Data processing method for monitoring deformation of rock-soil body

Abstract

The invention belongs to the technical field of data processing, and particularly relates to a data processing method for rock-soil body deformation monitoring. The method comprises the following steps: firstly, acquiring displacement time sequence data of multiple monitoring points, and constructing a space-time displacement field; deformation field topological features are extracted through field theory analysis, deformation energy source sinks and a streamline network are identified, and monitoring point topological role weights are calculated; thirdly, screening an instability related characteristic frequency band through multi-scale wavelet transform, constructing a phase coupling matrix, dividing sub-regions, and calculating a coupling index; and finally, fusing the topological role weight and the phase coupling index to construct a deformation state entropy model, and triggering graded early warning according to an entropy change rate. According to the method, the limitation of traditional single-point analysis is broken through, the deformation propagation rule is accurately captured, the instability precursor recognition capability and the early warning reliability are improved, and the multi-dimensional deformation monitoring requirements of complex geology are met.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and in particular relates to a data processing method for monitoring deformation of soil and rock masses. Background Technology

[0002] Soil and rock deformation monitoring is a core component of safety management in geotechnical engineering projects such as tunnel construction. Accurate deformation data processing is crucial for identifying early signs of instability and mitigating collapse hazards. Traditional monitoring methods rely on changes in single-point displacement curves for early warning, which has significant limitations when dealing with complex geological conditions and multi-dimensional deformation propagation mechanisms. While advancements in sensor technology have enabled monitoring systems to simultaneously collect three-dimensional displacement time-series data from multiple monitoring points, the complex spatiotemporal correlations of massive amounts of data present a significant technical challenge in extracting deformation evolution patterns from the data, making reliable early warning difficult to guarantee. Summary of the Invention

[0003] In view of the technical problems existing in the background art, the present invention proposes a data processing method for monitoring deformation of soil and rock.

[0004] To achieve the above objectives, the technical solution adopted by the present invention includes the following steps:

[0005] Obtain displacement time-series data from multiple monitoring points in the monitoring area to construct a spatiotemporal displacement field;

[0006] Field theory analysis is performed on the spatiotemporal displacement field to extract the topological features of the deformation field and identify the source and sink regions of deformation energy, as well as the deformation streamline network connecting the two.

[0007] Based on the spatial distribution characteristics of the topological feature structure, the topological role weight of each monitoring point in the deformation propagation path is calculated. The topological role weight represents the degree of influence of the monitoring point on the overall deformation field evolution.

[0008] For the displacement time series data of each monitoring point, multi-scale wavelet transform is performed to calculate the time-frequency coupling index of phase consistency and deformation energy, select characteristic frequency bands related to soil instability, construct a phase coupling matrix based on the characteristic frequency bands, apply the community detection algorithm to divide the monitoring area into sub-regions, and calculate the phase coupling index of each sub-region.

[0009] A deformation state entropy model is constructed by integrating the topological role weights of each monitoring point and the phase coupling index of each sub-region. The temporal evolution trajectory of the entropy value is analyzed. When the rate of change of the entropy value continuously exceeds the preset classification threshold, the corresponding level of deformation risk warning mechanism is triggered.

[0010] Preferably, the implementation of acquiring displacement time-series data of multiple monitoring points in the monitoring area and constructing a spatiotemporal displacement field includes:

[0011] N displacement detection points with fixed spatial locations are set up within the monitoring area;

[0012] Simultaneous displacement data is collected at each monitoring point using a preset sampling frequency to obtain the three-dimensional displacement components of each monitoring point i in the time series. ,in, The i-th monitoring point is located at The components of displacement in the x, y, and z directions at time t;

[0013] The displacement data is normalized to coordinate system 1 and reference surface, and the displacement data of all monitoring points at each time point are structured according to spatial location and timestamp to form a four-dimensional dataset. ;in, Let i be the spatial coordinates of the i-th monitoring point;

[0014] Based on the four-dimensional dataset D, a spatiotemporal interpolation algorithm is used to reconstruct the displacement field between monitoring points, generating a continuous spatiotemporal displacement field function. ,in, For spatiotemporal weighting coefficients, To monitor the coordinates of any spatial point within the area, The parameters are adaptive spatial scale parameters, ensuring that a displacement estimate can be obtained for any spatial location p and any time t. This completes the construction of the spatiotemporal displacement field.

[0015] As a preferred approach, field theory analysis is performed on the spatiotemporal displacement field to extract the topological features of the deformation field, and the identification of the source and sink regions of deformation energy, as well as the deformation streamline network connecting the two, is achieved through:

[0016] Constructing the displacement gradient vector field ,in, As a gradient operator, the deformation energy density function is calculated based on the displacement gradient vector field. ; Identify key topological structures in the deformable field, including source region, sink region, and saddle region; the source region is defined as satisfying and Within the spatial regions defined as the left and right neighborhoods of 0, where, The threshold for determining the source point. These represent the divergence and curl operators, respectively; the sink region is defined as satisfying... and The spatial region within the left and right neighborhoods of 0; the saddle point region is defined as satisfying... Within the defined left and right neighborhoods of 0 and Spatial region;

[0017] Starting from the source region, the deformable streamlines are traced along the direction of the displacement gradient vector, where the streamline tracing step size is... With deformation energy density Adaptive adjustment: ,in, These are the minimum and maximum step sizes, respectively. For adaptive coefficients, Energy threshold;

[0018] The streamline termination condition is determined when the deformable streamline reaches the sink region, or when the deformation energy density at the location of the deformable streamline is determined. When the energy level is less than the preset minimum energy threshold, the tracking of the deformed streamline stops, the construction of a single deformed streamline is completed, and a deformed streamline network is formed.

[0019] Preferably, based on the spatial distribution characteristics of the topological feature structure, the topological role weight of each monitoring point in the deformation propagation path is calculated. The topological role weight characterizes the degree of influence of the monitoring point on the overall deformation field evolution, including:

[0020] Based on deformable streamline networks, the front with the highest energy transfer efficiency e is selected. The streamlines serve as the primary deformation propagation path, among which, Let L be the energy transfer efficiency of the streamline, where L is the length of the streamline. For integration variables;

[0021] If monitoring point i is located on any of the main deformation propagation paths, then calculate its Euclidean distance to the nearest source region. And calculate the initial topology role weights. ,in To avoid small constants that divide by zero; if monitoring point i is not located on any major deformation propagation path, the topological role weight is 0;

[0022] The initial topological role weights of each monitoring point are normalized to obtain the topological role weights.

[0023] As a preferred approach, multi-scale wavelet transform is performed on the displacement time-series data of each monitoring point to calculate the time-frequency coupling index of phase consistency and deformation energy. Characteristic frequency bands related to soil instability are selected, a phase coupling matrix is ​​constructed based on these characteristic frequency bands, and a community detection algorithm is applied to divide the monitoring area into sub-regions. The specific implementation of calculating the phase coupling index for each sub-region includes:

[0024] The displacement time series data of the i-th monitoring point are obtained using the Morlet wavelet basis. Perform multi-scale continuous wavelet transform to obtain the time-frequency domain representation. Where s is the scale parameter, which is automatically optimized through maximum overlap discrete wavelet transform to satisfy... Less than the preset convergence threshold;

[0025] Compute the phase consistency index PC(s,t) and deformation energy index E(s,t) at scale s: , Where J is the imaginary unit, Let be the phase angle of the i-th monitoring point at scale s and time t, and N be the total number of monitoring points in the monitoring area. Let i be the topological role weight of the i-th monitoring point. For modulo operation;

[0026] Constructing time-frequency coupling index And select characteristic frequency bands to calculate the cumulative coupling strength at each scale s. ,in, At the start time, when When the value exceeds the set threshold and the duration exceeds the preset time, the frequency band corresponding to the judgment scale s is the characteristic frequency band related to the instability of the soil and rock mass.

[0027] Based on the identified characteristic frequency bands, the phase time series of each monitoring point in that frequency band is extracted. Construct a weighted phase coupling matrix C, where the elements of the matrix are... Elements in the matrix The implementation involves first calculating the phase lock value between monitoring points i and j within the time window, i.e., calculating the complex exponential function for all time points t. The average value is used to introduce a spatial attenuation factor, and the spatial Euclidean distance between monitoring points i and j is calculated. And apply the exponential decay function Weighting is performed, where The spatial attenuation coefficient characterizes the spatial influence range of soil and rock deformation. It is ultimately obtained by multiplying the phase-locked value by the spatial attenuation factor. , characterizing the phase synchronization strength between monitoring points i and j considering the influence of spatial distance;

[0028] The phase coupling matrix C is divided into sub-regions using a community detection algorithm to obtain... For each spatially continuous sub-region, calculate the phase coupling index of the k-th sub-region: ,in This represents the k-th sub-region. This is a modulo operation.

[0029] As a preferred approach, a deformation state entropy model is constructed by integrating the topological role weights of each monitoring point and the phase coupling index of each sub-region. The temporal evolution trajectory of the entropy value is analyzed, and the implementation of the corresponding level of deformation risk early warning mechanism is triggered when the rate of change of the entropy value continuously exceeds the preset classification threshold. This includes:

[0030] for For the k-th region among the sub-regions, normalize the topological role weights of each monitoring point within it to obtain the probability distribution. Calculate the local deformation entropy of subregion k. ,in Let be the phase coupling index of subregion k;

[0031] Based on the local deformation entropy of subregion k Obtain the global deformation state entropy value Establish an entropy time series and calculate the rate of change of entropy between adjacent time points. If three consecutive time points Warnings are issued when the value is less than 0 and below the set threshold.

[0032] Compared with existing technologies, the advantages and positive effects of this invention are as follows: It breaks through the limitations of traditional early warning methods that rely on single-point displacement curves. By constructing a spatiotemporal displacement field, it achieves continuous reconstruction of the deformation state, accurately capturing the spatial correlation and propagation characteristics of soil and rock deformation, and avoiding biased misjudgments caused by local data. Through field theory analysis, it deeply mines the topological characteristics of the deformation field, clarifies the source and sink points and streamline network of deformation energy, and clearly reveals the deformation evolution mechanism, solving the pain point of traditional methods' difficulty in grasping the overall deformation law. It innovatively integrates topological role weights and multi-scale time-frequency coupling analysis, effectively screening frequency bands related to instability, significantly suppressing noise interference, and improving the identification of key signals. By constructing a deformation state entropy model and combining it with the phase coupling characteristics of sub-regions, it achieves hierarchical early warning based on the entropy change rate, significantly enhancing the timeliness and reliability of early warning, and better adapting to the multidimensional deformation monitoring needs under complex geological conditions. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a schematic diagram of the structural flow of a data processing method for monitoring deformation of soil and rock masses. Detailed Implementation

[0035] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described below in conjunction with the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0036] Numerous specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways than those described herein, and therefore the invention is not limited to the specific embodiments disclosed in the following specification.

[0037] In geotechnical engineering projects such as tunnel excavation, the internal structure of soil and rock masses is complex, and their deformation processes often exhibit strong nonlinearity, heterogeneity, and abrupt changes. Traditional monitoring methods rely on changes in single-point displacement curves for early warning, which has significant limitations when dealing with complex geological conditions and multidimensional deformation propagation mechanisms. To identify instability signs earlier and provide reliable early warnings, it is necessary to integrate spatiotemporal data from multiple monitoring points, topological propagation characteristics, and energy evolution mechanisms to construct a more comprehensive deformation cognition model. Therefore, a data processing method for monitoring soil and rock deformation is proposed, specifically implemented as follows: Figure 1 As shown.

[0038] Firstly, to reveal the energy transfer path and key control areas of soil and rock deformation, and to address the issue of existing methods focusing only on single-point displacement values ​​while neglecting the overall structural characteristics of the deformation field, this method acquires time-series displacement data from multiple monitoring points within the monitoring area to construct a spatiotemporal displacement field. This is achieved by deploying N spatially fixed displacement detection points within the monitoring area; synchronously acquiring displacement data from each monitoring point at a preset sampling frequency; and obtaining the three-dimensional displacement components of each monitoring point i in the time series. ,in, The i-th monitoring point is located at The displacement components in the x, y, and z directions at each moment are analyzed. The displacement data is normalized using coordinate system one and a reference plane. The displacement data of all monitoring points at each moment are then structured according to spatial location and timestamp to form a four-dimensional dataset. ;in, Let be the spatial coordinates of the i-th monitoring point; based on the four-dimensional dataset D, a spatiotemporal interpolation algorithm is used to reconstruct the displacement field between the monitoring points, generating a continuous spatiotemporal displacement field function. ,in, For spatiotemporal weighting coefficients, To monitor the coordinates of any spatial point within the area, The parameters are adaptive spatial scale parameters, ensuring that a displacement estimate can be obtained for any spatial location p and any time t. This completes the construction of the spatiotemporal displacement field.

[0039] Specifically, N spatially fixed monitoring points are deployed within the target soil and rock mass area, and the three-dimensional displacement components of each monitoring point are synchronously collected at a preset sampling frequency at each moment, thereby obtaining discrete multi-point time-series data reflecting the actual deformation process. Subsequently, all monitoring point data undergo coordinate system standardization and reference surface normalization to eliminate interference from equipment differences, sensing errors, and spatial reference inconsistencies, ensuring data comparability and uniformity. The processed monitoring point displacement data is organized into a four-dimensional dataset according to spatial location and timestamp, laying the foundation for subsequent field function construction. Based on this four-dimensional dataset, a continuous spatiotemporal displacement field is reconstructed between monitoring points by introducing a spatiotemporal interpolation algorithm containing spatial adaptive scaling parameters, enabling the acquisition of corresponding displacement estimates for any spatial location p and any time t. The spatiotemporal displacement field constructed using the above method not only realizes the reconstruction of the deformation state from discrete monitoring points to continuous space but also provides a data foundation for subsequent deformation gradient analysis, energy density calculation, and topological feature extraction. Its advantages are: it can reveal the spatial correlation of deformation within the rock and soil mass from a holistic perspective, restore the continuity and propagation of displacement changes, avoid the local one-sidedness caused by the limited number of monitoring points, thereby significantly improving the accuracy and completeness of deformation mechanism analysis, and providing a reliable basis for identifying the source, sink and propagation path of deformation energy.

[0040] Then, considering that traditional methods cannot identify the source, sink, and propagation paths of deformation energy, leading to misjudgments of the deformation evolution mechanism, a field theory analysis is performed on the spatiotemporal displacement field to extract the topological features of the deformation field and identify the source and sink regions of deformation energy, as well as the deformation streamline network connecting them. This is achieved by constructing a displacement gradient vector field. ,in, As a gradient operator, the deformation energy density function is calculated based on the displacement gradient vector field. ; Identify key topological structures in the deformable field, including source region, sink region, and saddle region; the source region is defined as satisfying and Within the spatial regions defined as the left and right neighborhoods of 0, where, The threshold for determining the source point. These represent the divergence and curl operators, respectively; the sink region is defined as satisfying... and The spatial region within the left and right neighborhoods of 0; the saddle point region is defined as satisfying... Within the defined left and right neighborhoods of 0 and The spatial region; starting from the source region, tracing the deformable streamlines along the direction of the displacement gradient vector, where the streamline tracing step size is... With deformation energy density Adaptive adjustment: ,in, These are the minimum and maximum step sizes, respectively. For adaptive coefficients, The energy threshold is used to determine the streamline termination condition, which is when the deformed streamline reaches the sink region, or the deformation energy density at the location of the deformed streamline is determined. When the energy level is less than the preset minimum energy threshold, the tracking of the deformed streamline stops, the construction of a single deformed streamline is completed, and a deformed streamline network is formed.

[0041] Specifically, the displacement gradient vector field is calculated based on the spatiotemporal displacement field function. Based on this, the deformation energy density distribution is further derived from the gradient field. After obtaining the energy field, key topological structures in the deformation field of the soil and rock mass are identified through a comprehensive judgment of divergence, curl, and energy density. Regions satisfying divergence greater than a set threshold and curl close to zero are defined as energy release source regions; regions satisfying divergence less than the opposite threshold and curl close to zero are defined as energy convergence sink regions; and regions satisfying both divergence and curl close to zero but with abrupt changes in gradient direction are identified as saddle points. These regions are often key locations for deformation path inflection or energy redistribution. To reveal the propagation trajectory of deformation energy in space, streamline tracing is performed along the displacement gradient direction starting from each source region. Streamline tracing employs an adaptive step-size strategy: the step size is reduced when the local energy density is high to enhance path accuracy, and the step size is increased when the energy density is low to improve computational efficiency. The tracing process terminates when the streamline reaches the sink region or the energy density drops to the lowest threshold, thus obtaining a complete deformation streamline. By repeatedly tracing all source point regions, a deformation streamline network covering the entire monitoring area is ultimately constructed. Through the above field theory and topological analysis process, the deformation is transformed from a traditional discrete point change into a continuous field structure analysis.

[0042] Next, to accurately extract time-frequency features directly related to soil instability and address the limitations of existing methods in distinguishing effective signals from noise interference and locating precursory frequency bands in frequency domain analysis, the topological role weight of each monitoring point in the deformation propagation path is calculated based on the spatial distribution characteristics of the topological feature structure. This topological role weight characterizes the degree of influence of each monitoring point on the overall deformation field evolution. This is achieved by selecting the monitoring point with the highest energy transfer efficiency e based on the deformation streamline network. The streamlines serve as the primary deformation propagation path, among which, Let L be the energy transfer efficiency of the streamline, where L is the length of the streamline. Let i be the integral variable; if monitoring point i is located on any major deformation propagation path, then calculate its Euclidean distance to the nearest source region. And calculate the initial topology role weights. ,in To avoid small constants that divide by zero; if monitoring point i is not located on any major deformation propagation path, the topological role weight is 0; normalize the initial topological role weight of each monitoring point to obtain the topological role weight.

[0043] Specifically, based on the constructed deformation streamline network, all streamlines are sorted from high to low according to their energy transfer efficiency. Energy transfer efficiency is represented by the energy density integral over the streamline length, characterizing the continuity and intensity of energy propagation along that path. The top-ranked streamlines with the highest energy transfer efficiency are selected as the main deformation propagation paths. These paths correspond to the most significant deformation evolution channels within the soil mass and have a decisive influence on the instability process. Subsequently, it is determined whether each monitoring point is located on one of these main propagation paths. If a monitoring point falls on any of the main streamlines, the Euclidean distance between that monitoring point and the nearest source region is further calculated. The shorter the distance, the closer the monitoring point is to the energy initiation location, and the more sensitive its deformation signal is to the overall energy evolution. Therefore, the initial topological role weights are constructed using the reciprocal of the distance to avoid introducing small constants during division by zero, ensuring stable and reliable weight calculation. For monitoring points not falling on main streamlines, their role in deformation propagation is relatively weak, so their topological role weights are assigned zero. Finally, the initial weights of all monitoring points are normalized to form a topological role weight vector. This vector not only preserves the relative importance of different monitoring points in the propagation structure, but also allows for deep integration with subsequent time-frequency feature extraction, achieving dual enhancement of structure and signal. The above process, by introducing deformable streamline structure information, transforms the assessment of monitoring point importance from displacement amplitude to structural contribution, significantly improving the scientific rigor of signal selection. Secondly, the weights can be used to improve the signal-to-noise ratio of key frequency bands in time-frequency analysis, enhance the true precursory characteristics of instability, and suppress random noise.

[0044] To accurately extract time-frequency features directly related to soil and rock instability and address the limitations of existing methods in distinguishing effective signals from noise interference and locating precursory frequency bands in frequency domain analysis, this study performs multi-scale wavelet transform on the displacement time-series data of each monitoring point. It calculates the time-frequency coupling index of phase consistency and deformation energy, filters feature frequency bands related to soil and rock instability, constructs a phase coupling matrix based on these bands, and applies a community detection algorithm to divide the monitoring area into sub-regions. The phase coupling index for each sub-region is then calculated. This process involves using the Morlet wavelet basis to analyze the displacement time-series data of the i-th monitoring point. Perform multi-scale continuous wavelet transform to obtain the time-frequency domain representation. Where s is the scale parameter, which is automatically optimized through maximum overlap discrete wavelet transform to satisfy... Less than the preset convergence threshold; calculate the phase consistency index PC(s,t) and deformation energy index E(s,t) at scale s: , Where J is the imaginary unit, Let be the phase angle of the i-th monitoring point at scale s and time t, and N be the total number of monitoring points in the monitoring area. Let i be the topological role weight of the i-th monitoring point. For modulo operation; construct time-frequency coupling index And select characteristic frequency bands to calculate the cumulative coupling strength at each scale s. ,in, At the start time, when When the value exceeds the set threshold and the duration exceeds the preset time, the frequency band corresponding to the judgment scale s is identified as a characteristic frequency band related to soil and rock instability. Based on the identified characteristic frequency band, the phase time sequence of each monitoring point in that frequency band is extracted. Construct a weighted phase coupling matrix C, where the elements of the matrix are... Elements in the matrix The implementation involves first calculating the phase lock value between monitoring points i and j within the time window, i.e., calculating the complex exponential function for all time points t. The average value is used to introduce a spatial attenuation factor, and the spatial Euclidean distance between monitoring points i and j is calculated. And apply the exponential decay function Weighting is performed, where The spatial attenuation coefficient characterizes the spatial influence range of soil and rock deformation. It is ultimately obtained by multiplying the phase-locked value by the spatial attenuation factor. The phase synchronization strength between monitoring points i and j is characterized by considering the influence of spatial distance; the phase coupling matrix C is divided into sub-regions using a community detection algorithm to obtain... For each spatially continuous sub-region, calculate the phase coupling index of the k-th sub-region: ,in This represents the k-th sub-region. This is a modulo operation.

[0045] Specifically, a multi-scale continuous wavelet transform is performed on the original displacement time series of i monitoring points using the Morlet wavelet basis. The Morlet wavelet possesses excellent time-frequency localization performance, capable of capturing both short-term burst signals and identifying long-term, gradual trends. By setting multiple scale parameters s, deformation behavior in different frequency bands is decomposed. To ensure the rationality of scale selection, the maximum overlap discrete wavelet transform (MODWT) is introduced to automatically optimize the scale parameters, ensuring that the energy distribution difference between adjacent scales is less than a preset convergence threshold, thereby obtaining the scale sequence that best characterizes the deformation features. After completing the wavelet transform, the phase consistency index and deformation energy index at scale s and time t are calculated. Phase consistency is used to identify whether different monitoring points exhibit coordinated change behavior at the same scale; deformation energy represents the strength of displacement fluctuations at that scale. The phase angle is obtained from the complex form of the wavelet coefficients. To enhance the contribution of important monitoring points, topological role weights are introduced when calculating the phase consistency index, giving key control points a greater influence in the coupling analysis. To identify the main frequency bands associated with instability, a time-frequency coupling index is constructed. The cumulative coupling strength of scale *s* is obtained by accumulating and integrating the coupling strength between the phase consistency index and the deformation energy index at each scale. When this strength continuously exceeds a set threshold and persists for a preset time period, the frequency band corresponding to this scale is determined to be an instability-sensitive characteristic frequency band. This mechanism can automatically filter out noise-dominated scales and highlight structural deformation characteristics. Subsequently, based on the identified characteristic frequency bands, the phase time series of each monitoring point in that frequency band is extracted to construct a weighted phase coupling matrix *C*. The calculation process of the matrix elements includes three steps: first, calculating the complex exponential average of the phase difference between monitoring points *i* and *j* within the time window, i.e., the phase lock value; second, calculating the spatial Euclidean distance between the two monitoring points; and finally, multiplying the phase lock value by the spatial attenuation factor. This weighting method can more realistically reflect the deformation correlation between spatially close monitoring points and avoid misjudgments caused by accidental synchronization of distant monitoring points. The constructed coupling matrix C can be viewed as a weighted network. Through community detection algorithms, the Louvain modularity optimization algorithm can be used to iteratively maximize the network modularity, automatically clustering monitoring points with strong coupling strength into the same community. Alternatively, spectral clustering can be employed, projecting the network's clustering structure in high-dimensional space into separable sub-regions by calculating the eigenvectors of the Laplacian matrix. These algorithms can automatically form multiple spatially continuous and internally phase-synchronized region divisions based on coupling strength, achieving a structured expression of the deformation synergy within the monitoring area. The monitoring area is divided into several spatially continuous and internally phase-synchronized sub-regions. For each sub-region k, the phase coupling index is further calculated by taking a weighted average of the coupling strengths among all monitoring points within the sub-region, quantifying the consistency of deformation behavior within that region.

[0046] Finally, a deformation state entropy model is constructed by integrating the topological role weights of each monitoring point and the phase coupling index of each sub-region. The temporal evolution trajectory of the entropy value is analyzed, and a corresponding level of deformation risk warning mechanism is triggered when the rate of change of the entropy value continuously exceeds a preset grading threshold. Its implementation includes, for For the k-th region among the sub-regions, normalize the topological role weights of each monitoring point within it to obtain the probability distribution. Calculate the local deformation entropy of subregion k. ,in Let be the phase coupling index of subregion k; based on the local deformation entropy of subregion k. Obtain the global deformation state entropy value The core of global deformation state entropy is to measure the disorder and uncertainty of the system state. When entropy is high (disordered), deformation at monitoring points is randomly dispersed and phase-dependent, the deformation field lacks a unified development trend, and energy does not concentrate on a particular path. When entropy is low (ordered), deformation at monitoring points is highly synchronized, energy concentrates along a specific path, and the deformation field forms a unified evolution direction, corresponding to precursors of instability. Therefore, establishing an entropy time series and calculating the rate of change of entropy at adjacent time points is crucial. If three consecutive time points Warnings are issued when the value is less than 0 and below the set threshold.

[0047] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments for application in other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A data processing method for monitoring deformation of soil and rock masses, characterized in that, Includes the following steps: Obtain displacement time-series data from multiple monitoring points in the monitoring area to construct a spatiotemporal displacement field; Field theory analysis is performed on the spatiotemporal displacement field to extract the topological features of the deformation field and identify the source and sink regions of deformation energy, as well as the deformation streamline network connecting the two. Based on the spatial distribution characteristics of the topological feature structure, the topological role weight of each monitoring point in the deformation propagation path is calculated. The topological role weight represents the degree of influence of the monitoring point on the overall deformation field evolution. For the displacement time series data of each monitoring point, multi-scale wavelet transform is performed to calculate the time-frequency coupling index of phase consistency and deformation energy, select characteristic frequency bands related to soil instability, construct a phase coupling matrix based on the characteristic frequency bands, apply the community detection algorithm to divide the monitoring area into sub-regions, and calculate the phase coupling index of each sub-region. A deformation state entropy model is constructed by integrating the topological role weights of each monitoring point and the phase coupling index of each sub-region. The temporal evolution trajectory of the entropy value is analyzed. When the rate of change of the entropy value continuously exceeds the preset classification threshold, the corresponding level of deformation risk warning mechanism is triggered.

2. The data processing method for monitoring deformation of soil and rock mass according to claim 1, characterized in that, The implementation of acquiring displacement time-series data of multiple monitoring points in the monitoring area and constructing a spatiotemporal displacement field includes: N displacement detection points with fixed spatial locations are set up within the monitoring area; Simultaneous displacement data is collected at each monitoring point using a preset sampling frequency to obtain the three-dimensional displacement components of each monitoring point i in the time series. ,in, The i-th monitoring point is located at The components of displacement in the x, y, and z directions at time t; The displacement data is normalized to coordinate system 1 and reference surface, and the displacement data of all monitoring points at each time point are structured according to spatial location and timestamp to form a four-dimensional dataset. ;in, Let i be the spatial coordinates of the i-th monitoring point; Based on the four-dimensional dataset D, a spatiotemporal interpolation algorithm is used to reconstruct the displacement field between monitoring points, generating a continuous spatiotemporal displacement field function. ,in, For spatiotemporal weighting coefficients, To monitor the coordinates of any spatial point within the area, The parameters are adaptive spatial scale parameters, ensuring that a displacement estimate can be obtained for any spatial location p and any time t. This completes the construction of the spatiotemporal displacement field.

3. The data processing method for monitoring deformation of soil and rock mass according to claim 1, characterized in that, The implementation of field theory analysis of the spatiotemporal displacement field, extraction of the topological features of the deformation field, and identification of the source and sink regions of deformation energy, as well as the deformation streamline network connecting the two, includes: Constructing the displacement gradient vector field ,in, As a gradient operator, the deformation energy density function is calculated based on the displacement gradient vector field. ; The key topological structures in the deformable field are identified, including source point regions, sink point regions, and saddle point regions; the source point region is defined as satisfying... and Within the spatial regions defined as the left and right neighborhoods of 0, where, The threshold for determining the source point. These represent the divergence and curl operators, respectively; the sink region is defined as satisfying... and The spatial region within the left and right neighborhoods of 0; the saddle point region is defined as satisfying... Within the defined left and right neighborhoods of 0 and Spatial region; Starting from the source region, the deformable streamlines are traced along the direction of the displacement gradient vector, where the streamline tracing step size is... With deformation energy density Adaptive adjustment: ,in, These are the minimum and maximum step sizes, respectively. For adaptive coefficients, Energy threshold; The streamline termination condition is determined when the deformable streamline reaches the sink region, or when the deformation energy density at the location of the deformable streamline is determined. When the energy level is less than the preset minimum energy threshold, the tracking of the deformed streamline stops, the construction of a single deformed streamline is completed, and a deformed streamline network is formed.

4. The data processing method for monitoring deformation of soil and rock mass according to claim 1, characterized in that, Based on the spatial distribution characteristics of the aforementioned topological features, the topological role weight of each monitoring point in the deformation propagation path is calculated. This topological role weight characterizes the degree of influence of the monitoring point on the overall deformation field evolution, including: Based on deformable streamline networks, the front with the highest energy transfer efficiency e is selected. The streamlines serve as the primary deformation propagation path, among which, Let L be the energy transfer efficiency of the streamline, where L is the length of the streamline. For integration variables; If monitoring point i is located on any of the main deformation propagation paths, then calculate its Euclidean distance to the nearest source region. And calculate the initial topology role weights. ,in To avoid small constants that divide by zero; if monitoring point i is not located on any major deformation propagation path, the topological role weight is 0; The initial topological role weights of each monitoring point are normalized to obtain the topological role weights.

5. The data processing method for monitoring deformation of soil and rock mass according to claim 1, characterized in that, For the displacement time-series data of each monitoring point, multi-scale wavelet transform is performed to calculate the time-frequency coupling index of phase consistency and deformation energy. Feature frequency bands related to soil instability are screened, a phase coupling matrix is ​​constructed based on the feature frequency bands, and the monitoring area is divided into sub-regions using a community detection algorithm. The specific implementation of calculating the phase coupling index of each sub-region includes: The displacement time series data of the i-th monitoring point are obtained using the Morlet wavelet basis. Perform multi-scale continuous wavelet transform to obtain the time-frequency domain representation. Where s is the scale parameter, which is automatically optimized through maximum overlap discrete wavelet transform to satisfy... Less than the preset convergence threshold; Compute the phase consistency index PC(s,t) and deformation energy index E(s,t) at scale s: , Where J is the imaginary unit, Let be the phase angle of the i-th monitoring point at scale s and time t, and N be the total number of monitoring points in the monitoring area. Let i be the topological role weight of the i-th monitoring point. For modulo operation; Constructing time-frequency coupling index And select characteristic frequency bands to calculate the cumulative coupling strength at each scale s. ,in, At the start time, when When the value exceeds the set threshold and the duration exceeds the preset time, the frequency band corresponding to the judgment scale s is the characteristic frequency band related to the instability of the soil and rock mass. Based on the identified characteristic frequency bands, the phase time series of each monitoring point in that frequency band is extracted. Construct a weighted phase coupling matrix C, where the elements of the matrix are... Elements in the matrix The implementation involves first calculating the phase lock value between monitoring points i and j within the time window, i.e., calculating the complex exponential function for all time points t. The average value is used to introduce a spatial attenuation factor, and the spatial Euclidean distance between monitoring points i and j is calculated. And apply the exponential decay function Weighting is performed, where The spatial attenuation coefficient characterizes the spatial influence range of soil and rock deformation. It is ultimately obtained by multiplying the phase-locked value by the spatial attenuation factor. , characterizing the phase synchronization strength between monitoring points i and j considering the influence of spatial distance; The phase coupling matrix C is divided into sub-regions using a community detection algorithm to obtain... For each spatially continuous sub-region, calculate the phase coupling index of the k-th sub-region: ,in This represents the k-th sub-region. This is a modulo operation.

6. The data processing method for monitoring deformation of soil and rock mass according to claim 1, characterized in that, A deformation state entropy model is constructed by integrating the topological role weights of each monitoring point and the phase coupling index of each sub-region. The temporal evolution trajectory of entropy values ​​is analyzed. When the rate of change of entropy values ​​continuously exceeds the preset classification threshold, the corresponding level of deformation risk early warning mechanism is triggered. The implementation includes: for For the k-th region among the sub-regions, normalize the topological role weights of each monitoring point within it to obtain the probability distribution. Calculate the local deformation entropy of subregion k. ,in Let be the phase coupling index of subregion k; Based on the local deformation entropy of subregion k Obtain the global deformation state entropy value Establish an entropy time series and calculate the rate of change of entropy between adjacent time points. If three consecutive time points Warnings are issued when the value is less than 0 and below the set threshold.

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