dimensional geological model by using the three-dimensional slice method to obtain a plurality of three-dimensional slices; monitoring the three-dimensional slices, and based on the monitoring results and the three-dimensional geological model, extracting the spatial characteristics and monitoring time series data of the three-dimensional slices; constructing a three-dimensional slice stability prediction model, and predicting by using the three-dimensional slice stability prediction model according to the spatial characteristics and the monitoring time series data to obtain a three-dimensional slice stability prediction value; correcting the three-dimensional slice stability prediction value based on the real-time acquired seismic data and rainfall data to obtain a corrected stability prediction value; constructing a prediction value fusion model, and performing fusion calculation by using the prediction value fusion model according to the three-dimensional slice stability prediction value and the corrected stability prediction value to obtain a real-time prediction value of the slope stability.

[0009] By constructing a three-dimensional geological model and performing three-dimensional slicing, the present invention can more carefully and comprehensively reflect the geological structure characteristics of the slope, and is more conducive to subsequent calculation and analysis after modularization. Monitoring the three-dimensional slices and extracting spatial characteristics and time series data provide a rich and accurate data basis for subsequent stability prediction. The three-dimensional slice stability prediction model constructed by using these data can make scientific predictions based on historical and real-time data, improving the reliability of the prediction. At the same time, correcting the prediction value based on seismic and rainfall data fully considers the influence of external environmental factors on slope stability, further improving the accuracy of the prediction. By constructing a prediction value fusion model, the limitations of a single prediction model and correction model are avoided, and the prediction accuracy is improved again. Overall, dynamic and accurate monitoring and evaluation of slope stability are realized, providing strong support for slope safety management and decision-making, helping to detect potential risks in advance and reduce disaster losses.

[0010] Optionally, constructing the three-dimensional geological model of the slope includes: obtaining slope data of the slope, and decoupling the slope data into surface morphology data and internal structure data; constructing a digital elevation model by using the surface morphology data, and constructing a geological structural plane model by using the internal structure data; performing spatial coordinate alignment and geometric entity coupling on the digital elevation model and the geological structural plane model to form the three-dimensional geological model of the slope including external geometric characteristics and internal mechanical interfaces.

[0011] The present invention decouples slope data into surface morphology data and internal structure data, which can deeply analyze the external morphology and internal structure of the slope respectively, making data processing more targeted. By using the surface morphology data to construct a digital elevation model and the internal structure data to construct a geological structural plane model, the external and internal characteristics of the slope are clearly presented. Through spatial coordinate alignment and geometric entity coupling of the two models, a three-dimensional geological model containing external geometric features and internal mechanical interfaces is generated, comprehensively and accurately reflecting the actual situation of the slope and improving the accuracy of three-dimensional geological model construction.

[0012] Optionally, the construction of the three-dimensional block stability prediction model includes: obtaining the historical monitoring data of the three-dimensional block, and obtaining the historical spatial characteristics and historical monitoring time series data according to the historical monitoring data and the three-dimensional geological model; calculating the stability of the three-dimensional block according to the historical spatial characteristics and the historical monitoring time series data to obtain the historical stability; using the historical spatial characteristics, the historical monitoring time series data and the historical stability to construct sample data; constructing a framework model based on the spatio-temporal graph convolutional network, and training the framework model with the sample data to obtain a three-dimensional block stability prediction model.

[0013] In the present invention, by obtaining the historical monitoring data of the three-dimensional block and combining the three-dimensional geological model to obtain the historical spatial characteristics and the monitoring time series data, the historical information related to the block stability is comprehensively and deeply mined, providing rich data support for subsequent analysis. Calculating the historical stability based on these data provides a reliable reference standard for model training. Using the historical spatial characteristics, time series data and historical stability to construct sample data covers key information in multiple dimensions, improving the quality and representativeness of the samples. Constructing a framework model based on the spatio-temporal graph convolutional network can effectively capture the spatio-temporal relationships in the data. Training the framework model with the sample data enables the model to learn the internal laws between the block stability and related factors, thereby establishing an accurate three-dimensional block stability prediction model and improving the prediction effect of the three-dimensional block stability prediction model.

[0014] Optionally, the historical stability satisfies the following formula:

[0015] ,

[0016] where, is the historical stability of the th three-dimensional block, is the self-weight of the th three-dimensional block, The historical dip angle of the structural plane of the th three-dimensional block, is the historical pore water pressure of the th three-dimensional block, is the historical sliding surface area of the th 3D block, is the historical internal friction angle of the th 3D block, is the cohesion of the th 3D block.

[0017] The historical stability formula of the 3D block of the present invention comprehensively considers key factors such as the self - weight of the 3D block, the dip angle of the structural plane, the pore water pressure, the sliding surface area, the internal friction angle, and the cohesion, improving the accuracy of the calculation of the historical stability of the 3D block.

[0018] Optionally, the step of correcting the predicted stability value of the 3D block based on the real - time acquired seismic data and rainfall data to obtain the corrected predicted stability value includes: calculating the influence of the earthquake on the vibration of the 3D block in the horizontal direction according to the seismic data to obtain the horizontal seismic influence coefficient; calculating the vibration intensity of the earthquake on the 3D block according to the horizontal seismic influence coefficient to obtain the seismic inertia force; adjusting the cohesion of the 3D block according to the rainfall data to obtain the cohesion of the 3D block after rainfall weakening; and correcting the predicted stability value of the 3D block by using the seismic inertia force and the cohesion of the 3D block after rainfall weakening to obtain the corrected predicted stability value.

[0019] By calculating the influence of the earthquake on the vibration of the 3D block in the horizontal direction, the present invention obtains the horizontal seismic influence coefficient and further calculates the seismic inertia force, which can accurately quantify the interference of the seismic action on the slope stability and fully consider the dynamic load factor of the earthquake. Adjusting the cohesion of the 3D block according to the rainfall data takes into account that rainfall will weaken the mechanical properties of the soil, which is in line with the situation that rainfall reduces the slope stability in actual engineering. Using the seismic inertia force and the cohesion of the 3D block after rainfall weakening to correct the predicted value comprehensively considers two key disaster - causing factors in the external environment, making the corrected predicted stability value more in line with the true state of the slope under complex natural conditions, and improving the accuracy, reliability, and generalization ability of the slope stability assessment.

[0020] Optionally, the step of correcting the predicted stability value of the 3D block by using the seismic inertia force and the cohesion of the 3D block after rainfall weakening to obtain the corrected predicted stability value includes: correcting the sliding force of the 3D block by using the seismic inertia force to obtain the corrected sliding force; correcting the anti - sliding force of the 3D block by using the seismic inertia force and the cohesion of the 3D block after rainfall weakening to obtain the corrected anti - sliding force; and correcting the predicted stability value of the 3D block according to the corrected sliding force and the corrected anti - sliding force to obtain the corrected predicted stability value.

[0021] The present invention corrects the sliding force through seismic inertial force, fully considering the influence of the additional dynamic load on the slope blocks under seismic action, making the calculation of the sliding force more in line with the actual seismic conditions. The anti-sliding force is corrected by using the seismic inertial force and the cohesion weakened by rainfall, taking into account the dual weakening effects of earthquake and rainfall on the anti-sliding ability of the slope, and can more accurately reflect the true ability of the slope to resist sliding in a complex environment. The adverse effects of the two key factors of earthquake and rainfall on the slope stability are comprehensively considered, effectively improving the accuracy of the stability prediction value.

[0022] Optionally, the corrected stability prediction value satisfies the following formula:

[0023] ,

[0024] ,

[0025] ,

[0026] Wherein, is the corrected stability prediction value of the th three-dimensional block, is the corrected sliding force of the th three-dimensional block, is the corrected anti-sliding force of the th three-dimensional block, is the self-weight of the th three-dimensional block, is the dip angle of the structural plane of the th three-dimensional block, is the seismic inertial force, is the internal friction angle of the th three-dimensional block, is the cohesion of the th three-dimensional block weakened by rainfall, is the sliding surface area of the th three-dimensional block.

[0027] Through rigorous mechanical principles, the present invention comprehensively considers key elements such as the self-weight of the three-dimensional block, the dip angle of the structural plane, the seismic inertial force, the internal friction angle, the cohesion, and the sliding surface area, constructs a scientific and complete quantitative system for slope stability, and at the same time fully considers the dynamic influence of external adverse factors, greatly improving the accuracy of the calculation of the corrected stability prediction value.

[0028] Optionally, the construction of the predicted value fusion model includes: performing random perturbations within a predetermined range on the internal friction angle, cohesion, and rock and soil unit weight of the three-dimensional block, and calculating the stability of the three-dimensional block according to the results of the random perturbations; calculating the standard deviation of the stability of the three-dimensional block to obtain the standard deviation of the parameter perturbation error; obtaining multiple measured slope data, and using the three-dimensional block stability prediction model to perform predictions according to the measured slope data to obtain the prediction results of the three-dimensional block; calculating the standard deviation of the prediction results of the three-dimensional block to obtain the standard deviation of the prediction error; and constructing a predicted value fusion model according to the standard deviation of the parameter perturbation error and the standard deviation of the prediction error.

[0029] In the present invention, random perturbations are performed on key parameters such as the internal friction angle, cohesion, and rock and soil unit weight of the three-dimensional block, and the stability is calculated, which can simulate the uncertainty of the parameters. By calculating the standard deviation, the standard deviation of the parameter perturbation error is obtained, which quantifies the error caused by parameter fluctuations. Using the measured slope data in combination with the three-dimensional block stability prediction model to perform predictions and calculating the standard deviation of the prediction results, that is, the standard deviation of the prediction error, clarifies the error existing in the model prediction itself. Based on these two error standard deviations, a predicted value fusion model is constructed, which fully considers the two factors of parameter uncertainty and model prediction error, and improves the calculation accuracy of the predicted value fusion model.

[0030] Optionally, the predicted value fusion model satisfies the following formula:

[0031] ,

[0032] where, is the th final predicted value, is the standard deviation of the parameter perturbation error, is the th predicted value of the stability prediction model of the three-dimensional block, is the standard deviation of the prediction error, is the th corrected stability prediction value of the three-dimensional block.

[0033] The present invention comprehensively considers the standard deviation of the parameter perturbation error and the standard deviation of the prediction error, and can effectively fuse the predicted value of the stability prediction model and the corrected stability prediction value. This process fully weighs the parameter uncertainty and the model prediction error, making the finally obtained final predicted value more accurate and reliable, greatly improving the accuracy of the slope stability prediction, and providing strong data support for the risk prevention and control and decision-making of slope engineering.

[0034] Another aspect of the present invention further provides a real-time calculation system for slope stability. The system includes: a processor, an input device, an output device, and a memory. The processor, the input device, the output device, and the memory are interconnected. Among them, the memory is used to store a computer program, and the computer program includes program instructions. The processor is configured to call the program instructions to execute any one of the real-time calculation methods for slope stability described in the previous aspect of the present invention.

[0035] The real-time calculation system for slope stability of the present invention has a compact structure, stable performance, high integration, and simple composition. It can stably execute the real-time calculation method for slope stability provided in the previous aspect of the present invention, further improving the overall applicability and practical application ability of the present invention. Brief Description of the Drawings

[0036] Figure 1 It is a flowchart of a real-time calculation method for slope stability according to an embodiment of the present invention;

[0037] Figure 2 It is a schematic structural diagram of a real-time calculation system for slope stability according to an embodiment of the present invention. Detailed Description of the Embodiments

[0038] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described here are only for illustrative purposes and are not used to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that the present invention does not have to be practiced with these specific details. In other instances, well-known circuits, software, or methods have not been specifically described in order to avoid obscuring the present invention.

[0039] Throughout the specification, the reference to "an embodiment", "embodiments", "an example", or "examples" means that the specific features, structures, or characteristics described in connection with the embodiment or example are included in at least one embodiment of the present invention. Thus, the phrases "in an embodiment", "in embodiments", "an example", or "examples" appearing throughout the specification do not necessarily refer to the same embodiment or example. In addition, the specific features, structures, or characteristics may be combined in any suitable combination and / or sub-combination in one or more embodiments or examples. Moreover, those of ordinary skill in the art should understand that the drawings provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0040] In an alternative embodiment, to solve the problem of inaccurate and untimely slope calculation in the prior art, please refer to Figure 1 , Figure 1 The method shown includes the following steps:

[0041] Step S1, construct a three-dimensional geological model of the slope, and divide the three-dimensional geological model by using the three-dimensional slice method to obtain a plurality of three-dimensional slices.

[0042] Among them, the construction of the three-dimensional geological model of the slope specifically includes the following sub-steps:

[0043] Step S101, obtain the slope data of the slope, and decouple the slope data into surface morphology data and internal structure data.

[0044] In this embodiment, an unmanned aerial vehicle (UAV) equipped with a high-resolution camera is used for aerial survey. The UAV is controlled to fly over the slope along a predetermined route, and the camera takes pictures at a set frequency. Through the multi-view image matching technology, a large number of taken pictures are processed to construct a topographic contour model of the whole slope, accurately restoring the undulation of the slope surface. At the same time, with the help of terrestrial lidar scanning, in a non-contact measurement method, a laser beam is emitted to the slope surface and the reflected signal is received, quickly and accurately measuring the distance and angle information of each point on the slope surface. Through filtering, classification and other processing of the massive point cloud data, the details of the surface morphology are further improved, and high-precision three-dimensional data of the slope surface are obtained.

[0045] For the acquisition of internal structure data, geological exploration means are mainly relied on. Through geological drilling, core samples are drilled and taken out at different positions of the slope. In the laboratory, professional equipment such as polarizing microscopes and X-ray diffractometers are used to analyze the core samples to determine the lithology, stratigraphic layering, and to study in detail the development degree, occurrence and other characteristics of joint fissures. In addition, the seismic wave method is used. By artificially exciting seismic waves and receiving the reflected and refracted signals at different positions, the structure of the underground geological body is inverted based on information such as wave velocity and waveform. The electromagnetic method is used. Based on the electromagnetic property differences of underground geological bodies, the distribution and properties of underground geological bodies are detected, providing rich data support for comprehensively understanding the internal structure of the slope.

[0046] According to the slope characteristic attributes reflected by the data, the data with similar properties are classified. The information directly reflecting the external physical form of the slope, such as the overall shape of the slope, the inclination of the slope surface, the elevation of the slope top and bottom, etc. are classified as surface morphology data. These data can intuitively present the external contour and topographic features of the slope, and are an important basis for constructing the elevation digital model of the slope. And the data related to the internal material composition, geological structure, etc. of the slope, such as the specific type of rock and soil mass, the occurrence of geological structural planes, and the physical and mechanical parameters of the rock and soil mass, are classified as internal structure data. These data are the key basis for constructing the geological structural plane model, and are helpful for in-depth analysis of the mechanical properties and stability of the slope interior.

[0047] Step S102: Construct a digital elevation model using the surface morphology data, and construct a geological structural plane model using the internal structure data.

[0048] In this embodiment, when constructing a digital elevation model using the surface morphology data, first, preprocess the obtained surface morphology data, such as slope undulation, slope gradient, elevations of the slope top and bottom, etc., to remove the noise and outliers therein. Then, use the interpolation algorithm in geographic information system (GIS) software to estimate the elevation values of unknown points based on the discrete topographic survey point data, and convert these discrete data into continuous elevation data. By setting appropriate interpolation parameters, generate a high-precision digital elevation model (DEM), which can visually and accurately present the external topographic undulation of the slope.

[0049] For constructing a geological structural plane model using the internal structure data, first deeply analyze the internal structure data to clarify the types of rock and soil masses, the occurrence and distribution laws of geological structural planes. Then, use professional 3D modeling software, such as 3DMine or GOCAD, etc., and accurately draw each geological structural plane according to the parameter information of the geological structural planes. During the modeling process, fully consider the spatial relationships and interactions between different geological structural planes, and through operations such as Boolean operations, combine each geological structural plane into a complete geological structural plane model, which can clearly display the geological structural characteristics inside the slope and provide important basic data for subsequent stability analysis.

[0050] Step S103: Align the spatial coordinates and couple the geometric entities of the digital elevation model and the geological structural plane model to form a 3D geological model of the slope that includes external geometric features and internal mechanical interfaces.

[0051] In this embodiment, since the data sources and acquisition methods of these two models are different, their original spatial coordinate systems are not unified. First, by checking the current coordinate systems of the two models, use the "Projection and Transformation" tool in the "Data Management Tools" of ArcGIS software to unify the coordinate systems of the digital elevation model and the geological structural plane model. The coordinate system of the geological structural plane model is CGCS2000. The "Project Raster" tool can be used to convert the digital elevation model, and the "Project" tool can be used to convert the geological structural plane model, and convert them all to the same target coordinate system.

[0052] After completing the coordinate alignment, geometric entity coupling is performed. The geological structural plane model is imported into ArcScene, and using the "Align Shape with Terrain" tool, select the appropriate alignment function, such as "Project All", to project each structural plane in the geological structural plane model onto the terrain represented by the digital elevation model, so that the geological structural plane and the digital elevation model are combined with each other to form a complete three-dimensional geological model that includes the external geometric features and internal mechanical interfaces of the slope.

[0053] In this embodiment, when using the three-dimensional slice method to divide the constructed three-dimensional geological model, first, according to the geological conditions and mechanical characteristics of the slope, determine a reasonable slicing idea. Refer to factors such as the distribution of rock and soil layers in the slope, the strike and dip of geological structural planes (such as faults, joints, fissures, etc.), and the potential sliding direction, etc., to plan the slicing direction and approximate spacing.

[0054] Use the professional geotechnical engineering analysis software FLAC3D to perform the specific division operation. After importing the three-dimensional geological model into the software, use its built-in mesh generation or slicing function module to cut the model along the set direction according to the pre-determined slicing idea. During the cutting process, the software will automatically generate a series of three-dimensional slices in the shape of cuboids.

[0055] During the division process, it is necessary to ensure that the boundaries of each three-dimensional slice coincide with or are parallel to the geological structural plane as much as possible to accurately reflect the mechanical properties inside the slope. At the same time, control the size and quantity of the slices to avoid insufficient analysis accuracy due to overly large slices or a sharp increase in the calculation amount due to overly small slices. After the division is completed, number and assign attributes to the obtained multiple three-dimensional slices, record information such as the spatial position, geometric dimensions, and lithology parameters of each slice, and prepare for the subsequent independent mechanical analysis and stability calculation of each three-dimensional slice, so as to more accurately evaluate the overall stability of the slope.

[0056] Step S2, monitor the three-dimensional slices, and based on the results of the monitoring and the three-dimensional geological model, extract the spatial characteristics and monitoring time series data of the three-dimensional slices.

[0057] In this embodiment, the result of the monitoring is the monitoring data. The results of the monitoring include pore water pressure data, displacement data, and water content data. Due to the need for time series, dedicated sensors need to be installed near the slip surface, at the interface of rock and soil layers, or on the groundwater seepage path on the corresponding slope of the three-dimensional slice.

[0058] Install vibrating wire pore water pressure sensors. The sensor probe needs to be in close contact with the rock and soil mass inside the slice, connected to an automated data acquisition instrument through a data cable, and the pressure value is collected in real time at a frequency of once every 10 minutes, and the ambient temperature is synchronously recorded for data correction to obtain the pore water pressure data.

[0059] High-precision GNSS receivers are arranged on the surface of the strips and on both sides of the internal structural surface to monitor the displacement of the edge skin corresponding to the three-dimensional strips in real time in three-dimensional space, assisted by a total station for periodic calibration, focusing on monitoring the relative displacement between the strips to obtain displacement data.

[0060] TDR time domain reflectometer sensors are used and buried in layers at intervals of 0.5m along the depth direction of the strips. The moisture content is converted by measuring the soil dielectric constant. The data is collected every 30 minutes to avoid interference from instantaneous factors such as rainfall, and the moisture content data is obtained.

[0061] After the monitoring data is collected, it is necessary to pre-process the monitoring data. First, the 3σ rule is used to identify and eliminate the sudden noise of the monitoring data, such as the instantaneous fault data of the sensor. Then, based on the coordinate system of the three-dimensional geological model, the GNSS displacement data is converted into the relative displacement in the local coordinate system of the strip, eliminating the influence of the rigid body displacement of the overall slope and realizing time-space calibration. Finally, the pore water pressure, displacement, and water content data are aligned according to a unified timestamp accurate to seconds to form monitoring time series data indexed by the monitoring time.

[0062] The spatial features are extracted using the three-dimensional geological model. The spatial features include the surface inclination angle, sliding surface area, width, length and average thickness of the three-dimensional strips.

[0063] Each 3D strip is accurately extracted using the spatial analysis and measurement tools of ArcGIS, a professional geological modeling software. The built-in geological structural surface attribute query function of the model is used to directly obtain the structural surface inclination of each 3D strip, and the surface area calculation tool is used to calculate the area of ​​each 3D strip sliding surface. With the help of the 3D geometric measurement function, the width, length and average thickness of each 3D strip are measured. It should be noted that the strip number must be strictly compared during extraction to ensure that each spatial feature parameter is accurately associated with the corresponding 3D strip, and data verification is performed to avoid errors or mismatches.

[0064] Step S3, constructing a three-dimensional bar stability prediction model, and using the three-dimensional bar stability prediction model to perform predictions based on the spatial characteristics and the monitoring time series data to obtain a three-dimensional bar stability prediction value.

[0065] The construction of the three-dimensional bar stability prediction model specifically includes the following sub-steps:

[0066] Step S301, obtaining historical monitoring data of the three-dimensional strip, and obtaining historical spatial characteristics and historical monitoring time series data based on the historical monitoring data and the three-dimensional geological model.

[0067] In this embodiment, the method for obtaining the historical monitoring data of the three-dimensional strip and obtaining the historical spatial characteristics and historical monitoring time series data according to the historical monitoring data and the three-dimensional geological model is the same as the method for obtaining the monitoring data and obtaining the spatial characteristics and monitoring time series data of the three-dimensional strip described above, and will not be elaborated here.

[0068] Step S302: Calculate the stability of the three-dimensional strip according to the historical spatial characteristics and the historical monitoring time series data to obtain the historical stability.

[0069] The historical stability satisfies the following formula:

[0070] ,

[0071] where, is the historical stability of the th three-dimensional block, is the self-weight of the th three-dimensional block, the historical dip angle of the structural plane of the th three-dimensional strip, is the historical pore water pressure of the th three-dimensional strip, is the historical slip surface area of the th three-dimensional strip, is the historical internal friction angle of the th three-dimensional strip, is the cohesion of the th three-dimensional strip.

[0072] Step S303: Construct sample data by using the historical spatial characteristics, the historical monitoring time series data and the historical stability.

[0073] In this embodiment, the calculated historical stability value is accurately associated and matched with the spatial characteristics and monitoring data at the corresponding time. Taking the time point as the sample unit, the spatial characteristics, monitoring time series data and historical stability value of the three-dimensional strip at each moment are integrated to construct a standardized structured sample data (such as a two-dimensional table), ensuring that each data item in each row of the sample accurately corresponds to the same moment of the same three-dimensional strip, ensuring the integrity, accuracy and consistency of the data, providing a high-quality and unbiased data set for subsequent model training or analysis, and effectively supporting the construction and verification of the stability prediction model.

[0074] Step S304: Build a framework model based on the spatio-temporal graph convolutional network and train the framework model by using the sample data to obtain a three-dimensional strip stability prediction model.

[0075] In this embodiment, three-dimensional blocks are first abstracted as nodes in a graph structure, and an adjacency matrix of an undirected graph is constructed based on the spatial adjacency relationships between the blocks (such as adjacency, common structural planes, etc.). The node features fuse historical spatial features (structural plane dip angle, sliding surface area, geometric dimensions, etc.) and historical monitoring time series data (temporal variations of pore water pressure, displacement, water content). A network architecture stacked with multiple multi-layer spatio-temporal convolution modules is adopted: in the spatial dimension, the stability associations between adjacent blocks are captured through graph convolution layers, and the spatial dependencies between nodes are learned using Laplacian matrix decomposition or attention mechanisms; in the time dimension, the dynamic evolution laws of single-node features over time are extracted through one-dimensional convolution layers or temporal gating mechanisms.

[0076] In the model training stage, the sample data is divided into a training set, a validation set, and a test set according to the time series. The input layer encodes the spatio-temporal features of each node into a multi-dimensional tensor, with the dimension being the number of nodes multiplied by the time step and then multiplied by the feature dimension. After being alternately processed by the spatio-temporal convolution layers, the stability prediction values are output through a fully connected layer. The mean squared error is used as the loss function, and the optimizer AdamW is selected to balance the learning rate and weight decay. An early stopping mechanism is introduced during the training process to avoid overfitting, and at the same time, hyperparameters such as the number of spatial convolution kernels and the length of the time window are tuned through grid search. Finally, through end-to-end training, the model can effectively fuse the spatial correlation and time dynamics of three-dimensional blocks to generate a high-precision stability prediction model.

[0077] When using the three-dimensional block stability prediction model for prediction, first preprocess the spatial features of the current three-dimensional block to be predicted and the real-time monitoring time series data. Standardize and scale the spatial features, and slice the monitoring time series data according to the length of the time window during model training to form a feature sequence containing multiple time steps.

[0078] Each three-dimensional block is abstracted as a node in the graph structure, and a real-time adjacency matrix is constructed based on the spatial adjacency relationships between the blocks. It is combined with the preprocessed node features (spatial features + monitoring time series features) to form a multi-dimensional input tensor. This tensor is input into the trained three-dimensional block stability prediction model. The three-dimensional block stability prediction model captures the stability associations between adjacent blocks through spatial graph convolution layers, extracts the dynamic evolution laws of single-block features using time convolution layers, and after multi-layer spatio-temporal feature fusion, the stability prediction values of each three-dimensional block are output by the fully connected layer. The prediction process strictly follows the input specifications during model training to ensure the spatio-temporal consistency of the spatial adjacency relationships and real-time monitoring data, and finally obtains a quantitative prediction value reflecting the stability of the three-dimensional blocks under the current working conditions.

[0079] Step S4, based on the earthquake data and rainfall data obtained in real time, correct the three-dimensional block stability prediction value to obtain a corrected stability prediction value.

[0080] Among them, correcting the three-dimensional block stability prediction value based on the real-time obtained seismic data and rainfall data to obtain the corrected stability prediction value specifically includes the following sub-steps:

[0081] Step S401: Calculate the influence of the earthquake on the vibration of the three-dimensional block in the horizontal direction according to the seismic data to obtain the horizontal seismic influence coefficient.

[0082] In this embodiment, the seismic data mainly refers to the peak ground acceleration of the earthquake, which can be obtained through the local seismic monitoring network. The peak ground acceleration of the earthquake is obtained based on the real-time monitoring and recording of seismic waves by seismic monitoring instruments and is usually updated and released at regular time intervals.

[0083] The horizontal seismic influence coefficient satisfies the following formula:

[0084] ,

[0085] Among them, is the horizontal seismic influence coefficient, is the peak ground acceleration of the earthquake, is the acceleration of gravity.

[0086] Step S402: Calculate the vibration intensity of the earthquake on the three-dimensional block according to the horizontal seismic influence coefficient to obtain the seismic inertia force.

[0087] The seismic inertia force satisfies the following formula:

[0088] ,

[0089] Among them, is the seismic inertia force, is the horizontal seismic influence coefficient, is the self-weight of the

[0090] The self-weight of the

[0091] ,

[0092] Among them, is the self-weight of the is the unit volume weight of the is the average thickness of the three-dimensional block, is the width of the three-dimensional block, is the length of the three-dimensional block.

[0093] In this embodiment, the seismic inertial force refers to the force exerted on a slope due to its own inertia when the ground moves under the action of seismic waves during an earthquake. When seismic waves propagate to the ground and cause ground vibrations, a stationary slope, due to its inertia, attempts to maintain its original state of motion, and the movement of the ground forces the three-dimensional blocks in the slope to change their state. The resulting force is the seismic inertial force, and its magnitude is related to the mass of the three-dimensional blocks and the seismic acceleration. The greater the mass and the seismic acceleration, the greater the seismic inertial force.

[0094] Step S403: Adjust the cohesion of the three-dimensional blocks according to the rainfall data to obtain the cohesion of the three-dimensional blocks after rainfall weakening.

[0095] The cohesion after rainfall weakening satisfies the following formula:

[0096] ,

[0097] where, is the cohesion of the th three-dimensional block after rainfall weakening, is the cohesion of the th three-dimensional block, is the 24-hour cumulative rainfall.

[0098] In this embodiment, the 24-hour cumulative rainfall can be obtained through the rainfall monitoring equipment of the local meteorological station. These devices will record the rainfall in real time and perform statistics at hourly or shorter time intervals to finally obtain the 24-hour cumulative rainfall value.

[0099] The cohesion after rainfall weakening means that there is originally a certain cohesion between the particles of the rock and soil mass. Cohesion can be understood as a force within the rock and soil mass that resists mutual separation. When rainfall occurs, water seeps into the rock and soil mass, which will weaken this cohesion.

[0100] Step S404: Use the seismic inertial force and the cohesion after rainfall weakening to correct the stability prediction value of the three-dimensional blocks to obtain a corrected stability prediction value.

[0101] Among them, using the seismic inertial force and the cohesion after rainfall weakening to correct the stability prediction value of the three-dimensional blocks to obtain a corrected stability prediction value specifically includes the following sub-steps:

[0102] Step S40401: Use the seismic inertial force to correct the sliding force of the three-dimensional blocks to obtain a corrected sliding force.

[0103] In this embodiment, under normal circumstances, the downward sliding force of the three-dimensional block is mainly determined by the component of its own gravity along the structural plane, which is based on the tendency of the block to slide along the structural plane under the action of gravity. However, when an earthquake occurs, the seismic inertial force will change the stress state of the block. The horizontal component of the seismic inertial force will be superimposed or offset partially with the component of gravity along the structural plane, having an additional impact on the downward sliding tendency of the block.

[0104] The corrected downward sliding force satisfies the following formula:

[0105] ,

[0106] Wherein, is the corrected downward sliding force, The self-weight of the th three-dimensional block, is the dip angle of the structural plane of the

[0107] Step S40402: Correct the anti-sliding force of the three-dimensional block by using the seismic inertial force and the cohesion weakened by rainfall to obtain the corrected anti-sliding force.

[0108] In this embodiment, the anti-sliding force of the three-dimensional block mainly comes from two aspects: one is the friction between rock and soil masses, and the other is the cohesion of rock and soil masses. Under normal circumstances, the calculation of the anti-sliding force will consider the friction force generated by the component of the block's self-weight along the direction perpendicular to the structural plane. When an earthquake occurs, the seismic inertial force will change the stress state of the block. The seismic inertial force will generate a component force in the direction perpendicular to the structural plane, which will change the normal pressure of the block on the sliding surface, thereby affecting the magnitude of the friction force. In addition, rainfall will weaken the cohesion of rock and soil masses. When rainwater seeps into rock and soil masses, the bonding effect between particles will be reduced, and both the cohesion and the sliding surface area have an impact on the anti-sliding force.

[0109] The corrected anti-sliding force satisfies the following formula:

[0110] ,

[0111] is the corrected anti-sliding force, The self-weight of the th three-dimensional block, is the seismic inertial force, is the internal friction angle of the th three-dimensional block, is the The sliding surface area of the three-dimensional block.

[0112] The sliding surface area of the three-dimensional block satisfies the following formula:

[0113] ,

[0114] where is the sliding surface area of the th three-dimensional block, is the width of the three-dimensional block, is the length of the three-dimensional block, is the dip angle of the structural plane of the th three-dimensional block.

[0115] Step S40403: Correct the predicted value of the stability of the three-dimensional block according to the corrected sliding force and the corrected anti-sliding force to obtain a corrected predicted value of stability.

[0116] The corrected predicted value of stability satisfies the following formula:

[0117] ,

[0118] ,

[0119] ,

[0120] where is the corrected predicted value of stability of the th three-dimensional block, is the corrected sliding force of the th three-dimensional block, is the corrected anti-sliding force of the th three-dimensional block, is the self-weight of the th three-dimensional block, is the dip angle of the structural plane of the th three-dimensional block, is the seismic inertial force, is the th internal friction angle of the three-dimensional block, is the th cohesion of the three-dimensional block after weakening by rainfall, is the th sliding surface area of the three-dimensional block.

[0121] Step S5: Construct a predicted value fusion model, and perform fusion calculation on the predicted value of the stability of the three-dimensional block and the corrected predicted value of stability by using the predicted value fusion model to obtain a real-time predicted value of the stability of the slope.

[0122] Among them, constructing the predicted value fusion model specifically includes the following sub-steps:

[0123] Step S501: Randomly perturb the internal friction angle, cohesion, and unit weight of the soil mass of the three-dimensional block within a predetermined range, and calculate the stability of the three-dimensional block according to the results of the random perturbation.

[0124] In this embodiment, randomly perturbing the internal friction angle, cohesion, and unit weight of the soil mass of the three-dimensional block means randomly generating different parameter values within a predetermined range. According to past engineering experience, geological exploration data, and relevant specifications, determine the reasonable fluctuation range of each of these three parameters. The internal friction angle can fluctuate within ±5° of the measured value, the cohesion can vary within ±20% of the measured value, and the unit weight of the soil mass can float within ±5 kN / m³ of the measured value. Then, use the random number generation method in Monte Carlo simulation to randomly generate a series of different parameter combinations within the predetermined range, and then use these parameter combinations to calculate the stability of the three-dimensional block. Through no less than 1000 stability calculations, a large number of stability calculation results of the three-dimensional block are obtained.

[0125] Step S502: Calculate the standard deviation of the stability of the three-dimensional block to obtain the standard deviation of the parameter perturbation error.

[0126] In this embodiment, first, summarize and organize all the stability calculation values, and calculate the average value of these stability values. This step is to obtain a reference benchmark value to measure the deviation degree of each stability value from the average level. Then, calculate the difference between each stability value and the average value in turn, and square these differences. The purpose of the squaring operation is to eliminate the positive and negative effects of the differences, so that all deviation situations are reflected in positive numbers, so as to perform subsequent statistical calculations more reasonably. Then, sum up these squared differences and divide by the total number of stability values to obtain the variance. However, the numerical magnitude of the variance is quite different from the stability value itself, which is not conducive to intuitively reflecting the degree of data dispersion. Therefore, perform a square root operation on the obtained variance, and the finally obtained value is the standard deviation of the parameter perturbation error.

[0127] This standard deviation reflects the degree of dispersion of the stability calculation results caused by the random perturbation of the parameters. If the standard deviation is small, it means that these stability calculation results after random perturbation are relatively concentrated, and the uncertainty of the parameters has a relatively small impact on the stability. On the contrary, if the standard deviation is large, it means that the stability calculation results are more dispersed, and the uncertainty of the parameters has a more significant impact on the stability of the three-dimensional block.

[0128] Step S503: Obtain multiple measured slope data, and use the 3D strip stability prediction model to perform prediction based on the measured slope data to obtain the prediction result of the 3D strip.

[0129] In this embodiment, the data acquisition is the same as above and will not be elaborated again. The essence of the current embodiment is to verify the 3D strip stability prediction model. Through no less than 1000 times of prediction, a large number of prediction results are obtained.

[0130] Step S504: Calculate the standard deviation of the prediction results of the 3D strip to obtain the standard deviation of the prediction error.

[0131] In this embodiment, the principle is the same as that of step S502 and will not be elaborated.

[0132] Step S505: Construct a prediction value fusion model based on the standard deviation of the parameter perturbation error and the standard deviation of the prediction error.

[0133] The prediction value fusion model satisfies the following formula:

[0134] ,

[0135] where, is the th final prediction value, is the standard deviation of the parameter perturbation error, is the th prediction value of the 3D strip stability prediction model, is the standard deviation of the prediction error, is the th corrected stability prediction value of the 3D strip.

[0136] In this implementation, the construction of the prediction value fusion model is based on the principle of error weighted average. The standard deviation of the parameter perturbation error reflects the degree of dispersion of the stability calculation results caused by randomly perturbing parameters such as the internal friction angle, cohesion, and unit weight of rock and soil in the 3D strip, and it reflects the impact of parameter uncertainty on the results. The standard deviation of the prediction error measures the error fluctuation of the stability prediction model itself during the prediction process.

[0137] By weighting and summing the prediction value of the stability prediction model and the corrected stability prediction value according to the squares of their respective error standard deviations, and then dividing by the sum of the squares of the two error standard deviations, the final prediction value The purpose of doing this is that when the standard deviation of the error corresponding to a certain predicted value is small, it indicates that the reliability of this predicted value is relatively high, and a greater weight will be given to it in the final fusion result, so that the final predicted value will be more inclined to the prediction result with high reliability. By comprehensively considering the errors from two different sources, the accuracy and reliability of the prediction are improved.

[0138] Finally, substitute the obtained three-dimensional block stability predicted value and the corrected stability predicted value into the stability prediction model of the three-dimensional block to obtain the final predicted value.

[0139] It should be clear that when there is no influence of earthquake and heavy rainfall factors, the predicted value of the three-dimensional block stability prediction model will not be corrected. Then, the predicted value of the stability prediction model of the th three-dimensional block is equal to the corrected stability predicted value of the th three-dimensional block, satisfying the formula: Substitute this formula into the predicted value fusion model to obtain the formula: . It shows that when there is no influence of earthquake and heavy rainfall factors, the final fusion value is the predicted value of the three-dimensional block stability prediction model.

[0140] Associate and bind the number of each three-dimensional block with the corresponding final predicted value. In the GIS software, set the color mapping rule according to the stability threshold. For example, set the area with a stability value greater than 1.2 to green, indicating a stable state; set the area with a stability value between 1.0 - 1.2 to yellow, representing a potentially unstable area; set the area with a stability value less than 1.0 to red, that is, a dangerous area. Through this way of color gradient, the spatial distribution of the stability value can be clearly shown on the three-dimensional slope model. At the same time, use the isoline generation tool to generate a stability isoline map based on the final predicted value. The density of the isolines can further reflect the change gradient of the stability value, enabling users to more intuitively identify the areas with lower stability and the areas with larger stability changes, thus providing strong support for slope stability analysis and the formulation of protection measures.

[0141] Such as Figure 2 shown. On the other hand, the present invention also provides a real-time calculation system for slope stability, including: a processor, an input device, an output device, and a memory. The processor, the input device, the output device, and the memory are interconnected. Among them, the memory is used to store computer programs, and the computer programs include program instructions. The processor is configured to call the program instructions to execute the relevant steps of the relevant embodiments in a real-time calculation method for slope stability of the present invention.

[0142] A real-time calculation system for slope stability provided by the present invention, each functional component can be integrated in a processing component, or each component can exist physically alone, or two or more components can be integrated in one component. The above integrated components can be implemented in the form of hardware or in the form of software functions, further improving the overall applicability and practical application ability of the present invention.

[0143] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered by the scope of the claims and the specification of the present invention.

Claims

1. A real-time calculation method for slope stability, characterized in that, The method includes: Construct a three-dimensional geological model of the slope, and divide the three-dimensional geological model by using the three-dimensional slice method to obtain a plurality of three-dimensional slices; Monitor the three-dimensional slices, and based on the monitoring results and the three-dimensional geological model, extract the spatial characteristics and monitoring time series data of the three-dimensional slices; Construct a three-dimensional slice stability prediction model, and perform prediction by using the three-dimensional slice stability prediction model according to the spatial characteristics and the monitoring time series data to obtain a three-dimensional slice stability prediction value; Correct the three-dimensional slice stability prediction value based on the real-time acquired seismic data and rainfall data to obtain a corrected stability prediction value; Construct a prediction value fusion model, and perform fusion calculation by using the prediction value fusion model according to the three-dimensional slice stability prediction value and the corrected stability prediction value to obtain a real-time stability prediction value of the slope; The constructing of the three-dimensional geological model of the slope includes: Obtain the slope data of the slope, and decouple the slope data into surface morphology data and internal structure data; Construct a digital elevation model by using the surface morphology data, and construct a geological structural plane model by using the internal structure data; Perform spatial coordinate alignment and geometric entity coupling on the digital elevation model and the geological structural plane model to form the three-dimensional geological model of the slope including external geometric features and internal mechanical interfaces; The constructing of the three-dimensional slice stability prediction model includes: Obtain the historical monitoring data of the three-dimensional slices, and obtain historical spatial characteristics and historical monitoring time series data according to the historical monitoring data and the three-dimensional geological model; Calculate the stability of the three-dimensional slices according to the historical spatial characteristics and the historical monitoring time series data to obtain historical stability; Construct sample data by using the historical spatial characteristics, the historical monitoring time series data and the historical stability; Construct a framework model based on a spatio-temporal graph convolutional network, and train the framework model by using the sample data to obtain a three-dimensional slice stability prediction model; The constructing of the prediction value fusion model includes: Perform random perturbations within a predetermined range on the internal friction angle, cohesion and unit weight of the soil and rock of the three-dimensional slices, and calculate the stability of the three-dimensional slices according to the results of the random perturbations; Calculate the standard deviation of the stability of the three-dimensional slices to obtain a parameter perturbation error standard deviation; Obtain a plurality of measured slope data, and perform prediction by using the three-dimensional slice stability prediction model according to the measured slope data to obtain a prediction result of the three-dimensional slices; Calculate the standard deviation of the prediction result of the three-dimensional slices to obtain a prediction error standard deviation; Construct a prediction value fusion model according to the parameter perturbation error standard deviation and the prediction error standard deviation.

2. The real-time calculation method for slope stability according to claim 1, characterized in that The historical stability satisfies the following formula: ， Among them, is the historical stability of the th three-dimensional block, is the self-weight of the th three-dimensional block, The dip angle of the historical structural plane of the th three-dimensional strip block, is the historical pore water pressure of the th three-dimensional strip block, is the historical sliding surface area of the th three-dimensional strip block, is the historical internal friction angle of the th three-dimensional strip block, is the cohesion of the th three-dimensional strip block.

3. The real-time calculation method for slope stability according to claim 1, characterized in that, The correcting the three-dimensional slice stability prediction value based on the real-time acquired seismic data and rainfall data to obtain a corrected stability prediction value includes: According to the seismic data, calculate the influence of the earthquake on the horizontal vibration of the three-dimensional slices to obtain a horizontal seismic influence coefficient; Calculate the seismic vibration intensity on the three-dimensional block according to the horizontal seismic influence coefficient to obtain the seismic inertial force; Adjust the cohesion of the three-dimensional block according to the rainfall data to obtain the cohesion of the three-dimensional block after rainfall weakening; Use the seismic inertial force and the cohesion of the three-dimensional block after rainfall weakening to correct the stability prediction value of the three-dimensional block to obtain the corrected stability prediction value.

4. A real-time calculation method for slope stability according to claim 3, characterized in that, The step of using the seismic inertial force and the cohesion of the three-dimensional block after rainfall weakening to correct the stability prediction value of the three-dimensional block to obtain the corrected stability prediction value includes: Use the seismic inertial force to correct the sliding force of the three-dimensional block to obtain the corrected sliding force; Use the seismic inertial force and the cohesion of the three-dimensional block after rainfall weakening to correct the anti-sliding force of the three-dimensional block to obtain the corrected anti-sliding force; Correct the stability prediction value of the three-dimensional block according to the corrected sliding force and the corrected anti-sliding force to obtain the corrected stability prediction value.

5. A real-time calculation method for slope stability according to claim 4, characterized in that, The corrected stability prediction value satisfies the following formula: ， ， ， Among them, is the predicted value of the corrected stability of the th three-dimensional block, is the corrected downslope force of the th three-dimensional block, is the corrected anti-sliding force of the th three-dimensional block, is the self-weight of the th three-dimensional block, is the dip angle of the structural plane of the th three-dimensional block, is the seismic inertial force, is the internal friction angle of the th three-dimensional block, is the cohesion of the th three-dimensional block after rainfall weakening, is the sliding surface area of the th three-dimensional block.

6. A real-time calculation method for slope stability according to claim 1, characterized in that, The prediction value fusion model satisfies the following formula: ， Among them, is the th final predicted value, is the standard deviation of the parameter perturbation error, is the predicted value of the stability prediction model of the th three-dimensional strip, is the standard deviation of the prediction error, is the corrected stability predicted value of the th three-dimensional strip.

7. A real-time calculation system for slope stability, characterized in that, including: A processor, an input device, an output device, and a memory. The processor, the input device, the output device, and the memory are interconnected. Among them, the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute a real-time calculation method for slope stability according to any one of claims 1 to 6.

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