Side slope monitoring-based twin risk analysis method for apparent displacement field and related equipment

Abstract

The invention provides an apparent displacement field twinborn risk analysis method based on slope monitoring and related equipment, and relates to the technical field of slope instability monitoring, and the method comprises the steps: obtaining the spatial position information of each monitoring point and a first time sequence monitoring signal, and carrying out the correlation analysis of each monitoring point, determining a monitoring area based on a correlation analysis result, generating a space displacement field of the monitoring area, and correcting material parameters of the rock mass by utilizing forward modeling-inversion; extracting displacement data of each monitoring point under different slope safety coefficients in the instability process so as to construct a correlation quantitative relation; and constructing a plurality of safety early warning levels, and determining the safety early warning level of the monitoring point. According to the method and the device, accurate mapping of the early warning area is realized, the displacement data and coordinate association are combined, the area of the instability risk area is estimated, and the technical problems of expensive monitoring equipment, insufficient point monitoring and subjective threshold setting in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the technical field of slope instability, and in particular to a twin risk analysis method and related equipment for apparent displacement field based on slope monitoring. Background Technology

[0002] Slope stability is a core safety issue in geotechnical engineering, transportation engineering, water conservancy engineering, and port and coastal engineering, as its stability directly determines the operational safety during the construction phase and the long-term service reliability during the maintenance phase. In engineering practice, slope instability is often caused by the accumulation of displacement due to stress imbalance within the soil mass. Therefore, displacement monitoring is a core technical means for slope stability assessment and risk warning, and is also recognized as the foundation for slope safety management within the industry.

[0003] While traditional monitoring methods such as radar monitoring systems can provide displacement data within a certain range, they suffer from several significant technical drawbacks. First, the high cost of purchasing and deploying radar monitoring systems limits their widespread application in large-scale slope monitoring projects. Second, data acquisition from radar monitoring systems is susceptible to interference from environmental factors such as severe weather and obstructions, affecting the accuracy and continuity of the data.

[0004] Furthermore, the frequent equipment calibration and troubleshooting required during long-term operation and maintenance increase maintenance costs and workload, making it impractical for long-term continuous monitoring at engineering sites. These shortcomings prevent existing technologies from simultaneously achieving low cost, spatial coverage, and long-term stability for large-area slope monitoring, and also hinder the accurate location, quantitative assessment, and graded early warning of high-risk areas based on full-area displacement field data. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a twin risk analysis method and related equipment for apparent displacement field based on slope monitoring, in order to solve at least some of the above-mentioned problems.

[0006] In a first aspect, embodiments of the present invention provide a twin risk analysis method for apparent displacement fields based on slope monitoring, including: The spatial location information of each monitoring point and the first time-series monitoring signal are obtained, and the first time-series monitoring signal is denoised using the discrete wavelet method. The first time-series monitoring signal is used to monitor the displacement changes of each monitoring point. Based on the first time-series monitoring signal after noise reduction, a correlation analysis is performed on each monitoring point, and the monitoring area is determined based on the correlation analysis results. The noise reduction result of the first time-series monitoring signal is geospatially interpolated within the monitoring area using the Kriging interpolation algorithm to generate the spatial displacement field of the monitoring area. Obtain rock mass parameters, construct a slope stability numerical analysis model based on the rock mass parameters, the geometry of the slope in the monitoring area, and the spatial displacement field of the monitoring area, and perform parameter inversion on the material parameters of the rock mass to correct the material parameters of the rock mass; The overall safety factor of the slope is calculated using the strength reduction method based on the corrected material parameters of the rock mass, and the displacement data of each monitoring point under different slope safety factors during the instability process are extracted to construct a correlation and quantitative relationship. Multiple safety warning levels are constructed based on the degree of slope instability risk. For each safety warning level, the displacement threshold corresponding to each monitoring point under different safety warning levels is determined based on the safety coefficient corresponding to the safety warning level and the correlation quantification relationship. If a slope instability warning is triggered, the spatial displacement field is associated with the GIS scene using geographic information system geographic registration to achieve regional mapping. For each monitoring point, the displacement value of the monitoring point is compared with the displacement threshold corresponding to each safety warning level to determine the safety warning level of the monitoring point. The percentage of pixels exceeding the warning threshold is counted to estimate the area of ​​the instability risk zone.

[0007] One possible design is that the step of acquiring the spatial location information of each monitoring point and the first time-series monitoring signal, and denoising the first time-series monitoring signal using the discrete wavelet method, includes: Remove outliers from the first time-series monitoring signal and use cubic spline interpolation to correct the first time-series monitoring signal to generate a second time-series monitoring signal; The second time-series monitoring signal is decomposed into high-frequency and low-frequency signals, and threshold processing is performed on the second time-series monitoring signal. The high-frequency signal after threshold processing is superimposed with the low-frequency signal in the second time-series monitoring signal to perform discrete wavelet reconstruction and generate the first time-series monitoring signal after noise reduction.

[0008] One possible design is that the thresholding process for the second timing monitoring signal includes both soft thresholding and hard thresholding.

[0009] One possible design is that, in the step of performing correlation analysis on each monitoring point based on the denoised first time-series monitoring signal, and determining the monitoring area based on the correlation analysis results, The monitoring points are calculated using the following formula. and monitoring points Correlation: ; —Number of timing sequences; —Pearson correlation coefficient; —No. Monitoring points under time series The amount of displacement; —No. Monitoring points under time series The amount of displacement; —Monitoring points during the monitoring period The average displacement of all displacement values; —Monitoring points during the monitoring period The average displacement of all displacement values.

[0010] One possible design is that, in the step of using the Kriging interpolation algorithm to perform geospatial interpolation on the noise reduction result of the first time-series monitoring signal within the monitoring area to generate the spatial displacement field of the monitoring area, any unknown point is determined using the following formula. The estimated value : The semivariance of each monitoring point is calculated using the following formula: ; —Known monitoring points Observational data; —Weighting coefficient; in, ; —The semivariance between monitoring point i and monitoring point j is known; —Unknown points and the unknown semivariance ; in, ; —Known monitoring points and monitoring points The semivariance; —Known monitoring points Observational data.

[0011] One possible design is that the steps of acquiring rock mass parameters, constructing a slope stability numerical analysis model based on the rock mass parameters, the geometry of the slope in the monitoring area, and the spatial displacement field of the monitoring area, and performing parameter inversion on the material parameters of the rock mass to correct the material parameters of the rock mass include: Sensitivity analysis is performed on the material parameters of the rock mass, and based on the results of the sensitivity analysis, the material parameters are divided into a first target parameter and a second target parameter, and the first target parameter is set as an empirical value; Construct an objective function, and perform forward and inverse iterations with the objective function as the goal to obtain the optimization results of the first objective parameter and the second objective parameter; The objective function is: ； —Objective function; —Weight of monitoring points; —No. Measured displacement at each monitoring point; —Given the first objective parameter and the second objective parameter, the first Calculated displacement of each monitoring point.

[0012] One possible design is to construct multiple safety warning levels based on the degree of slope instability risk. For each safety warning level, in the step of determining the displacement threshold corresponding to each monitoring point under different safety warning levels based on the safety coefficient corresponding to the safety warning level and the correlation quantification relationship, the slope danger is divided into blue warning, yellow warning and red warning. When a blue alert is triggered at the monitoring point, the displacement threshold of the monitoring point is: the cumulative displacement of the monitoring point is greater than or equal to the displacement value corresponding to a safety factor of 1.15; When a yellow alert is triggered at the monitoring point, the displacement threshold of the monitoring point is: the cumulative displacement of the monitoring point is greater than or equal to the displacement value corresponding to a safety factor of 1.10; When a red alert is triggered at the monitoring point, the displacement threshold of the monitoring point is: the cumulative displacement of the monitoring point is greater than or equal to the displacement value corresponding to a safety factor of 1.05.

[0013] Secondly, embodiments of this application provide a device for apparent displacement field twinning and risk analysis based on slope monitoring, including: Noise reduction module: used to acquire the spatial location information of each monitoring point and the first time-series monitoring signal, and to reduce the noise of the first time-series monitoring signal using the discrete wavelet method. The first time-series monitoring signal is used to monitor the displacement changes of each monitoring point. First determination module: used to perform correlation analysis on each monitoring point based on the first time-series monitoring signal after noise reduction, and determine the monitoring area based on the correlation analysis results; Generation module: used to perform geospatial interpolation on the noise reduction result of the first time-series monitoring signal within the monitoring area using the Kriging interpolation algorithm, and generate the spatial displacement field of the monitoring area; Correction module: acquires rock mass parameters, constructs a slope stability numerical analysis model based on the rock mass parameters, the geometry of the slope in the monitoring area, and the spatial displacement field of the monitoring area, and performs parameter inversion on the material parameters of the rock mass to correct the material parameters of the rock mass; The construction module is used to calculate the overall safety factor of the slope based on the corrected material parameters of the rock mass using the strength reduction method, and to extract the displacement data of each monitoring point under different slope safety factors during the instability process, so as to build a correlation and quantitative relationship. The second determining module is used to construct multiple safety warning levels based on the degree of slope instability risk. For each safety warning level, based on the safety coefficient corresponding to the safety warning level and the correlation quantification relationship, the displacement threshold corresponding to each monitoring point under different safety warning levels is determined. The statistics module is used to determine the safety warning level of a monitoring point by comparing its displacement value with the corresponding displacement threshold under each safety warning level after the spatial displacement field is associated with the GIS scene through geographic registration in the Geographic Information System (GIS) to achieve regional mapping if a slope instability warning is triggered. The module also calculates the percentage of pixels exceeding the warning threshold to estimate the area of ​​the instability risk zone.

[0014] Thirdly, embodiments of this application provide an electronic device, including: At least one processor; and At least one memory communicatively connected to the processor, wherein: The memory stores program instructions that can be executed by the processor, which can invoke the program instructions to perform the method as described in the first aspect.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions that cause the computer to perform the method described in the first aspect.

[0016] The embodiments of the present invention bring about the following beneficial effects: This application has at least the following beneficial effects: 1. By optimizing the data acquisition and preprocessing scheme, Ming eliminates the need for expensive radar monitoring systems, greatly reducing equipment purchase and deployment costs and making large-area slope monitoring more economical and feasible.

[0017] 2. By establishing a coordinate system for the observation area and using the Kriging interpolation method, this invention achieves accurate construction from discrete monitoring points to a global displacement field, which can comprehensively reflect the displacement situation of the monitoring area and make up for the shortcomings of traditional single-point monitoring.

[0018] 3. By correcting the material parameters of the model through parameter inversion and calculating the overall safety factor of the slope using the strength reduction method, a quantitative correlation between the safety factor and the displacement of monitoring points was established. This enabled a precise link between the early warning level and the actual stability of the slope, avoiding the problem of subjective setting of traditional early warning thresholds.

[0019] 4. By combining GIS geographic registration with over-threshold pixel statistics, we can achieve accurate location and area estimation of risk areas. Combined with a warning mechanism that combines manual preliminary judgment with tiered release, we can form a closed loop of the entire process from risk identification and level determination to area location and information push. This not only solves the problems of delayed early warning response and ambiguous risk scope in traditional early warning systems, but also provides accurate decision-making basis for engineering operation and maintenance, taking into account both the scientific nature of monitoring and its practicality on site.

[0020] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0022] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 A flowchart illustrating a twin risk analysis method for apparent displacement fields based on slope monitoring, provided in an embodiment of this application; Figure 2 A schematic diagram of the monitoring area for apparent displacement field twinning and risk analysis based on slope monitoring, provided for an embodiment of this application; Figure 3 A flowchart illustrating another apparent displacement field twin risk analysis method based on slope monitoring provided in this application embodiment; Figure 4A structural diagram of another apparent displacement field twinning and risk analysis device based on slope monitoring provided in the embodiments of this application; Figure 5 A structural diagram of an electronic device is provided for the embodiments of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Slope stability is a core safety issue in geotechnical engineering, transportation engineering, water conservancy engineering, and port and coastal engineering, as its stability directly determines the operational safety during the construction phase and the long-term service reliability during the maintenance phase. In engineering practice, slope instability is often caused by the accumulation of displacement due to stress imbalance within the soil mass. Therefore, displacement monitoring is a core technical means for slope stability assessment and risk warning, and is also recognized as the foundation for slope safety management within the industry.

[0026] While traditional monitoring methods such as radar monitoring systems can provide displacement data within a certain range, they suffer from several significant technical drawbacks. First, the high cost of purchasing and deploying radar monitoring systems limits their widespread application in large-scale slope monitoring projects. Second, data acquisition from radar monitoring systems is susceptible to interference from environmental factors such as severe weather and obstructions, affecting the accuracy and continuity of the data.

[0027] Furthermore, the frequent equipment calibration and troubleshooting required during long-term operation and maintenance increase maintenance costs and workload, making it impractical for long-term continuous monitoring at engineering sites. These shortcomings prevent existing technologies from simultaneously achieving low cost, spatial coverage, and long-term stability for large-area slope monitoring, and also hinder the accurate location, quantitative assessment, and graded early warning of high-risk areas based on full-area displacement field data.

[0028] Based on this, embodiments of the present invention provide a twin risk analysis method and related equipment for apparent displacement field based on slope monitoring, in order to solve the above-mentioned problems.

[0029] To facilitate understanding of this embodiment, a twin risk analysis method for apparent displacement field based on slope monitoring, disclosed in this embodiment of the invention, will first be described in detail.

[0030] Reference Figure 1 This application provides a twin risk analysis method for apparent displacement fields based on slope monitoring, including: S1: Obtain the spatial location information of each monitoring point and the first time-series monitoring signal, and use the discrete wavelet method to denoise the first time-series monitoring signal.

[0031] In this step, the first time-series monitoring signal is used to monitor the displacement changes of each monitoring point. As one possible approach, firstly, outliers in the first time-series monitoring signal are removed, and cubic spline interpolation is used to interpolate and correct the first time-series monitoring signal to generate a second time-series monitoring signal. Then, the second time-series monitoring signal is decomposed into high-frequency signals and low-frequency signals, and threshold processing is performed on the second time-series monitoring signal.

[0032] One feasible approach is to utilize the Pauta criterion for outlier handling. The Pauta criterion is an outlier detection method based on normal distribution and standard deviation in statistics, primarily used to identify anomalous points in a dataset. For the original slope displacement data, assuming a set of monitoring data contains random errors, the mean and standard deviation are calculated and processed to establish intervals based on a specific probability range. When the absolute value of the residual error of a monitoring value in the dataset exceeds the interval error range, the corresponding monitoring value is classified as invalid and subsequently removed. Generally, valid values ​​almost always concentrate in the range (μ-3σ, μ+3σ), with a probability of 0.9974, and the probability of exceeding this range is less than 0.0027.

[0033] To interpolate and correct the aforementioned first time-series monitoring signal, one possible approach is to use the cubic spline method to interpolate and correct the first time-series monitoring signal.

[0034] Specifically, when measured data is missing due to various subjective and objective factors, it is necessary to interpolate using existing adjacent data. Generally, there are two methods: interpolation based on physical meaning and interpolation based on mathematical theory. Since our current understanding of slope deformation is insufficient to accurately calculate the displacement at a specific moment, mathematical methods are used for data interpolation correction. Specific methods include Lagrange interpolation, Newton interpolation, Hermite interpolation, and spline functions. Cubic spline interpolation is chosen because of its advantages of flexibility, smoothness, accuracy, and ease of interpretation.

[0035] Cubic spline interpolation uses a set of cubic polynomials To represent the interpolation function between every two adjacent data points, each polynomial The form is: Where i = 0, 1, ..., n-1.

[0036] To ensure the entire difference function The smoothness and connectivity of [the system] need to meet the following conditions: (1) The polynomial must pass through the corresponding function value at each data point. , ; (2) At each node, the first and second derivatives of the interpolation function must be continuous. ; Here, let's talk about step length. Based on the above conditions, we can obtain: ; ; Therefore, we can conclude that: ; in, ; Here, boundary conditions are set, including: natural boundary conditions, fixed boundary conditions, and non-node boundary conditions. Specifically, the natural boundary conditions are ; Fixed boundary conditions are , ; Non-node boundary conditions: , .

[0037] Construct the matrix equation: Solving using the matrix method yields the following results. Then, the solutions can be obtained sequentially. , and After obtaining all coefficients, the cubic polynomial over each interval... It can be completely determined, thus constructing the overall cubic spline function. This function connects smoothly between each data point, and its first and second derivatives are continuous at these points.

[0038] Finally, the high-frequency signal after threshold processing is superimposed with the low-frequency signal in the second time-series monitoring signal to perform discrete wavelet reconstruction and generate the first time-series monitoring signal after noise reduction.

[0039] The entire noise reduction process begins by using Empirical Mode Decomposition (EMD) to decompose the noisy signal into Intrinsic Mode Functions (IMFs) of different frequencies. Then, the amplitude and frequency of the IMFs are analyzed, further classifying them into high-frequency and low-frequency IMFs. Since noise is predominantly present in high-frequency signals, wavelet decomposition and reconstruction using DWT on the high-frequency IMFs can reduce high-frequency noise. Finally, the low-frequency IMFs are added to the reconstructed wavelet (the denoised high-frequency IMFs) to achieve data smoothing.

[0040] DWT is a wavelet decomposition method based on filtering and downsampling. Its core principle is to utilize signals of different frequencies, mainly including high-pass and low-pass filters. Wavelet functions and scaling functions are used to analyze high-frequency and low-frequency signals respectively. DWT decomposes a discrete signal x[n] through a high-pass filter h[n] and a low-pass filter l[n], performs a 2x downsampling to obtain high-frequency and low-frequency coefficients, and finally reconstructs the wavelet.

[0041] Here, the formula for a linear DWT is defined as follows: ; ; in, Low-frequency coefficients These are high-frequency coefficients.

[0042] Using Discrete Wavelet Transform (DWT) decomposes a high-frequency signal into high- and low-frequency components. The process of reconstructing the original signal is the Inverse Discrete Wavelet Transform (IDWT), which means that the original input signal can be obtained by performing reconstruction filtering on the Discrete Wavelet Transform. First, the high-frequency coefficients undergo range processing. Common thresholding methods include soft thresholding and hard thresholding. Below are two common formulas for thresholding high-frequency coefficients: (1) Soft threshold processing: For each high-frequency coefficient If its absolute value is less than the threshold If so, set it to 0 and subtract the absolute value of the threshold: ; Here, (∙)+ represents the function that takes positive values, that is, (∙)+= max(∙,0).

[0043] (2) Hard thresholding: For each high-frequency coefficient If its absolute value is less than the threshold If so, then set it to zero: ; in, The sign indicates the selection of high-frequency coefficients, and λ is the threshold parameter used to control the threshold value. These are the processed high-frequency coefficients. Depending on the specific situation, appropriate thresholding methods and parameters can be selected to achieve signal denoising.

[0044] Finally, the denoised high-frequency IMF and low-frequency IMF are added together to achieve DWT-based reconstruction of the monitoring data, removing noise from the high-frequency components of the monitoring data. The wavelet reconstruction formula is as follows: ; in, It is the wavelet basis function corresponding to the low-frequency coefficients of the last layer. It is the wavelet basis function corresponding to the high-frequency coefficients of the j-th layer.

[0045] The above methods can reduce noise in time-series monitoring signals, solving the problem of low accuracy in subsequent analysis caused by noise interference in traditional monitoring data, ensuring the validity of the data foundation, and improving the accuracy of subsequent analysis. S2: Based on the first time-series monitoring signal after noise reduction, perform correlation analysis on each monitoring point, and determine the monitoring area based on the correlation analysis results.

[0046] To simplify calculations, the monitoring area is mapped to a two-dimensional plane, and a Cartesian coordinate system is established. The Y-axis is preferably aligned with the line connecting the top of a slope section to the slope angle, while the X-axis should be perpendicular to the slope section. The monitoring area is set as a rectangle, and the positions of the four corners are determined to define the extent of the monitoring area. The monitoring area is as follows: Figure 2 As shown A preferred approach is to smooth and denoise the aforementioned time-series monitoring signals, and then perform correlation analysis on all monitoring points. Correlation analysis is used to determine whether different monitoring data are correlated, and whether a change in one data point affects the others. Only monitoring points showing correlation can be used to construct the surface displacement field; otherwise, they are considered invalid points. Commonly used correlation analysis methods include the Pearson correlation coefficient method and the Spearman correlation coefficient method. In these two methods, the closer the absolute value of the correlation coefficient is to 1, the stronger the correlation between the two variables; the closer the correlation coefficient is to 0, the weaker the correlation.

[0047] Specifically, the monitoring points are calculated using the following formula. and monitoring points Correlation: ; —Number of timing sequences; —Pearson correlation coefficient; —No. Monitoring points under time series The amount of displacement; —No. Monitoring points under time series The amount of displacement; —Monitoring points during the monitoring period The average displacement of all displacement values; —Monitoring points during the monitoring period The average displacement of all displacement values.

[0048] S3: Using the Kriging interpolation algorithm, the noise reduction result of the first time-series monitoring signal is geospatially interpolated within the monitoring area to generate the spatial displacement field of the monitoring area.

[0049] Kriging interpolation is a commonly used geospatial interpolation method that estimates values ​​at unknown locations using known discrete point data. It has wide applications in geographic information systems, meteorology, geology, and other fields. The core idea of ​​Kriging interpolation is to predict the values ​​of unknown points based on the spatial correlation between known points. This will be explained in detail later.

[0050] S4: Obtain rock mass parameters, construct a slope stability numerical analysis model based on the rock mass parameters, the geometry of the slope in the monitoring area, and the spatial displacement field of the monitoring area, and perform parameter inversion on the material parameters of the rock mass to correct the material parameters of the rock mass.

[0051] In this step, the rock mass parameters specifically include the layered distribution of the soil and rock mass and the parameters of the soil and rock materials (cohesion, internal friction angle, Poisson's ratio, elastic modulus, unit weight, etc.). HyperMesh can be used to build a two-dimensional geometric model of the slope, divide the soil and rock layers into material zones, and assign mechanical parameters to each layer of soil and rock mass. At the same time, the mesh is refined for key areas such as the slope toe, slope crest, and potential sliding surface, while sparse mesh is used for non-critical areas (balancing computational accuracy and efficiency). Then, the parameters are corrected using a parameter inversion-forward iterative method.

[0052] S5: Based on the corrected material parameters of the rock mass, the overall safety factor of the slope is calculated using the strength reduction method, and the displacement data of each monitoring point under different slope safety factors during the instability process are extracted to construct a correlation and quantitative relationship.

[0053] In this step, based on the modified material parameters, the slope safety factor is calculated using the strength reduction method (the mainstream algorithm of the finite difference method). The cohesion and internal friction angle of the soil and rock are gradually reduced until the model reaches the critical instability state. At the same time, the displacement data of each monitoring point in the model (preset points such as the top of the slope, the toe of the slope, and the potential sliding surface) are extracted. The displacement data of each monitoring point under different slope safety factors during the critical instability process are extracted, and the correlation and quantification relationship is constructed based on this.

[0054] S6. Construct multiple safety warning levels based on the degree of slope instability risk. For each safety warning level, determine the displacement threshold corresponding to each monitoring point under different safety warning levels based on the safety coefficient corresponding to the safety warning level and the correlation quantification relationship.

[0055] Specifically, in the embodiments provided in this application, different safety warning levels correspond to different safety coefficients. The larger the safety coefficient, the lower the risk of slope instability.

[0056] One feasible approach is to categorize slope hazards into blue, yellow, and red alerts.

[0057] When a blue alert is triggered at the monitoring point, the displacement threshold of the monitoring point is: the cumulative displacement of the monitoring point is greater than or equal to the displacement value corresponding to a safety factor of 1.15.

[0058] When a yellow alert is triggered at the monitoring point, the displacement threshold of the monitoring point is: the cumulative displacement of the monitoring point is greater than or equal to the displacement value corresponding to a safety factor of 1.10; When a red alert is triggered at the monitoring point, the displacement threshold of the monitoring point is: the cumulative displacement of the monitoring point is greater than or equal to the displacement value corresponding to a safety factor of 1.05.

[0059] S7: If a slope instability warning is triggered, the spatial displacement field is associated with the GIS scene and mapped to the region using geographic information system geographic registration. For each monitoring point, the displacement value of the monitoring point is compared with the displacement threshold corresponding to each safety warning level to determine the safety warning level of the monitoring point. The percentage of pixels exceeding the warning threshold is counted to estimate the area of ​​the instability risk zone.

[0060] As a preferred embodiment, on the spatial displacement field image to be registered and the existing geographic coordinate GIS base map (or other reference layer), feature points (i.e., control points) with one-to-one correspondence are selected respectively. Points that are easy to identify and have fixed positions, such as road intersections, corners of ground features, and landmark facilities, are given priority. The association links of the control points are created to establish the mapping relationship between the image pixel coordinates and the real geographic coordinates.

[0061] Simultaneously, if the cumulative displacement of a monitoring point reaches a displacement threshold, a slope instability warning is triggered. By comparing the displacement value of the monitoring point with the corresponding displacement threshold at each safety warning level, the safety warning level of the monitoring point is determined, thereby accurately estimating the area of ​​the slope in the instability risk zone under the warning state. The coordinates of the instability risk zone are also extracted and sent to staff for early warning response.

[0062] This application has at least the following beneficial effects: 1. By optimizing the data acquisition and preprocessing scheme, this invention eliminates the need for expensive radar monitoring systems, greatly reducing equipment purchase and deployment costs, and making large-area slope monitoring more economical and feasible.

[0063] 2. By establishing a coordinate system for the observation area and using the Kriging interpolation method, this invention achieves accurate construction from discrete monitoring points to a global displacement field, which can comprehensively reflect the displacement situation of the monitoring area and make up for the shortcomings of traditional single-point monitoring.

[0064] 3. By correcting the material parameters of the model through parameter inversion and calculating the overall safety factor of the slope using the strength reduction method, a quantitative correlation between the safety factor and the displacement of monitoring points was established. This enabled a precise link between the early warning level and the actual stability of the slope, avoiding the problem of subjective setting of traditional early warning thresholds.

[0065] 4. By combining GIS geographic registration with over-threshold pixel statistics, the risk area can be accurately located and its area estimated. Combined with a warning mechanism that combines manual preliminary judgment with tiered release, a closed loop is formed from risk identification and level determination to area location and information push. This not only solves the problems of delayed response and ambiguous risk scope in traditional early warning systems, but also provides accurate decision-making basis for engineering operation and maintenance, taking into account both the scientific nature of monitoring and its practicality on site.

[0066] The following section will describe the method for constructing the spatial displacement field in this application: In this application, regarding unknown points The estimated value is determined using the following formula: ; —Known monitoring points Observational data; —Weighting coefficients, and .

[0067] The method for determining the weights will be explained below: Here, the semivariance between any two monitoring points is calculated using the following formula: ; —Known monitoring points and monitoring points The semivariance; —Known monitoring points Observational data; Then, a fitting curve (or mutation model) is found to simulate the relationship between distance and semivariance, so that the corresponding semivariance can be calculated based on any distance. Commonly used mutation models include linear models, exponential models, spherical models, Gaussian models, etc.

[0068] For unknown points Calculate it to all known monitoring points distance And using the previously obtained fitted curve, estimate the unknown points. With monitoring points semivariance .

[0069] Finally, the final coefficient is obtained using the following formula. : ; —The semivariance between monitoring point i and monitoring point j is known; —Unknown points and the unknown semivariance ; in, ; —Known monitoring points and monitoring points The semivariance; —Known monitoring points Observational data.

[0070] Using the above method, geospatial interpolation can be performed using the Kriging interpolation algorithm, thereby constructing a spatial displacement field.

[0071] The following section will elaborate on the methods for correcting the material parameters of rock masses: Reference Figure 3 S4: Obtaining rock mass parameters, constructing a slope stability numerical analysis model based on the rock mass parameters, the geometry of the slope in the monitoring area, and the spatial displacement field of the monitoring area, and performing parameter inversion on the material parameters of the rock mass to correct the material parameters of the rock mass specifically includes the following steps: S41: Perform sensitivity analysis on the material parameters of the rock mass, and based on the sensitivity analysis results, divide the material parameters into a first target parameter and a second target parameter, and set the first target parameter as an empirical value.

[0072] As can be seen from the foregoing, the rock mass material parameters here include: cohesion, internal friction angle, Poisson's ratio, elastic modulus and unit weight. The parameters that are highly sensitive to displacement response are set as the first target parameters, and the parameters that are highly sensitive to displacement response (such as cohesion, internal friction angle and elastic modulus) are set as the second target parameters. For the first target parameters, the initial empirical values ​​are fixed to reduce the inversion parameter space.

[0073] S42: Construct an objective function, and perform forward and inverse iterations with the objective function as the goal, to obtain the optimization results of the first objective parameter and the second objective parameter; The objective function is: ； —Objective function; —Weight of monitoring points; —No. Measured displacement at each monitoring point; —Given the first objective parameter and the second objective parameter, the first Calculated displacement of each monitoring point.

[0074] Here, points with high displacement monitoring accuracy and significant impact on slope stability are given higher weight, such as the slope crest settlement point. =1.2, ordinary point position =1.0.

[0075] By performing forward and inverse iterations using the objective function until the objective function reaches the convergence threshold, the material parameters of the rock mass can be optimized.

[0076] Based on the aforementioned embodiments, in some embodiments, the early warning is issued through multiple channels. Specifically, if it is a blue warning, it is pushed to the on-site monitors and inspection teams through the project work group and the operation and maintenance terminal APP, with details of the monitoring data exceeding the standard and the "encrypted monitoring" instruction; if it is a yellow warning, in addition to internal group push, project management personnel and construction team leaders are notified by SMS and telephone, and a paper "Early Warning Notice" is issued simultaneously, clarifying the hidden danger area and control requirements; if it is a red warning, emergency broadcasts and audible and visual alarms are activated to warn the workers within the slope's impact range on-site; all relevant units (construction, supervision, construction, and surrounding enterprises and institutions) are notified through emergency communication groups and telephone lines, and on-site personnel are notified to evacuate using the positioning system.

[0077] Reference Figure 4 Based on the foregoing embodiments, this application provides a device for apparent displacement field twinning and risk analysis based on slope monitoring, comprising: Noise reduction module: used to acquire the spatial location information of each monitoring point and the first time-series monitoring signal, and to perform noise reduction on the first time-series monitoring signal using the discrete wavelet method. The first time-series monitoring signal is used to monitor the displacement changes of each monitoring point. First determination module: used to perform correlation analysis on each monitoring point based on the first time-series monitoring signal after noise reduction, and determine the monitoring area based on the correlation analysis results; Generation module: used to perform geospatial interpolation on the noise reduction result of the first time-series monitoring signal within the monitoring area using the Kriging interpolation algorithm, and generate the spatial displacement field of the monitoring area; Correction module: acquires rock mass parameters, constructs a slope stability numerical analysis model based on the rock mass parameters, the geometry of the slope in the monitoring area, and the spatial displacement field of the monitoring area, and performs parameter inversion on the material parameters of the rock mass to correct the material parameters of the rock mass; The construction module is used to calculate the overall safety factor of the slope based on the corrected material parameters of the rock mass using the strength reduction method, and to extract the displacement data of each monitoring point under different slope safety factors during the instability process, so as to build a correlation and quantitative relationship. The second determining module is used to construct multiple safety warning levels based on the degree of slope instability risk. For each safety warning level, based on the safety coefficient corresponding to the safety warning level and the correlation quantification relationship, the displacement threshold corresponding to each monitoring point under different safety warning levels is determined. The statistics module is used to determine the safety warning level of a monitoring point by comparing its displacement value with the corresponding displacement threshold under each safety warning level after the spatial displacement field is associated with the GIS scene through geographic registration in the Geographic Information System (GIS) to achieve regional mapping if a slope instability warning is triggered. The module also calculates the percentage of pixels exceeding the warning threshold to estimate the area of ​​the instability risk zone.

[0078] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0079] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0080] Figure 5 A block diagram is shown that is suitable for implementing embodiments of the present invention. Figure 5 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0081] like Figure 5 As shown, the electronic device is represented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: one or more processors 410, memory 430, and communication bus 440 connecting different system components (including memory 430 and processor 410).

[0082] Communication bus 440 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) buses, Micro Channel Architecture (MAC) buses, Enhanced ISA buses, Video Electronics Standards Association (VESA) local buses, and Peripheral Component Interconnect (PCI) buses.

[0083] Electronic devices typically include a variety of computer-readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, and removable and non-removable media.

[0084] Memory 430 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. Memory 430 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present invention.

[0085] A program / utility having a set (at least one) of program modules can be stored in memory 430. Such program modules include—but are not limited to—an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. The program modules typically perform the functions and / or methods described in the embodiments of this invention.

[0086] Processor 410 executes various functional applications and data processing by running programs stored in memory 430, such as implementing embodiments of the present invention. Figure 1 The method provided in the illustrated embodiment.

[0087] This invention provides a non-transitory computer-readable storage medium that stores computer instructions, which cause the computer to execute embodiments of this invention. Figure 1 The method provided in the illustrated embodiment.

[0088] The aforementioned computer-readable storage medium may be any combination of one or more computer-readable media. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium that contains or stores a program that may be used by or in connection with an instruction execution system, apparatus, or device.

[0089] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0090] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0091] Computer program code for performing the operations of embodiments of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0092] The foregoing has described specific embodiments of the present invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0093] In the description of the embodiments of the present invention, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In the embodiments of the present invention, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in the embodiments of the present invention, as well as the features of the different embodiments or examples.

[0094] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of embodiments of the present invention, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0095] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of the invention pertain.

[0096] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0097] It should be noted that the terminals involved in the embodiments of the present invention may include, but are not limited to, personal computers (PCs), personal digital assistants (PDAs), wireless handheld devices, tablet computers, mobile phones, MP3 players, MP4 players, etc.

[0098] In the several embodiments provided in this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0099] Furthermore, in the various embodiments of the present invention, the functional units can be integrated into a single processor, or each unit can exist physically separately, or two or more units can be integrated into a single unit. The integrated units described above can be implemented in hardware or in a combination of hardware and software functional units.

[0100] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0101] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A twin risk analysis method for apparent displacement fields based on slope monitoring, characterized in that, include: The spatial location information of each monitoring point and the first time-series monitoring signal are obtained, and the first time-series monitoring signal is denoised using the discrete wavelet method. The first time-series monitoring signal is used to monitor the displacement changes of each monitoring point. Based on the first time-series monitoring signal after noise reduction, a correlation analysis is performed on each monitoring point, and the monitoring area is determined based on the correlation analysis results. The noise reduction result of the first time-series monitoring signal is geospatially interpolated within the monitoring area using the Kriging interpolation algorithm to generate the spatial displacement field of the monitoring area. Obtain rock mass parameters, construct a slope stability numerical analysis model based on the rock mass parameters, the geometry of the slope in the monitoring area, and the spatial displacement field of the monitoring area, and perform parameter inversion on the material parameters of the rock mass to correct the material parameters of the rock mass; The overall safety factor of the slope is calculated using the strength reduction method based on the corrected material parameters of the rock mass, and the displacement data of each monitoring point under different slope safety factors during the instability process are extracted to construct a correlation and quantitative relationship. Multiple safety warning levels are constructed based on the degree of slope instability risk. For each safety warning level, the displacement threshold corresponding to each monitoring point under different safety warning levels is determined based on the safety coefficient corresponding to the safety warning level and the correlation quantification relationship. If a slope instability warning is triggered, the spatial displacement field is associated with the GIS scene using geographic information system geographic registration to achieve regional mapping. For each monitoring point, the displacement value of the monitoring point is compared with the displacement threshold corresponding to each safety warning level to determine the safety warning level of the monitoring point. The percentage of pixels exceeding the warning threshold is counted to estimate the area of ​​the instability risk zone.

2. The method according to claim 1, characterized in that, The steps of acquiring the spatial location information of each monitoring point and the first time-series monitoring signal, and denoising the first time-series monitoring signal using the discrete wavelet method, include: Remove outliers from the first time-series monitoring signal and use cubic spline interpolation to correct the first time-series monitoring signal to generate a second time-series monitoring signal; The second time-series monitoring signal is decomposed into high-frequency and low-frequency signals, and threshold processing is performed on the second time-series monitoring signal. The high-frequency signal after threshold processing is superimposed with the low-frequency signal in the second time-series monitoring signal to perform discrete wavelet reconstruction and generate the first time-series monitoring signal after noise reduction.

3. The method according to claim 2, characterized in that, In the step of thresholding the second time-series monitoring signal, the thresholding process includes soft thresholding and hard thresholding.

4. The method according to claim 1, characterized in that, In the step of performing correlation analysis on each monitoring point based on the denoised first time-series monitoring signal, and determining the monitoring area based on the correlation analysis results... The monitoring points are calculated using the following formula. and monitoring points Correlation: ； —Number of timing sequences; —Pearson correlation coefficient; —No. Monitoring points under time series The amount of displacement; —No. Monitoring points under time series The amount of displacement; —Monitoring points during the monitoring period The average displacement of all displacement values; —Monitoring points during the monitoring period The average displacement of all displacement values.

5. The method according to claim 1, characterized in that, In the step of using the Kriging interpolation algorithm to perform geospatial interpolation on the noise reduction result of the first time-series monitoring signal within the monitoring area to generate the spatial displacement field of the monitoring area, the following formula is used to determine any unknown point. The estimated value : The semivariance of each monitoring point is calculated using the following formula: ； —Known monitoring points Observational data; —Weighting coefficient; in, ； —The semivariance between monitoring point i and monitoring point j is known; —Unknown points and the unknown semivariance ; in, ; —Known monitoring points and monitoring points The semivariance; —Known monitoring points Observational data.

6. The method according to any one of claims 1 to 5, characterized in that, The steps of acquiring rock mass parameters, constructing a slope stability numerical analysis model based on the rock mass parameters, the geometry of the slope in the monitoring area, and the spatial displacement field of the monitoring area, and performing parameter inversion on the material parameters of the rock mass to correct the material parameters of the rock mass include: Sensitivity analysis is performed on the material parameters of the rock mass, and based on the results of the sensitivity analysis, the material parameters are divided into a first target parameter and a second target parameter, and the first target parameter is set as an empirical value; Construct an objective function, and perform forward and inverse iterations with the objective function as the goal to obtain the optimization results of the first objective parameter and the second objective parameter; The objective function is: ； —Objective function; —Weight of monitoring points; —No. Measured displacement at each monitoring point; —Given the first objective parameter and the second objective parameter, the first Calculated displacement of each monitoring point.

7. The method according to claim 5, characterized in that, Multiple safety warning levels are constructed based on the degree of slope instability risk. For each safety warning level, the displacement threshold corresponding to each monitoring point under different safety warning levels is determined based on the safety coefficient corresponding to the safety warning level and the correlation quantification relationship. The slope hazard is divided into blue warning, yellow warning and red warning. When a blue alert is triggered at the monitoring point, the displacement threshold of the monitoring point is: the cumulative displacement of the monitoring point is greater than or equal to the displacement value corresponding to a safety factor of 1.15; When a yellow alert is triggered at the monitoring point, the displacement threshold of the monitoring point is: the cumulative displacement of the monitoring point is greater than or equal to the displacement value corresponding to a safety factor of 1.10; When a red alert is triggered at the monitoring point, the displacement threshold of the monitoring point is: the cumulative displacement of the monitoring point is greater than or equal to the displacement value corresponding to a safety factor of 1.

05.

8. A device for twinning and risk analysis of apparent displacement fields based on slope monitoring, characterized in that, include: Noise reduction module: used to acquire the spatial location information of each monitoring point and the first time-series monitoring signal, and to reduce the noise of the first time-series monitoring signal using the discrete wavelet method. The first time-series monitoring signal is used to monitor the displacement changes of each monitoring point. First determination module: used to perform correlation analysis on each monitoring point based on the first time-series monitoring signal after noise reduction, and determine the monitoring area based on the correlation analysis results; Generation module: used to perform geospatial interpolation on the noise reduction result of the first time-series monitoring signal within the monitoring area using the Kriging interpolation algorithm, and generate the spatial displacement field of the monitoring area; Correction module: acquires rock mass parameters, constructs a slope stability numerical analysis model based on the rock mass parameters, the geometry of the slope in the monitoring area, and the spatial displacement field of the monitoring area, and performs parameter inversion on the material parameters of the rock mass to correct the material parameters of the rock mass; The construction module is used to calculate the overall safety factor of the slope based on the corrected material parameters of the rock mass using the strength reduction method, and to extract the displacement data of each monitoring point under different slope safety factors during the instability process, so as to build a correlation and quantitative relationship. The second determining module is used to construct multiple safety warning levels based on the degree of slope instability risk. For each safety warning level, based on the safety coefficient corresponding to the safety warning level and the correlation quantification relationship, the displacement threshold corresponding to each monitoring point under different safety warning levels is determined. The statistics module is used to determine the safety warning level of a monitoring point by comparing its displacement value with the corresponding displacement threshold under each safety warning level after the spatial displacement field is associated with the GIS scene through geographic registration in the Geographic Information System (GIS) to achieve regional mapping if a slope instability warning is triggered. The module also calculates the percentage of pixels exceeding the warning threshold to estimate the area of ​​the instability risk zone.

9. An electronic device, characterized in that, include: At least one processor; as well as At least one memory communicatively connected to the processor, wherein: The memory stores program instructions that can be executed by the processor, and the processor can invoke the program instructions to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause the computer to perform the method as described in any one of claims 1 to 7.

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