Slope monitoring and early warning method and device, storage medium and electronic equipment

Abstract

The invention provides a slope monitoring and early warning method and device, a storage medium and electronic equipment, and relates to the technical field of reservoir safety. The method comprises the following steps: acquiring multi-modal data of different monitoring points in a target slope area; performing principal component analysis on the multi-modal data of each monitoring point to obtain a corresponding slope feature score; constructing a geographic space continuous model of the target slope area, determining a reference feature score of a spatial neighborhood of each monitoring point in the geographic space continuous model, and determining a local deviation degree of each monitoring point according to the slope feature score and the reference feature score; and performing spatial correlation analysis by using each local deviation degree to obtain a global deviation degree of the target slope area, and generating slope early warning information according to the global deviation degree. According to the method and the device, the multi-modal data are deeply integrated, so that the slope stability can be accurately evaluated.

Description

Technical Field

[0001] The present disclosure relates to the field of reservoir safety technology, and in particular to a slope monitoring and early warning method, device, storage medium, and electronic equipment. Background Art

[0002] Reservoir slope early warning is a key component of reservoir safety. It typically uses sensor monitoring combined with early warning algorithms. Sensors used include image sensors, ultrasonic detection sensors, and vibration monitoring sensors. The collected characteristic information includes displacement, crack detection, and soil moisture, all of which can directly or indirectly affect slope geological hazards.

[0003] Furthermore, each sensor is processed independently, with different types of data simply overlaid or compared in subsequent analysis. This approach makes it difficult to fully explore the potential connections between multimodal data, ignoring the inherent coupling of different modal data in time, space, and physical properties, and limiting the ability to fully perceive slope warnings.

[0004] Therefore, there is an urgent need to provide a more efficient and scientific method that can deeply process multimodal data in reservoir slope monitoring, so as to improve the accuracy and reliability of slope monitoring and early warning.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0006] The purpose of the embodiments of the present disclosure is to provide a slope monitoring and early warning method, device, storage medium and electronic device, thereby at least to a certain extent solving the problem in related technologies of low accuracy and reliability of slope monitoring and early warning due to the difficulty in fully exploring the potential correlations between multimodal data.

[0007] According to a first aspect of an embodiment of the present disclosure, a slope monitoring and early warning method is provided, comprising: Acquire multimodal data from different monitoring points within the target slope area; Performing principal component analysis on the multimodal data of each monitoring point to obtain a corresponding slope characteristic score; Constructing a geospatial continuity model of the target slope area, determining a reference characteristic score of a spatial neighborhood of each monitoring point in the geospatial continuity model, and determining a local deviation degree of each monitoring point based on the slope characteristic score and the reference characteristic score; A spatial correlation analysis is performed using the local deviation degrees to obtain a global deviation degree of the target slope area, and slope warning information is generated according to the global deviation degree.

[0008] In an exemplary embodiment of the present disclosure, performing principal component analysis on the multimodal data of each monitoring point to obtain a corresponding slope characteristic score includes: generating a standardized matrix based on the multimodal data of each monitoring point, and extracting slope characteristic data corresponding to each modal data from the standardized matrix; Determining the pairing priority between any two modal data according to a preset combination rule, and determining the target slope characteristic data pair according to the pairing priority; constructing a covariance matrix based on the mean difference between the target slope characteristic data pairs; Performing eigenvalue decomposition on the covariance matrix, and determining a principal component matrix based on the eigenvalues obtained by the decomposition; Matrix operations are performed on the standardized matrix and the principal component matrix to obtain the slope characteristic score of each monitoring point.

[0009] In an exemplary embodiment of the present disclosure, performing eigenvalue decomposition on the covariance matrix and determining a principal component matrix based on the eigenvalues obtained by the decomposition includes: Performing eigenvalue decomposition on the covariance matrix to obtain the eigenvector and corresponding eigenvalue of each principal component direction; The target principal component direction is determined according to the magnitude of the eigenvalue, and the principal component matrix is generated according to the eigenvector of the target principal component direction.

[0010] In an exemplary embodiment of the present disclosure, determining a reference characteristic score of a spatial neighborhood of each monitoring point in the geographic spatial continuity model includes: The slope characteristic scores of the monitoring points in the geographic space continuous model are spatially interpolated to obtain reference characteristic scores of the spatial neighborhood of the monitoring points.

[0011] In an exemplary embodiment of the present disclosure, performing spatial correlation analysis using the local deviation degrees to obtain the global deviation degree of the target slope area includes: Based on the geographical distribution information of each monitoring point in the target slope area, a graph structure is constructed with the monitoring points as nodes and the spatial adjacency relationship between the monitoring points as edges, the local deviation degree of each monitoring point is used as a node feature vector, and initial graph data is generated based on the graph structure and the node feature vector; Inputting the initial graph data into the trained graph neural network model, aggregating the neighbor features of each node through the graph neural network model to obtain an updated node feature vector; Based on the updated node feature vectors, the spatial weighted deviation value of each node is calculated in combination with the edge weight information, and a node deviation feature map of the target slope area is generated according to each of the spatial weighted deviation values; The node deviation feature graph is globally aggregated to obtain the global deviation degree of the target slope area.

[0012] In an exemplary embodiment of the present disclosure, generating slope warning information according to the global deviation degree includes: Setting multi-level warning thresholds based on historical monitoring data of the target slope area; Performing graded judgment on the global deviation degree according to the multi-level warning threshold to determine the current warning level of the target slope area; Based on the current warning level and combined with risk triggering conditions, corresponding slope warning information is generated; Among them, the slope warning information at least includes the warning level, risk area, risk prediction time and recommended response measures; the risk triggering conditions at least include rainfall triggering analysis results, earthquake triggering analysis results and static instability analysis results.

[0013] In an exemplary embodiment of the present disclosure, the step of acquiring multimodal data from different monitoring points within a target slope area includes: Deploy multimodal sensors based on the terrain characteristics and risk distribution of the target slope area; wherein the multimodal sensors include micro-vibration sensors, acoustic wave sensors, displacement sensors, rainfall sensors, tilt sensors, and thermal imaging sensors; The multimodal data of each monitoring point is collected by the multimodal sensor; wherein the multimodal data includes micro-vibration signals, acoustic wave signals, surface displacement data, rainfall data, slope node inclination data and surface temperature data in the target slope area.

[0014] According to a second aspect of an embodiment of the present disclosure, a slope monitoring and early warning device is provided, comprising: Multimodal data acquisition module, used to obtain multimodal data from different monitoring points in the target slope area; a characteristic score determination module, configured to perform principal component analysis on the multimodal data of each monitoring point to obtain a corresponding slope characteristic score; a deviation degree determination module, configured to construct a geospatial continuity model of the target slope area, determine a reference characteristic score of a spatial neighborhood of each monitoring point in the geospatial continuity model, and determine a local deviation degree of each monitoring point based on the slope characteristic score and the reference characteristic score; The warning information generating module is used to perform spatial correlation analysis using the local deviation degrees to obtain the global deviation degree of the target slope area, and generate slope warning information according to the global deviation degree.

[0015] According to a third aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, any step of the slope monitoring and early warning method described in the first aspect is implemented.

[0016] According to a fourth aspect of the present disclosure, there is provided an electronic device, including: A processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any step of the slope monitoring and early warning method described in the first aspect is implemented.

[0017] The technical solutions provided by the embodiments of the present disclosure may have the following beneficial effects: The slope monitoring and early warning method provided in the example embodiments of the present disclosure obtains multimodal data from different monitoring points within a target slope area; performs principal component analysis on the multimodal data of each monitoring point to obtain a corresponding slope characteristic score; constructs a geospatial continuity model of the target slope area, determines a reference characteristic score of the spatial neighborhood of each monitoring point in the geospatial continuity model, and determines the local deviation degree of each monitoring point based on the slope characteristic score and the reference characteristic score; performs spatial correlation analysis using each local deviation degree to obtain a global deviation degree of the target slope area, and generates slope early warning information based on the global deviation degree. On the one hand, by performing principal component analysis on multimodal data, it is possible to extract correlation information between features from multiple dimensions, thereby achieving deep integration of complex multimodal data, making full use of the inherent coupling relationship of different modal data, and avoiding the potential information loss problem caused by single data analysis in related technologies; on the other hand, the spatial neighborhood and reference feature scores of the monitoring points are determined through the geospatial continuity model, dynamically reflecting the relative status of the monitoring points in the local environment, accurately quantifying the degree of local anomalies, and quickly locating potential risk areas; further, the local deviation degrees are integrated through spatial correlation analysis to form an overall deviation representation of the target slope area, thereby providing a quantitative assessment of the global risk; on the other hand, the global deviation degree is used to dynamically generate warning information, so that the slope monitoring system can quickly respond to changes in the slope environment and have the ability to identify sudden risks in real time, thereby providing a more scientific and effective solution for landslide warning.

[0018] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0020] Figure 1 A system architecture diagram is shown to which the slope monitoring and early warning method according to the embodiment of the present disclosure can be applied.

[0021] Figure 2 A schematic flow chart of a slope monitoring and early warning method in an embodiment of the present disclosure is shown.

[0022] Figure 3 A schematic diagram of a process for determining a slope characteristic score of a monitoring point in an embodiment of the present disclosure is shown.

[0023] Figure 4 A schematic diagram of a process for determining the global deviation degree of a target slope area in an embodiment of the present disclosure is shown.

[0024] Figure 5 A schematic diagram of a slope monitoring and early warning device in an embodiment of the present disclosure is shown.

[0025] Figure 6 A schematic structural diagram of an electronic device suitable for implementing the embodiments of the present disclosure is shown.

[0026] In the drawings, the same or corresponding reference numerals denote the same or corresponding parts. DETAILED DESCRIPTION

[0027] The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this specification. As used in this specification and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0028] It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, such information should not be limited to these terms. These terms are merely used to distinguish information of the same type from one another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information without departing from the scope of this specification. Depending on the context, the term "if" as used herein may be interpreted as "when," "when," or "in response to determining."

[0029] Figure 1 A schematic diagram of the system architecture of a slope monitoring and early warning method to which the embodiments of the present disclosure can be applied is shown.

[0030] like Figure 1 As shown, system architecture 100 may include one or more terminal devices such as a smartphone 101, a portable computer 102, and a desktop computer 103, a network 104, and a server 105. Network 104 is a medium for providing a communication link between the terminal device and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0031] The terminal device can be any electronic device with data processing capabilities, having a display screen for displaying multimodal data, slope characteristic scores, reference characteristic scores, local deviations, global deviations, and slope warning information from different monitoring points within the target slope area. Such electronic devices include, but are not limited to, the aforementioned desktop computers, portable computers, smartphones, and tablet computers.

[0032] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as needed. For example, the server 105 may be a server cluster consisting of multiple servers.

[0033] The slope monitoring and early warning method provided in the embodiments of the present disclosure can be executed by a terminal device, and accordingly, the slope monitoring and early warning device can be installed in the terminal device. However, those skilled in the art will readily appreciate that the slope monitoring and early warning method provided in the embodiments of the present disclosure can also be executed by server 105, and accordingly, the slope monitoring and early warning device can also be installed in server 105, and this exemplary embodiment does not specifically limit this.

[0034] The present disclosure first provides a slope monitoring and early warning method. The method is described below using server execution as an example.

[0035] Figure 2The flowchart of a slope monitoring and early warning method in an embodiment of the present disclosure is schematically shown. Figure 2 As shown, the method may include steps S210 to S240: Step S210, acquiring multimodal data of different monitoring points in the target slope area; Step S220, performing principal component analysis on the multimodal data of each monitoring point to obtain a corresponding slope characteristic score; Step S230: constructing a geospatial continuity model of the target slope area, determining a reference characteristic score of a spatial neighborhood of each monitoring point in the geospatial continuity model, and determining a local deviation degree of each monitoring point based on the slope characteristic score and the reference characteristic score; Step S240 , performing spatial correlation analysis using the local deviation degrees to obtain a global deviation degree of the target slope area, and generating slope warning information according to the global deviation degree.

[0036] The slope monitoring and early warning method in this exemplary embodiment will be described in detail below.

[0037] In step S210, multimodal data of different monitoring points in the target slope area are obtained.

[0038] In the present embodiment, the target slope area refers to a slope geological area that needs to be monitored and studied. For example, the target slope area can be a slope area where cracks, settlement, or displacement occur, and landslides or rockfalls are likely to occur. It can also be a slope area where the strata are loose and there are weak soil layers or fault zones. It can also be a slope area with a large slope gradient and low stability. This disclosure does not limit this. Multimodal data refers to data obtained by deploying multiple types of sensors and acquisition equipment to conduct comprehensive monitoring of the target slope area.

[0039] For example, multimodal sensors can be deployed based on the terrain characteristics and risk distribution of the target slope area. These sensors include micro-vibration sensors, acoustic wave sensors, displacement sensors, rainfall sensors, tilt sensors, and thermal imaging sensors. For example, based on the terrain characteristics of the target slope area and known landslide or collapse risk points, sensors can be deployed preferentially in high-risk areas. The sensor density can be adjusted based on the importance and risk level of the area to ensure that sensors cover different areas of the slope.

[0040] Specifically, micro-vibration sensors can be deployed in key monitoring areas, such as around cracks, at the top and toe of the slope, to capture subtle vibration signals from the slope and identify crack expansion or landslide precursors. Acoustic wave sensors can be deployed in the middle of the slope and in areas prone to landslide activity to monitor acoustic signals generated by landslides or rockfall, thereby identifying landslide triggering events. Displacement sensors can be deployed at key points on the slope surface, such as crack edges, to monitor changes in horizontal and vertical displacement and determine deformation trends. Rainfall sensors can be deployed at the top of the slope and at representative hydrological locations within the area, recording local rainfall intensity and accumulated rainfall to analyze the potential for rainfall-induced landslides. Inclination sensors can be embedded within the slope or installed at locations prone to slippage. They measure the slope's inclination angle and changes, which can indicate landslide precursors. Thermal imaging sensors can be deployed on the slope surface, covering cracks and areas of thermal anomalies, to monitor surface temperature distribution and identify groundwater activity or abnormal heat sources.

[0041] Multimodal sensors are then used to collect multimodal data from each monitoring point. This multimodal data includes microvibration signals, acoustic signals, surface displacement data, rainfall data, slope node inclination data, and surface temperature data within the target slope area. Microvibration signals, including acceleration, spectral characteristics, and vibration energy, are collected by microvibration sensors. By capturing rapid vibration changes, they can identify local slope instability phenomena such as crack propagation and slip initiation. Acoustic signals, including the frequency and intensity of landslides or rockfalls, can be collected by acoustic sensors to identify landslide triggering events and provide dynamic information about rockfall or slope sliding processes. Surface displacement data, including horizontal and vertical displacement changes at surface points, can be collected by surface displacement sensors for long-term monitoring of slope deformation. Precipitation data, including real-time rainfall intensity and cumulative rainfall, is collected by rain gauge sensors and used to analyze the impact of hydrological factors on slope instability. Slope node inclination data, including the inclination value and rate of change, provides information about the slope's inclination trend and assists in landslide risk assessment. A slope node can be a specific location on the slope or the location of a rockfall. This data is collected using an inclination sensor. Surface temperature data, including the slope surface temperature distribution and temperature changes, is collected using a thermal imaging sensor.

[0042] It is important to note that the data collected by each sensor is recorded with a unified timestamp to ensure time synchronization of data from different modalities. Furthermore, data from different modalities are labeled according to the spatial coordinates of the sensor locations to form a unified monitoring network. When preprocessing the collected multimodal data, filtering algorithms can be used to denoise the microvibration and acoustic signals. Standardization methods are used to unify the dimensions of data from different modalities. The preprocessed data is then structured and stored to form standardized data tables divided by monitoring points.

[0043] Through the joint deployment and coordination of multimodal sensors, comprehensive physical, meteorological and thermal information of the target slope area can be obtained, providing reliable data support for subsequent slope stability analysis and slope early warning.

[0044] In step S220, principal component analysis is performed on the multimodal data of each monitoring point to obtain a corresponding slope characteristic score.

[0045] In the embodiments of this specification, principal component analysis is performed on the multimodal data of the monitoring points to extract key features reflecting slope stability from the multimodal data. Optionally, the covariance matrix can be calculated by evaluating the correlation between the modal data, and the principal components that can be used to express the comprehensive characteristics of the multimodal data can be determined based on the covariance matrix. The slope characteristic score obtained by the final analysis is the projection value of the principal component in the low-dimensional space, which reflects the comprehensive stability of the monitoring point.

[0046] refer to Figure 3 As shown, the process of performing principal component analysis on the multimodal data of each monitoring point to obtain the corresponding slope characteristic score may include steps S310 to S350: Step S310, generating a standardized matrix based on the multimodal data of each monitoring point, and extracting slope characteristic data corresponding to each modal data from the standardized matrix; The standardized data table obtained in step S210 generates a standardized matrix, which is recorded as Z={Z ij}, where i is the monitoring point number, j is the type of modal data, and each column in the matrix represents the standardized data of a certain mode. Based on this, each modal data can be extracted from the standardized matrix Z of the i-th monitoring point as a slope characteristic submatrix Z j , Z j Represents the slope characteristic data corresponding to the jth modal data, which is used for subsequent pairing analysis.

[0047] Step S320: determining a pairing priority between any two modal data according to a preset combination rule, and determining a target slope characteristic data pair according to the pairing priority; In an example implementation, combination rules can be defined based on the triggering mechanism and correlation of landslides. Specifically, abnormal signals of microvibration may be an early sign of surface structural instability, and changes in inclination can further reflect the displacement trend of the slope. The combination of the two helps to capture early characteristics before a landslide occurs, so microvibration data can be paired with inclination data. Rainfall is one of the main causes of landslides, and changes in surface displacement are often directly related to rainfall intensity and accumulation. Therefore, by pairing surface displacement data with rainfall data, the real-time impact of rainfall on slope stability can be analyzed. Surface temperature changes can affect water evaporation and soil strength of the slope, thereby affecting changes in inclination. Therefore, pairing inclination data with surface temperature data can reveal the indirect impact of temperature changes on slope stability. Crack expansion before a landslide may be accompanied by abnormal signals of low-frequency sound waves and microvibration. Therefore, pairing microvibration data with sound wave data can detect hidden slope crack changes. Rapid changes in inclination are often a precursor to surface displacement. Pairing surface displacement data with inclination data can capture the core characteristics of the overall slope instability process.

[0048] Furthermore, the pairing priority between any two modal data can be determined according to the preset combination rules, that is, the pairing priority between micro-vibration data and inclination data, surface displacement data and rainfall data, inclination data and surface temperature data, micro-vibration data and acoustic wave data, and surface displacement data and inclination data is higher, and the slope feature data pair corresponding to the modal data pair with higher pairing priority is used as the target slope feature data pair.

[0049] The preset combination rules closely integrate the physical connection and risk logic between modal data, which can improve the relevance of data analysis and provide more accurate and comprehensive support for landslide early warning.

[0050] Step S330, constructing a covariance matrix based on the mean difference between the target slope characteristic data pairs; For the target slope characteristic data pair set D={(Z j ,Z k )},Z j represents the slope characteristic data corresponding to the jth modal data, Z k Represents the slope characteristic data corresponding to the kth modal data. For each data pair (Z j ,Z k ), calculate the mean difference between the two ,have: (1) in, 、 are the means of the j-th modal data and the k-th modal data respectively.

[0051] Then, the covariance of the j-th modal data and the k-th modal data is calculated according to formula (2): ,have: (2) in, is the mean difference between the j-th modal data and the k-th modal data, which is used to adjust the covariance weight. 、 are the standard deviations of the j-th modal data and the k-th modal data, respectively, indicating the overall volatility of each modal data.

[0052] According to formulas (1) and (2), the covariance of all target slope characteristic data pairs can be calculated, and the covariance matrix can be constructed based on all covariances. The larger the covariance, the stronger the correlation between the two modal data, that is, the consistent changes in the data values. For slope monitoring, a data pair with a large covariance indicates that the two modal data have a strong coupling relationship in the landslide development process and can jointly describe a certain landslide feature or stage.

[0053] For example, a sudden increase in microvibration signals occurring simultaneously with an increase in the rate of change in inclination angle may indicate an accelerated landslide development phase. After rainfall exceeds a certain threshold, the displacement rate increases significantly, potentially reflecting rainfall-induced soil slip. In the short period following a period of high temperature and drought, increased inclination angle changes may be related to soil expansion and weakening. The simultaneous increase in the low-frequency acoustic signal and the expansion of the microvibration frequency range may reflect the propagation of surface cracks. A sharp change in inclination angle in a specific area, accompanied by a sudden increase in displacement rate, may indicate a critical landslide state.

[0054] Step S340, performing eigenvalue decomposition on the covariance matrix, and determining a principal component matrix based on the eigenvalues obtained by the decomposition; For example, the covariance matrix is subjected to eigenvalue decomposition to obtain the eigenvectors and corresponding eigenvalues of each principal component direction. The target principal component direction is then determined based on the magnitude of the eigenvalues, and the principal component matrix is generated based on the eigenvectors of the target principal component direction. For example, the eigenvector directions corresponding to the first K largest eigenvalues are selected as the target principal component directions, and the eigenvectors of the first K largest eigenvalues are used to construct the principal component matrix.

[0055] Step S350: performing a matrix operation on the standardized matrix and the principal component matrix to obtain the slope characteristic score of each monitoring point.

[0056] A matrix operation is performed on the standardized matrix and the principal component matrix to obtain the slope characteristic score matrix. In the slope characteristic score matrix, a higher slope characteristic score indicates that the area is stable, while a lower slope characteristic score indicates that the area is at risk of landslide or rockfall.

[0057] By integrating the features of multimodal data, the disclosed embodiments can effectively capture the dynamic changes of slope areas and provide an important scientific basis for landslide prediction and early warning.

[0058] In step S230, a geospatial continuity model of the target slope area is constructed, a reference characteristic score of the spatial neighborhood of each monitoring point in the geospatial continuity model is determined, and the local deviation degree of each monitoring point is determined based on the slope characteristic score and the reference characteristic score.

[0059] The geospatial continuity model is constructed based on monitoring point data within the target slope region, incorporating topography, geology, and sensor distribution. For example, the target slope region can be divided into a continuous geospatial grid using finite element meshing or point cloud interpolation. The local deviation degree of a monitoring point quantifies the degree to which the slope characteristic score at that point deviates from the reference characteristic score of its spatial neighborhood and is used to identify abnormalities at that point.

[0060] Given the spatial location and slope characteristic score of each monitoring point within the target slope area, the constructed geospatial continuity model is used to determine the spatial neighborhood of each monitoring point. For example, the set of all reference points within a specified radius from each monitoring point is selected as the neighborhood points. For another example, the multiple reference points closest to each monitoring point are selected as the neighborhood points. For another example, the neighborhood can be defined based on the slope topography trend, such as along the main landslide direction or fault line, although this disclosure does not limit this.

[0061] For example, the slope characteristic scores of each monitoring point in the geographic spatial continuous model are spatially interpolated to obtain a reference characteristic score for the spatial neighborhood of each monitoring point. For example, the slope characteristic scores of each monitoring point are spatially predicted using methods such as kriging interpolation or Gaussian process regression to obtain the reference characteristic scores of all neighboring points within the corresponding spatial neighborhood.

[0062] After obtaining the reference characteristic score of the spatial neighborhood of each monitoring point, the local deviation degree of each monitoring point can be determined according to the slope characteristic score and the reference characteristic score, as follows: (3) in, is the local deviation degree of each monitoring point, is the slope characteristic score of the i-th monitoring point, is the reference feature score of the spatial neighborhood of the i-th monitoring point, for example, it can be the weighted average of the reference feature scores of all neighboring points in the spatial neighborhood of the i-th monitoring point.

[0063] By constructing a geographic spatial continuity model, calculating the reference characteristic score of the spatial neighborhood of each monitoring point, and quantifying the degree of local deviation based on the difference between the slope characteristic score and the reference characteristic score, the spatial variation of slope characteristics can be effectively captured, providing an accurate quantitative analysis method for dynamic identification, risk assessment and early warning of landslides.

[0064] In step S240, spatial correlation analysis is performed using the local deviation degrees to obtain a global deviation degree of the target slope area, and slope warning information is generated according to the global deviation degree.

[0065] In the examples of this specification, the purpose of spatial correlation analysis is to integrate local deviations into a global deviation, assessing the overall slope stability by analyzing the spatial relationships between monitoring points. The global deviation can be defined as an instability index for the entire target slope region, calculated based on multiple local deviations.

[0066] refer to Figure 4 As shown, the process of determining the global deviation degree of the target slope area may include steps S410 to S440: Step S410: Based on the geographical distribution information of each monitoring point in the target slope area, a graph structure is constructed with the monitoring points as nodes and the spatial adjacency relationships between the monitoring points as edges. The local deviation degree of each monitoring point is used as a node feature vector, and initial graph data is generated based on the graph structure and the node feature vector. Exemplarily, the specific location coordinates of each monitoring point are recorded, such as longitude and latitude or three-dimensional spatial coordinates. The adjacency relationship between the monitoring points is defined according to the specific location coordinates of each monitoring point, such as using Euclidean distance to define adjacent nodes. The monitoring points are regarded as nodes, and the adjacency relationship between the monitoring points is regarded as edges, and an undirected weighted graph G=(V, E, A) is generated, where V is a node set, E is an edge set, and A is edge weight information, such as an adjacency matrix composed of the inverse of the distance. The local deviation degree of each monitoring point is used as a node feature vector to form a node feature matrix. The initial graph data includes an adjacency matrix and a node feature matrix, which represent the spatial distribution of the current state of the target slope area.

[0067] Step S420: Input the initial graph data into the trained graph neural network model, aggregate the neighbor features of each node through the graph neural network model, and obtain an updated node feature vector; The graph neural network model uses a message-passing mechanism to enable each node to exchange information with its neighbors, thereby updating the node feature vector. Specifically, when the trained graph neural network model is used to process the initial graph data, for each node, the corresponding node feature matrix and neighbor features are aggregated to obtain the updated node feature vector.

[0068] For example, the node feature vector is updated according to formula (4): (4) in, is the updated node feature vector of node i, is the activation function, is the linear transformation matrix of node features, is the node feature matrix before node i is updated, is the linear transformation matrix of neighbor features, is the neighbor feature of node i, is the edge weight, which reflects the strength of the relationship between node i and node j. is the number of nodes.

[0069] By superimposing multiple layers of graph neural networks, we can capture spatial correlations over a wider range, ultimately obtaining updated node feature vectors. At this point, each node's feature vector has integrated the feature information of its neighbors.

[0070] Step S430, calculating a spatial weighted deviation value of each node based on the updated node feature vector and edge weight information, and generating a node deviation feature map of the target slope area according to each of the spatial weighted deviation values; For example, for node i, based on the updated node feature vector and edge weight information Calculate the spatial weighted deviation value of the node ,have: (5) Furthermore, the spatial weighted deviation value of each node is calculated according to formula (5). The spatial weighted deviation value reflects the degree of deviation of the node after integrating its own state and the influence of the neighborhood, which is a further aggregation of the node features.

[0071] Step S440 : Globally aggregate the node deviation feature graph to obtain a global deviation degree of the target slope area.

[0072] By globally aggregating the spatially weighted deviation values of all nodes in the node deviation feature graph, the global deviation degree of the target slope area can be obtained: (6) in, is the global deviation degree of each monitoring point, is the weight of node i, is the spatial weighted deviation value of node i, is the number of nodes.

[0073] The global deviation degree reflects the current stability of the target slope area. A higher global deviation degree indicates a higher instability of the target slope area and a greater potential landslide or rockfall risk.

[0074] After determining the global deviation degree of the target slope area, a multi-level warning threshold can be set based on historical monitoring data for the target slope area. The global deviation degree is then graded using these thresholds to determine the current warning level for the target slope area. Based on the current warning level and combined with risk trigger conditions, corresponding slope warning information is generated. This slope warning information includes at least the warning level, risk area, risk prediction time, and recommended response measures. Risk trigger conditions include at least rainfall trigger analysis results, earthquake trigger analysis results, and static instability analysis results.

[0075] It is important to note that the risk prediction time in slope warning information can include the time the warning information was generated, the time the risk occurred (or the time of emergency response), etc., and this disclosure does not limit this. The risk area can be the entire slope area, a specific high-risk area, or a monitoring point number.

[0076] For example, a multi-level warning threshold is set based on the global deviation degree corresponding to the historical monitoring data collected in the historical landslide events. , the warning level is green warning, indicating that the global deviation degree is within the normal range and there is no landslide risk; if , the warning level is yellow, indicating that there is a slight landslide risk, and monitoring needs to be strengthened and relevant departments should be notified to pay attention; if , the warning level is orange, indicating a high possibility of landslides, and it is recommended to take preventive measures, such as evacuating personnel and vehicles, and blocking areas; if , the warning level is red, indicating that the risk of landslide is extremely high and requires emergency response, such as emergency evacuation, full blockade of the area, and initiation of landslide emergency response.

[0077] The current warning level of the target slope area is determined by comparing the current global deviation degree of the target slope area with the set multi-level warning threshold. For example, if the current global deviation degree of the target slope area is , the corresponding warning level is orange warning.

[0078] It should be noted that risks such as slope landslides are usually related to external triggering conditions, such as rainfall intensity, earthquake fluctuations, etc. Therefore, in the embodiments of this specification, on the basis of determining the warning level, a comprehensive analysis can be conducted in combination with the risk triggering conditions, such as predicting the risk time point, which helps to generate more accurate slope warning information.

[0079] For example, the real-time rainfall and the predicted rainfall trend are combined to use the cumulative curve to predict the time point when the rainfall reaches the threshold. Among them, the changing trend of future rainfall can be obtained through the rainfall forecast data of the meteorological department. The real-time monitored rainfall is superimposed with the future rainfall forecast to calculate the cumulative rainfall at a certain moment in the future. The time when the cumulative rainfall reaches the set threshold is the time range when the landslide risk may occur. Similarly, by monitoring seismic waves or vibration frequencies, the time range that may cause landslides can be identified. The time of critical instability can also be calculated in combination with the current slope inclination and stress state. This specification does not go into detail in the embodiments.

[0080] In addition, slope warning information can be released through multiple channels, such as text messages, APP (application) push, broadcast systems, etc. For different levels of warning, appropriate release ranges can also be selected, for example, expanding coverage during red warnings.

[0081] The slope monitoring and early warning method provided by the present invention, on the one hand, can extract the correlation information between features from multiple dimensions by performing principal component analysis on multimodal data, thereby realizing deep integration of complex multimodal data, making full use of the inherent coupling relationship of different modal data, and avoiding the potential information loss problem caused by single data analysis in related technologies; on the other hand, the spatial neighborhood and reference feature scores of the monitoring points are determined through the geospatial continuity model, dynamically reflecting the relative status of the monitoring points in the local environment, accurately quantifying the degree of local anomalies, and quickly locating potential risk areas; further, the local deviation degrees are integrated through spatial correlation analysis to form an overall deviation representation of the target slope area, thereby providing a quantitative assessment of the global risk; on the other hand, the global deviation degree is used to dynamically generate early warning information, so that the slope monitoring system can quickly respond to changes in the slope environment and have the real-time identification capability of sudden risks, thereby providing a more scientific and effective solution for landslide early warning.

[0082] In this exemplary embodiment, a slope monitoring and early warning device is also provided. Figure 5 As shown, the slope monitoring and early warning device 500 may include a multimodal data acquisition module 510, a feature score determination module 520, a deviation degree determination module 530, and an early warning information generation module 540, wherein: A multimodal data acquisition module 510 is used to acquire multimodal data from different monitoring points within the target slope area; A characteristic score determination module 520 is configured to perform principal component analysis on the multimodal data of each monitoring point to obtain a corresponding slope characteristic score; a deviation degree determination module 530 for constructing a geospatial continuity model of the target slope area, determining a reference characteristic score of a spatial neighborhood of each monitoring point in the geospatial continuity model, and determining a local deviation degree of each monitoring point based on the slope characteristic score and the reference characteristic score; The warning information generating module 540 is configured to perform spatial correlation analysis using the local deviation degrees to obtain a global deviation degree of the target slope area, and generate slope warning information according to the global deviation degree.

[0083] In an exemplary embodiment of the present disclosure, when the feature score determination module 520 performs principal component analysis on the multimodal data of each monitoring point to obtain the corresponding slope feature score, it is specifically configured to: generating a standardized matrix based on the multimodal data of each monitoring point, and extracting slope characteristic data corresponding to each modal data from the standardized matrix; Determining the pairing priority between any two modal data according to a preset combination rule, and determining the target slope characteristic data pair according to the pairing priority; constructing a covariance matrix based on the mean difference between the target slope characteristic data pairs; Performing eigenvalue decomposition on the covariance matrix, and determining a principal component matrix based on the eigenvalues obtained by the decomposition; Matrix operations are performed on the standardized matrix and the principal component matrix to obtain the slope characteristic score of each monitoring point.

[0084] In an exemplary embodiment of the present disclosure, when performing eigenvalue decomposition on the covariance matrix and determining the principal component matrix based on the eigenvalues obtained by the decomposition, the eigenscore determination module 520 is specifically configured to: Performing eigenvalue decomposition on the covariance matrix to obtain the eigenvector and corresponding eigenvalue of each principal component direction; The target principal component direction is determined according to the magnitude of the eigenvalue, and the principal component matrix is generated according to the eigenvector of the target principal component direction.

[0085] In an exemplary embodiment of the present disclosure, when determining the reference feature score of the spatial neighborhood of each monitoring point in the geographic spatial continuity model, the deviation degree determination module 530 is specifically configured to: The slope characteristic scores of the monitoring points in the geographic space continuous model are spatially interpolated to obtain reference characteristic scores of the spatial neighborhood of the monitoring points.

[0086] In an exemplary embodiment of the present disclosure, when the warning information generating module 540 performs spatial correlation analysis using the local deviation degrees to obtain the global deviation degree of the target slope area, it is specifically configured to: Based on the geographical distribution information of each monitoring point in the target slope area, a graph structure is constructed with the monitoring points as nodes and the spatial adjacency relationship between the monitoring points as edges, the local deviation degree of each monitoring point is used as a node feature vector, and initial graph data is generated based on the graph structure and the node feature vector; Inputting the initial graph data into the trained graph neural network model, aggregating the neighbor features of each node through the graph neural network model to obtain an updated node feature vector; Based on the updated node feature vectors, the spatial weighted deviation value of each node is calculated in combination with the edge weight information, and a node deviation feature map of the target slope area is generated according to each of the spatial weighted deviation values; The node deviation feature graph is globally aggregated to obtain the global deviation degree of the target slope area.

[0087] In an exemplary embodiment of the present disclosure, when generating slope warning information according to the global deviation degree, the warning information generating module 540 is specifically configured to: Setting multi-level warning thresholds based on historical monitoring data of the target slope area; Performing graded judgment on the global deviation degree according to the multi-level warning threshold to determine the current warning level of the target slope area; Based on the current warning level and combined with risk triggering conditions, corresponding slope warning information is generated; Among them, the slope warning information at least includes the warning level, risk area, risk prediction time and recommended response measures; the risk triggering conditions at least include rainfall triggering analysis results, earthquake triggering analysis results and static instability analysis results.

[0088] In an exemplary embodiment of the present disclosure, when executing the acquisition of multimodal data of different monitoring points in the target slope area, the multimodal data acquisition module 510 is specifically used to: Deploy multimodal sensors based on the terrain characteristics and risk distribution of the target slope area; wherein the multimodal sensors include micro-vibration sensors, acoustic wave sensors, displacement sensors, rainfall sensors, tilt sensors, and thermal imaging sensors; The multimodal data of each monitoring point is collected by the multimodal sensor; wherein the multimodal data includes micro-vibration signals, acoustic wave signals, surface displacement data, rainfall data, slope node inclination data and surface temperature data in the target slope area.

[0089] The specific details of each module of the above-mentioned slope monitoring and early warning device have been described in detail in the corresponding slope monitoring and early warning method, so they will not be repeated here.

[0090] The exemplary embodiments of the present disclosure further provide a computer-readable storage medium having stored thereon a program product capable of implementing the methods described above in this specification. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product comprising program code that, when executed on an electronic device, causes the electronic device to perform the steps described in the "Exemplary Methods" section above according to various exemplary embodiments of the present disclosure.

[0091] The program product may be a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on an electronic device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, a readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0092] The program product may utilize any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0093] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0094] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0095] Program code for performing the operations of the present disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C#, C++, and the like, as well as conventional procedural programming languages such as "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0096] In addition, an exemplary embodiment of the present disclosure further provides an electronic device capable of implementing the above-mentioned slope monitoring and early warning method.

[0097] Refer to the following Figure 6 6 to describe the electronic device 600 according to such an embodiment of the present disclosure. Figure 6 The electronic device 600 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0098] like Figure 6 As shown, electronic device 600 is implemented as a general-purpose computing device. Components of electronic device 600 may include, but are not limited to, the aforementioned at least one processing unit 610, the aforementioned at least one storage unit 620, a bus 630 connecting various system components (including storage unit 620 and processing unit 610), and a display unit 640.

[0099] The storage unit 620 stores program code that can be executed by the processing unit 610, causing the processing unit 610 to perform the steps described in the "Exemplary Method" section above according to various exemplary embodiments of the present disclosure. For example, the processing unit 610 can perform the method steps in the exemplary embodiments of the present disclosure.

[0100] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 621 and / or a cache memory unit (Cache) 622 , and may further include a read-only memory unit (ROM) 623 .

[0101] The storage unit 620 may also include a program / utility 624 having a set (at least one) of program modules 625, such program modules 625 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0102] Bus 630 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0103] The electronic device 600 can also communicate with one or more external devices 700 (e.g., a keyboard, pointing device, Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and / or any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., a router, modem, etc.). This communication can occur via an input / output (I / O) interface 650. Furthermore, the electronic device 600 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 660. As shown, the network adapter 660 communicates with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 600, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0104] Through the description of the above embodiments, it will be readily understood by those skilled in the art that the example embodiments described herein can be implemented via software or via a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or mobile hard drive) or on a network and includes several instructions for causing a computing device (such as a personal computer, server, terminal device, or network device) to execute the methods according to the embodiments of the present disclosure.

[0105] Furthermore, the figures above are merely illustrative of the processes included in the methods according to exemplary embodiments of the present disclosure and are not intended to be limiting. It is readily understood that the processes illustrated in the figures above do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0106] Through the description of the above embodiments, it will be readily understood by those skilled in the art that the example embodiments described herein can be implemented via software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or mobile hard drive) or on a network and includes several instructions to enable a computing device (such as a personal computer, server, touch terminal, or network device) to execute the methods according to the embodiments of the present disclosure.

[0107] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow from the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the claims.

[0108] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A slope monitoring and early warning method, characterized in that: include: Acquire multimodal data from different monitoring points within the target slope area; Performing principal component analysis on the multimodal data of each monitoring point to obtain a corresponding slope characteristic score; Constructing a geospatial continuity model of the target slope area, determining a reference characteristic score of a spatial neighborhood of each monitoring point in the geospatial continuity model, and determining a local deviation degree of each monitoring point based on the slope characteristic score and the reference characteristic score; A spatial correlation analysis is performed using the local deviation degrees to obtain a global deviation degree of the target slope area, and slope warning information is generated according to the global deviation degree.

2. The slope monitoring and early warning method according to claim 1, characterized in that: The principal component analysis is performed on the multimodal data of each monitoring point to obtain the corresponding slope characteristic score, including: generating a standardized matrix based on the multimodal data of each monitoring point, and extracting slope characteristic data corresponding to each modal data from the standardized matrix; Determining the pairing priority between any two modal data according to a preset combination rule, and determining the target slope characteristic data pair according to the pairing priority; constructing a covariance matrix based on the mean difference between the target slope characteristic data pairs; Performing eigenvalue decomposition on the covariance matrix, and determining a principal component matrix based on the eigenvalues obtained by the decomposition; Matrix operations are performed on the standardized matrix and the principal component matrix to obtain the slope characteristic score of each monitoring point.

3. The slope monitoring and early warning method according to claim 2 is characterized in that: The performing eigenvalue decomposition on the covariance matrix and determining the principal component matrix based on the eigenvalues obtained by the decomposition includes: Performing eigenvalue decomposition on the covariance matrix to obtain the eigenvector and corresponding eigenvalue of each principal component direction; The target principal component direction is determined according to the magnitude of the eigenvalue, and the principal component matrix is generated according to the eigenvector of the target principal component direction.

4. The slope monitoring and early warning method according to claim 1 is characterized in that: Determining the reference characteristic score of the spatial neighborhood of each monitoring point in the geographic space continuity model includes: The slope characteristic scores of the monitoring points in the geographic space continuous model are spatially interpolated to obtain reference characteristic scores of the spatial neighborhood of the monitoring points.

5. The slope monitoring and early warning method according to claim 1 is characterized in that: The spatial correlation analysis is performed using the local deviation degrees to obtain the global deviation degree of the target slope area, including: Based on the geographical distribution information of each monitoring point in the target slope area, a graph structure is constructed with the monitoring points as nodes and the spatial adjacency relationship between the monitoring points as edges, the local deviation degree of each monitoring point is used as a node feature vector, and initial graph data is generated based on the graph structure and the node feature vector; Inputting the initial graph data into the trained graph neural network model, aggregating the neighbor features of each node through the graph neural network model to obtain an updated node feature vector; Based on the updated node feature vectors, the spatial weighted deviation value of each node is calculated in combination with the edge weight information, and a node deviation feature map of the target slope area is generated according to each of the spatial weighted deviation values; The node deviation feature graph is globally aggregated to obtain the global deviation degree of the target slope area.

6. The slope monitoring and early warning method according to claim 1, characterized in that: Generating slope warning information according to the global deviation degree includes: Setting multi-level warning thresholds based on historical monitoring data of the target slope area; Performing graded judgment on the global deviation degree according to the multi-level warning threshold to determine the current warning level of the target slope area; Based on the current warning level and combined with risk triggering conditions, corresponding slope warning information is generated; Among them, the slope warning information at least includes the warning level, risk area, risk prediction time and recommended response measures; the risk triggering conditions at least include rainfall triggering analysis results, earthquake triggering analysis results and static instability analysis results.

7. The slope monitoring and early warning method according to claim 1, characterized in that: The obtaining of multimodal data from different monitoring points within the target slope area includes: Deploy multimodal sensors based on the terrain characteristics and risk distribution of the target slope area; wherein the multimodal sensors include micro-vibration sensors, acoustic wave sensors, displacement sensors, rainfall sensors, tilt sensors, and thermal imaging sensors; The multimodal data of each monitoring point is collected by the multimodal sensor; wherein the multimodal data includes micro-vibration signals, acoustic wave signals, surface displacement data, rainfall data, slope node inclination data and surface temperature data in the target slope area.

8. A slope monitoring and early warning device, characterized in that: include: Multimodal data acquisition module, used to obtain multimodal data from different monitoring points in the target slope area; a characteristic score determination module, configured to perform principal component analysis on the multimodal data of each monitoring point to obtain a corresponding slope characteristic score; a deviation degree determination module, configured to construct a geospatial continuity model of the target slope area, determine a reference characteristic score of a spatial neighborhood of each monitoring point in the geospatial continuity model, and determine a local deviation degree of each monitoring point based on the slope characteristic score and the reference characteristic score; The warning information generating module is used to perform spatial correlation analysis using the local deviation degrees to obtain the global deviation degree of the target slope area, and generate slope warning information according to the global deviation degree.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the slope monitoring and early warning method according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: processor; and A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the slope monitoring and early warning method according to any one of claims 1 to 7.

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