A method, system, terminal and storage medium for evaluating slope stability based on an attention mechanism-based graph convolution integration network

Abstract

The present invention belongs to the technical field of slope stability analysis, and discloses a slope stability assessment method, system, terminal and storage medium based on an attention mechanism-based graph convolution integration network. The method includes: obtaining initial InSAR ground deformation point data and landslide point data and performing preprocessing to obtain InSAR ground deformation points and node feature sample data sets in the adjacent area of the landslide point; constructing an initial attention mechanism-based graph convolution integration network, training and testing the initial attention mechanism-based graph convolution integration network by using the node feature sample data set to obtain an attention mechanism-based graph convolution integration network; and using the attention mechanism-based graph convolution integration network to perform slope stability assessment. The present invention faces multi-source heterogeneous slope stability sample data, takes into account the spatial correlation between ground deformation points in the global area, considers the landslide hazards in the adjacent area of the landslide, and realizes high-precision slope stability assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of slope stability analysis, and particularly relates to a slope stability assessment method, system, terminal and computer-readable storage medium based on an attention mechanism graph convolutional integration network. Background Art

[0002] Landslides caused by slope instability have a significant impact on public infrastructure and the ecological environment. Therefore, slope stability assessment is of great significance for preventing and reducing landslides.

[0003] With the development of artificial intelligence technology, various existing machine learning methods have been applied to slope stability assessment. For example, a single neural network uses slope stability influencing factors as input data and slope safety factor values as output data. Although it has good performance, almost all of them predict the slope safety factor values of individual slope points, without considering the influence of the spatial correlation of ground deformation between all slope points in the study area on the surrounding area, resulting in the potential slope instability risks in adjacent areas being ignored, that is, the influence of the spatial correlation between ground deformation points in the global area on the slope stability prediction result is not considered. Moreover, a single machine learning network is suitable for homogeneous data structures and not suitable for multi-source heterogeneous slope stability sample data.

[0004] Therefore, the existing technology still needs to be improved and developed. Summary of the Invention

[0005] The main purpose of the present invention is to provide a slope stability assessment method, system, terminal and computer-readable storage medium based on an attention mechanism graph convolutional integration network, aiming to solve the problems that the existing slope stability assessment method based on a single network is not suitable for multi-source heterogeneous landslide data and does not consider the influence of the spatial correlation between ground deformation points in the global area on slope stability.

[0006] To achieve the above-mentioned invention purpose, the present invention provides a slope stability assessment method based on an attention mechanism graph convolutional integration network. The slope stability assessment method based on an attention mechanism graph convolutional integration network includes:

[0007] Obtain initial InSAR ground deformation point data and landslide point data, and preprocess the initial InSAR ground deformation point data and the landslide point data to obtain InSAR ground deformation points in the adjacent area of the landslide point and a node feature sample data set based on the InSAR ground deformation points;

[0008] Construct an initial graph convolutional integration network based on the attention mechanism, and use the node feature sample data set to train and test the initial graph convolutional integration network based on the attention mechanism to obtain a graph convolutional integration network based on the attention mechanism whose error index meets the evaluation accuracy requirements;

[0009] Obtain the data of the InSAR ground deformation points to be evaluated, and input the data of the InSAR ground deformation points to be evaluated into the graph convolutional integration network based on the attention mechanism, and output the slope stability evaluation result corresponding to the data of the InSAR ground deformation points to be evaluated.

[0010] Optionally, the obtaining of the initial InSAR ground deformation point data and landslide point data, and the preprocessing of the initial InSAR ground deformation point data and the landslide point data to obtain the InSAR ground deformation points in the adjacent area of the landslide point and the node feature sample data set based on the InSAR ground deformation points specifically includes:

[0011] Obtain the initial InSAR ground deformation point data, landslide point data, publicly available multi-source landslide inducing factor data, ground elevation data and slope data of the research area;

[0012] According to the initial InSAR ground deformation point data, the landslide point data, the publicly available multi-source landslide inducing factor data, the ground elevation data and the slope data, through spatial correlation analysis, spatial multi-value extraction analysis, importance ranking and safety factor value calculation, obtain the associated slope stability influence factor sequence in the adjacent area of the landslide point and the InSAR ground deformation points with the safety factor value attribute;

[0013] Based on the InSAR ground deformation points with the associated slope stability influence factor sequence and the safety factor value attribute, through spatial correlation calculation, construct an undirected network graph, and use the undirected network graph to obtain the node feature sample data set based on the InSAR ground deformation points.

[0014] Optionally, the obtaining of the associated slope stability influence factor sequence in the adjacent area of the landslide point and the InSAR ground deformation points with the safety factor value attribute according to the initial InSAR ground deformation point data, the landslide point data, the publicly available multi-source landslide inducing factor data, the ground elevation data and the slope data, through spatial correlation analysis, spatial multi-value extraction analysis, importance ranking and safety factor value calculation specifically includes:

[0015] Based on the initial InSAR ground deformation point data and the landslide point data, through spatial correlation analysis, an attribute correlation between the landslide point and the initial InSAR ground deformation point is established to obtain the InSAR ground deformation point associated with the landslide attribute, where the InSAR ground deformation point is the initial InSAR ground deformation point in the adjacent area of the landslide point;

[0016] Based on the InSAR ground deformation point and the publicly available multi-source landslide inducing factor data, through spatial multi-value extraction analysis, a plurality of slope stability influencing factors are obtained;

[0017] The information gain algorithm is used to analyze the importance index of each of the slope stability influencing factors, and each of the slope stability influencing factors is sorted in descending order according to the importance index of each of the slope stability influencing factors to obtain a slope stability influencing factor sequence;

[0018] Based on the ground elevation data and the slope data corresponding to the InSAR ground deformation point, the finite element method is used to calculate the safety factor value of the InSAR ground deformation point;

[0019] Based on the InSAR ground deformation point, the slope stability influencing factor sequence, and the safety factor value, the InSAR ground deformation point associated with the slope stability influencing factor sequence and the safety factor value attribute in the adjacent area of the landslide point is obtained;

[0020] Among them, the InSAR ground deformation point data corresponding to the InSAR ground deformation point associated with the slope stability influencing factor sequence and the safety factor value attribute includes the safety factor value, the deformation rate, the slope stability influencing factor sequence, and the safety factor value.

[0021] Optionally, based on the InSAR ground deformation point associated with the slope stability influencing factor sequence and the safety factor value attribute, through spatial correlation calculation, an undirected network graph is constructed, and the node feature sample data set based on the InSAR ground deformation point is obtained by using the undirected network graph, specifically including:

[0022] Based on the InSAR ground deformation point associated with the slope stability influencing factor sequence and the safety factor value attribute, through spatial correlation calculation, the spatial correlation data based on the InSAR ground deformation point is obtained, where the spatial correlation data includes the spatial correlation value, and the spatial correlation value is calculated by a Gaussian similarity function:

[0023] =exp ;

[0024] Among them, represents the The spatial correlation value between the th InSAR ground deformation point and the th InSAR ground deformation point, The th InSAR ground deformation point and the th InSAR ground deformation point represents the Euclidean distance therebetween, The th InSAR ground deformation point and the th InSAR ground deformation point represents the standard deviation of the Euclidean distance therebetween; exp represents the exponential function;

[0025] According to the InSAR ground deformation points of the associated slope stability influence factor sequence and the safety factor value attribute and the spatial correlation data based on the InSAR ground deformation points, construct an undirected network graph, use the InSAR ground deformation points as the nodes of the undirected network graph, use the deformation rate and the slope stability influence factor sequence of the InSAR ground deformation points as the deformation rate and the slope stability influence factor sequence of the corresponding nodes, and use the spatial correlation value between the InSAR ground deformation points as the spatial similarity value and the node edge weight between the corresponding nodes to obtain an undirected network graph wherein, v represents the set of nodes in the undirected network graph g, represents the edge data set, and A represents the node edge weight matrix;

[0026] Use the undirected network graph to obtain a node feature sample data set, wherein the node feature sample data set includes input features corresponding to the nodes, and the input features include the deformation rate and the slope stability influence factor sequence:

[0027] ;

[0028] ;

[0029] wherein, represents the node feature sample data set, represents the input feature corresponding to the 1st node, represents the input feature corresponding to the 2nd node, represents the input feature corresponding to the th node, represents the input feature corresponding to the th node, represents the total number of nodes, represents the th node's deformation rate, represents the The first slope stability influence factor corresponding to a node, denotes the second slope stability influence factor corresponding to a node, denotes the th slope stability influence factor corresponding to a node, th slope stability influence factor, denotes the total number of slope stability influence factors in the slope stability influence factor sequence.

[0030] Optionally, to construct the initial graph convolutional integration network based on the attention mechanism, use the node feature sample data set to train and test the initial graph convolutional integration network based on the attention mechanism, and obtain a graph convolutional integration network based on the attention mechanism whose error index meets the evaluation accuracy requirements, specifically including:

[0031] Construct an initial graph convolutional integration network based on the attention mechanism, where the initial graph convolutional integration network based on the attention mechanism includes an initial graph convolutional neural network based on the attention mechanism and an initial hybrid integration learning network. The initial graph convolutional neural network based on the attention mechanism includes an attention mechanism layer and a graph convolutional neural network, and the initial hybrid integration learning network includes a sub-network model layer and a meta-machine learning classifier;

[0032] Input the node feature sample data set into the initial graph convolutional neural network based on the attention mechanism. The initial graph convolutional neural network based on the attention mechanism extracts spatial correlation features from the node feature sample data set to obtain the spatial correlation features corresponding to the node feature sample data set;

[0033] Input the spatial correlation features corresponding to the node feature sample data set into the initial hybrid integration learning network. The initial hybrid integration learning network performs slope stability evaluation on the spatial correlation features to obtain the slope stability evaluation result corresponding to the node feature sample data set, where the slope stability evaluation result corresponding to the node feature sample data set is the slope stability evaluation result corresponding to the InSAR ground deformation point;

[0034] Compare the slope stability evaluation result corresponding to the InSAR ground deformation point with the slope stability calculation result corresponding to the safety factor value in the InSAR ground deformation point data, and perform iterative training and testing on the initial graph convolutional integration network based on the attention mechanism according to the comparison result until the error index of the initial graph convolutional integration network based on the attention mechanism meets the evaluation accuracy requirements, and obtain a graph convolutional integration network based on the attention mechanism;

[0035] Among them, the error index includes mean absolute error, mean square error, and root mean square error.

[0036] Optionally, when inputting the node feature sample data set into the initial graph convolutional neural network based on the attention mechanism, the initial graph convolutional neural network based on the attention mechanism extracts spatial correlation features from the node feature sample data set to obtain the spatial correlation features corresponding to the node feature sample data set, which specifically includes:

[0037] Input the node feature sample data set into the attention mechanism layer. The activation function of the attention mechanism layer calculates the weight value of each node according to the deformation rate of each node and the slope stability influence factor sequence in the node feature sample data set, and calculates the attention matrix based on the weight value of each node and the input features:

[0038] ;

[0039] Among them, represents the attention matrix, represents the activation function of the attention mechanism layer, represents the weight value of the th node, represents the deviation of the

[0040] th node;

[0041] ;

[0042] Among them, represents the component of the normalized attention matrix ', represents the component of the attention matrix ;

[0043] Multiply the normalized attention matrix by the node edge weight matrix to obtain the attention feature set ;

[0044] Regarding the attention feature set Input into the graph convolutional neural network, and each sub-graph convolutional neural network layer in the graph convolutional neural network outputs spatial correlation features based on the attention feature set and the node-edge weight matrix, and uses the spatial correlation features as the input data for the next sub-graph convolutional neural network layer until the spatial correlation features output by the last sub-graph convolutional neural network layer are obtained. Among them, the spatial correlation features output by the last sub-graph convolutional neural network layer are the spatial correlation features corresponding to the node feature sample data set:

[0045] = A+ ;

[0046] ;

[0047] ;

[0048] ;

[0049] Among them, represents the intermediate node-edge weight matrix, represents the Laplace equation matrix coefficient, represents the identification matrix of represents the new node-edge weight matrix, represents the degree matrix of, and are respectively the spatial correlation features output by the th sub-graph convolutional neural network layer and the th sub-graph convolutional neural network layer in the graph convolutional neural network, and are respectively the initial activation function and the initial weight value of the graph convolutional neural network, and are respectively the activation function and the weight value of the th sub-graph convolutional neural network layer in the graph convolutional neural network, and are respectively the activation function and the weight value of the th sub-graph convolutional neural network layer in the graph convolutional neural network.

[0050] Optionally, inputting the spatial correlation features corresponding to the node feature sample data set into the initial hybrid ensemble learning network, and the initial hybrid ensemble learning network performs slope stability evaluation on the spatial correlation features to obtain the slope stability evaluation result corresponding to the node feature sample data set, specifically including:

[0051] Input the spatial correlation features corresponding to the node feature sample data set into the sub-network model layer. Multiple basic neural network models in the sub-network model layer perform preliminary slope stability evaluation based on the spatial correlation features and output multiple preliminary slope stability evaluation results.

[0052] Input multiple preliminary slope stability evaluation results into the meta-machine learning classifier. The meta-machine learning classifier performs logistic regression processing based on multiple preliminary slope stability evaluation results and outputs the safety factor value of the InSAR ground deformation point corresponding to the node feature sample data set.

[0053] When the safety factor value of the InSAR ground deformation point is greater than the preset threshold, determine that the slope stability evaluation result of the InSAR ground deformation point is stable.

[0054] When the safety factor value of the InSAR ground deformation point is not greater than the preset threshold, determine that the slope stability evaluation result of the InSAR ground deformation point is unstable.

[0055] To achieve the above invention purpose, the present invention also provides a slope stability evaluation system based on an attention mechanism graph convolution integration network. The slope stability evaluation system based on an attention mechanism graph convolution integration network includes:

[0056] Sample data set construction module: used to obtain initial InSAR ground deformation point data and landslide point data, and preprocess the initial InSAR ground deformation point data and the landslide point data to obtain InSAR ground deformation points in the adjacent area of the landslide point and a node feature sample data set based on the InSAR ground deformation points.

[0057] Network construction module: used to construct an initial attention mechanism graph convolution integration network, train and test the initial attention mechanism graph convolution integration network using the node feature sample data set, and obtain an attention mechanism graph convolution integration network with an error index meeting the evaluation accuracy requirements.

[0058] Slope stability evaluation module: used to obtain InSAR ground deformation point data to be evaluated, input the InSAR ground deformation point data to be evaluated into the attention mechanism graph convolution integration network, and output the slope stability evaluation result corresponding to the InSAR ground deformation point data to be evaluated.

[0059] To achieve the above invention object, the present invention further provides a terminal, which includes: a memory, a processor, and a slope stability evaluation program of a graph convolutional integration network based on an attention mechanism stored on the memory and operable on the processor. When the slope stability evaluation program of the graph convolutional integration network based on the attention mechanism is executed by the processor, the steps of the slope stability evaluation method of the graph convolutional integration network based on the attention mechanism as described above are implemented.

[0060] To achieve the above invention object, the present invention further provides a computer-readable storage medium, which stores a slope stability evaluation program of a graph convolutional integration network based on an attention mechanism. When the slope stability evaluation program of the graph convolutional integration network based on the attention mechanism is executed by a processor, the steps of the slope stability evaluation method of the graph convolutional integration network based on the attention mechanism as described above are implemented.

[0061] In the present invention, initial InSAR (Interferometric Synthetic Aperture Radar) ground deformation point data and landslide point data are obtained, and the initial InSAR ground deformation point data and the landslide point data are preprocessed to obtain InSAR ground deformation points in the adjacent area of the landslide point and a node feature sample data set based on the InSAR ground deformation points; an initial graph convolutional integration network based on an attention mechanism is constructed, and the initial graph convolutional integration network based on the attention mechanism is trained and tested by using the node feature sample data set to obtain a graph convolutional integration network based on the attention mechanism whose error index meets the evaluation accuracy requirement; the InSAR ground deformation point data to be evaluated is obtained, and the InSAR ground deformation point data to be evaluated is input into the graph convolutional integration network based on the attention mechanism, and a slope stability evaluation result corresponding to the InSAR ground deformation point data to be evaluated is output. The present invention combines the performance of a deep learning network and an ensemble learning network, faces multi-source heterogeneous slope stability sample data, takes into account the spatial correlation between global regional InSAR ground deformation points, and realizes high-precision slope stability evaluation. Description of the Drawings

[0062] Figure 1 is a flowchart of a preferred embodiment of the slope stability evaluation method of the graph convolutional integration network based on the attention mechanism of the present invention;

[0063] Figure 2 is a structural diagram of a preferred embodiment of the slope stability evaluation method of the graph convolutional integration network based on the attention mechanism of the present invention;

[0064] Figure 3 is a framework diagram of the AGCN-HEN network of the present invention;

[0065] Figure 4 It is the framework diagram of the AGCN network of the present invention;

[0066] Figure 5 It is the framework diagram of the HEN network of the present invention;

[0067] Figure 6 It is the structural diagram of a preferred embodiment of the slope stability evaluation system of the graph convolution integration network based on the attention mechanism of the present invention;

[0068] Figure 7 It is the structural diagram of a preferred embodiment of the terminal of the present invention. Detailed implementation manners

[0069] To make the objectives, technical solutions and advantages of the present invention clearer and more definite, the following further describes the present invention in detail with reference to the accompanying drawings and by way of examples. It should be understood that the specific examples described herein are only used to explain the present invention and are not used to limit the present invention.

[0070] Landslides caused by slope instability have a significant impact on public infrastructure and the ecological environment. Therefore, slope stability evaluation is of great significance for preventing and reducing landslides.

[0071] At present, there are many methods for slope stability evaluation. For example, the analytical method constructs a mathematical model of slope stability and uses relevant analytical formulas for solution, but it is not suitable for complex and large-scale slope stability evaluation. In addition, the finite element method and the finite difference method are commonly used numerical analysis methods. This method regards slope stability evaluation as a discrete finite-dimensional mathematical calculation problem that can obtain an approximate solution and can be used for complex slope stability evaluation. However, this method takes a long time to calculate, and relevant parameters need to be determined through field measurements. At the same time, accurate boundary conditions are required for high-precision slope stability results, and it is difficult to obtain such conditions in practical applications. The InSAR technology is one of the commonly used remote sensing means for monitoring surface deformation, and it evaluates slope stability through the temporal variation of ground displacement. However, in mountainous jungle areas with large undulations, there are problems of geometric distortion in the InSAR images obtained by this technology, including foreshortening, layover and shadow. Although the InSAR technology can be used for large-scale slope stability evaluation, accurate evaluation results need to be combined with topographic and geological data, and even field investigations for comprehensive judgment of the slope instability state in the area.

[0072] With the development of artificial intelligence technology, various existing machine learning methods are applied to the evaluation of slope stability, such as single neural networks: random forest, logistic regression, support vector machine, artificial neural network, etc. These network models use the influencing factors of slope stability as input data and the factor of safety (FOS) value of the slope as output data. Although good performance results are obtained, almost all of them predict the slope safety factor value of a single slope point, without considering the influence of the spatial correlation of ground deformation among all slope points in the study area on the surrounding area, resulting in the potential slope instability risk (i.e., landslide hazard) in the adjacent area being ignored, that is, the influence of the spatial correlation among ground deformation points in the global area on the slope stability prediction result is not considered. Moreover, a single machine learning network is suitable for homogeneous data structures and is not applicable to the analysis of multi-source heterogeneous slope stability sample data (including multi-source heterogeneous landslide data).

[0073] Since a single machine learning network is suitable for homogeneous data structures, it is necessary to integrate the performance of multiple single networks for the analysis of multi-source heterogeneous slope stability sample data. In view of the fact that the existing deep learning and ensemble learning networks have better performance in slope stability evaluation than single machine learning networks, therefore, the present invention integrates the performance of deep learning networks and ensemble learning networks, faces multi-source heterogeneous slope stability sample data, takes InSAR ground deformation as the guide, and takes into account the spatial correlation among ground deformation points in the global area, and proposes an attention mechanism-based graph convolutional ensemble network AGCN-HEN (attention-graph convolutional network-hybrid ensemble learning network) for slope stability evaluation, obtains the initial InSAR ground deformation point data and landslide point data, and preprocesses the initial InSAR ground deformation point data and the landslide point data to obtain the InSAR ground deformation points in the adjacent area of the landslide point and the node feature sample data set based on the InSAR ground deformation points; constructs an initial attention mechanism-based graph convolutional ensemble network, uses the node feature sample data set to train and test the initial attention mechanism-based graph convolutional ensemble network, and obtains an attention mechanism-based graph convolutional ensemble network with an error index meeting the evaluation accuracy requirements; obtains the InSAR ground deformation point data to be evaluated, inputs the InSAR ground deformation point data to be evaluated into the attention mechanism-based graph convolutional ensemble network, and outputs the slope stability evaluation result corresponding to the InSAR ground deformation point data to be evaluated. The present invention integrates the performance of deep learning networks and ensemble learning networks, faces multi-source heterogeneous slope stability sample data, takes into account the spatial correlation among InSAR ground deformation points in the global area, and realizes high-precision slope stability evaluation.

[0074] The following further describes the application content by describing the embodiments in conjunction with the accompanying drawings.

[0075] A preferred embodiment of the slope stability assessment method of the graph convolutional integration network based on the attention mechanism of the present invention is as Figure 1 and Figure 2 shown, and specifically includes:

[0076] S1. Obtain the initial InSAR ground deformation point data and landslide point data, and preprocess the initial InSAR ground deformation point data and the landslide point data to obtain the InSAR ground deformation points in the adjacent area of the landslide point and the node feature sample data set based on the InSAR ground deformation points.

[0077] In an implementation manner of this embodiment, the obtaining the initial InSAR ground deformation point data and landslide point data, and preprocessing the initial InSAR ground deformation point data and the landslide point data to obtain the InSAR ground deformation points in the adjacent area of the landslide point and the node feature sample data set based on the InSAR ground deformation points specifically includes:

[0078] Obtain the initial InSAR ground deformation point data, landslide point data, publicly available multi-source landslide inducing factor data, ground elevation data, and slope data of the research area;

[0079] According to the initial InSAR ground deformation point data, the landslide point data, the publicly available multi-source landslide inducing factor data, the ground elevation data, and the slope data, through spatial association analysis, spatial multi-value extraction analysis, importance ranking, and safety factor value calculation, obtain the associated slope stability influence factor sequence in the adjacent area of the landslide point and the InSAR ground deformation points with safety factor value attributes;

[0080] Based on the associated slope stability influence factor sequence and the InSAR ground deformation points with safety factor value attributes, construct an undirected network graph through spatial correlation calculation, and use the undirected network graph to obtain the node feature sample data set based on the InSAR ground deformation points.

[0081] In an implementation manner of this embodiment, the according to the initial InSAR ground deformation point data, the landslide point data, the publicly available multi-source landslide inducing factor data, the ground elevation data, and the slope data, through spatial association analysis, spatial multi-value extraction analysis, importance ranking, and safety factor value calculation, obtain the associated slope stability influence factor sequence in the adjacent area of the landslide point and the InSAR ground deformation points with safety factor value attributes specifically includes:

[0082] Based on the initial InSAR ground deformation point data and the landslide point data, through spatial correlation analysis, an attribute association between the landslide point and the initial InSAR ground deformation point is established to obtain the InSAR ground deformation point associated with the landslide attribute, where the InSAR ground deformation point is the initial InSAR ground deformation point in the adjacent area of the landslide point;

[0083] Based on the InSAR ground deformation point and the publicly available multi-source landslide inducing factor data, through spatial multi-value extraction analysis, a plurality of slope stability influencing factors are obtained;

[0084] The information gain algorithm is used to analyze the importance index of each of the slope stability influencing factors, and the slope stability influencing factors are sorted in descending order according to the importance index of each of the slope stability influencing factors to obtain a sequence of slope stability influencing factors;

[0085] Based on the ground elevation data and the slope data corresponding to the InSAR ground deformation point, the finite element method is used to calculate the safety factor value of the InSAR ground deformation point;

[0086] Based on the InSAR ground deformation point, the sequence of slope stability influencing factors, and the safety factor value, the InSAR ground deformation point associated with the sequence of slope stability influencing factors in the adjacent area of the landslide point and the safety factor value attribute is obtained;

[0087] Among them, the InSAR ground deformation point data corresponding to the InSAR ground deformation point associated with the sequence of slope stability influencing factors and the safety factor value attribute includes the safety factor value, the deformation rate, the sequence of slope stability influencing factors, and the safety factor value.

[0088] In an implementation manner of this embodiment, based on the InSAR ground deformation point associated with the sequence of slope stability influencing factors and the safety factor value attribute, an undirected network graph is constructed through spatial correlation calculation, and a node feature sample data set based on the InSAR ground deformation point is obtained by using the undirected network graph, specifically including:

[0089] Based on the InSAR ground deformation point associated with the sequence of slope stability influencing factors and the safety factor value attribute, spatial correlation data based on the InSAR ground deformation point is obtained through spatial correlation calculation, where the spatial correlation data includes spatial correlation values, and the spatial correlation values are calculated by a Gaussian similarity function:

[0090] =exp ;

[0091] Among them, represents the The spatial correlation value between the th and the th InSAR ground deformation points, where the th and the th represent the Euclidean distance between the th and the th InSAR ground deformation points, and exp represents the exponential function;

[0092] Construct an undirected network graph based on the InSAR ground deformation points with the associated slope stability influence factor sequence and safety factor value attributes and the spatial correlation data of the InSAR ground deformation points. Use the InSAR ground deformation points as the nodes of the undirected network graph, the deformation rate and the slope stability influence factor sequence of the InSAR ground deformation points as the deformation rate and the slope stability influence factor sequence of the corresponding nodes, and the spatial correlation value between the InSAR ground deformation points as the spatial similarity value and the node edge weight between the corresponding nodes, to obtain the undirected network graph , where v represents the set of nodes in the undirected network graph g, represents the edge dataset, and A represents the node edge weight matrix;

[0093] Use the undirected network graph to obtain the node feature sample dataset, where the node feature sample dataset includes input features corresponding to nodes, and the input features include the deformation rate and the slope stability influence factor sequence:

[0094] ;

[0095] ;

[0096] where, represents the node feature sample dataset, represents the input feature corresponding to the 1st node, represents the input feature corresponding to the 2nd node, represents the input feature corresponding to the th node, represents the input feature corresponding to the th node, represents the total number of nodes, represents the deformation rate corresponding to the th node, represents the The first slope stability influence factor corresponding to the th node, The second slope stability influence factor corresponding to the th node, The th slope stability influence factor corresponding to the th node,

[0097] Specifically, in order to extract the spatial correlation characteristics between ground deformation points, the present invention needs to construct a slope stability sample data set (i.e., a node feature sample data set) based on InSAR points (i.e., InSAR ground deformation points), including a slope stability influence sequence factor data set (i.e., a slope stability influence factor sequence) and spatial correlation data (i.e., spatial correlation data) based on InSAR ground deformation points. The specific process is as follows:

[0098] Construct a dataset of sequence factors affecting slope stability based on InSAR points: First, according to the obtained landslide points, InSAR ground deformation points (original InSAR ground deformation points, i.e., initial InSAR ground deformation points), and publicly available multi-source landslide inducing factors (such as slope, slope height, and soil unit weight, etc.) data, use the spatial join method to establish the attribute association between the landslide points and InSAR ground deformation points in the study area, and obtain the InSAR ground deformation points in the adjacent area of the landslide points (i.e., InSAR ground deformation points associated with landslide attributes / InSAR ground deformation points with potential landslide hazards). Then, based on this data, use the spatial multi-value extraction analysis tool of the ArcGIS (Arc Geographic Information System) platform to obtain the InSAR ground deformation point data associated with the attributes of slope stability influencing factors (which can be understood as obtaining the slope stability influencing factor data of InSAR ground deformation points). Since the Information Gain (IG) algorithm can measure the contribution degree of variable features to the target classification result, the present invention uses the IG algorithm to analyze the importance index of each of the above slope stability influencing factors, eliminates the influencing factors with extremely small influence on slope stability, and then arranges them in descending order according to their importance index to obtain a dataset of sequence factors affecting slope stability (i.e., the sequence of slope stability influencing factors), which is used as the input data of the slope stability assessment model constructed by the present invention. And, taking the slope safety factor value FOS extracted based on the above InSAR ground deformation points as the output data of the model, the present invention uses the ground elevation and slope data of the study area and calculates the safety factor value FOS of the study area by the finite element method in SAGA GIS (System for Automated Geoscientific Analyse). Among them, the study area with the FOS value less than or equal to 1 is classified as an unstable state, that is, FOS <= 1, which is expressed as an unstable state and coded as 0; otherwise, it is a stable state and coded as 1.

[0099] Constructing spatial correlation data based on InSAR ground deformation points: To consider the influence of the spatial correlation characteristics of ground deformation points on slope stability, it is necessary to establish spatial correlation data. To represent the spatial correlation characteristics of InSAR ground deformation points throughout the study area, an undirected network graph (GGDN, global ground deformation network) structure is constructed based on the above-obtained sequence of associated slope stability influence factors and the InSAR ground deformation points with FOS safety coefficient value attributes, which is used to describe the spatial topological structure relationship between InSAR ground deformation points. Each InSAR ground deformation point in the undirected network graph is regarded as a graph node, and the edge dataset reflects the spatial connectivity between nodes. Each value in the node-edge weight matrix represents the weight value (edge weight value) between nodes. It should be noted that the edge dataset consists of spatial similarity values between two nodes, and these similarity values represent the connection weights between the positions of ground deformation points. The greater the weight, the greater the spatial correlation it represents, that is, spatial similarity value = edge weight value = spatial correlation value.

[0100] The present invention obtains a large amount of ground deformation point data based on InSAR technology, and proposes a construction method for a slope stability sample dataset based on InSAR points, which solves the problem that the sparsity of landslide point data cannot meet the requirements of a large number of training samples needed for deep learning; at the same time, aiming at the problem that not all InSAR ground deformation points will cause landslides, based on the first law of geography, a sample dataset of InSAR ground deformation points with potential landslide hazards is constructed, providing a data basis for slope stability assessment.

[0101] S2. Construct an initial graph convolutional integration network based on the attention mechanism, and use the node feature sample dataset to train and test the initial graph convolutional integration network based on the attention mechanism to obtain a graph convolutional integration network based on the attention mechanism with an error index meeting the evaluation accuracy requirements.

[0102] In an implementation manner of this embodiment, the construction of the initial graph convolutional integration network based on the attention mechanism, using the node feature sample dataset to train and test the initial graph convolutional integration network based on the attention mechanism to obtain a graph convolutional integration network based on the attention mechanism with an error index meeting the evaluation accuracy requirements specifically includes:

[0103] Construct an initial graph convolutional integration network based on the attention mechanism, where the initial graph convolutional integration network based on the attention mechanism includes an initial graph convolutional neural network based on the attention mechanism and an initial hybrid ensemble learning network. The initial graph convolutional neural network based on the attention mechanism includes an attention mechanism layer and a graph convolutional neural network, and the initial hybrid ensemble learning network includes a sub-network model layer and a meta-machine learning classifier;

[0104] Input the node feature sample data set into the initial graph convolutional neural network based on the attention mechanism. The initial graph convolutional neural network based on the attention mechanism extracts spatial correlation features from the node feature sample data set to obtain the spatial correlation features corresponding to the node feature sample data set.

[0105] Input the spatial correlation features corresponding to the node feature sample data set into the initial hybrid ensemble learning network. The initial hybrid ensemble learning network performs slope stability evaluation on the spatial correlation features to obtain the slope stability evaluation result corresponding to the node feature sample data set. Among them, the slope stability evaluation result corresponding to the node feature sample data set is the slope stability evaluation result corresponding to the InSAR ground deformation point.

[0106] Compare the slope stability evaluation result corresponding to the InSAR ground deformation point with the slope stability calculation result corresponding to the safety factor value in the InSAR ground deformation point data. Iteratively train and test the initial graph convolutional ensemble network based on the attention mechanism according to the comparison result until the error index of the initial graph convolutional ensemble network based on the attention mechanism meets the evaluation accuracy requirement, and obtain the graph convolutional ensemble network based on the attention mechanism.

[0107] Among them, the error index includes mean absolute error, mean square error and root mean square error.

[0108] In an implementation manner of this embodiment, the step of inputting the node feature sample data set into the initial graph convolutional neural network based on the attention mechanism, where the initial graph convolutional neural network based on the attention mechanism extracts spatial correlation features from the node feature sample data set to obtain the spatial correlation features corresponding to the node feature sample data set specifically includes:

[0109] Input the node feature sample data set into the attention mechanism layer. The activation function of the attention mechanism layer calculates the weight value of each node according to the deformation rate of each node and the slope stability influence factor sequence in the node feature sample data set, and calculates the attention matrix based on the weight value of each node and the input features:

[0110] ;

[0111] Among them, denotes M the attention matrix of dimensions, denotes the activation function of the attention mechanism layer, The weight value of a node, denotes the bias of the

[0112] th node; It should be noted that the activation function of the attention mechanism layer is ReLU (Rectified Linear Unit);

[0113] ;

[0114] Among them, denotes the component of the normalized attention matrix '; denotes the component of the attention matrix and represents the degree of spatial correlation between node and node ; ' is the result of normalizing the matrix with the purpose of ensuring that the sum of the weights of all nodes is 1;

[0115] Multiply the normalized attention matrix by the node edge weight matrix to obtain an attention feature set ;

[0116] Input the attention feature set into the graph convolutional neural network. Each layer of sub-graph convolutional neural network in the graph convolutional neural network outputs spatial correlation features based on the attention feature set and the node edge weight matrix, and uses the spatial correlation features as the input data for the next layer of sub-graph convolutional neural network until obtaining the spatial correlation features output by the last layer of sub-graph convolutional neural network. Among them, the spatial correlation features output by the last layer of sub-graph convolutional neural network are the spatial correlation features corresponding to the node feature sample dataset:

[0117] = A+ ;

[0118] ;

[0119] ;

[0120] ;

[0121] Among them, denotes the intermediate node edge weight matrix, denotes the Laplace equation matrix coefficient, denotes the identification matrix of (recognition matrix of ground deformation points), represents the edge weight matrix of the new node; represents the degree matrix of, which is used for standard normalization during graph convolution operation; and are respectively the spatial correlation features output by the -th sub-graph convolution neural network and the -th sub-graph convolution neural network in the graph convolution neural network, and are respectively the initial activation function and the initial weight value of the graph convolution neural network, and are respectively the activation function and the weight value of the -th sub-graph convolution neural network in the graph convolution neural network, and are respectively the activation function and the weight value of the -th sub-graph convolution neural network in the graph convolution neural network. It should be noted that the initial activation function of the graph convolution neural network, the activation function of the -th sub-graph convolution neural network in the graph convolution neural network, and the activation function of the -th sub-graph convolution neural network in the graph convolution neural network are all ReLU (Rectified Linear Unit).

[0122] In an implementation manner of this embodiment, when inputting the spatial correlation features corresponding to the node feature sample data set into the initial hybrid ensemble learning network, the initial hybrid ensemble learning network performs slope stability evaluation on the spatial correlation features to obtain the slope stability evaluation result corresponding to the node feature sample data set, which specifically includes:

[0123] Input the spatial correlation features corresponding to the node feature sample data set into the sub-network model layer, and multiple basic neural network models in the sub-network model layer perform preliminary slope stability evaluation based on the spatial correlation features and output multiple preliminary slope stability evaluation results;

[0124] Input multiple preliminary slope stability evaluation results into the meta-machine learning classifier, and the meta-machine learning classifier performs logistic regression processing based on multiple preliminary slope stability evaluation results and outputs the safety factor value of the InSAR ground deformation points corresponding to the node feature sample data set;

[0125] When the safety factor value of the InSAR ground deformation point is greater than the preset threshold (in this implementation, the preset threshold is taken as 1), it is determined that the slope stability evaluation result of the InSAR ground deformation point is stable;

[0126] When the safety factor value of the InSAR ground deformation point is not greater than the preset threshold (in this implementation, the preset threshold is taken as 1), it is determined that the slope stability evaluation result of the InSAR ground deformation point is unstable.

[0127] Specifically, as Figure 3 shown, the attention-based graph convolutional ensemble network AGCN-HEN mainly consists of two parts: AGCN (attention-graph convolutional network, an attention-based graph convolutional neural network) and HEN (hybrid ensemble learning network, a hybrid ensemble learning network). Among them, the AGCN network mainly embeds the attention mechanism Attention in the graph convolutional neural network GCN (graph convolutional network) to realize the extraction of spatial correlation features between ground deformation points; the HEN network includes a first-level sub-network model and a second-level meta-machine learning classifier to realize slope stability evaluation and output the safety factor value (i.e., the slope stability evaluation result).

[0128] As Figure 4 shown, the specific process of extracting the spatial correlation features between ground deformation points based on the graph convolutional neural network AGCN embedded with the attention mechanism includes: Let the node feature sample data set of the GGDN undirected network graph be , including input features of nodes, specifically including the deformation rates corresponding to each of the nodes and the values of slope stability influence factors arranged in descending order based on the importance index. After the node feature sample data set extracted by the nodes of the GGDN undirected network graph enters the attention mechanism Attention layer, according to the ground deformation rate of each node in the graph and the slope stability influence factors arranged in descending order based on the importance index, the activation function in the Attention layer respectively obtains the weight values of nodes; and based on the weight value of each node and the slope stability input feature vector data (i.e., input feature) of the node, an attention matrix is obtained; then based on the attention matrix and the node edge weight matrix, the attention mechanism result (i.e., the attention feature set ) is obtained as the input data of the graph convolutional neural network GCN layer.

[0129] The attention vector features (i.e., the attention feature set) obtained by the attention mechanism and the node-edge weight matrix (i.e., the adjacency matrix) are used as the input data of the GCN network, and the spatial correlation features of ground deformation of all nodes in the GGDN undirected network graph are obtained through the GCN network. By constructing a stacked GCN network structure, the spatial correlation feature data of ground deformation output by the previous layer GCN network structure of each node is used as the input data of the next layer GCN network structure, and so on, until the spatial correlation features between ground deformation points in the global area are obtained. The spatial correlation features between ground deformation points are extracted through this GCN network structure.

[0130] As Figure 3 and Figure 5 shown, the specific process of the hybrid integrated learning network HEN for slope stability prediction (i.e., slope stability assessment) includes: The HEN network model mainly consists of two parts, including the sub-network model of the first level and the meta-machine learning classifier of the second level. The sub-network model of the first level of the HEN network model includes 11 basic neural network models: LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), SRU (Simple Recurrent Unit), RF (Random Forest), AdaBoost (Adaptive Boosting), GBM (Gradient Boosting Machine), XGB (Extreme Gradient Boosting), SVM (Support Vector Machine), KNN (K-Nearest Neighbors), DT (Decision Tree), GNB (Gaussian Naive Bayes). The prediction results of these basic neural network models are used as the input data of the meta-machine learning classifier in the second level of the HEN model. The HEN network uses the above-mentioned spatial correlation features extracted by the AGCN network as a new sample data set (i.e., new input data), and sets the network model parameters using the grid search algorithm. Each sub-network model in the HEN network model outputs the slope stability prediction result with the minimum error through continuous iterative training. Among them, LR represents Logistic Regression, represents the first new input data, represents the second new input data, represents the third new input data, Represents the nth new input data, L represents the number of prediction objects (i.e., the number of InSAR ground deformation points), and P represents the number of sub-network models.

[0131] In one implementation of this embodiment, the error index of the model evaluation accuracy is used to describe the difference between the model prediction value (slope stability evaluation result) and the true value (slope stability calculation result), and can quantify the performance effect of the model. The present invention uses three statistical indexes, namely, the mean absolute error MAE (mean absolute error), the mean square error MSE (mean square error), and the root mean square error RMSE (Root Mean Square Error), to evaluate the evaluation performance effect of the slope stability evaluation model (i.e., the graph convolution integration network based on the attention mechanism). The smaller the three indexes are, the better the model performance effect is. The corresponding calculation formulas are as follows:

[0132] ;

[0133] ;

[0134] ;

[0135] Among them, represents the number of the test set, and respectively represent the th true value and the th predicted value. It should be noted that the present invention divides the node feature sample data set into a training set and a test set. In addition, slope stability prediction refers to slope stability evaluation, and the prediction result is the evaluation result.

[0136] The present invention proposes a graph convolution neural network AGCN model embedded with an attention mechanism to realize the extraction of spatial correlation features between ground deformation points, and solves the problem that the existing slope stability evaluation method does not consider the potential landslide hazards in the adjacent areas of the landslide. At the same time, aiming at the problem that a single neural network is suitable for the analysis of homogeneous data, for the landslide data set with multi-source heterogeneity, by integrating the advantages of multiple single networks, a HEN hybrid integration network model is proposed to realize the application evaluation of multi-source heterogeneous structure data.

[0137] S3. Obtain the InSAR ground deformation point data to be evaluated, input the InSAR ground deformation point data to be evaluated into the graph convolution integration network based on the attention mechanism, and output the slope stability evaluation result corresponding to the InSAR ground deformation point data to be evaluated.

[0138] In another implementation of this embodiment, the overall idea framework of the slope stability evaluation method based on the attention mechanism graph convolution integration network is as follows Figure 2 shown, which is mainly divided into three parts: 1. Data preprocessing: First, through InSAR ground deformation point data and landslide point data, obtain InSAR ground deformation points related to landslide attributes, and extract multi-source slope stability influence factor attribute data based on this, and conduct importance analysis to obtain a dataset of slope stability influence sequence factors based on InSAR points. On this basis, construct global regional ground deformation spatial correlation data and the corresponding undirected network graph GGDN to provide data support for slope stability evaluation. 2. AGCN-HEN network model: First, according to the proposed AGCN network, extract the spatial correlation features between InSAR ground deformation points, and on this basis, use the proposed hybrid ensemble learning network HEN model to output the slope stability prediction result with the minimum error. 3. Accuracy evaluation: Use three error indices, namely mean absolute error MAE, mean square error MSE, and root mean square error RMSE, to evaluate the prediction accuracy of the AGCN-HEN network model.

[0139] In addition, based on the above slope stability evaluation method based on the attention mechanism graph convolution integration network, the present invention also provides a slope stability evaluation system based on the attention mechanism graph convolution integration network. Among them, a preferred embodiment of the slope stability evaluation system based on the attention mechanism graph convolution integration network is as follows Figure 6 shown, specifically including:

[0140] Sample dataset construction module 01: Used to obtain initial InSAR ground deformation point data and landslide point data, and preprocess the initial InSAR ground deformation point data and the landslide point data to obtain InSAR ground deformation points in the adjacent area of the landslide point and a node feature sample dataset based on the InSAR ground deformation points;

[0141] Network construction module 02: Used to construct an initial attention mechanism graph convolution integration network, train and test the initial attention mechanism graph convolution integration network using the node feature sample dataset, and obtain an attention mechanism graph convolution integration network whose error index meets the evaluation accuracy requirements;

[0142] Slope stability evaluation module 03: Used to obtain InSAR ground deformation point data to be evaluated, input the InSAR ground deformation point data to be evaluated into the attention mechanism graph convolution integration network, and output the slope stability evaluation result corresponding to the InSAR ground deformation point data to be evaluated.

[0143] In addition, based on the above slope stability evaluation method and system of the graph convolution integration network based on the attention mechanism, the present invention also correspondingly provides a terminal. In a preferred embodiment of the terminal, as Figure 7 shown, it specifically includes a processor 10, a memory 20, and a display 30. Figure 7 Only some components of the terminal are shown. However, it should be understood that it is not required to implement all the shown components, and more or fewer components can be alternatively implemented.

[0144] The memory 20 can be an internal storage unit of the terminal in some embodiments, such as the hard disk or memory of the terminal. The memory 20 can also be an external storage device of the terminal in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, and a Flash Card equipped on the terminal, etc. Further, the memory 20 can also include both the internal storage unit and the external storage device of the terminal. The memory 20 is used to store application software installed on the terminal and various types of data, such as storing the program code of the terminal, etc. The memory 20 can also be used to temporarily store data that has been output or will be output. In an embodiment, a slope stability evaluation program 40 of the graph convolution integration network based on the attention mechanism is stored on the memory 20, and the slope stability evaluation program 40 of the graph convolution integration network based on the attention mechanism can be executed by the processor 10, so as to implement the steps of the slope stability evaluation method of the graph convolution integration network based on the attention mechanism in the present application.

[0145] The processor 10 can be a Central Processing Unit (CPU), a microprocessor, or other data processing chips in some embodiments, and is used to run the program code stored in the memory 20 or process data, such as executing the slope stability evaluation program 40 of the graph convolution integration network based on the attention mechanism, etc.

[0146] The display 30 can be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. in some embodiments. The display 30 is used to display information on the terminal and to display a visual user interface.

[0147] In an embodiment, when the processor 10 executes the slope stability evaluation program 40 of the graph convolution integration network based on the attention mechanism in the memory 20, the steps of the slope stability evaluation method of the graph convolution integration network based on the attention mechanism as described above are implemented.

[0148] The present invention also correspondingly provides a computer-readable storage medium. The computer-readable storage medium stores a slope stability evaluation program of a graph convolutional integration network based on an attention mechanism. When the slope stability evaluation program of the graph convolutional integration network based on the attention mechanism is executed by a processor, it implements the steps of the slope stability evaluation method of the graph convolutional integration network based on the attention mechanism as described above.

[0149] It should be noted that in this text, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or terminal. Without more limitations, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or terminal including that element.

[0150] Certainly, those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware (such as a processor, a controller, etc.) through a computer program. The program can be stored in a computer-readable storage medium that can be read by a computer. When the program is executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be a memory, a magnetic disk, an optical disc, etc.

[0151] It should be understood that the application of the present invention is not limited to the above examples. For those of ordinary skill in the art, improvements or transformations can be made according to the above description. All such improvements and transformations should fall within the protection scope of the appended claims of the present invention.

Claims

1. A slope stability evaluation method based on an attention mechanism-based graph convolution integration network, characterized in that The slope stability evaluation method based on the attention mechanism graph convolution integration network includes: Obtain the initial InSAR ground deformation point data and landslide point data, and preprocess the initial InSAR ground deformation point data and the landslide point data to obtain the InSAR ground deformation points in the adjacent area of the landslide point and the node feature sample data set based on the InSAR ground deformation points; Construct an initial attention mechanism-based graph convolution integration network, and use the node feature sample data set to train and test the initial attention mechanism-based graph convolution integration network to obtain an attention mechanism-based graph convolution integration network with an error index meeting the evaluation accuracy requirements; Obtain the InSAR ground deformation point data to be evaluated, input the InSAR ground deformation point data to be evaluated into the attention mechanism-based graph convolution integration network, and output the slope stability evaluation result corresponding to the InSAR ground deformation point data to be evaluated; The construction of the initial attention mechanism-based graph convolution integration network, using the node feature sample data set to train and test the initial attention mechanism-based graph convolution integration network to obtain an attention mechanism-based graph convolution integration network with an error index meeting the evaluation accuracy requirements specifically includes: Construct an initial attention mechanism-based graph convolution integration network, where the initial attention mechanism-based graph convolution integration network includes an initial attention mechanism-based graph convolution neural network and an initial hybrid integration learning network. The initial attention mechanism-based graph convolution neural network includes an attention mechanism layer and a graph convolution neural network, and the initial hybrid integration learning network includes a sub-network model layer and a meta-machine learning classifier; Input the node feature sample data set into the initial attention mechanism-based graph convolution neural network, and the initial attention mechanism-based graph convolution neural network extracts the spatial correlation features of the node feature sample data set to obtain the spatial correlation features corresponding to the node feature sample data set; Input the spatial correlation features corresponding to the node feature sample data set into the initial hybrid integration learning network, and the initial hybrid integration learning network conducts slope stability evaluation on the spatial correlation features to obtain the slope stability evaluation result corresponding to the node feature sample data set, where the slope stability evaluation result corresponding to the node feature sample data set is the slope stability evaluation result corresponding to the InSAR ground deformation point; Compare the slope stability evaluation result corresponding to the InSAR ground deformation point with the slope stability calculation result corresponding to the safety factor value in the InSAR ground deformation point data, and perform iterative training and testing on the initial attention mechanism-based graph convolution integration network according to the comparison result until the error index of the initial attention mechanism-based graph convolution integration network meets the evaluation accuracy requirements to obtain an attention mechanism-based graph convolution integration network; Among them, the error index includes mean absolute error, mean square error, and root mean square error.

2. The slope stability evaluation method of the graph convolution integration network based on the attention mechanism according to claim 1, characterized in that, Obtaining the initial InSAR ground deformation point data and landslide point data, and preprocessing the initial InSAR ground deformation point data and the landslide point data to obtain InSAR ground deformation points in the adjacent area of the landslide point and a node feature sample data set based on the InSAR ground deformation points, specifically including: Obtaining the initial InSAR ground deformation point data, landslide point data, publicly available multi-source landslide inducing factor data, ground elevation data, and slope data of the research area; According to the initial InSAR ground deformation point data, the landslide point data, the publicly available multi-source landslide inducing factor data, the ground elevation data, and the slope data, through spatial correlation analysis, spatial multi-value extraction analysis, importance ranking, and safety factor value calculation, obtaining an InSAR ground deformation point with an associated slope stability influence factor sequence and safety factor value attribute in the adjacent area of the landslide point; Based on the InSAR ground deformation point with the associated slope stability influence factor sequence and safety factor value attribute, through spatial correlation calculation, constructing an undirected network graph, and using the undirected network graph to obtain a node feature sample data set based on the InSAR ground deformation point.

3. The slope stability evaluation method of the graph convolution integration network based on the attention mechanism according to claim 2, characterized in that, The step of, according to the initial InSAR ground deformation point data, the landslide point data, the publicly available multi-source landslide inducing factor data, the ground elevation data, and the slope data, through spatial correlation analysis, spatial multi-value extraction analysis, importance ranking, and safety factor value calculation, obtaining an InSAR ground deformation point with an associated slope stability influence factor sequence and safety factor value attribute in the adjacent area of the landslide point, specifically including: According to the initial InSAR ground deformation point data and the landslide point data, through spatial correlation analysis, establishing an attribute association between the landslide point and the initial InSAR ground deformation point, obtaining an InSAR ground deformation point with an associated landslide attribute, where the InSAR ground deformation point is the initial InSAR ground deformation point in the adjacent area of the landslide point; According to the InSAR ground deformation point and the publicly available multi-source landslide inducing factor data, through spatial multi-value extraction analysis, obtaining a plurality of slope stability influence factors; Using the information gain algorithm to analyze the importance index of each of the slope stability influence factors, and arranging each of the slope stability influence factors in descending order according to the importance index of each of the slope stability influence factors, obtaining a slope stability influence factor sequence; According to the ground elevation data and the slope data corresponding to the InSAR ground deformation point, using the finite element method to calculate the safety factor value of the InSAR ground deformation point; Based on the InSAR ground deformation point, the slope stability influence factor sequence, and the safety factor value, obtaining an InSAR ground deformation point with an associated slope stability influence factor sequence and safety factor value attribute in the adjacent area of the landslide point; Among them, the InSAR ground deformation point data corresponding to the associated slope stability influence factor sequence and the safety factor value attribute of the InSAR ground deformation point includes the safety factor value, the deformation rate, the slope stability influence factor sequence, and the safety factor value.

4. The slope stability assessment method of the graph convolution integration network based on the attention mechanism according to claim 3, characterized in that, Based on the InSAR ground deformation point of the associated slope stability influence factor sequence and the safety factor value attribute, an undirected network graph is constructed through spatial correlation calculation, and a node feature sample data set based on the InSAR ground deformation point is obtained by using the undirected network graph. Specifically, it includes: Based on the InSAR ground deformation point of the associated slope stability influence factor sequence and the safety factor value attribute, spatial correlation data based on the InSAR ground deformation point is obtained through spatial correlation calculation. Among them, the spatial correlation data includes spatial correlation values, and the spatial correlation values are calculated through a Gaussian similarity function: =exp ; Among them, represents the spatial correlation value between the th InSAR ground deformation point and the th InSAR ground deformation point, represents the Euclidean distance between the th InSAR ground deformation point and the th InSAR ground deformation point, represents the standard deviation of the Euclidean distance between the th InSAR ground deformation point and the th InSAR ground deformation point; exp represents the exponential function. Construct an undirected network graph based on the InSAR ground deformation points according to the associated slope stability influence factor sequence and the safety factor value attribute, and the spatial correlation data based on the InSAR ground deformation points. Take the InSAR ground deformation points as the nodes of the undirected network graph, take the deformation rate of the InSAR ground deformation points and the slope stability influence factor sequence as the deformation rate and slope stability influence factor sequence of the corresponding nodes, and take the spatial correlation value between the InSAR ground deformation points as the spatial similarity value and node edge weight between the corresponding nodes, so as to obtain an undirected network graph , where v represents the set of nodes in the undirected network graph g, represents the edge data set, and A represents the node edge weight matrix; Obtain a node feature sample data set by using the undirected network graph, where the node feature sample data set includes input features corresponding to nodes, and the input features include a strain rate and a slope stability influence factor sequence: ； ； Among them, represents the node feature sample data set, represents the input feature corresponding to the 1st node, represents the input feature corresponding to the 2nd node, represents the input feature corresponding to the th node, input feature corresponding to the represents the total number of nodes, represents the deformation rate corresponding to the th node, represents the 1st slope stability influence factor corresponding to the th node, represents the 2nd slope stability influence factor corresponding to the th node, represents the th slope stability influence factor corresponding to the represents the total number of slope stability influence factors in the slope stability influence factor sequence.

5. The slope stability evaluation method of the graph convolution integration network based on the attention mechanism according to claim 4, characterized in that, Inputting the node feature sample data set into the initial graph convolutional neural network based on the attention mechanism, the initial graph convolutional neural network based on the attention mechanism extracts spatial correlation features from the node feature sample data set, and obtains the spatial correlation features corresponding to the node feature sample data set. Specifically, it includes: Inputting the node feature sample data set into the attention mechanism layer, the activation function of the attention mechanism layer calculates the weight value of each node according to the deformation rate and the slope stability influence factor sequence of each node in the node feature sample data set, and calculates the attention matrix based on the weight value of each node and the input features: ； Among them, represents the attention matrix, represents the activation function of the attention mechanism layer, represents the weight value of the th node, and represents the bias of the Performing normalization processing on the attention matrix to obtain a normalized attention matrix: ； Among them, represents the component of the normalized attention matrix '; represents the component of the attention matrix ; Multiply the normalized attention matrix by the node-edge weight matrix to obtain an attention feature set ; Input the attention feature set into the graph convolutional neural network. Each sub-graph convolutional neural network layer in the graph convolutional neural network outputs spatial correlation features based on the attention feature set and the node-edge weight matrix, and uses the spatial correlation features as the input data for the next sub-graph convolutional neural network layer until the spatial correlation features output by the last sub-graph convolutional neural network layer are obtained. Among them, the spatial correlation features output by the last sub-graph convolutional neural network layer are the spatial correlation features corresponding to the node feature sample data set: = A+ ； ； ； ； Among them, represents the edge weight matrix of the intermediate node, represents the matrix coefficient of the Laplace equation, represents the identification matrix of nodes, represents the edge weight matrix of the new node, represents the degree matrix of and are the spatial correlation features output by the -th layer subgraph convolutional neural network and the -th layer subgraph convolutional neural network in the graph convolutional neural network respectively, and are the initial activation function and the initial weight value of the graph convolutional neural network respectively, and are the activation function and the weight value of the -th layer subgraph convolutional neural network in the graph convolutional neural network respectively, and are the activation function and the weight value of the -th layer subgraph convolutional neural network in the graph convolutional neural network respectively.

6. The slope stability evaluation method of the graph convolution integration network based on the attention mechanism according to claim 5, characterized in that Inputting the spatial correlation features corresponding to the node feature sample data set into the initial hybrid ensemble learning network, the initial hybrid ensemble learning network performs slope stability evaluation on the spatial correlation features, and obtains the slope stability evaluation result corresponding to the node feature sample data set. Specifically, it includes: Inputting the spatial correlation features corresponding to the node feature sample data set into the sub-network model layer, and multiple basic neural network models in the sub-network model layer perform preliminary slope stability evaluation based on the spatial correlation features, and output multiple preliminary slope stability evaluation results; Inputting multiple preliminary slope stability evaluation results into the meta-machine learning classifier, and the meta-machine learning classifier performs logistic regression processing based on multiple preliminary slope stability evaluation results, and outputs the safety factor value of the InSAR ground deformation point corresponding to the node feature sample data set; When the safety factor value of the InSAR ground deformation point is greater than the preset threshold, it is determined that the slope stability evaluation result of the InSAR ground deformation point is stable; When the safety factor value of the InSAR ground deformation point is not greater than the preset threshold, it is determined that the slope stability evaluation result of the InSAR ground deformation point is unstable.

7. A slope stability evaluation system based on an attention mechanism-based graph convolution integration network, characterized in that The slope stability evaluation system of the graph convolutional integration network based on the attention mechanism includes: Sample dataset construction module: used to obtain initial InSAR ground deformation point data and landslide point data, and preprocess the initial InSAR ground deformation point data and the landslide point data to obtain InSAR ground deformation points in the adjacent area of the landslide point and a node feature sample dataset based on the InSAR ground deformation points; Network construction module: used to construct an initial graph convolutional integration network based on the attention mechanism, train and test the initial graph convolutional integration network based on the attention mechanism using the node feature sample dataset, and obtain a graph convolutional integration network based on the attention mechanism whose error index meets the evaluation accuracy requirements; Slope stability evaluation module: used to obtain the InSAR ground deformation point data to be evaluated, input the InSAR ground deformation point data to be evaluated into the graph convolutional integration network based on the attention mechanism, and output the slope stability evaluation result corresponding to the InSAR ground deformation point data to be evaluated; The construction of the initial graph convolutional integration network based on the attention mechanism, training and testing the initial graph convolutional integration network based on the attention mechanism using the node feature sample dataset, and obtaining a graph convolutional integration network based on the attention mechanism whose error index meets the evaluation accuracy requirements specifically includes: Construct an initial graph convolutional integration network based on the attention mechanism, where the initial graph convolutional integration network based on the attention mechanism includes an initial graph convolutional neural network based on the attention mechanism and an initial hybrid ensemble learning network, the initial graph convolutional neural network based on the attention mechanism includes an attention mechanism layer and a graph convolutional neural network, and the initial hybrid ensemble learning network includes a sub-network model layer and a meta-machine learning classifier; Input the node feature sample dataset into the initial graph convolutional neural network based on the attention mechanism, and the initial graph convolutional neural network based on the attention mechanism extracts spatial correlation features from the node feature sample dataset to obtain the spatial correlation features corresponding to the node feature sample dataset; Input the spatial correlation features corresponding to the node feature sample dataset into the initial hybrid ensemble learning network, and the initial hybrid ensemble learning network conducts slope stability evaluation on the spatial correlation features to obtain the slope stability evaluation result corresponding to the node feature sample dataset, where the slope stability evaluation result corresponding to the node feature sample dataset is the slope stability evaluation result corresponding to the InSAR ground deformation point; Compare the slope stability evaluation result corresponding to the InSAR ground deformation point with the slope stability calculation result corresponding to the safety factor value in the InSAR ground deformation point data, and perform iterative training and testing on the initial graph convolutional integration network based on the attention mechanism according to the comparison result until the error index of the initial graph convolutional integration network based on the attention mechanism meets the evaluation accuracy requirements, and obtain a graph convolutional integration network based on the attention mechanism; Among them, the error index includes mean absolute error, mean square error, and root mean square error.

8. A terminal, characterized in that, The terminal includes: a memory, a processor, and a slope stability evaluation program of a graph convolutional integration network based on an attention mechanism stored on the memory and operable on the processor. When the slope stability evaluation program of the graph convolutional integration network based on the attention mechanism is executed by the processor, the steps of the slope stability evaluation method of the graph convolutional integration network based on the attention mechanism as described in any one of claims 1-6 are implemented.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a slope stability evaluation program of a graph convolutional integration network based on an attention mechanism. When the slope stability evaluation program of the graph convolutional integration network based on the attention mechanism is executed by a processor, the steps of the slope stability evaluation method of the graph convolutional integration network based on the attention mechanism as described in any one of claims 1-6 are implemented.

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