Mine rock stratum displacement deformation detection method and system for deep mining

Abstract

The present application provides a mine rock stratum displacement deformation detection method and system for deep mining, relates to the technical field of mine detection, and comprises the following steps: obtaining rock stratum multidimensional physical field data through a detection sensor, performing spatial discretization processing, identifying a stress gradient abnormal area, establishing a correlation between the stress gradient abnormal area and a displacement field direction feature, calculating a rock stratum residual bearing capacity, and generating safety margin distribution data. The present application can accurately identify rock stratum damage precursors, provide a scientific basis for pressure relief measures, and improve deep mining safety.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mine detection, and in particular to a mine rock stratum displacement deformation detection method and system for deep mining. BACKGROUND

[0002] With the exploitation of mineral resources advancing to the deep, underground mining faces complex geological environments such as high stress, high ground temperature, and high surrounding rock pressure. The deformation and failure mechanism of deep rock strata is more complex, and the risk of rock stratum catastrophe is significantly increased. Under the condition of deep mining, monitoring of rock stratum displacement and deformation is of great significance for preventing and controlling mine rock stratum disasters and ensuring mine safety production.

[0003] Traditional mine rock stratum monitoring mainly relies on the measurement of a single physical quantity, such as displacement, stress, or acoustic emission signals, using devices such as displacement meters, stress meters, and acoustic emission instruments. These monitoring methods usually only provide monitoring data at local points and cannot fully reflect the overall deformation state of the rock stratum. At the same time, existing monitoring systems mainly focus on data acquisition and lack quantitative evaluation and early warning mechanisms for rock stratum stability. SUMMARY

[0004] The present application provides a mine rock stratum displacement deformation detection method and system for deep mining, which can solve the problems in the prior art.

[0005] In a first aspect of the present application, a mine rock stratum displacement deformation detection method for deep mining is provided, comprising:

[0006] Obtaining multi-dimensional physical field data of the rock stratum in the monitoring area through a detection sensor, the multi-dimensional physical field data including displacement field data reflecting the deformation state of the rock stratum and stress field data reflecting the stress state of the rock stratum;

[0007] Performing spatial discretization processing on the multi-dimensional physical field data, dividing the monitoring area into three-dimensional grid elements, calculating the gradient values between adjacent nodes in the three-dimensional grid elements, identifying the stress concentration position of the rock stratum through the spatial distribution of the gradient values, defining a node set with a gradient value exceeding a preset critical change rate as a stress gradient abnormal area, and extracting the spatial geometric features of the stress gradient abnormal area and the directional features in the displacement field data, establishing an association between the spatial position and directional features of the stress gradient abnormal area;

[0008] Based on the aforementioned correlation, by tracking the expansion rate of the stress gradient anomaly region and the degradation rate of the shear strength of the rock material, the additional load required for the rock material to reach the critical failure state from the current stress state is calculated. The additional load is used as a quantitative indicator of the remaining bearing capacity of the rock layer, generating safety margin distribution data for each spatial location within the monitoring area, and determining the implementation time window for pressure relief measures based on the safety margin decay rate of the target area.

[0009] The multi-dimensional physical field data is spatially discretized, dividing the area to be monitored into three-dimensional grid cells. The gradient values ​​between adjacent nodes in the three-dimensional grid cells are calculated as follows:

[0010] The spatial second derivative distribution of displacement field data within the monitored area is calculated. Based on the spatial second derivative distribution, the scale division criterion of the three-dimensional grid cells is determined. The monitored area is then spatially discretized according to the scale division criterion to obtain a set of three-dimensional grid cells.

[0011] The displacement field data and the stress field data are respectively mapped to nodes at corresponding spatial locations in the three-dimensional mesh element set. Each node stores the displacement vector value and stress tensor value at the spatial coordinates of that node as physical field attribute values.

[0012] For each target node in the three-dimensional mesh unit set, all adjacent nodes that share a mesh surface with the target node are obtained. The spatial vector distance between the target node and each adjacent node and their respective stored physical field attribute values ​​are extracted. The physical field attribute values ​​of the target node and each adjacent node are differentially calculated to obtain the attribute difference. The attribute difference is divided by the magnitude of the corresponding spatial vector distance to obtain the directional gradient component of the direction from the target node to each adjacent node. Vector synthesis is performed on all directional gradient components to obtain the gradient value at the target node.

[0013] Extracting the spatial geometric features of the stress gradient anomaly region and the directional features in the displacement field data, and establishing the correlation between the spatial location and directional features of the stress gradient anomaly region includes:

[0014] Obtain the set of boundary contour points of the stress gradient anomaly region, perform three-dimensional spatial fitting on the set of boundary contour points to obtain the closed spatial surface of the stress gradient anomaly region, calculate the geometric center coordinates, principal axis direction vector and spatial extension scale of the closed spatial surface, and combine the geometric center coordinates, principal axis direction vector and spatial extension scale to form the spatial geometric features of the stress gradient anomaly region;

[0015] The displacement vectors at each spatial location within the monitored area are extracted from the displacement field data. The direction angle and magnitude of each displacement vector are calculated. The distribution of the direction angles of the displacement vectors at all spatial locations within the stress gradient anomaly area is statistically analyzed. The dominant direction of the displacement vectors within the stress gradient anomaly area is identified. The unit direction vector corresponding to the dominant direction is extracted as the direction feature in the displacement field data.

[0016] Establish a spatial mapping relationship between the geometric center coordinates of the stress gradient anomaly region and the directional features, thereby forming a correlation between the spatial location and directional features of the stress gradient anomaly region.

[0017] Based on the aforementioned correlation, tracking the expansion rate of stress gradient anomaly regions and the degradation rate of rock material shear strength includes:

[0018] Obtain the spatial boundary coordinates of the stress gradient anomaly region, calculate the spatial boundary displacement of the stress gradient anomaly region between adjacent time series nodes, divide the spatial boundary displacement by the time interval between adjacent time series nodes to obtain the expansion rate vector of the stress gradient anomaly region in each spatial direction, and calculate the magnitude of the expansion rate vector to obtain the overall expansion rate of the stress gradient anomaly region;

[0019] Based on the correlation, the spatial location of the rock strata structure corresponding to the stress gradient anomaly region is determined. The measured values ​​of the shear strength of the rock strata material at multiple time series nodes are extracted. The difference between the measured values ​​of the shear strength between adjacent time series nodes is calculated. The difference is divided by the time interval between adjacent time series nodes to obtain the degradation rate of the shear strength of the rock strata material.

[0020] The calculation of the additional load required for rock material to reach the critical failure state from its current stress state includes:

[0021] A coupled calculation relationship is established between the overall expansion rate of the stress gradient anomaly region and the shear strength degradation rate of the rock material. By multiplying the overall expansion rate by the elastic modulus of the rock material, the stress increment rate caused by the expansion of the stress gradient anomaly region is obtained.

[0022] The rate of shear strength degradation is correlated with the internal friction angle in the Mohr-Coulomb failure criterion of rock materials to obtain the rate of decay of the bearing capacity of rock materials.

[0023] The stress increment rate and the bearing capacity decay rate are superimposed to obtain the comprehensive evolution rate of the rock material stress state to the critical failure state. The stress difference between the current stress state and the critical failure stress state of the rock material is obtained. The stress difference is divided by the comprehensive evolution rate to obtain the estimated time for the rock material to reach the critical failure state.

[0024] Multiplying the stress increment rate by the estimated time yields the additional load that the rock material needs to withstand to reach the critical failure state from its current stress state.

[0025] The additional load is used as a quantitative indicator of the remaining bearing capacity of the rock strata. Safety margin distribution data for each spatial location within the monitored area are generated. The implementation time window for pressure relief measures is determined based on the safety margin decay rate of the target area, including:

[0026] The additional load values ​​corresponding to each spatial location within the monitored area are obtained, and the ultimate bearing capacity value of the rock material at each spatial location is extracted. The ratio of the additional load value to the ultimate bearing capacity value is calculated to obtain the safety margin value for each spatial location. The safety margin value represents the remaining bearing capacity reserve of the rock material from the current stress state to the failure state. The safety margin value of each spatial location is bound and mapped to the corresponding spatial coordinates to generate safety margin distribution data for each spatial location within the monitored area.

[0027] The spatial regions in the safety margin distribution data whose safety margin values ​​are lower than the benchmark values ​​for rock strata stability assessment are marked as target regions;

[0028] Calculate the change in safety margin value of the target region between adjacent time series nodes, divide the change in safety margin value by the time interval between adjacent time series nodes to obtain the safety margin decay rate of the target region, take the difference between the safety margin value of the target region at the current moment and the rock instability judgment threshold as the safety margin remaining amount, divide the safety margin remaining amount by the safety margin decay rate to obtain the predicted time value required for the target region to reach the rock instability judgment threshold from the current moment;

[0029] Based on the predicted time value, the implementation cycle and effective delay time of the pressure relief measures are deducted to obtain the latest time node at which the pressure relief measures must be initiated. The time period between the current time and the latest time node is determined as the implementation time window of the pressure relief measures.

[0030] A second aspect of the present invention provides a rock strata displacement and deformation detection system for deep mining, comprising:

[0031] The first unit is used to acquire multi-dimensional physical field data of rock strata in the area to be monitored through detection sensors. The multi-dimensional physical field data includes displacement field data reflecting the deformation state of the rock strata and stress field data reflecting the stress state of the rock strata.

[0032] The second unit is used to spatially discretize the multi-dimensional physical field data, divide the area to be monitored into three-dimensional grid cells, calculate the gradient values ​​between adjacent nodes in the three-dimensional grid cells, identify the stress concentration locations of the rock strata through the spatial distribution of the gradient values, define the set of nodes whose gradient values ​​exceed a preset critical rate of change as stress gradient anomaly regions, and extract the spatial geometric features of the stress gradient anomaly regions and the directional features in the displacement field data to establish the correlation between the spatial location and directional features of the stress gradient anomaly regions.

[0033] The third unit is used to calculate the additional load required for the rock layer material to reach the critical failure state from the current stress state by tracking the expansion rate of the stress gradient anomaly region and the degradation rate of the shear strength of the rock layer material, based on the aforementioned correlation. The additional load is used as a quantitative indicator of the remaining bearing capacity of the rock layer. The unit generates safety margin distribution data for each spatial location within the monitoring area and determines the implementation time window of the pressure relief measures based on the safety margin decay rate of the target area.

[0034] A third aspect of the embodiments of the present invention,

[0035] An electronic device is provided, comprising:

[0036] processor;

[0037] Memory used to store processor-executable instructions;

[0038] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0039] Fourth aspect of the present invention,

[0040] A computer-readable storage medium is provided, having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0041] The beneficial effects of this application are as follows:

[0042] By establishing the correlation between the spatial location and directional characteristics of stress gradient anomaly regions, the predictive analysis of rock strata deformation trends was achieved, improving the accuracy of early warning.

[0043] This paper innovatively proposes to calculate a quantitative index of the remaining bearing capacity of rock strata by tracking the expansion rate of anomaly regions of stress gradient and the degradation rate of shear strength of rock materials. This transforms the safety status of rock strata from a qualitative assessment to a quantitative analysis, significantly improving the scientific nature of safety assessment.

[0044] The timing window for implementing pressure relief measures is determined based on the rate of decrease in the safety margin of the target area, thus realizing the transformation of mine safety management from passive response to proactive prevention. Attached Figure Description

[0045] Figure 1 This is a schematic flowchart of a method for detecting displacement and deformation of rock strata in deep mining, according to an embodiment of the present invention.

[0046] Figure 2 A schematic diagram illustrating the process of tracking the expansion of anomaly regions in stress gradients and the degradation of rock shear strength. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments 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, and 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.

[0048] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0049] refer to Figure 1 and Figure 2 The present invention provides a method for detecting displacement and deformation of rock strata in deep mining operations, comprising:

[0050] Multi-dimensional physical field data of rock strata in the area to be monitored are acquired by detection sensors. The multi-dimensional physical field data includes displacement field data reflecting the deformation state of the rock strata and stress field data reflecting the stress state of the rock strata.

[0051] The multi-dimensional physical field data is spatially discretized, dividing the area to be monitored into three-dimensional grid cells. The gradient values ​​between adjacent nodes in the three-dimensional grid cells are calculated, and the location of stress concentration in the rock strata is identified by the spatial distribution of the gradient values. The set of nodes whose gradient values ​​exceed a preset critical rate of change is defined as a stress gradient anomaly region. The spatial geometric features of the stress gradient anomaly region and the directional features in the displacement field data are extracted to establish the correlation between the spatial location and directional features of the stress gradient anomaly region.

[0052] Based on the aforementioned correlation, by tracking the expansion rate of the stress gradient anomaly region and the degradation rate of the shear strength of the rock material, the additional load required for the rock material to reach the critical failure state from the current stress state is calculated. The additional load is used as a quantitative indicator of the remaining bearing capacity of the rock layer, generating safety margin distribution data for each spatial location within the monitoring area, and determining the implementation time window for pressure relief measures based on the safety margin decay rate of the target area.

[0053] In one optional implementation, the multi-dimensional physical field data is spatially discretized to divide the area to be monitored into three-dimensional grid cells, and the gradient values ​​between adjacent nodes in the three-dimensional grid cells are calculated as follows:

[0054] The spatial second derivative distribution of displacement field data within the monitored area is calculated. Based on the spatial second derivative distribution, the scale division criterion of the three-dimensional grid cells is determined. The monitored area is then spatially discretized according to the scale division criterion to obtain a set of three-dimensional grid cells.

[0055] The displacement field data and the stress field data are respectively mapped to nodes at corresponding spatial locations in the three-dimensional mesh element set. Each node stores the displacement vector value and stress tensor value at the spatial coordinates of that node as physical field attribute values.

[0056] For each target node in the three-dimensional mesh unit set, all adjacent nodes that share a mesh surface with the target node are obtained. The spatial vector distance between the target node and each adjacent node and their respective stored physical field attribute values ​​are extracted. The physical field attribute values ​​of the target node and each adjacent node are differentially calculated to obtain the attribute difference. The attribute difference is divided by the magnitude of the corresponding spatial vector distance to obtain the directional gradient component of the direction from the target node to each adjacent node. Vector synthesis is performed on all directional gradient components to obtain the gradient value at the target node.

[0057] The process of spatially discretizing the multi-dimensional physical field data, dividing the area to be monitored into three-dimensional grid cells, and calculating the gradient values ​​between adjacent nodes in the three-dimensional grid cells is as follows:

[0058] In practical applications, spatial discretization first requires determining a suitable mesh generation method. By calculating the spatial second derivative distribution of displacement field data within the monitored area, regions of severe rock deformation can be effectively identified. Displacement field data is typically acquired by micro-deformation sensor arrays embedded in the rock strata; these sensors can capture minute deformation information of the rock strata. For the acquired raw displacement data, the spatial second derivative is calculated using the finite difference method. Specifically, for the displacement vector u(x,y,z) at any point (x,y,z) in three-dimensional space, its spatial second derivative can be approximated by the difference in displacement values ​​between adjacent sampling points. The distribution of the second derivative of the displacement field directly reflects the curvature change of the rock strata; regions with drastic curvature changes are often stress concentration areas or dangerous areas prone to impending failure.

[0059] Based on the calculated spatial second derivative distribution, an adaptive 3D mesh cell scaling criterion is established. In regions with drastic displacement gradient changes, smaller mesh cells are selected to improve computational accuracy; while in regions with gentle deformation gradients, larger mesh cells can be used to improve computational efficiency. The scaling criterion can be set as follows: when the second derivative of displacement between adjacent measurement points exceeds a preset threshold, the mesh size in that region is dynamically adjusted according to the magnitude of the second derivative of displacement; the larger the second derivative, the smaller the mesh size. This adaptive meshing method can optimize the allocation of computational resources while ensuring computational accuracy.

[0060] After setting the scale division criteria, the entire area to be monitored is spatially discretized. A three-dimensional mesh model is constructed using basic geometric units such as tetrahedrons or hexahedrons, forming a complete topological structure containing nodes, edges, and faces. During discretization, mesh quality must be ensured to avoid highly distorted units, thus guaranteeing the accuracy of subsequent calculations. Mesh generation algorithms can employ Delaunay triangulation or forward hexahedral mesh generation methods, ultimately yielding a set of three-dimensional mesh units adapted to the geometric characteristics of the area to be monitored.

[0061] After obtaining the 3D mesh set, multi-dimensional physical field data needs to be mapped to the mesh nodes. Displacement field data mainly comes from measurement results from devices such as micro-deformation sensors, fiber optic gratings, or displacement gauges, while stress field data is obtained through stress gauges, strain gauges, or microseismic monitoring systems. For spatial locations not directly covered by sensors, 3D interpolation algorithms are used to calculate the physical field attribute values ​​of the corresponding nodes. Commonly used interpolation methods include inverse distance weighted interpolation, radial basis function interpolation, or Kriging interpolation. The interpolation process considers the relationship between the physical field values ​​of surrounding known measurement points and spatial distances to ensure the physical rationality of the interpolation results. After data mapping is completed, each node in the 3D mesh set stores the physical field attribute information for that spatial location, including displacement vector values ​​and stress tensor values.

[0062] Calculating the gradient value for each target node in a 3D mesh set requires considering the physical field changes between it and its neighboring nodes. First, all neighboring nodes sharing a mesh surface with the target node are identified. These nodes are directly connected to the target node and are typically six or more surrounding nodes, depending on the mesh topology. The spatial vector distance between the target node and each neighboring node is extracted; this distance is the 3D vector formed by the difference in coordinates between the two nodes. Simultaneously, the stored physical field property values ​​of the target node and each neighboring node are obtained, including displacement vectors and stress tensors.

[0063] For each adjacent node, the physical field attribute values ​​of the target node and the adjacent node are subtracted. For the displacement field, the difference between the displacement vectors of the two nodes is directly calculated; for the stress field, the difference between the components of the stress tensor is calculated. The resulting attribute difference reflects the change in the physical field between the two nodes. Dividing this attribute difference by the magnitude of the corresponding spatial vector distance yields the directional gradient component in the direction from the target node to the adjacent node. The directional gradient component represents the rate of change of the physical field in a specific direction and is a vector quantity.

[0064] After calculating the gradient components in all adjacent directions, a vector synthesis operation is performed on these gradient components to obtain the total gradient value at the target node. Vector synthesis can employ a weighted average method, using the reciprocal of the spatial distance as the weight to ensure that nodes closer to each other contribute more to the gradient calculation. The result is the gradient vector of the physical field at the target node; its magnitude reflects the drastic change in the physical field, and its direction points in the direction of the fastest increase in the physical field.

[0065] In practical applications, such as geological structural deformation monitoring, the above methods can efficiently calculate the gradient field of rock strata displacement and stress distribution, thereby analyzing fault activity and potential slip zones, and providing a scientific basis for geological disaster early warning.

[0066] In one optional implementation, extracting the spatial geometric features of the stress gradient anomaly region and the directional features in the displacement field data, and establishing the correlation between the spatial location and directional features of the stress gradient anomaly region includes:

[0067] Obtain the set of boundary contour points of the stress gradient anomaly region, perform three-dimensional spatial fitting on the set of boundary contour points to obtain the closed spatial surface of the stress gradient anomaly region, calculate the geometric center coordinates, principal axis direction vector and spatial extension scale of the closed spatial surface, and combine the geometric center coordinates, principal axis direction vector and spatial extension scale to form the spatial geometric features of the stress gradient anomaly region;

[0068] The displacement vectors at each spatial location within the monitored area are extracted from the displacement field data. The direction angle and magnitude of each displacement vector are calculated. The distribution of the direction angles of the displacement vectors at all spatial locations within the stress gradient anomaly area is statistically analyzed. The dominant direction of the displacement vectors within the stress gradient anomaly area is identified. The unit direction vector corresponding to the dominant direction is extracted as the direction feature in the displacement field data.

[0069] Establish a spatial mapping relationship between the geometric center coordinates of the stress gradient anomaly region and the directional features, thereby forming a correlation between the spatial location and directional features of the stress gradient anomaly region.

[0070] Obtain the set of boundary contour points for the stress gradient anomaly region. Extract the coordinates of the boundary points of the anomaly region obtained through stress gradient analysis. These point sets can be extracted from the original stress field data using stress gradient thresholding or region growing algorithms.

[0071] A three-dimensional spatial fitting is performed on the set of boundary contour points to obtain a closed spatial surface in the stress gradient anomaly region. The fitting process employs a three-dimensional surface reconstruction algorithm, such as a three-dimensional ellipsoidal fitting or a B-spline surface fitting method. For the ellipsoidal fitting method, the parameters of the ellipsoidal equation can be solved using the least squares method; for complex shapes, a piecewise surface fitting approach is used to construct a closed surface model S(u, v). The fitted closed spatial surface should satisfy the best approximation of the boundary points while maintaining the smoothness and closure of the surface.

[0072] The geometric center coordinates C = (x0, y0, z0) of a closed spatial surface are calculated by taking the arithmetic mean of the coordinates of all points on the surface. For an ellipsoid, the geometric center is the ellipsoid center; for irregular shapes, the position of the centroid is obtained by volume integration and taken as the geometric center.

[0073] Calculate the principal axis direction vectors of the closed spatial surface, construct the covariance matrix of the surface point set, and then perform eigenvalue decomposition on the covariance matrix to obtain three eigenvalues ​​λ1, λ2, and λ3 and their corresponding eigenvectors v1, v2, and v3. Among them, the eigenvector v1 corresponding to the largest eigenvalue represents the principal axis direction of the anomaly region, that is, the direction in which the anomaly region extends most significantly in space.

[0074] Calculate the spatial extension scale D = (d1, d2, d3) of the closed spatial surface, representing the extension distance along the three principal axes. Specifically, project all points on the surface onto the principal axes and calculate twice the maximum projected distance as the extension scale in that direction. For example, d1 represents the extension scale along the principal axis v1, calculated as the maximum distance difference between all points on the surface and the plane perpendicular to v1.

[0075] The spatial geometric features G = {C, v1, D} of the stress gradient anomaly region are formed by combining the geometric center coordinates C, the principal axis direction vector v1, and the spatial extension scale D. This feature set completely describes the spatial location, direction, and size of the anomaly region.

[0076] In displacement field data analysis, the first step is to extract the set of displacement vectors U = {u1, u2, ..., un} for each spatial location within the monitored area, where u represents a three-dimensional displacement vector and n represents the number of spatial locations. For each displacement vector, its orientation angles α, β, and γ (angles with the x, y, and z axes, respectively) and magnitude |u| are calculated. The orientation angle is obtained by the ratio of the vector component to the magnitude, such as α = arccos(u...). x / |u|).

[0077] The angular distribution of all displacement vectors within the anomaly region of stress gradient is statistically analyzed. The spatial direction is divided into several angular intervals, and the number of displacement vectors or their cumulative magnitude within each interval is calculated to generate a histogram of direction distribution. Histogram analysis identifies the direction intervals with the highest frequency or largest cumulative magnitude; the directions corresponding to these intervals are defined as the dominant directions of displacement vectors within the anomaly region.

[0078] Select representative directions from the dominant direction interval and extract the corresponding unit direction vector d = (d x The displacement vector (d, dy, dz) serves as the directional feature in the displacement field data. This unit direction vector can be obtained by weighted averaging of all displacement vectors within the dominant direction interval, with the weights set as the magnitudes of the displacement vectors.

[0079] A spatial mapping relationship is established between the geometric center coordinates C and the directional characteristics d of the stress gradient anomaly region. This mapping relationship can be expressed as a vector function F(C) = d, describing the correspondence from the spatial location of the anomaly region to the displacement direction. In practical applications, regression models, such as linear or nonlinear regression models, can be established using data from multiple anomaly regions to predict the possible displacement direction of the anomaly region at a given location.

[0080] By establishing the aforementioned correlations, it is possible to predict the development trend of stress gradient anomaly areas, providing a basis for geological disaster early warning. For example, in a mining environment, when a new stress gradient anomaly area is identified, its spatial location can be used to predict the possible deformation direction, thereby assessing potential hazards and taking corresponding preventive measures.

[0081] In one alternative implementation, based on the correlation, tracking the expansion rate of the stress gradient anomaly region and the degradation rate of the shear strength of the rock material includes:

[0082] Obtain the spatial boundary coordinates of the stress gradient anomaly region, calculate the spatial boundary displacement of the stress gradient anomaly region between adjacent time series nodes, divide the spatial boundary displacement by the time interval between adjacent time series nodes to obtain the expansion rate vector of the stress gradient anomaly region in each spatial direction, and calculate the magnitude of the expansion rate vector to obtain the overall expansion rate of the stress gradient anomaly region;

[0083] Based on the correlation, the spatial location of the rock strata structure corresponding to the stress gradient anomaly region is determined. The measured values ​​of the shear strength of the rock strata material at multiple time series nodes are extracted. The difference between the measured values ​​of the shear strength between adjacent time series nodes is calculated. The difference is divided by the time interval between adjacent time series nodes to obtain the degradation rate of the shear strength of the rock strata material.

[0084] In this embodiment, by tracking the correlation between the expansion rate of the stress gradient anomaly region and the degradation rate of the shear strength of the rock stratum material, an accurate assessment and prediction of the stability of the rock stratum structure can be achieved.

[0085] The spatial boundary coordinates of the stress gradient anomaly region are obtained. Stress distribution data within the rock strata are acquired using a distributed sensor network, and the boundary point coordinates of the anomaly region are determined through stress gradient calculation. Boundary points are typically represented by three-dimensional spatial coordinates (x, y, z), forming a closed surface describing the spatial boundary of the anomaly region. Specifically, locations where the stress gradient value between adjacent measuring points exceeds a preset threshold are used as boundary points. A boundary surface model is constructed using a triangulation algorithm, and the precise spatial coordinates of each boundary point are recorded.

[0086] Next, the spatial boundary displacement of the stress gradient anomaly region between adjacent time series nodes is calculated. The time series is selected as the observation node, and for each boundary point, its position change vector between adjacent time nodes is calculated. For boundary point P, its displacement can be expressed as ΔP = P(T2) - P(T1), where P(T1) and P(T2) represent the spatial coordinates of the point at times T1 and T2, respectively. To improve calculation accuracy, a nearest-point matching algorithm is introduced to ensure the correct correspondence between boundary points at different times.

[0087] Dividing the aforementioned spatial boundary displacement by the time interval between adjacent time series nodes yields the expansion rate vectors of the stress gradient anomaly region in each spatial direction. For a time interval ΔT = T2 - T1, the expansion rate vector V = ΔP / ΔT contains components in the x, y, and z directions. To reduce the influence of random errors, a sliding window averaging method is used to smooth the rate vectors.

[0088] The magnitude of the propagation rate vector is calculated to obtain the overall propagation rate of the stress gradient anomaly region. The rate magnitude S = |V| = √(Vx)2 + Vy 2 + Vz 2 ), representing the velocity scalar of the comprehensive expansion of the anomalous region in all directions. Based on the different expansion directions, the expansion rate is divided into two components: radial expansion and tangential expansion, each assigned a different weight to reflect the dominant direction of the anomalous region's expansion.

[0089] Based on the established correlations, the spatial location of the rock strata corresponding to the stress gradient anomaly region is determined. Using a geological structure model, the spatial boundary of the anomaly region is mapped onto actual rock strata units, establishing a spatial correspondence between the anomaly region and the rock strata units. A coordinate transformation matrix is ​​established to convert the anomaly region location in the sensor network coordinate system to the rock strata location in the geological structure coordinate system, ensuring positioning accuracy.

[0090] Measured shear strength values ​​of rock strata at multiple time series nodes were extracted. Shear strength data for corresponding rock strata regions were obtained at each time node using methods such as borehole sampling or in-situ shear tests. For deep rock strata where direct sampling is difficult, indirect measurement methods, such as acoustic velocity inversion technology, were employed to derive shear strength values ​​based on a model relating wave velocity and shear strength.

[0091] Calculate the difference in measured shear strength between adjacent time series nodes. For time points T1 and T2, the shear strength difference ΔS = S(T1) - S(T2), where S(T1) and S(T2) are the shear strength values ​​at the two time points, respectively. Considering measurement errors, confidence interval analysis is introduced to ensure the reliability of the difference calculation.

[0092] Dividing the above difference by the time interval between adjacent time series nodes yields the degradation rate of the rock strata's shear strength. The degradation rate R = ΔS / ΔT represents the degree of decrease in the rock strata's shear strength per unit time. For nonlinear degradation processes, a piecewise linear fitting method is used to divide the overall degradation process into multiple stages, and the degradation rate for each stage is calculated separately.

[0093] In practical applications, taking a mining area as an example, a stress monitoring network comprising fifty monitoring points was deployed. Through six consecutive months of monitoring, a significant anomaly in the stress gradient was observed in the northwest region. The calculated expansion rate of this area was approximately three centimeters per month, primarily expanding in a southeast-northwest direction. Correspondingly, the shear strength of the rock strata in this area decreased by 8% from its initial value within six months, resulting in a calculated degradation rate of approximately 1.3% per month.

[0094] By establishing a correlation model between the propagation rate and the degradation rate, it was found that when the propagation rate exceeds 5 cm per month, the shear strength degradation rate exhibits an accelerated growth trend, and this threshold can be used as an early warning indicator. This method was successfully applied to predict the risk of rock strata instability in a mining area, providing a two-week advance warning of possible rock strata slip events and offering important support for safe production.

[0095] In one alternative implementation, calculating the additional load that the rock material needs to withstand to reach the critical failure state from its current stress state includes:

[0096] A coupled calculation relationship is established between the overall expansion rate of the stress gradient anomaly region and the shear strength degradation rate of the rock material. By multiplying the overall expansion rate by the elastic modulus of the rock material, the stress increment rate caused by the expansion of the stress gradient anomaly region is obtained.

[0097] The rate of shear strength degradation is correlated with the internal friction angle in the Mohr-Coulomb failure criterion of rock materials to obtain the rate of decay of the bearing capacity of rock materials.

[0098] The stress increment rate and the bearing capacity decay rate are superimposed to obtain the comprehensive evolution rate of the rock material stress state to the critical failure state. The stress difference between the current stress state and the critical failure stress state of the rock material is obtained. The stress difference is divided by the comprehensive evolution rate to obtain the estimated time for the rock material to reach the critical failure state.

[0099] Multiplying the stress increment rate by the estimated time yields the additional load that the rock material needs to withstand to reach the critical failure state from its current stress state.

[0100] A coupled calculation relationship was established between the overall expansion rate of stress gradient anomaly regions and the shear strength degradation rate of rock strata. Stress gradient anomaly regions typically refer to areas within rock strata where stress distribution is uneven and highly variable, making these areas prone to becoming failure initiation points. Morphological change data of stress gradient anomaly regions in the rock strata were acquired using field monitoring equipment, and their volumetric expansion per unit time was calculated to determine the overall expansion rate. For example, if a stress gradient anomaly region is monitored to expand from an initial volume of 2 cubic meters to 2.5 cubic meters within 24 hours, the overall expansion rate is 0.5 cubic meters per day.

[0101] Multiplying the overall expansion rate by the elastic modulus of the rock material yields the stress increment rate caused by the expansion of the stress gradient anomaly region. For example, if the elastic modulus of the rock material is 30 GPa and the overall expansion rate is 0.5 cubic meters per day, the stress increment rate can be calculated to be 15 MPa per day based on the stress-strain relationship.

[0102] Simultaneously, the shear strength degradation of the rock strata was analyzed. Long-term exposure to high stress conditions leads to internal structural damage in rock strata, causing a gradual decrease in shear strength. By collecting rock core samples and conducting indoor tests, the shear strength values ​​at different time points were measured to determine the shear strength degradation rate. Correlating this degradation rate with the internal friction angle in the Mohr-Coulomb failure criterion yields the rate of decrease in the bearing capacity of the rock strata. For example, if the internal friction angle decreases by 0.1 degrees per day as measured in the test, the bearing capacity degradation rate can be calculated to be 3 MPa / day according to the Mohr-Coulomb failure criterion.

[0103] The stress increment rate and the bearing capacity decay rate are superimposed to obtain the comprehensive evolution rate of the rock material's stress state towards the critical failure state. In the example above, the stress increment rate is 15 MPa / day, and the bearing capacity decay rate is 3 MPa / day, so the comprehensive evolution rate is 18 MPa / day.

[0104] Next, the stress difference between the current stress state and the critical failure stress state of the rock strata is determined. The current stress state and the critical failure stress state can be obtained through in-situ stress measurements and rock material strength tests. Assuming the current principal stress of the rock strata is 80 MPa, and the critical failure stress calculated according to the Mohr-Coulomb failure criterion is 170 MPa, the stress difference is 90 MPa.

[0105] Dividing the stress difference by the overall evolution rate yields the estimated time when the rock material reaches its critical failure state. In this example, the stress difference is 90 MPa, and the overall evolution rate is 18 MPa / day, so the estimated time is 5 days.

[0106] Finally, the stress increment rate is multiplied by the estimated time to calculate the additional load required for the rock material to reach the critical failure state from the current stress state. In the example above, the stress increment rate is 15 MPa / day, the estimated time is 5 days, therefore the required additional load is 75 MPa.

[0107] In practical engineering applications, parameters can be adjusted according to different rock strata types and engineering conditions. For example, in the design of coal mine roadway support, a suitable support scheme can be selected based on the calculated additional load; during tunnel excavation, the excavation speed and support timing can be adjusted according to the estimated time to ensure construction safety.

[0108] Furthermore, this method can be combined with numerical simulation techniques to establish a rock stratum failure early warning system. By monitoring the expansion of anomaly regions in stress gradients and changes in rock material strength parameters in real time, the calculation results can be dynamically updated, providing a scientific basis for engineering decisions.

[0109] It should be noted that the mechanical properties of rock strata are spatially heterogeneous. The differences in parameters at different locations should be considered during the calculation process. If necessary, probabilistic and statistical methods can be used to handle the data dispersion and improve the reliability of the calculation results.

[0110] In one optional implementation, the additional load is used as a quantitative indicator of the remaining bearing capacity of the rock strata to generate safety margin distribution data for each spatial location within the monitoring area, and the implementation time window for pressure relief measures is determined based on the safety margin decay rate of the target area, including:

[0111] The additional load values ​​corresponding to each spatial location within the monitored area are obtained, and the ultimate bearing capacity value of the rock material at each spatial location is extracted. The ratio of the additional load value to the ultimate bearing capacity value is calculated to obtain the safety margin value for each spatial location. The safety margin value represents the remaining bearing capacity reserve of the rock material from the current stress state to the failure state. The safety margin value of each spatial location is bound and mapped to the corresponding spatial coordinates to generate safety margin distribution data for each spatial location within the monitored area.

[0112] The spatial regions in the safety margin distribution data whose safety margin values ​​are lower than the benchmark values ​​for rock strata stability assessment are marked as target regions;

[0113] Calculate the change in safety margin value of the target region between adjacent time series nodes, divide the change in safety margin value by the time interval between adjacent time series nodes to obtain the safety margin decay rate of the target region, take the difference between the safety margin value of the target region at the current moment and the rock instability judgment threshold as the safety margin remaining amount, divide the safety margin remaining amount by the safety margin decay rate to obtain the predicted time value required for the target region to reach the rock instability judgment threshold from the current moment;

[0114] Based on the predicted time value, the implementation cycle and effective delay time of the pressure relief measures are deducted to obtain the latest time node at which the pressure relief measures must be initiated. The time period between the current time and the latest time node is determined as the implementation time window of the pressure relief measures.

[0115] A network of stress monitoring sensors deployed within the rock strata collects stress state data in real time at different spatial coordinates within the monitored area. These sensors, employing fiber optic gratings, vibrating wire, or resistance strain gauges, are installed at key locations in a three-dimensional grid layout, forming a high-density monitoring network. The collected raw stress data undergoes signal filtering, temperature compensation, and calibration conversion to obtain the real-time stress tensor for each measuring point. Subtracting the current stress state from the initial geostress state yields the numerical matrix of the additional load caused by mining activities.

[0116] The ultimate bearing strength values ​​of rock strata at various spatial locations were extracted. Based on borehole core tests and laboratory rock mechanics test data, a database of rock strata strength parameters for the monitored area was established. For different types of rock strata, their uniaxial compressive strength, tensile strength, cohesion, and internal friction angle were measured. Considering the anisotropy and discontinuities of the rock strata, the Hoek-Brown criterion or the Mohr-Coulomb criterion was used to calculate the ultimate bearing strength values ​​under different stress states. Through geological structure modeling and parameter interpolation methods, the discrete strength parameters were mapped onto a three-dimensional spatial grid to form a continuous strength distribution field.

[0117] The safety margin value for each spatial location is obtained by comparing the additional load value with the ultimate bearing strength value. For each spatial grid point, the safety margin value S = (ultimate bearing strength value - additional load value) / ultimate bearing strength value is calculated. This safety margin value characterizes the percentage of remaining bearing capacity reserve of the rock material from the current stress state to the failure state. The larger the safety margin value, the higher the stability of the rock layer; a safety margin value close to zero or negative indicates that the rock layer is close to or has reached the failure state.

[0118] The safety margin values ​​at each spatial location are bound and mapped to their corresponding spatial coordinates to generate safety margin distribution data for each spatial location within the monitored area. A 3D visualization model is constructed, using different color gradients to represent different safety margin values, such as green for high safety margin, yellow for medium safety margin, and red for critical or unsafe safety margin. The model can be segmented, its transparency adjusted, and locally magnified as needed to intuitively display the spatial distribution characteristics of the safety margin.

[0119] Spatial regions in the safety margin distribution data whose safety margin values ​​are lower than the benchmark value for rock strata stability assessment are marked as target regions. Based on rock strata engineering experience and mechanical theory analysis, the benchmark value for rock strata stability assessment is determined, typically set between 0.3 and 0.5. The safety margin distribution data is then segmented using a threshold to filter out spatial regions with safety margin values ​​lower than this benchmark value, and these regions are marked as target regions through regional connectivity analysis. Based on the geometry and size of the target regions, their impact on overall engineering safety is assessed.

[0120] Calculate the change in safety margin value of the target area between adjacent time series nodes. Calculate the weighted average of the safety margin values ​​of all grid points within the target area to obtain the regional average safety margin value at different time points. Select an appropriate time sampling interval, typically once per hour or per shift, to record the safety margin value sequence data for multiple consecutive time points.

[0121] The change in the safety margin value is divided by the time interval between adjacent time series nodes to obtain the safety margin decay rate of the target region. By calculating the decay rate over multiple time windows and combining it with time series preprocessing methods such as weighted moving average or exponential smoothing, the influence of random fluctuations is eliminated, and a stable decay rate trend is obtained.

[0122] The difference between the current safety margin value of the target area and the rock instability judgment threshold is taken as the remaining safety margin. The rock instability judgment threshold is usually set to 0.1 to 0.15, indicating that the rock strata are in a high-risk state and engineering measures must be taken. The remaining safety margin SR = S(current) - instability judgment threshold, represents how much safety margin is left from the current state to the critical instability state.

[0123] Dividing the remaining safety margin by the safety margin decay rate yields the predicted time required for the target area to reach the rock instability threshold from the current time. The predicted value T = SR / R indicates that, without any intervention, the target area will reach the instability threshold after time T. If the decay rate exhibits nonlinear characteristics, nonlinear fitting or machine learning methods can be used to improve prediction accuracy.

[0124] Based on the predicted time, subtracting the implementation period and effectiveness delay of the pressure relief measures, the latest time node at which the pressure relief measures must be initiated is obtained. Pressure relief measures may include engineering methods such as pressure relief drilling, pre-splitting blasting, and support reinforcement, each with a specific implementation period and effectiveness delay characteristics. Latest initiation time = T - implementation period - effectiveness delay, ensuring that the pressure relief measures can fully function before the rock strata reach the instability threshold.

[0125] The time period between the current moment and the latest time node is defined as the implementation time window for pressure relief measures. Within this time window, pressure relief measures should be initiated at an appropriate time based on on-site construction conditions, personnel and equipment allocation, and production schedule. To ensure a safety margin, it is recommended to implement the measures as early as possible, and to continuously monitor changes in the safety margin during the implementation process, dynamically evaluate the pressure relief effect, and adjust the pressure relief plan parameters or add supplementary measures as necessary.

[0126] This invention provides a rock strata displacement and deformation detection system for deep mining operations, comprising:

[0127] The first unit is used to acquire multi-dimensional physical field data of rock strata in the area to be monitored through detection sensors. The multi-dimensional physical field data includes displacement field data reflecting the deformation state of the rock strata and stress field data reflecting the stress state of the rock strata.

[0128] The second unit is used to spatially discretize the multi-dimensional physical field data, divide the area to be monitored into three-dimensional grid cells, calculate the gradient values ​​between adjacent nodes in the three-dimensional grid cells, identify the stress concentration locations of the rock strata through the spatial distribution of the gradient values, define the set of nodes whose gradient values ​​exceed a preset critical rate of change as stress gradient anomaly regions, and extract the spatial geometric features of the stress gradient anomaly regions and the directional features in the displacement field data to establish the correlation between the spatial location and directional features of the stress gradient anomaly regions.

[0129] The third unit is used to calculate the additional load required for the rock layer material to reach the critical failure state from the current stress state by tracking the expansion rate of the stress gradient anomaly region and the degradation rate of the shear strength of the rock layer material, based on the aforementioned correlation. The additional load is used as a quantitative indicator of the remaining bearing capacity of the rock layer. The unit generates safety margin distribution data for each spatial location within the monitoring area and determines the implementation time window of the pressure relief measures based on the safety margin decay rate of the target area.

[0130] A third aspect of the present invention provides an electronic device, comprising:

[0131] processor;

[0132] Memory used to store processor-executable instructions;

[0133] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0134] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0135] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting displacement deformation of a mine rock stratum oriented to deep mining, characterized in that, The method comprises the following steps: acquiring multi-dimensional physical field data of the rock stratum in the monitoring area by a detection sensor, the multi-dimensional physical field data including displacement field data reflecting the deformation state of the rock stratum and stress field data reflecting the stress state of the rock stratum; performing spatial discretization processing on the multi-dimensional physical field data, dividing the monitoring area into three-dimensional grid units, calculating the gradient value between adjacent nodes in the three-dimensional grid units, identifying the stress concentration position of the rock stratum through the spatial distribution of the gradient value, defining a node set with a gradient value exceeding a preset critical change rate as a stress gradient abnormal area, and extracting the spatial geometric features of the stress gradient abnormal area and the directional features in the displacement field data, establishing an association between the spatial position of the stress gradient abnormal area and the directional features; according to the association, tracking the expansion rate of the stress gradient abnormal area and the degradation rate of the shear strength of the rock stratum material, calculating the additional load amount that the rock stratum material needs to bear from the current stress state to the critical failure state, taking the additional load amount as a quantitative indicator of the residual bearing capacity of the rock stratum, generating safety margin distribution data of each spatial position in the monitoring area, and determining the implementation time window of the pressure relief measures according to the safety margin decay rate of the target area; according to the association, tracking the expansion rate of the stress gradient abnormal area and the degradation rate of the shear strength of the rock stratum material includes: obtaining the spatial boundary coordinates of the stress gradient abnormal area, calculating the spatial boundary displacement amount of the stress gradient abnormal area between adjacent time sequence nodes, dividing the spatial boundary displacement amount by the time interval of adjacent time sequence nodes to obtain the expansion rate vector of the stress gradient abnormal area in each spatial direction, performing module length calculation on the expansion rate vector to obtain the overall expansion rate of the stress gradient abnormal area; determining the spatial position of the rock stratum structure corresponding to the stress gradient abnormal area based on the association, extracting the measured values of the shear strength of the rock stratum material at multiple time sequence nodes, calculating the difference value of the measured values of the shear strength between adjacent time sequence nodes, dividing the difference value by the time interval of adjacent time sequence nodes to obtain the degradation rate of the shear strength of the rock stratum material; calculating the additional load amount that the rock stratum material needs to bear from the current stress state to the critical failure state includes: The coupling calculation relationship between the overall extension rate of the stress gradient abnormal area and the shear strength degradation rate of the rock stratum material is established, the stress increment rate caused by the stress gradient abnormal area extension is obtained by multiplying the overall extension rate and the elastic modulus of the rock stratum material, and the bearing capacity decay rate of the rock stratum material is obtained by correlating the shear strength degradation rate and the internal friction angle in the Mohr-Coulomb failure criterion; the comprehensive evolution rate of the stress state of the rock stratum material to the critical failure state is obtained by superimposing the stress increment rate and the bearing capacity decay rate, the stress difference between the current stress state and the critical failure stress state of the rock stratum material is obtained, and the estimated time for the rock stratum material to reach the critical failure state is obtained by dividing the stress difference by the comprehensive evolution rate; the additional load amount borne by the rock stratum material from the current stress state to the critical failure state is obtained by multiplying the stress increment rate and the estimated time; The additional load amount is taken as a quantitative index of the residual bearing capacity of the rock stratum, the safety margin distribution data of each spatial position in the target area is generated, and the implementation time window of the pressure relief measure is determined according to the safety margin decay rate of the target area, including: The additional load amount value corresponding to each spatial position in the monitored area is obtained, the ultimate bearing strength value of the rock stratum material at each spatial position is extracted, the additional load amount value and the ultimate bearing strength value are subjected to ratio operation to obtain the safety margin value of each spatial position, the safety margin value represents the residual bearing capacity reserve of the rock stratum material from the current stress state to the failure state, the safety margin value of each spatial position is bound and mapped with the corresponding spatial coordinates to generate the safety margin distribution data of each spatial position in the monitored area; the spatial area with a safety margin value lower than the rock stratum stability evaluation reference value in the safety margin distribution data is marked as a target area; the safety margin value change amount of the target area between adjacent time sequence nodes is calculated, the safety margin decay rate of the target area is obtained by dividing the safety margin value change amount by the time interval of adjacent time sequence nodes, the difference between the safety margin value of the target area at the current time and the rock stratum instability judgment threshold value is taken as the safety margin remaining amount, and the time prediction value of the target area from the current time to the rock stratum instability judgment threshold value is obtained by dividing the safety margin remaining amount by the safety margin decay rate; the latest time node at which the pressure relief measure must be started and implemented is obtained based on the time prediction value, the implementation cycle of the pressure relief measure and the effective delay time, the time period between the current time and the latest time node is determined as the implementation time window of the pressure relief measure.

2. The method of claim 1, wherein, The multi-dimensional physical field data is subjected to spatial discretization processing, the monitored area is divided into three-dimensional grid units, and the gradient value between adjacent nodes in the three-dimensional grid units is calculated, including: The spatial second-order derivative distribution of displacement field data in the to-be-monitored region is calculated, a scale division criterion of three-dimensional grid cells is determined based on the spatial second-order derivative distribution, and the to-be-monitored region is discretized according to the scale division criterion to obtain a three-dimensional grid cell set; The displacement field data and the stress field data are respectively mapped to nodes at corresponding spatial positions in the three-dimensional grid cell set, and each node stores a displacement vector value and a stress tensor value at the spatial coordinates of the node as physical field attribute values; For each target node in the three-dimensional grid cell set, all adjacent nodes sharing a grid face with the target node are obtained, the spatial vector distance between the target node and each adjacent node and the physical field attribute values stored by the target node and each adjacent node are extracted, the physical field attribute values of the target node and the physical field attribute values of each adjacent node are subjected to difference operation to obtain attribute difference values, the attribute difference values are divided by the lengths of the corresponding spatial vector distances to obtain directional gradient components of the target node in the direction of each adjacent node, and all directional gradient components are subjected to vector composition operation to obtain a gradient value at the target node.

3. The method of claim 1, wherein, The spatial geometric features of the stress gradient anomaly region and the directional features in the displacement field data are extracted, and a correlation between the spatial position and the directional features of the stress gradient anomaly region is established, including: A boundary contour point set of the stress gradient anomaly region is obtained, the boundary contour point set is subjected to three-dimensional space fitting to obtain a closed spatial surface of the stress gradient anomaly region, geometric center coordinates, a principal axis direction vector and a spatial extension scale of the closed spatial surface are calculated, and the geometric center coordinates, the principal axis direction vector and the spatial extension scale are combined to form the spatial geometric features of the stress gradient anomaly region; Displacement vectors of each spatial position in the to-be-monitored region in the displacement field data are extracted, the direction angles and lengths of the displacement vectors are calculated, the distribution of the direction angles of the displacement vectors of all spatial positions in the stress gradient anomaly region is counted, the dominant direction of the displacement vectors in the stress gradient anomaly region is identified, and a unit direction vector corresponding to the dominant direction is extracted as the directional feature in the displacement field data; A spatial mapping relationship between the geometric center coordinates of the stress gradient anomaly region and the directional features is established to form a correlation between the spatial position and the directional features of the stress gradient anomaly region.

4. A system for detecting displacement deformation of rock strata in a mine for deep mining, for implementing the method according to any one of claims 1 to 3, characterized in that, The first unit is configured to acquire multi-dimensional physical field data of a rock stratum in a to-be-monitored region through a detection sensor, the multi-dimensional physical field data including displacement field data reflecting a deformation state of the rock stratum and stress field data reflecting a stress state of the rock stratum. ​ The second unit is configured to perform spatial discretization processing on the multi-dimensional physical field data, divide the monitoring area into three-dimensional grid units, calculate gradient values between adjacent nodes in the three-dimensional grid units, identify a stress concentration position of the rock stratum through spatial distribution of the gradient values, define a node set with a gradient value exceeding a preset critical change rate as a stress gradient abnormal area, extract spatial geometric features of the stress gradient abnormal area and directional features in the displacement field data, and establish an association between the spatial position of the stress gradient abnormal area and the directional features. The third unit is configured to calculate, according to the association, an additional load amount that the rock stratum needs to bear from a current stress state to a critical failure state through tracking an expansion rate of the stress gradient abnormal area and a degradation rate of a shear strength of the rock stratum, take the additional load amount as a quantitative index of a residual bearing capacity of the rock stratum, generate safety margin distribution data of each spatial position in the monitoring area, and determine an implementation time window of a pressure relief measure according to a safety margin decay rate of a target area.

5. An electronic device, comprising: The computer program instructions are executed by the processor to implement the method in any one of claims 1 to 3. The computer program instructions are executed by the processor to implement the method in any one of claims 1 to 3. ​ ​ 6. A computer-readable storage medium having stored thereon computer program instructions, wherein, ​

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