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

Abstract

The invention provides a mine rock stratum displacement deformation detection method and system for deep mining, and relates to the technical field of mine detection, and the method comprises the steps: obtaining rock stratum multi-dimensional physical field data through a detection sensor, carrying out the spatial discretization processing, and recognizing a stress gradient abnormal region; and establishing an association relationship between the stress gradient abnormal region and the displacement field direction characteristics, calculating the residual bearing capacity of the rock stratum, and generating safety margin distribution data. According to the method, the rock stratum failure precursor can be accurately identified, a scientific basis is provided for pressure relief measures, and the deep mining safety is improved.

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, the monitoring of rock stratum displacement and deformation is of great significance to prevent and control mine rock stratum disasters and ensure 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, through displacement meters, stress meters, acoustic emission instruments, and other equipment. 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 of rock stratum stability and early warning mechanisms. 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: acquiring multi-dimensional physical field data of the rock stratum in the monitoring area through detection sensors, 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 cells, calculating the gradient values between adjacent nodes in the three-dimensional grid cells, identifying the stress concentration position of the rock stratum through the spatial distribution of the gradient values, defining the node set with gradient values exceeding the preset critical change rate as the 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 the correlation between the spatial position and directional features of the stress gradient abnormal area; According to the correlation, the extension rate of the stress gradient abnormal area and the degradation rate of the rock stratum material shear strength are tracked, the additional load required for the rock stratum material to reach the critical failure state from the current stress state is calculated, the additional load is taken as a quantitative indicator of the rock stratum residual bearing capacity, the safety margin distribution data of each spatial position in the monitoring area is generated, and the implementation time window of the pressure relief measures is determined according to the safety margin decay rate of the target area.

[0006] The multi-dimensional physical field data is subjected to spatial discretization processing, and the monitoring 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 the displacement field data in the monitoring area is calculated, the scale division criterion of the three-dimensional grid units is determined based on the spatial second-order derivative distribution, the monitoring area is subjected to spatial discretization processing according to the scale division criterion, and a three-dimensional grid unit set is obtained; The displacement field data and the stress field data are respectively mapped to the nodes at the corresponding spatial positions in the three-dimensional grid unit set, and each node stores the displacement vector value and the stress tensor value at the spatial coordinates of the node as the physical field attribute value; For each target node in the three-dimensional grid unit set, all adjacent nodes sharing a grid surface 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 respectively 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, the attribute difference value is obtained, the attribute difference value is divided by the module length of the corresponding spatial vector distance, the directional gradient component of the target node pointing to each adjacent node direction is obtained, and all directional gradient components are subjected to vector synthesis operation, and the gradient value at the target node is obtained.

[0007] The spatial geometric features of the stress gradient anomaly area and the directional features in the displacement field data are extracted, and the correlation between the spatial position and the directional features of the stress gradient anomaly area is established, including: A boundary contour point set of the stress gradient anomaly area is obtained, the boundary contour point set is subjected to three-dimensional space fitting, a closed space surface of the stress gradient anomaly area is obtained, the geometric center coordinates, the principal axis direction vector and the spatial extension scale of the closed space 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 area; The displacement vectors of each spatial position in the monitoring area in the displacement field data are extracted, the direction angle and the module length of each displacement vector are calculated, the distribution of the displacement vector direction angle of all spatial positions in the stress gradient anomaly area is counted, the dominant direction of the displacement vector in the stress gradient anomaly area is identified, and the unit direction vector corresponding to the dominant direction is extracted as the directional feature in the displacement field data; The spatial mapping relationship between the geometric center coordinates of the stress gradient anomaly area and the directional features is established, and the correlation between the spatial position and the directional features of the stress gradient anomaly area is formed.

[0008] According to the correlation, the expansion rate of the stress gradient abnormal area and the degradation rate of the shear strength of the rock stratum material are tracked, including: The spatial boundary coordinates of the stress gradient abnormal area are obtained, the spatial boundary displacement amount of the stress gradient abnormal area between adjacent time sequence nodes is calculated, the spatial boundary displacement amount is divided by the time interval of the adjacent time sequence nodes to obtain the expansion rate vector of the stress gradient abnormal area in each spatial direction, and the expansion rate vector is subjected to a module length calculation to obtain the overall expansion rate of the stress gradient abnormal area. Based on the correlation, the spatial position of the rock stratum structure corresponding to the stress gradient abnormal area is determined, the measured values of the shear strength of the rock stratum material at multiple time sequence nodes are extracted, the difference value of the measured values of the shear strength between adjacent time sequence nodes is calculated, and the difference value is divided by the time interval of the adjacent time sequence nodes to obtain the degradation rate of the shear strength of the rock stratum material.

[0009] The additional load amount that the rock stratum material needs to bear from the current stress state to reach the critical failure state is calculated, including: A coupling calculation relationship between the overall expansion rate of the stress gradient abnormal area and the degradation rate of the shear strength of the rock stratum material is established, the overall expansion rate is multiplied by the elastic modulus of the rock stratum material to obtain a stress increment rate caused by the expansion of the stress gradient abnormal area, The degradation rate of the bearing capacity of the rock stratum material is obtained by correlating the degradation rate of the shear strength with the internal friction angle in the Mohr-Coulomb failure criterion of the rock stratum material; The stress increment rate and the bearing capacity degradation rate are superimposed to obtain a comprehensive evolution rate of the stress state of the rock stratum material evolving to the critical failure state, the stress difference between the current stress state and the critical failure stress state of the rock stratum material is obtained, and the stress difference is divided by the comprehensive evolution rate to obtain the estimated time for the rock stratum material to reach the critical failure state; The stress increment rate is multiplied by the estimated time to obtain the additional load amount that the rock stratum material needs to bear from the current stress state to reach the critical failure state.

[0010] The additional load amount is taken as a quantitative indicator of the residual bearing capacity of the rock stratum, safety margin distribution data of each spatial position in the region to be monitored is generated, and the implementation time window of the pressure relief measures is determined according to the safety margin degradation rate of the target region, including: Obtain the corresponding additional load quantity value of each spatial position in the monitoring area, extract the ultimate bearing strength value of the rock stratum material at each spatial position, and perform ratio operation on the additional load quantity value and the ultimate bearing strength value to obtain the safety margin value of each spatial position, wherein the safety margin value represents the remaining bearing capacity reserve of the rock stratum material from the current stress state to the failure state, and 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 monitoring area. Mark the spatial region with a safety margin value lower than the rock stratum stability evaluation reference value in the safety margin distribution data as a target region. Calculate the safety margin value change amount of the target region between adjacent time sequence nodes, divide the safety margin value change amount by the time interval of adjacent time sequence 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 time and the rock stratum instability judgment threshold as the safety margin remaining amount, and divide the safety margin remaining amount by the safety margin decay rate to obtain the time prediction value required for the target region to reach the rock stratum instability judgment threshold from the current time. Subtract the implementation cycle and effective delay time of the pressure relief measure based on the time prediction value to obtain the latest time node at which the pressure relief measure must be started and implemented, and determine the time period between the current time and the latest time node as the implementation time window of the pressure relief measure.

[0011] In a second aspect of the embodiments of the present application, a mine rock stratum displacement and deformation detection system for deep mining is provided, which comprises: A first unit is configured to obtain multi-dimensional physical field data of a rock stratum in a monitoring area through a detection sensor, wherein the multi-dimensional physical field data comprises displacement field data reflecting the deformation state of the rock stratum and stress field data reflecting the stress state of the rock stratum. A 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 the gradient value between adjacent nodes in the three-dimensional grid units, identify the stress concentration position of the rock stratum through the spatial distribution of the gradient value, define a node set with a gradient value exceeding a preset critical change rate as a stress gradient abnormal region, extract the spatial geometric features of the stress gradient abnormal region and the directional features in the displacement field data, and establish an association relationship between the spatial position and the directional features of the stress gradient abnormal region. The third unit is configured to calculate an additional load amount that needs to be borne by the rock stratum material from a current stress state to a critical failure state according to the correlation relationship, an extension rate of the stress gradient abnormal area and a degradation rate of the rock stratum material shear strength, generate safety margin distribution data of each spatial position in the to-be-monitored area by taking the additional load amount as a quantitative index of the rock stratum residual bearing capacity, and determine an implementation time window of the pressure relief measure according to a safety margin decay rate of the target area.

[0012] A third aspect of the embodiment of the present application, An electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; The processor is configured to invoke the instructions stored in the memory to execute the method described above.

[0013] A fourth aspect of the embodiment of the present application, A computer-readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the method described above.

[0014] The beneficial effects of the present application are as follows: By establishing the correlation relationship between the spatial position and the direction feature of the stress gradient abnormal area, the prediction and analysis of the rock stratum deformation trend are realized, and the accuracy of the early warning is improved.

[0015] The quantitative index of the rock stratum residual bearing capacity is calculated by tracking the extension rate of the stress gradient abnormal area and the degradation rate of the rock stratum shear strength, the rock stratum safety state is changed from qualitative evaluation to quantitative analysis, and the scientificity of the safety evaluation is significantly improved.

[0016] The implementation time window of the pressure relief measure is determined according to the safety margin decay rate of the target area, and the change from passive response to active prevention of the mine safety management is realized. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 Fig. 1 is a flowchart of a mine rock stratum displacement deformation detection method for deep mining of the embodiment of the present application; Figure 2 Fig. 2 is a flowchart of tracking the extension of the stress gradient abnormal area and the degradation of the rock stratum shear strength. DETAILED DESCRIPTION

[0018] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0019] The technical solutions of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and some embodiments can not be described again for the same or similar concepts or processes.

[0020] With reference to Figure 1 and Figure 2 , the mine rock stratum displacement deformation detection method for deep mining in the embodiments of the present application comprises: acquiring multi-dimensional physical field data of rock strata in a to-be-monitored area by a detection sensor, wherein the multi-dimensional physical field data comprises displacement field data reflecting a deformation state of the rock strata and stress field data reflecting a stress state of the rock strata; performing spatial discretization processing on the multi-dimensional physical field data, dividing the to-be-monitored area into three-dimensional grid units, calculating gradient values between adjacent nodes in the three-dimensional grid units, identifying a stress concentration position of the rock strata through 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 spatial geometric features of the stress gradient abnormal area and directional features in the displacement field data, to establish an association relationship between spatial positions of the stress gradient abnormal area and the directional features; according to the association relationship, calculating an additional load amount borne by the rock strata 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 strata, taking the additional load amount as a quantitative index of a residual bearing capacity of the rock strata, generating safety margin distribution data of each spatial position in the to-be-monitored area, and determining an implementation time window of a pressure relief measure according to a safety margin decay rate of a target area.

[0021] In an optional implementation, the spatial discretization processing on the multi-dimensional physical field data, the division of the to-be-monitored area into three-dimensional grid units, and the calculation of the gradient values between adjacent nodes in the three-dimensional grid units comprise: calculating a spatial second-order derivative distribution of the displacement field data in the to-be-monitored area, determining a scale division criterion of the three-dimensional grid units based on the spatial second-order derivative distribution, performing spatial discretization processing on the to-be-monitored area according to the scale division criterion, and obtaining a three-dimensional grid unit set; mapping the displacement field data and the stress field data to nodes corresponding to spatial positions in the set of three-dimensional grid cells respectively, each node storing 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 set of three-dimensional grid cells, all neighboring nodes sharing a grid face with the target node are obtained, the spatial vector distance between the target node and each neighboring node and the respective stored physical field attribute values are extracted, the physical field attribute value of the target node is subjected to a difference operation with the physical field attribute value of each neighboring node to obtain an attribute difference value, the attribute difference value is divided by the length of the corresponding spatial vector distance to obtain a directional gradient component of the target node in the direction of each neighboring node, and all directional gradient components are subjected to a vector composition operation to obtain a gradient value at the target node.

[0022] The process of calculating the gradient values between neighboring nodes in the three-dimensional grid cells after spatial discretization of the multi-dimensional physical field data is as follows: In actual application, spatial discretization first needs to determine a suitable grid division method. The spatial second-order derivative distribution of displacement field data in the monitored area can effectively identify areas of intense deformation of the rock stratum. Displacement field data is usually obtained by an array of micro-deformation sensors buried in the rock stratum, which can capture small deformation information of the rock stratum. For the original displacement data obtained, the spatial second-order 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, the spatial second-order derivative can be obtained by difference approximation of the displacement values of neighboring sampling points. The second-order derivative distribution of the displacement field directly reflects the curvature change of the rock stratum, and areas with intense curvature change are often dangerous areas with stress concentration or impending failure.

[0023] Based on the calculated spatial second-order derivative distribution, an adaptive three-dimensional grid cell size division criterion is established. In areas with intense displacement gradient change, smaller grid cells are selected to improve calculation accuracy, while in areas with gentle deformation gradient, larger grid cells can be used to improve calculation efficiency. The size division criterion can be set as follows: when the displacement second-order derivative value between adjacent sampling points exceeds a predetermined threshold, the grid size in this area is dynamically adjusted according to the size of the displacement second-order derivative value, and the larger the second-order derivative value, the smaller the grid size. This adaptive grid division method can optimize the allocation of computing resources while ensuring calculation accuracy.

[0024] After the completion of the scale division criterion setting, the entire monitoring area is discretized. A tetrahedron or hexahedron is used as a basic geometric unit to construct a three-dimensional grid model, forming a complete topological structure containing nodes, edges, and faces. During the discretization process, the grid quality needs to be ensured to avoid the presence of highly distorted cells, in order to ensure the accuracy of subsequent calculations. The grid generation algorithm can use the Delaunay triangulation or the forward hexahedral grid generation method, and finally obtain a three-dimensional grid cell set suitable for the geometric characteristics of the monitoring area.

[0025] After obtaining the three-dimensional grid cell set, the multi-dimensional physical field data needs to be mapped to the grid nodes. Displacement field data is mainly derived from the measurement results of micro-deformation sensors, fiber Bragg gratings, or displacement meters, while stress field data is obtained through stress meters, strain gauges, or microseismic monitoring systems. For spatial positions not directly covered by sensors, a three-dimensional interpolation algorithm is used to calculate the physical field attribute values of the corresponding nodes. Common 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 the surrounding known measurement points and the spatial distance, ensuring that the interpolation results are physically reasonable. After completing the data mapping, each node in the three-dimensional grid cell set stores the physical field attribute information of the spatial position, including displacement vector values and stress tensor values.

[0026] For each target node in the three-dimensional grid cell set, the gradient value needs to consider the physical field change relationship between the adjacent nodes. First, determine all adjacent nodes that share a grid face with the target node. These nodes are directly connected to the target node, usually six or more nodes around the target node, depending on the grid topology. Extract the spatial vector distance between the target node and each adjacent node, which is the three-dimensional vector formed by the difference between the coordinates of the two nodes. At the same time, obtain the physical field attribute values stored by the target node and each adjacent node, including displacement vectors and stress tensors.

[0027] For each adjacent node, perform a difference operation on the physical field attribute values of the target node and the adjacent node. For the displacement field, directly calculate the difference between the displacement vectors of the two nodes; for the stress field, calculate the difference between the components of the stress tensor. The attribute difference value reflects the change in the physical field between the two nodes. Divide this attribute difference value by the modulus of the corresponding spatial vector distance to obtain the directional gradient component of the target node in the direction of 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.

[0028] After the gradient component calculation of all adjacent directions is completed, vector composition operation is performed on the direction gradient components to obtain the total gradient value at the target node. The vector composition can adopt a weighted average method, and the reciprocal of the spatial distance is used as the weight to ensure that the closer the nodes, the greater the contribution to the gradient calculation. The calculation result is the gradient vector of the physical field at the target node, and the size reflects the degree of change of the physical field, and the direction points to the direction in which the physical field increases the fastest.

[0029] In actual application scenarios, such as geological structure deformation monitoring, the gradient field of rock layer displacement and stress distribution can be efficiently calculated by the above method, and then the fault activity and potential slip area are analyzed to provide a scientific basis for geological disaster warning.

[0030] In an optional implementation, the spatial geometric features of the stress gradient anomaly region and the direction features in the displacement field data are extracted, and a correlation between the spatial position of the stress gradient anomaly region and the direction features is established. A set of boundary contour points of the stress gradient anomaly region is obtained, the set of boundary contour points is fitted in three-dimensional space to obtain a closed spatial surface of the stress gradient anomaly region, geometric center coordinates, principal axis direction vectors and spatial extension scales of the closed spatial surface are calculated, and the geometric center coordinates, the principal axis direction vectors and the spatial extension scales are combined to form the spatial geometric features of the stress gradient anomaly region. The displacement vectors of each spatial position in the to-be-monitored region in the displacement field data are extracted, the direction angle and the module length of each displacement vector are calculated, the distribution of the displacement vector direction angle of all spatial positions in the stress gradient anomaly region is counted, the dominant direction of the displacement vector in the stress gradient anomaly region is identified, and the unit direction vector corresponding to the dominant direction is extracted as the direction feature in the displacement field data. A spatial mapping relationship between the geometric center coordinates of the stress gradient anomaly region and the direction features is established to form a correlation between the spatial position of the stress gradient anomaly region and the direction features.

[0031] A set of boundary contour points of the stress gradient anomaly region is obtained. The boundary point coordinates of the anomaly region calculated by the stress gradient analysis are extracted. These point sets can be extracted from the original stress field data by stress gradient threshold segmentation or region growing algorithm.

[0032] The boundary contour point set is fitted in three-dimensional space to obtain a closed spatial surface of the stress gradient anomaly region. The fitting process adopts a three-dimensional surface reconstruction algorithm, such as three-dimensional ellipsoid fitting or B-spline surface fitting method. For the ellipsoid fitting method, the parameters of the ellipsoid equation can be solved by the least square method; for a complex shape, a segmented surface fitting method is adopted to construct a closed surface model S(u, v). The closed spatial surface obtained by fitting should satisfy the best approximation of the boundary points, while maintaining the smoothness and closedness of the surface.

[0033] The geometric center coordinates C = (x0, y0, z0) of the closed spatial surface are calculated by taking the arithmetic mean of all point coordinates on the surface. For an ellipsoid, the geometric center is the center of the ellipsoid; for an irregular shape, the centroid position obtained by volume integration is used as the geometric center.

[0034] The principal axis direction vector of the closed spatial surface is calculated, the covariance matrix of the surface point set is constructed, and then the eigenvalue decomposition of the covariance matrix is performed to obtain three eigenvalues λ1, λ2, λ3 and corresponding eigenvectors v1, v2, v3. The eigenvector v1 corresponding to the largest eigenvalue represents the principal axis direction of the anomaly region, i.e., the most significant direction of the anomaly region in space.

[0035] The spatial extension scale D = (d1, d2, d3) of the closed spatial surface is calculated, which represents the extension distance along the three principal axis directions. The specific method is to project all points on the surface onto the principal axis direction and calculate the maximum projection distance as twice the extension scale in that direction. For example, d1 represents the extension scale along the principal axis direction v1, and the calculation method is to calculate the maximum distance difference of all points on the surface to the plane perpendicular to v1.

[0036] The geometric center coordinates C, the principal axis direction vector v1 and the spatial extension scale D are combined to form the spatial geometric features G = {C, v1, D} of the stress gradient anomaly region, which completely describes the spatial position, direction and size of the anomaly region.

[0037] In the displacement field data analysis, first, the displacement vector set U = {u1, u2,..., un} of each spatial position in the monitored area is extracted, where u represents a three-dimensional displacement vector and n represents the number of spatial positions. For each displacement vector, the direction angles α, β, γ (the angles with x, y, z axes respectively) and the module |u| are calculated. The direction angle is obtained by the ratio of the vector component and the module, such as α = arccos(u x / |u|).

[0038] The direction angle distribution of all displacement vectors in the stress gradient anomaly region is counted. The spatial direction is divided into several angle intervals, and the number or cumulative length of displacement vectors in each interval is calculated to generate a direction distribution histogram. Through histogram analysis, the direction interval with the highest frequency of occurrence or the maximum cumulative length is identified, and the direction corresponding to the interval is defined as the dominant direction of the displacement vectors in the anomaly region.

[0039] A representative direction is selected from the dominant direction interval, and the corresponding unit direction vector d = (dx, dy, dz) is extracted as the direction feature in the displacement field data. The unit direction vector can be obtained by weighted averaging of all displacement vectors in the dominant direction interval, and the weight can be set as the length of the displacement vector.

[0040] A spatial mapping relationship between the geometric center coordinates C of the stress gradient anomaly region and the direction feature d is established. This mapping relationship can be represented as a vector function F(C) = d, which describes the corresponding relationship from the spatial position of the anomaly region to the displacement direction. In practical applications, a regression model such as linear regression or nonlinear regression model can be established through multiple anomaly region data to predict the possible displacement direction of the anomaly region at a given location.

[0041] Through the establishment of the above correlation, the development trend of the stress gradient anomaly region can be predicted, providing a basis for geological disaster warning. For example, in a mining environment, when a new stress gradient anomaly region is identified, its spatial position can be used to predict the possible deformation direction, thereby evaluating the potential danger and taking appropriate preventive measures.

[0042] In an alternative embodiment, according to the correlation, the expansion rate of the stress gradient anomaly region and the degradation rate of the shear strength of the rock mass material are tracked, including: The spatial boundary coordinates of the stress gradient anomaly region are obtained, the spatial boundary displacement amount of the stress gradient anomaly region between adjacent time series nodes is calculated, the spatial boundary displacement amount is divided by the time interval of adjacent time series nodes to obtain the expansion rate vector of the stress gradient anomaly region in each spatial direction, and the length of the expansion rate vector is calculated to obtain the overall expansion rate of the stress gradient anomaly region. Based on the correlation, the spatial position of the rock mass structure corresponding to the stress gradient anomaly region is determined, the measured values of the shear strength of the rock mass material at multiple time series nodes are extracted, the difference between the measured values of the shear strength of the rock mass material between adjacent time series nodes is calculated, and the difference is divided by the time interval of adjacent time series nodes to obtain the degradation rate of the shear strength of the rock mass material.

[0043] In this embodiment, by tracking the correlation between the expansion rate of the stress gradient abnormal area and the degradation rate of the shear strength of the rock stratum material, accurate evaluation and prediction of the stability of the rock stratum structure can be realized.

[0044] The spatial boundary coordinates of the stress gradient abnormal area are obtained, the stress distribution data inside the rock stratum structure are obtained by using a distributed sensing network, and the boundary point coordinates of the abnormal area are determined by stress gradient calculation. The boundary points are usually represented as three-dimensional spatial coordinates (x, y, z) to form a closed surface to describe the spatial boundary of the abnormal area. Specifically, the positions where the stress gradient values between adjacent measurement points exceed a preset threshold are used as boundary points, a boundary surface model is constructed by a triangulation algorithm, and the accurate spatial coordinates of each boundary point are recorded.

[0045] Next, the spatial boundary displacement of the stress gradient abnormal area between adjacent time series nodes is calculated. Selecting a time series as an observation node, for each boundary point, the position change vector between adjacent time nodes is calculated. For the boundary point P, its displacement can be represented as ΔP = P(T2) - P(T1), where P(T1) and P(T2) represent the spatial coordinates of the point at T1 and T2 respectively. To improve the calculation accuracy, the nearest point matching algorithm is introduced to ensure the correct correspondence of the boundary points between different time instants.

[0046] The spatial boundary displacement is divided by the time interval of adjacent time series nodes to obtain the expansion rate vector of the stress gradient abnormal area in each spatial direction. For a time interval ΔT = T2 - T1, the expansion rate vector V = ΔP / ΔT contains three direction components x, y, z. To reduce the influence of random errors, the sliding window average method is used to smooth the rate vector.

[0047] The expansion rate vector is calculated to obtain the overall expansion rate of the stress gradient abnormal area. The rate module S = |V| = √(Vx 2 + Vy 2 + Vz 2 ) represents the speed scalar of the comprehensive expansion of the abnormal area in each direction. According to the different expansion directions, the expansion rate is divided into radial expansion and tangential expansion components, and different weights are assigned to reflect the dominant direction of the expansion of the abnormal area.

[0048] Based on the established correlation, the spatial position of the rock stratum structure corresponding to the stress gradient abnormal area is determined. The spatial boundary of the abnormal area is mapped to the actual rock stratum structure unit by using the geological structure model, forming the spatial correspondence between the abnormal area and the rock stratum unit. The coordinate transformation matrix is established to convert the position of the abnormal area in the sensing network coordinate system to the position of the rock stratum in the geological structure coordinate system, ensuring the positioning accuracy.

[0049] Extracting the measured shear strength values of the rock mass material at multiple time-series nodes. Utilize methods such as borehole sampling or in-situ shear tests to obtain shear strength data for the corresponding rock mass region at the time nodes. For deep rock layers that are difficult to sample directly, use indirect measurement methods such as acoustic velocity inversion techniques to derive shear strength values based on the relationship model between wave velocity and shear strength.

[0050] Calculating the difference in measured shear strength values 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. Considering measurement errors, introduce confidence interval analysis to ensure the reliability of the difference calculation.

[0051] Divide the above difference by the time interval between adjacent time-series nodes to obtain the degradation rate of the shear strength of the rock mass material. The degradation rate R = ΔS / ΔT represents the degree of decline of the rock shear strength per unit time. For nonlinear degradation processes, use piecewise linear fitting methods to divide the overall degradation process into multiple stages, and calculate the degradation rate of each stage.

[0052] In practical applications, take a certain mining area as an example, a stress monitoring network containing fifty measurement points is laid out. Through six months of continuous monitoring, it is found that there is a clear stress gradient abnormal area in the northwest region. The expansion rate of this region is calculated to be about three centimeters per month, mainly expanding along the southeast-northwest direction. Correspondingly, the shear strength of the rock mass in this region has decreased by 8% in six months, and the degradation rate is calculated to be about 1.3% per month.

[0053] By establishing the correlation model between the expansion rate and the degradation rate, it is found that when the expansion rate exceeds five centimeters per month, the shear strength degradation rate will show an accelerating growth trend, and this threshold can be used as a warning indicator. This method is successfully used to predict the rock mass instability risk in a certain mining area, and a possible rock mass sliding event is warned two weeks in advance, providing important support for safety production.

[0054] In an alternative embodiment, calculating the amount of additional load that the rock mass material needs to withstand to reach a critical failure state from the current stress state includes: Establishing a coupling calculation relationship between the overall expansion rate of the stress gradient abnormal area and the degradation rate of the shear strength of the rock mass material, and multiplying the overall expansion rate by the elastic modulus of the rock mass material to obtain the stress increment rate caused by the expansion of the stress gradient abnormal area, By correlating the shear strength degradation rate with the internal friction angle in the Mohr-Coulomb failure criterion of the rock mass material, the decay rate of the bearing capacity of the rock mass material is obtained; The stress increment rate and the bearing capacity decay rate are superimposed to obtain a comprehensive evolution rate of the stress state of the rock stratum material evolving to a critical failure state, and a stress difference between the current stress state and the critical failure stress state of the rock stratum material is obtained, and the stress difference is divided by the comprehensive evolution rate to obtain an estimated time for the rock stratum material to reach the critical failure state. The stress increment rate and the estimated time are multiplied to obtain an additional load amount that the rock stratum material needs to bear from the current stress state to the critical failure state.

[0055] A coupling calculation relationship is established between the overall expansion rate of the stress gradient abnormal area and the shear strength degradation rate of the rock stratum material. The stress gradient abnormal area generally refers to an area in which the stress distribution in the rock stratum is uneven and changes greatly, and these areas are prone to become starting points of failure. The morphological change data of the stress gradient abnormal area in the rock stratum are obtained through the field monitoring equipment, the volume expansion tensor thereof in a unit time is calculated, and then the overall expansion rate is determined. For example, if it is monitored that a certain stress gradient abnormal area expands from an initial 2 cubic meters to 2.5 cubic meters in 24 hours, the overall expansion rate is 0.5 cubic meters / day.

[0056] The overall expansion rate obtained is multiplied by the elastic modulus of the rock stratum material to obtain a stress increment rate caused by the expansion of the stress gradient abnormal area. For example, if the elastic modulus of the rock stratum material is 30 GPa and the overall expansion rate is 0.5 cubic meters / day, according to the stress-strain relationship, the stress increment rate can be calculated to be 15 MPa / day.

[0057] At the same time, the shear strength degradation of the rock stratum material is analyzed. The internal structure of the rock stratum material is damaged under a long-term high stress environment, which gradually reduces the shear strength. The shear strength values at different time points are measured through indoor tests on rock core samples, so as to determine the shear strength degradation rate. The degradation rate is associated with the internal friction angle in the Mohr-Coulomb failure criterion to obtain the decay rate of the bearing capacity of the rock stratum material. For example, if the test measures that the internal friction angle decreases by 0.1 degree per day, according to the Mohr-Coulomb failure criterion, the bearing capacity decay rate can be calculated to be 3 MPa / day.

[0058] The stress increment rate and the bearing capacity decay rate are superimposed to obtain a comprehensive evolution rate of the stress state of the rock stratum material evolving to a critical failure state. In the above example, the stress increment rate is 15 MPa / day, the bearing capacity decay rate is 3 MPa / day, and the comprehensive evolution rate is 18 MPa / day.

[0059] Next, determine the stress difference between the current stress state of the rock material and the critical failure stress state. Through field stress measurement and rock material strength test, the current stress state and the critical failure stress state can be obtained. Assuming that the current rock principal stress is 80 MPa, and the critical failure stress is calculated to be 170 MPa according to the Mohr-Coulomb failure criterion, the stress difference is 90 MPa.

[0060] Divide the stress difference by the comprehensive evolution rate to obtain the estimated time for the rock material to reach the critical failure state. In this example, the stress difference is 90 MPa, and the comprehensive evolution rate is 18 MPa / day, so the estimated time is 5 days.

[0061] Finally, multiply the stress increment rate 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 above example, the stress increment rate is 15 MPa / day, and the estimated time is 5 days, so the additional load required is 75 MPa.

[0062] In actual engineering applications, parameters can be adjusted according to different rock types and engineering conditions. For example, in the design of coal mine roadway support, appropriate support schemes can be selected according to the calculated additional load; in the process of tunnel excavation, the excavation speed and support timing can be adjusted according to the estimated time to ensure construction safety.

[0063] In addition, this method can also be combined with numerical simulation technology to establish a rock failure warning system. By monitoring the expansion of the stress gradient anomaly area and the change of the rock material strength parameters in real time, the calculation results are dynamically updated to provide scientific basis for engineering decision-making.

[0064] It should be noted that the mechanical properties of rock materials have spatial heterogeneity, and the differences in parameters at different positions should be considered in the calculation process. If necessary, probability and statistics methods can be used to handle data dispersion to improve the reliability of the calculation results.

[0065] In an alternative embodiment, the additional load is taken as a quantitative indicator of the remaining bearing capacity of the rock, and the safety margin distribution data of each spatial position in the monitored area is generated, and the implementation time window of the pressure relief measures is determined according to the safety margin decay rate of the target area, including: The additional load values corresponding to each spatial position in the monitored area are obtained, the ultimate bearing strength values of the rock material at each spatial position are extracted, the additional load values and the ultimate bearing strength values are subjected to ratio operation, and the safety margin values of each spatial position are obtained, which represent the remaining bearing capacity reserve of the rock material from the current stress state to the failure state. The safety margin values of each spatial position are bound and mapped with the corresponding spatial coordinates to generate the safety margin distribution data of each spatial position in the monitored area. Mark the space region in which the safety margin value in the safety margin distribution data is lower than the rock stability evaluation reference value as a target region; Calculate the safety margin value variation amount of the target region between adjacent time sequence nodes, divide the safety margin value variation amount by the time interval of adjacent time sequence 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 time and the rock instability judgment threshold value as the safety margin remaining amount, and divide the safety margin remaining amount by the safety margin decay rate to obtain the time prediction value required for the target region to reach the rock instability judgment threshold value from the current time; Subtract the implementation cycle of the pressure relief measure and the effective delay time based on the time prediction value to obtain the latest time node at which the pressure relief measure must be started to be implemented, and determine the time period between the current time and the latest time node as the implementation time window of the pressure relief measure.

[0066] Through the stress monitoring sensor network arranged in the rock stratum, the stress state data of different spatial coordinate points in the region to be monitored is collected in real time. These sensors can use fiber grating, vibrating wire or resistance strain stress meters, which are installed at key positions according to a three-dimensional grid layout to form a high-density monitoring network. After the original stress data collected is filtered, temperature compensated and calibrated, the real-time stress tensor of each measuring point is obtained. The additional load value matrix caused by the mining activity can be obtained by subtracting the current stress state from the initial in-situ stress state.

[0067] The limit bearing strength value of the rock stratum material at each spatial position is extracted, and a rock stratum material strength parameter database of the region to be monitored is established based on the drilling core test and indoor rock mechanics test data. For different types of rock strata, the uniaxial compressive strength, tensile strength, cohesion and internal friction angle and other mechanical parameters are measured respectively. Considering the influence of anisotropy and discontinuous surface of the rock stratum, the limit bearing strength value under different stress states is calculated by using Hoek-Brown criterion or Mohr-Coulomb criterion. Through geological structure modeling and parameter interpolation method, the discrete strength parameters are mapped to the three-dimensional space grid to form a continuous strength distribution field.

[0068] The additional load value and the limit bearing strength value are subjected to ratio operation to obtain the safety margin value of each spatial position. For each spatial grid point, the safety margin value S = (limit bearing strength value - additional load value) / limit bearing strength value is calculated. The safety margin value represents the remaining bearing capacity reserve percentage of the rock stratum material from the current stress state to the failure state. The larger the safety margin value, the higher the stability of the rock stratum; and the safety margin value close to zero or negative value indicates that the rock stratum has approached or reached the failure state.

[0069] The safety margin value of each spatial position is bound and mapped with the corresponding spatial coordinates to generate safety margin distribution data of each spatial position in the monitoring area. A three-dimensional visualization model is constructed, and different color gradients are used to represent different safety margin values, such as green for high safety margin value, yellow for medium, and red for critical or unsafe. The model can be cut, transparency adjusted, and locally enlarged as needed to visually display the spatial distribution characteristics of the safety margin.

[0070] The spatial region in the safety margin distribution data with a safety margin value lower than the rock stability evaluation reference value is marked as a target region. The rock stability evaluation reference value is determined through rock engineering experience and mechanical theory analysis, and can generally be set to between 0.3 and 0.5. The safety margin distribution data is threshold segmented to filter out the spatial region with a safety margin value lower than the reference value, which is marked as the target region through region connectivity analysis. The influence degree of the target region on the overall engineering safety is evaluated according to its geometric shape and volume size.

[0071] The safety margin value change amount of the target region between adjacent time sequence nodes is calculated. The safety margin values of all grid points in the target region are weighted and averaged to obtain the region average safety margin values at different time points. A suitable time sampling interval, generally once per hour or per shift, is selected to record the safety margin value sequence data at multiple consecutive time points.

[0072] The safety margin value change amount is divided by the time interval of adjacent time sequence nodes to obtain the safety margin decay rate of the target region. Through decay rate calculation of multiple time windows, combined with time series preprocessing methods such as weighted moving average or exponential smoothing, the influence of random fluctuations is eliminated to obtain a stable decay rate trend.

[0073] The difference between the current safety margin value of the target region and the rock instability judgment threshold is taken as the safety margin remaining amount. The rock instability judgment threshold is usually set to 0.1 to 0.15, indicating that the rock has entered a high-risk state and engineering measures must be taken. The safety margin remaining amount SR = S(current) - instability judgment threshold, indicating how much safety margin is left from the current state to the instability critical state.

[0074] The safety margin remaining amount is divided by the safety margin decay rate to obtain the time prediction value of the target region from the current time to reach the rock instability judgment threshold. The prediction value T = SR / R, indicating that without taking any measures, the target region will reach the instability judgment threshold after T time. If the decay rate has nonlinear characteristics, nonlinear fitting or machine learning methods can be used to improve the prediction accuracy.

[0075] Subtract the implementation cycle of the pressure relief measure and the delay time from the time prediction value to obtain the latest time node at which the pressure relief measure must be started. The pressure relief measure can include pressure release boreholes, pre-splitting blasting, support reinforcement, etc. Each measure has a specific implementation cycle and delay characteristics. The latest start time = T - implementation cycle - delay time, which ensures that the pressure relief measure can fully function before the rock mass reaches the instability threshold.

[0076] Determine the time period between the current time and the latest time node as the implementation time window of the pressure relief measure. Within this time window, according to the site construction conditions, personnel and equipment deployment, and production plan arrangement, select the appropriate time to start the pressure relief measure. To ensure safety margin, it is recommended to implement as soon as possible, and continuously monitor the safety margin change during the implementation of the pressure relief measure, dynamically evaluate the pressure relief effect, and adjust the pressure relief scheme parameters or add supplementary measures if necessary.

[0077] The embodiment of the present application faces a mine rock mass displacement deformation detection system for deep mining, comprising: A first unit for obtaining multi-dimensional physical field data of rock mass in a monitored area through a detection sensor, wherein the multi-dimensional physical field data includes displacement field data reflecting the deformation state of the rock mass and stress field data reflecting the stress state of the rock mass; A second unit for spatially discretizing the multi-dimensional physical field data, dividing the monitored 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 mass through the spatial distribution of the gradient value, defining the 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 the correlation between the spatial position and the directional features of the stress gradient abnormal area; A third unit for calculating the additional load amount that the rock mass needs to withstand from the current stress state to the critical failure state according to the correlation by tracking the expansion rate of the stress gradient abnormal area and the degradation rate of the shear strength of the rock mass, taking the additional load amount as a quantitative indicator of the remaining bearing capacity of the rock mass, generating safety margin distribution data of each spatial position in the monitored area, and determining the implementation time window of the pressure relief measure according to the safety margin decay rate of the target area.

[0078] In a third aspect of the embodiment of the present application, an electronic device is provided, comprising: A processor; A memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method described above.

[0079] In a fourth aspect, the present application provides a computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method described above.

[0080] The present application can be a method, an apparatus, a system, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for performing various aspects of the present application.

[0081] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

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, by a detection sensor, multi-dimensional physical field data of a rock stratum in a region to be monitored, the multi-dimensional physical field data comprising displacement field data reflecting a deformation state of the rock stratum and stress field data reflecting a stress state of the rock stratum; performing spatial discretization processing on the multi-dimensional physical field data, dividing the region to be monitored into three-dimensional grid units, calculating gradient values between adjacent nodes in the three-dimensional grid units, identifying a stress concentration position of the rock stratum through 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 region, and extracting spatial geometric features of the stress gradient abnormal region and directional features in the displacement field data, to establish an association between spatial positions and directional features of the stress gradient abnormal region; according to the association, tracking an expansion rate of the stress gradient abnormal region and a degradation rate of a shear strength of the rock stratum, calculating an additional load amount that the rock stratum needs to bear from a current stress state to a critical failure state, taking the additional load amount as a quantitative index of a residual bearing capacity of the rock stratum, generating safety margin distribution data of each spatial position in the region to be monitored, and determining an implementation time window of a pressure relief measure according to a safety margin decay rate of a target region.

2. The method of claim 1, wherein, The spatial discretization processing on the multi-dimensional physical field data, the division of the region to be monitored into three-dimensional grid units, and the calculation of gradient values between adjacent nodes in the three-dimensional grid units comprise the following steps: calculating a spatial second-order derivative distribution of the displacement field data in the region to be monitored, determining a scale division criterion of the three-dimensional grid units based on the spatial second-order derivative distribution, performing spatial discretization processing on the region to be monitored according to the scale division criterion, and obtaining a three-dimensional grid unit set; mapping the displacement field data and the stress field data to nodes at corresponding spatial positions in the three-dimensional grid unit set respectively, and storing a displacement vector value and a stress tensor value at the spatial coordinates of each node as physical field attribute values; for each target node in the three-dimensional grid unit set, acquiring all adjacent nodes sharing a grid face with the target node, extracting spatial vector distances between the target node and each adjacent node and the physical field attribute values stored respectively, performing difference operation on the physical field attribute values of the target node and each adjacent node to obtain attribute difference values, dividing the attribute difference values 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 performing vector composition operation on all directional gradient components to obtain a gradient value at the target node.

3. The method of claim 1, wherein, The extraction of the spatial geometric features of the stress gradient abnormal region and the directional features in the displacement field data, and the establishment of the association between the spatial positions and the directional features of the stress gradient abnormal region comprise the following steps: The boundary contour point set of the stress gradient anomaly region is acquired, and three-dimensional space fitting is performed on the boundary contour point set to obtain a closed space curve of the stress gradient anomaly region. The geometric center coordinates, principal axis direction vector and space extension scale of the closed space curve are calculated, and the geometric center coordinates, the principal axis direction vector and the space extension scale are combined to form the spatial geometric features of the stress gradient anomaly region. The displacement vector of each spatial position in the monitoring area in the displacement field data is extracted, the direction angle and module length of each displacement vector are calculated, the distribution of the displacement vector direction angle of all spatial positions in the stress gradient anomaly region is counted, and the dominant direction of the displacement vector in the stress gradient anomaly region is identified. The unit direction vector corresponding to the dominant direction is extracted as the direction feature in the displacement field data. The spatial mapping relationship between the geometric center coordinates of the stress gradient anomaly region and the direction feature is established to form the correlation between the spatial position and the direction feature of the stress gradient anomaly region.

4. The method of claim 1, wherein, According to the correlation, the extension rate of the stress gradient anomaly region and the degradation rate of the shear strength of the rock stratum material are tracked, including: The spatial boundary coordinates of the stress gradient anomaly region are acquired, the spatial boundary displacement amount of the stress gradient anomaly region between adjacent time sequence nodes is calculated, the spatial boundary displacement amount is divided by the time interval of adjacent time sequence nodes to obtain the extension rate vector of the stress gradient anomaly region in each spatial direction, and the module length of the extension rate vector is calculated to obtain the overall extension rate of the stress gradient anomaly region. Based on the correlation, the spatial position of the rock stratum structure corresponding to the stress gradient anomaly region is determined, the measured values of the shear strength of the rock stratum material at multiple time sequence nodes are extracted, the difference value of the measured values of the shear strength between adjacent time sequence nodes is calculated, and the difference value is divided by the time interval of adjacent time sequence nodes to obtain the degradation rate of the shear strength of the rock stratum material.

5. The method of claim 4, wherein, The additional load amount that the rock stratum material needs to bear from the current stress state to reach the critical failure state is calculated, including: A coupling calculation relationship between the overall extension rate of the stress gradient anomaly region and the degradation rate of the shear strength of the rock stratum material is established, the overall extension rate is multiplied by the elastic modulus of the rock stratum material to obtain the stress increment rate caused by the extension of the stress gradient anomaly region, The degradation rate of the bearing capacity of the rock stratum material is obtained by correlating the degradation rate of the shear strength with the internal friction angle in the Mohr-Coulomb failure criterion of the rock stratum material; The stress increment rate and the bearing capacity decay rate are superimposed to obtain the comprehensive evolution rate of the stress state of the rock stratum material to the critical failure state, the stress difference between the current stress state and the critical failure stress state of the rock stratum material is obtained, the stress difference is divided by the comprehensive evolution rate to obtain the estimated time for the rock stratum material to reach the critical failure state; The stress increment rate and the estimated time are multiplied to obtain the additional load amount that the rock stratum material needs to bear from the current stress state to reach the critical failure state.

6. The method of claim 1, wherein, The additional load amount is taken as a quantitative index of the residual bearing capacity of the rock stratum, safety margin distribution data of each spatial position in the region to be monitored is generated, and a time window for implementing pressure relief measures is determined according to the safety margin decay rate of the target region, including: The additional load amount corresponding to each spatial position in the region to be monitored is obtained, the ultimate bearing strength value of the rock stratum material at each spatial position is extracted, the additional load amount value is subjected to ratio operation with the ultimate bearing strength value, the safety margin value of each spatial position is obtained, 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, and safety margin distribution data of each spatial position in the region to be monitored is generated; The spatial region 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 region; The safety margin value change amount of the target region between adjacent time sequence nodes is calculated, the safety margin value change amount is divided by the time interval of adjacent time sequence nodes, the safety margin decay rate of the target region is obtained, the difference between the safety margin value of the target region at the current time and the rock stratum instability judgment threshold value is taken as the safety margin remaining amount, the safety margin remaining amount is divided by the safety margin decay rate, and the time prediction value required for the target region to reach the rock stratum instability judgment threshold value from the current time is obtained; The latest time node at which the pressure relief measures must be started and implemented is obtained based on the time prediction value, the implementation cycle of the pressure relief measures, and the effective delay time, and the time period between the current time and the latest time node is determined as the implementation time window of the pressure relief measures.

7. A system for detecting displacement deformation of a rock stratum in a mine for deep mining, for implementing the method according to any one of claims 1 to 6, characterized in that, It includes: A first unit is configured to acquire multi-dimensional physical field data of a rock stratum in a region to be monitored through a detection sensor, and the multi-dimensional physical field data includes displacement field data reflecting a deformation state of the rock stratum and stress field data reflecting a stress state of the rock stratum; A second unit is configured to perform spatial discretization processing on the multi-dimensional physical field data, divide the region to be monitored 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 region, and extract spatial geometric features of the stress gradient abnormal region and directional features in the displacement field data, to establish an association relationship between the spatial position and the directional features of the stress gradient abnormal region; A third unit is configured to calculate an additional load amount borne by the rock stratum material from a current stress state to a critical failure state according to the association relationship by tracking an expansion rate of the stress gradient abnormal region and a degradation rate of the shear strength of the rock stratum material, take the additional load amount as a quantitative index of the residual bearing capacity of the rock stratum, generate safety margin distribution data of each spatial position in the region to be monitored, and determine a time window for implementing pressure relief measures according to a safety margin decay rate of a target region.

8. An electronic device, comprising: It includes: A processor; A memory for storing processor-executable instructions; The processor is configured to invoke the instructions stored in the memory to execute the method in any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon computer program instructions, wherein, The computer program instructions, when executed by the processor, implement the method in any one of claims 1 to 6.

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