Dynamic three-dimensional modeling method for coal mining subsidence basin

Abstract

The invention relates to the technical field of settlement monitoring, in particular to a coal mining subsidence basin dynamic three-dimensional modeling method. According to the method, the geological information of the coal mining area is systematically called, and the settlement data of the coal mining area is comprehensively and accurately obtained by combining multiple methods such as drilling, geological exploration and seismic exploration; the actual settlement amount and settlement data are reversely deduced through difference value operation of discontinuous areas, and the coverage range of settlement monitoring is widened; the collected data is cleaned through Kalman filtering, so that the data error is reduced; a fault of a coal mine area is accurately positioned by combining a geological radar; by constructing the dynamic three-dimensional model of the coal mining subsidence basin, the subsidence process and the stratum change can be visually displayed, and an analysis tool is provided for subsequent risk assessment, environment monitoring and subsidence prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of subsidence monitoring, in particular to a method for dynamic three-dimensional modeling of a coal mining subsidence basin. Background Art

[0002] Settlement monitoring technology is a technical means for real-time tracking and measuring the settlement of the ground or structures. This technology is widely used in construction projects, underground projects, subway construction, transportation infrastructure and other fields to ensure the safety and stability of engineering structures. The core purpose of settlement monitoring is to monitor the settlement of soil, buildings or other facilities under the influence of factors such as force, construction or environmental changes, to promptly identify potential safety hazards and take measures to repair them. Coal mining subsidence monitoring refers to the real-time monitoring and analysis of surface subsidence or building subsidence caused by mining activities during the coal mining process through various monitoring means, assessing the stability of the surface, buildings, transportation facilities, etc. around the mining area, and predicting the risk of disasters such as subsidence or landslides in advance.

[0003] However, existing coal mining subsidence monitoring technologies have numerous shortcomings. First, most current coal mine subsidence monitoring methods rely primarily on traditional drilling, geological surveys, and seismic surveys. These methods can suffer from poor data timeliness, low accuracy, large data errors, and limited coverage. Traditional monitoring methods often only provide data from a limited number of monitoring points, failing to fully and accurately reflect overall subsidence changes in the mining area, potentially missing critical subsidence information during data collection.

[0004] Second, existing subsidence monitoring technologies mostly focus on post-analysis or periodic inspections, lacking the ability to monitor in real time and respond quickly to sudden subsidence changes during coal mining. Furthermore, due to the limitations of monitoring methods and tools, effective early warning of subsidence is often not possible, posing certain safety risks.

[0005] Finally, while existing technologies have begun to attempt to develop 3D models of coal mining subsidence, the accuracy and adaptability of existing 3D models remain limited due to incomplete data acquisition and inadequate treatment of differences between different strata and faults during model construction. In complex geological environments, existing models often struggle to accurately predict subsidence trends, hindering subsequent risk assessment and decision support.

[0006] In view of the above problems, it is necessary to propose a method for dynamic three-dimensional modeling of coal mining subsidence basins. Summary of the Invention

[0007] The purpose of the present invention is to solve the problems existing in the background technology and to propose a method for dynamic three-dimensional modeling of coal mining subsidence basins.

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] A method for dynamic three-dimensional modeling of a coal mining subsidence basin comprises the following steps:

[0010] Step 1: Settlement data collection and preprocessing;

[0011] The geological exploration report is retrieved to obtain the geological information of the coal mining area, and each stratum is numbered with the number symbol i, i = 1, 2, 3, ..., n; n is the total number of strata.

[0012] Obtain basic geological information of each bottom layer through drilling, geological exploration and seismic survey, including but not limited to: formation thickness Hi, elastic modulus Ei, water content Wi, Poisson's ratio υi and gravity γi of each formation;

[0013] As a preferred method of the present invention, the position and direction of all faults are determined by geological radar; all faults are numbered with a number symbol j, where j = 1, 2, 3, ..., m; each fault is fitted into a plane to generate a spatial distribution model of each fault j: Among them A j x+B j y+C j z+D j =0 is the fitting plane equation of fault j, where A j 、B j 、C j and D j are the spatial distribution characteristic parameters of fault j; j is the strike direction vector of fault j, u1 j 、u2 j and u3 j They are all directional characteristic elements and satisfy u j Fault fitting surface A j x+B j y+C j z+D j =0 vertical; where S j is the fitted plane area of fault j, L j is the length of the fitting plane, W j is the fitting plane width.

[0014] Deploy settlement monitoring equipment, including GPS, ground laser scanning, ground settlement meters, and ground deformation monitors, to collect real-time settlement data for the coal mining area and surrounding areas, including: the spatial distribution coordinates (xk, yk, zk) of the monitoring point, the settlement amount S(k) of the monitoring point, and the settlement rate SV(k) of the monitoring point. Where k is the settlement monitoring point number, and k = 1, 2, 3, ..., o; where o is the total number of settlement monitoring points.

[0015] As a preferred method of the present invention, interpolation processing is performed on discontinuous monitoring data in the spatial dimension to ensure that the settlement data can be spatially connected with the geological data and the three-dimensional model; and filtering and cleaning of the collected settlement data is performed in the time dimension to ensure that the settlement data can accurately reflect the continuous changes of the settlement data in time.

[0016] The specific process of the difference processing is: extracting the settlement amount S(k) and the settlement rate SV(k) of all monitoring points, and performing discontinuous area detection:

[0017] By formula Calculate the distance d(k1, k2) between any two adjacent settlement monitoring points k1 and k2. When the condition d(k1, k2) is greater than the preset threshold dMax, it means that there is a discontinuity between the settlement monitoring points k1 and k2, and interpolation calculation is required to mark k1 and k2 as discontinuous points.

[0018] Perform an inverse distance weighted difference calculation on the area between discontinuities: Calculate the difference point coordinates Difference point settlement and sedimentation rate Where S(u) and SV(u) are the settlement amount and settlement rate of the u-th settlement monitoring point, respectively. u is the number of all settlement monitoring points adjacent to the two discontinuities k1 and k2. N is the total number of settlement monitoring points adjacent to the two discontinuities k1 and k2. Where p is a preset weight index, which can be 1 or 2. When p = 1, it corresponds to a simple linear inverse distance weighted difference calculation; when p = 2, it corresponds to a squared inverse distance weighted difference calculation.

[0019] The filter cleaning process is specifically as follows:

[0020] The settlement amount S(k) and settlement rate SV(k) of each settlement monitoring point k are recorded at preset time intervals to generate vector The current time t is used as the timestamp to mark the settlement state vector of each preset time, and the settlement state vector is generated. and the settlement observation vector The settlement state vector represents the true value of the settlement amount and settlement rate at time t, that is, S^(k) is the estimated true settlement amount, and SV^(k) is the estimated true settlement rate; the settlement observation vector represents the observed value of the settlement amount and settlement rate at time t, that is, S(k) is the observed value of the settlement amount, and SV(k) is the observed value of the settlement rate;

[0021] Build a linear dynamic system model: where X t-1 represents the settlement state vector at time t-1; where H is the preset observation matrix, which represents the mapping relationship from the true value to the observed value; where A is the preset state transfer matrix, which describes the mapping relationship of the true value of the time series data from time t-1 to time t; where w t is process noise, specifically Gaussian noise with variance q and mean 0; where v t is the measurement noise, specifically Gaussian noise with variance r and mean 0;

[0022] Every preset time interval t, by formula Calculate the predicted settlement state vector at the current time t in is the predicted settlement state vector at the last preset time;

[0023] By formula Calculate the forecast covariance at the current moment in is the covariance of the last preset moment, where Q is the process noise w t The covariance matrix of

[0024] By formula Calculate the Kalman gain K at the current moment t ; Where H is the preset observation matrix, H T is the transposed matrix of the observation matrix; where R is the measurement noise v t The covariance matrix of

[0025] By formula Update the predicted value and covariance matrix; where I is the identity matrix; where P t is the updated covariance matrix.

[0026] Record the settlement state vectors of all settlement monitoring points k at each time t obtained through calculation And output as the filtering result.

[0027] Step 2: Establish a preliminary three-dimensional model of the subsidence basin;

[0028] A three-dimensional rectangular coordinate system XOY is established. A three-dimensional model of the subsidence basin is constructed within this system using the finite element method. The model comprises several finite element units. Over time, all finite element units dynamically fit the topographic map of the subsidence basin. Each finite element unit is numbered, with the symbol p = 1, 2, 3, ..., q, where q is the total number of finite element cells. The volume Vp and the stratum number i of each finite element unit are obtained. Each finite element cell is annotated with the corresponding basic geological information, including the elastic modulus Ep, water content Wp, Poisson's ratio υp, and gravity γp.

[0029] Establish the stress balance equation for each finite element model: σ d1，d2 +f d = 0. Among them, σ d1，d2 is the stress tensor in all directions, including the normal stress σ along the X axis xx , normal stress along the Y axis σ yy , normal stress along the Z axis σ zz , shear stresses σ in the X and Y directions xy , shear stresses σ in the X and Z directions xz and the shear stresses σ in the Y and Z directions yz Where d1 and d2 are direction indexes, and d1 = x, y, z; d2 = x, y, z; where f d External force refers to the external force applied to the finite element, including but not limited to body force, external load and gravity.

[0030] Obtain the layer i, elastic modulus Ei and Poisson's ratio υi of each finite element p, and establish the stress-strain-displacement relationship constitutive model of each finite element p: where κ is the elastic modulus matrix, and

[0031]

[0032] ; where σ xx , σ yy and σ zz is the principal stress; where σ xy , σ yz and σ xz is the shear stress; where ε xx , ε yy and ε zz is the principal strain; xy , γ yz and γ xz is the shear strain; where u, v, and w represent the displacement components of the finite element in the directions x, y, and z.

[0033] In the finite element model, input the real-time coordinates of each settlement monitoring point k and the settlement state vector The settlement S^(k) and settlement rate SV^(k) in the stress-strain relationship are calculated. The displacement of each finite element is simulated by combining the equilibrium equation of the finite element network model and the stress-strain-displacement relationship constitutive model.

[0034] Step 3: Model visualization;

[0035] Revit modeling software is used to input the settlement data, settlement rate data of each settlement monitoring point and the differential settlement data and differential settlement rate data of discontinuous areas into the three-dimensional model of the subsidence basin according to coordinates to display the spatial distribution and timeliness of the settlement data.

[0036] The spatial distribution display is specifically as follows:

[0037] The maximum and minimum settlements, Smax, Smin, were obtained and a set of equal settlement difference thresholds was calculated through interpolation: SC = (Smax - Smin) × (C / Cmax) + Smin. The actual settlement at each settlement observation point was calculated, where Cmax is the preset equal-division precision and C is the difference layer number, C = 1, 2, 3, ..., Cmax. All settlement data were divided into C groups according to the equal settlement difference thresholds to generate the equal-fall surface model.

[0038] The timeliness display is specifically as follows: whenever the monitoring data of a settlement monitoring point is updated, that is, the settlement amount S^(k) and the settlement rate SV^(k), the settlement amount and settlement rate are input into the three-dimensional model of the subsidence basin in real time and compared with the data collected last time. If the comparison result is that the change in settlement amount is greater than the preset threshold or the settlement rate is greater than the preset threshold, the settlement observation point will be highlighted.

[0039] Whenever new subsidence monitoring data are collected, these data are used to dynamically update the display results of spatial distribution display, timeliness display and subsidence trend prediction display in the existing subsidence basin visualization model.

[0040] Step 4: Multi-dimensional analysis and assessment of subsidence;

[0041] Combine the data to analyze the sedimentation rate and acceleration, and use the formula Calculate the settlement acceleration a of each settlement monitoring point t (k); where SV t+Δt (k) is the sedimentation rate collected at the last preset time; SV t (k) is the sedimentation rate collected at the current time; where Δt is the interval between two preset times.

[0042] As a preferred method of the present invention, a sedimentation acceleration-time graph is drawn. The sedimentation acceleration data points (t, at (k) Input the image and obtain the sedimentation acceleration-time curve.

[0043] Determine whether the curve converges. If so, obtain the convergence value of the settlement acceleration If not, a settlement alarm signal is generated.

[0044] As a preferred embodiment of the present invention, if the settlement acceleration converges to If the value is greater than the preset threshold, a settlement warning signal is generated;

[0045] Step 5: Risk assessment and emergency response;

[0046] When a settlement alarm signal is identified, the location of the settlement monitoring point that generates the alarm signal will be highlighted.

[0047] When a settlement warning signal is identified, the inspection personnel are reminded to pay close attention to the updates and changes of the relevant settlement data.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. The present invention can comprehensively and accurately obtain the settlement data of the coal mining area by systematically retrieving the geological information of the coal mining area and combining various methods such as drilling, geological exploration, and seismic survey. In particular, by collecting detailed physical parameters such as the numbering of the strata and elastic modulus, water content, and Poisson's ratio, it provides a scientific basis for subsequent dynamic three-dimensional modeling, ensures the high accuracy of the settlement data, and provides reliable data support for further analysis and prediction; in addition, the present invention improves the coverage of settlement monitoring by inferring the actual settlement amount and settlement data through difference calculation of discontinuous areas; and cleans the collected data through Kalman filtering to reduce data errors;

[0050] 2. This invention uses geological radar to accurately locate faults in coal mine areas, effectively identifying and tracking the location and direction of faults, and identifying potential geological risks in advance. This not only improves the comprehensiveness of coal mine subsidence monitoring, but also enables real-time tracking and timely warning of geological changes during coal mining, reducing safety hazards caused by geological changes.

[0051] 3. By constructing a dynamic three-dimensional model of a coal mining subsidence basin, this invention can visually demonstrate the subsidence process and stratigraphic changes, providing a powerful tool for subsequent risk assessment, environmental monitoring, and subsidence prediction. Dynamic modeling can simulate the impact of coal mining on underground structures and the surface in real time, foresee possible subsidence trends, and help decision-makers take effective preventive and intervention measures, thereby effectively ensuring the safety of mining areas and the sustainable development of the environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings:

[0053] Figure 1 is a flow chart of the method of the present invention;

[0054] Figure 2 Schematic diagram of the iso-drop surface model of the present invention. DETAILED DESCRIPTION

[0055] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0056] See also Figure 1 As shown, a method for dynamic three-dimensional modeling of a coal mining subsidence basin specifically includes the following steps:

[0057] Step 1: Settlement data collection and preprocessing;

[0058] The geological exploration report is retrieved to obtain the geological information of the coal mining area, and each stratum is numbered with the number symbol i, i = 1, 2, 3, ..., n; n is the total number of strata.

[0059] Obtain basic geological information of each bottom layer through drilling, geological exploration and seismic survey, including but not limited to: formation thickness Hi, elastic modulus Ei, water content Wi, Poisson's ratio υi and gravity γi of each formation;

[0060] Furthermore, the location and direction of all faults are determined by geological radar; all faults are numbered with the number symbol j, where j = 1, 2, 3, ..., m; each fault is fitted into a plane to generate a spatial distribution model of each fault j: Among them A j x+B j y+C j z+D j =0 is the fitting plane equation of fault j, where A j 、B j 、C j and D j are the spatial distribution characteristic parameters of fault j; j is the strike direction vector of fault j, u1 j 、u2 j and u3 j They are all directional characteristic elements and satisfy u j Fault fitting surface A jx+B j y+C j z+D j =0 vertical; where S j is the fitted plane area of fault j, L j is the length of the fitting plane, W j is the fitting plane width.

[0061] Deploy settlement monitoring equipment, including GPS, ground laser scanning, ground settlement meters, and ground deformation monitors, to collect real-time settlement data for the coal mining area and surrounding areas, including: the spatial distribution coordinates (xk, yk, zk) of the monitoring point, the settlement amount S(k) of the monitoring point, and the settlement rate SV(k) of the monitoring point. Where k is the settlement monitoring point number, and k = 1, 2, 3, ..., o; where o is the total number of settlement monitoring points.

[0062] Furthermore, the discontinuous monitoring data are interpolated in the spatial dimension to ensure that the settlement data can be spatially connected with the geological data and the three-dimensional model; the collected settlement data are filtered and cleaned in the time dimension to ensure that the settlement data can accurately reflect the continuous changes in time.

[0063] The specific process of the difference processing is: extracting the settlement amount S(k) and the settlement rate SV(k) of all monitoring points, and performing discontinuous area detection:

[0064] By formula Calculate the distance d(k1, k2) between any two adjacent settlement monitoring points k1 and k2. When the condition d(k1, k2) is greater than the preset threshold dMax, it means that there is a discontinuity between the settlement monitoring points k1 and k2, and interpolation calculation is required to mark k1 and k2 as discontinuous points.

[0065] Perform an inverse distance weighted difference calculation on the area between discontinuities: Calculate the difference point coordinates Difference point settlement and sedimentation rate Where S(u) and SV(u) are the settlement amount and settlement rate of the u-th settlement monitoring point, respectively. u is the number of all settlement monitoring points adjacent to the two discontinuities k1 and k2. N is the total number of settlement monitoring points adjacent to the two discontinuities k1 and k2. Where p is a preset weight index, which can be 1 or 2. When p = 1, it corresponds to a simple linear inverse distance weighted difference calculation; when p = 2, it corresponds to a squared inverse distance weighted difference calculation.

[0066] The filter cleaning process is specifically as follows:

[0067] The settlement amount S(k) and settlement rate SV(k) of each settlement monitoring point k are recorded at preset time intervals to generate vector The current time t is used as the timestamp to mark the settlement state vector of each preset time, and the settlement state vector is generated. and the settlement observation vector The settlement state vector represents the true value of the settlement amount and settlement rate at time t, that is, S^(k) is the estimated true settlement amount, and SV^(k) is the estimated true settlement rate; the settlement observation vector represents the observed value of the settlement amount and settlement rate at time t, that is, S(k) is the observed value of the settlement amount, and SV(k) is the observed value of the settlement rate;

[0068] Build a linear dynamic system model: where X t-1 represents the settlement state vector at time t-1; where H is the preset observation matrix, which represents the mapping relationship from the true value to the observed value; where A is the preset state transfer matrix, which describes the mapping relationship of the true value of the time series data from time t-1 to time t; where w t is process noise, specifically Gaussian noise with variance q and mean 0; where v t is the measurement noise, specifically Gaussian noise with variance r and mean 0;

[0069] Every preset time interval t, by formula Calculate the predicted settlement state vector at the current time t in is the predicted settlement state vector at the last preset time;

[0070] By formula Calculate the forecast covariance at the current moment in is the covariance of the last preset moment, where Q is the process noise w t The covariance matrix of

[0071] By formula Calculate the Kalman gain K at the current moment t ; Where H is the preset observation matrix, H T is the transposed matrix of the observation matrix; where R is the measurement noise v t The covariance matrix of

[0072] By formula Update the predicted value and covariance matrix; where I is the identity matrix; where P t is the updated covariance matrix.

[0073] Record the settlement state vectors of all settlement monitoring points k at each time t obtained through calculation And output as the filtering result.

[0074] Step 2: Establish a preliminary three-dimensional model of the subsidence basin;

[0075] A three-dimensional rectangular coordinate system XOY is established. A three-dimensional model of the subsidence basin is constructed within this system using the finite element method. The model comprises several finite element units. Over time, all finite element units dynamically fit the topographic map of the subsidence basin. Each finite element unit is numbered, with the symbol p = 1, 2, 3, ..., q, where q is the total number of finite element cells. The volume Vp and the stratum number i of each finite element unit are obtained. Each finite element cell is annotated with the corresponding basic geological information, including the elastic modulus Ep, water content Wp, Poisson's ratio υp, and gravity γp.

[0076] Establish the stress balance equation for each finite element model: σ d1，d2 +f d = 0. Among them, σ d1，d2 is the stress tensor in all directions, including the normal stress σ along the X axis xx , normal stress along the Y axis σ yy , normal stress along the Z axis σ zz , shear stresses σ in the X and Y directions xy , shear stresses σ in the X and Z directions xz and the shear stresses σ in the Y and Z directions yz Where d1 and d2 are direction indexes, and d1 = x, y, z; d2 = x, y, z; where f d External force refers to the external force applied to the finite element, including but not limited to body force, external load and gravity.

[0077] Obtain the layer i, elastic modulus Ei and Poisson's ratio υi of each finite element p, and establish the stress-strain-displacement relationship constitutive model of each finite element p: where κ is the elastic modulus matrix, and

[0078]

[0079] ; where σ xx , σ yy and σ zz is the principal stress; where σ xy , σ yz and σ xz is the shear stress; where ε xx , ε yy and ε zz is the principal strain; xy , γ yz and γ xzis the shear strain; where u, v, and w represent the displacement components of the finite element in the directions x, y, and z.

[0080] In the finite element model, input the real-time coordinates of each settlement monitoring point k and the settlement state vector The settlement S^(k) and settlement rate SV^(k) in the stress-strain relationship are calculated. The displacement of each finite element is simulated by combining the equilibrium equation of the finite element network model and the stress-strain-displacement relationship constitutive model.

[0081] Step 3: Model visualization;

[0082] Revit modeling software is used to input the settlement data, settlement rate data of each settlement monitoring point and the differential settlement data and differential settlement rate data of discontinuous areas into the three-dimensional model of the subsidence basin according to coordinates to display the spatial distribution and timeliness of the settlement data.

[0083] The spatial distribution display is specifically as follows:

[0084] See also Figure 2 As shown in the figure, the maximum settlement Smax and minimum settlement Smin are obtained, and a set of equal settlement difference thresholds are obtained through difference calculation: SC = (Smax - Smin) × (C / Cmax) + Smin. The actual settlement of each settlement observation point is calculated, where Cmax is the preset difference division accuracy and C is the difference layer number, C = 1, 2, 3, ..., Cmax. All settlement data are divided into C groups according to the equal settlement difference thresholds to generate the equal settlement surface model.

[0085] The timeliness display is specifically as follows: whenever the monitoring data of a settlement monitoring point is updated, that is, the settlement amount S^(k) and the settlement rate SV^(k), the settlement amount and settlement rate are input into the three-dimensional model of the subsidence basin in real time and compared with the data collected last time. If the comparison result is that the change in settlement amount is greater than the preset threshold or the settlement rate is greater than the preset threshold, the settlement observation point will be highlighted.

[0086] Whenever new subsidence monitoring data are collected, these data are used to dynamically update the display results of spatial distribution display, timeliness display and subsidence trend prediction display in the existing subsidence basin visualization model.

[0087] Step 4: Multi-dimensional analysis and assessment of subsidence;

[0088] Combine the data to analyze the sedimentation rate and acceleration, and use the formula Calculate the settlement acceleration a of each settlement monitoring point t (k); where SV t+Δt (k) is the sedimentation rate collected at the last preset time; SVt (k) is the sedimentation rate collected at the current time; where Δt is the interval between two preset times;

[0089] Furthermore, a sedimentation acceleration-time graph is drawn. The sedimentation acceleration data points (t, a t (k) Input the image and obtain the sedimentation acceleration-time curve.

[0090] Determine whether the curve converges. If so, obtain the convergence value of the settlement acceleration If not, a settlement alarm signal is generated.

[0091] Furthermore, if the settlement acceleration converges to If the value is greater than the preset threshold, a settlement warning signal is generated;

[0092] Step 5: Risk assessment and emergency response;

[0093] When a settlement alarm signal is identified, the location of the settlement monitoring point that generates the alarm signal will be highlighted.

[0094] When a settlement warning signal is identified, the inspection personnel are reminded to pay close attention to the updates and changes of the relevant settlement data.

[0095] Based on a dynamically updated 3D model of the subsidence basin and the distribution of the surrounding environment, risk assessments are conducted, highlighting buildings, infrastructure, faults, and water bodies within a preset distance threshold when subsidence warning signals and early warning signals are triggered. This helps monitoring personnel assess the impact of potential ground subsidence during mining on surrounding buildings, infrastructure, groundwater levels, and more.

[0096] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0097] It should also be understood that the terms used in this disclosure are for the purpose of describing particular embodiments only and are not intended to limit the disclosure. As used in this disclosure and the claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and the claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations;

[0098] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for dynamic three-dimensional modeling of coal mining subsidence basins, characterized in that: The following steps are involved: Step 1: Settlement data collection and preprocessing; Retrieve geological exploration reports, obtain geological information of the coal mining area, and number the strata; obtain basic geological information of each stratum through drilling, geological exploration and seismic survey; The location and direction of all faults are determined through geological radar, and a mathematical model of the faults is established. Subsidence monitoring equipment is deployed to collect subsidence data in real time, and discontinuous monitoring data are interpolated in the spatial dimension and filtered and cleaned in the temporal dimension. Step 2: Establish a preliminary three-dimensional model of the subsidence basin; Based on the finite element method, a three-dimensional model of the subsidence basin is established in a three-dimensional rectangular coordinate system, including several finite element units. The corresponding basic geological information is annotated for each finite element, and a stress equilibrium equation and a constitutive model of the stress-strain-displacement relationship are established. The real-time data of the settlement monitoring points and the settlement state vector are input to simulate the displacement of each finite element. Step 3: Model visualization; Use modeling software to input subsidence data into the three-dimensional model of the subsidence basin to display the spatial distribution and timeliness of subsidence data; Step 4: Multi-dimensional analysis and assessment of subsidence; Combine the data to analyze the settlement rate and acceleration, determine whether the settlement acceleration curve has converged, and generate a settlement alarm signal or settlement warning signal based on the judgment result; Step 5: Risk assessment and emergency response; Based on the generated alarm signal or early warning signal, the settlement monitoring point is highlighted or the inspection personnel are reminded to pay close attention to the relevant data updates and changes.

2. The method for dynamic three-dimensional modeling of a coal mining subsidence basin according to claim 1, characterized in that: The specific process of establishing the fault mathematical model is as follows; Determine the location and direction of all faults using geological radar; number all faults with the symbol j, where j = 1, 2, 3, ..., m; fit each fault into a plane to generate a spatial distribution model for each fault j: Among them A j x+B j y+C j z+D j =0 is the fitting plane equation of fault j, where A j 、B j 、C j and D j are the spatial distribution characteristic parameters of fault j; u j is the strike direction vector of fault j, u1 j 、u2 j and u3 j They are all directional characteristic elements and satisfy u j Fault fitting surface A j x+B j y+C j z+D j =0 vertical; where S j is the fitted plane area of fault j, L j is the length of the fitting plane, W j is the fitting plane width.

3. The method for dynamic three-dimensional modeling of a coal mining subsidence basin according to claim 1, characterized in that: The specific process of interpolating settlement data in the spatial dimension is as follows: Extract the settlement amount S(k) and settlement rate SV(k) of all monitoring points and perform discontinuous area detection: By formula Calculate the distance d(k1, k2) between any two adjacent settlement monitoring points k1 and k2. When the condition d(k1, k2) is greater than the preset threshold dMax, it means that there is a discontinuity between the settlement monitoring points k1 and k2, and interpolation calculation is required. Mark k1 and k2 as discontinuous points; Perform an inverse distance weighted difference calculation on the area between discontinuities: Calculate the difference point coordinates Difference point settlement and sedimentation rate Where S(u) and SV(u) are the settlement amount and settlement rate of the u-th settlement monitoring point, respectively, u is the number of all settlement monitoring points adjacent to the two discontinuous points k1 and k2, and N is the total number of all settlement monitoring points adjacent to the two discontinuous points k1 and k2; Where p is a preset weight index, which takes a value of 1 or 2. When p = 1, it corresponds to a simple linear inverse distance weighted difference calculation; when p = 2, it corresponds to a squared inverse distance weighted difference calculation.

4. The method for dynamic three-dimensional modeling of a coal mining subsidence basin according to claim 1, characterized in that: The specific process of filtering and cleaning the sedimentation data in the time dimension is as follows: The settlement amount S(k) and settlement rate SV(k) of each settlement monitoring point k are recorded at preset time intervals to generate vector The current time t is used as the timestamp to mark the settlement state vector of each preset time, and the settlement state vector is generated. and the settlement observation vector The settlement state vector represents the true value of the settlement amount and settlement rate at time t, that is, S^(k) is the estimated true settlement amount, and SV^(k) is the estimated true settlement rate; The settlement observation vector represents the observed values of settlement amount and settlement rate at time t, that is, S(k) is the observed value of settlement amount, and SV(k) is the observed value of settlement rate; Build a linear dynamic system model: where X t-1 Represents the settlement state vector at time t-1; where H is the preset observation matrix, which represents the mapping relationship from true value to observation value; Where A is the preset state transfer matrix, which describes the mapping relationship between the true value of the time series data from time t-1 to time t; where w t is process noise, specifically Gaussian noise with variance q and mean 0; where v t is the measurement noise, specifically Gaussian noise with variance r and mean 0; Every preset time interval t, by formula Calculate the predicted settlement state vector at the current time t in is the predicted settlement state vector at the last preset time; By formula Calculate the forecast covariance at the current moment in is the covariance of the last preset moment, where Q is the process noise w t The covariance matrix of By formula Calculate the Kalman gain K at the current moment t ; Where H is the preset observation matrix, H T is the transposed matrix of the observation matrix; where R is the measurement noise v t The covariance matrix of By formula Update the predicted value and covariance matrix; Where I is the identity matrix; where P t To complete the updated covariance matrix; Record the settlement state vectors of all settlement monitoring points k at each time t obtained through calculation And output as the filtering result.

5. The method for dynamic three-dimensional modeling of a coal mining subsidence basin according to claim 1, characterized in that: The specific process of establishing a three-dimensional model of a subsidence basin is as follows: A three-dimensional rectangular coordinate system XOY is established, and a three-dimensional model of a subsidence basin is established in the three-dimensional rectangular coordinate system based on the finite element method, wherein the three-dimensional model of the subsidence basin includes a plurality of finite element units; over time, all the finite element units perform dynamic fitting on a topographic map of the subsidence basin area; the finite element units are numbered with a number symbol p, where p = 1, 2, 3, ..., q; q is the total number of finite element meshes; the volume Vp and the stratum number i of each finite element unit are obtained, and each finite element mesh is annotated with corresponding basic geological information, including elastic modulus Ep, water content Wp, Poisson's ratio υp, and gravity γp.

6. The method for dynamic three-dimensional modeling of a coal mining subsidence basin according to claim 1, characterized in that: The stress balance equation is specifically: σ d1，d2 +f d =0; where σ d1，d2 is the stress tensor in all directions, including the normal stress σ along the X axis xx , normal stress along the Y axis σ yy , normal stress along the Z axis σ zz , shear stress σ in the X and Y directions xy , shear stresses σ in the X and Z directions xz and the shear stresses σ in the Y and Z directions yz ; Where d1 and d2 are direction indexes, and d1 = x, y, z; d2 = x, y, z; where f d External force represents the external force applied to the finite element, including but not limited to body force, external load and gravity; Based on the stress equilibrium equation, a stress-strain-displacement constitutive model is further established.

7. A method for dynamic three-dimensional modeling of a coal mining subsidence basin according to claim 1 or 6, characterized in that: The stress-strain-displacement constitutive model is specifically: Obtain the layer i, elastic modulus Ei and Poisson's ratio υi of each finite element p, and establish the stress-strain-displacement relationship constitutive model of each finite element p: where κ is the elastic modulus matrix, and where σ xx , σ yy and σ zz is the principal stress; where σ xy , σ yz and σ xz is the shear stress; where ε xx , ε yy and ε zz is the principal strain; xy , γ yz and γ xz is the shear strain; where u, v, and w represent the displacement components of the finite element in the directions x, y, and z.

8. The method for dynamic three-dimensional modeling of a coal mining subsidence basin according to claim 1, characterized in that: The spatial distribution display is specifically as follows: The maximum settlement Smax and the minimum settlement Smin are obtained, and a set of equal settlement difference thresholds are obtained through difference calculation: SC = (Smax-Smin) × (C / Cmax) + Smin; the actual settlement of each settlement observation point, where Cmax is the preset difference division accuracy, and C is the difference layer number, C = 1, 2, 3, ..., Cmax; all settlement data are divided into C groups of data according to the equal settlement difference thresholds to generate the equal settlement surface model.

9. The method for dynamic three-dimensional modeling of a coal mining subsidence basin according to claim 1, characterized in that: The timeliness display is specifically as follows: whenever the monitoring data of a settlement monitoring point is updated, that is, the settlement amount S^(k) and the settlement rate SV^(k), the settlement amount and settlement rate are input into the three-dimensional model of the subsidence basin in real time and compared with the data collected last time. If the comparison result is that the change in settlement amount is greater than the preset threshold or the settlement rate is greater than the preset threshold, the settlement observation point will be highlighted.

10. The method for dynamic three-dimensional modeling of coal mining subsidence basin according to claim 1, characterized in that: The sedimentation rate and acceleration analysis are specifically as follows: Combine the data to analyze the sedimentation rate and acceleration, and use the formula Calculate the settlement acceleration a of each settlement monitoring point t (k); where SV t+Δt (k) is the sedimentation rate collected at the last preset time; SV t (k) is the sedimentation rate collected at the current time; where Δt is the interval between two preset times; Draw the sedimentation acceleration-time graph; and plot the sedimentation acceleration data points (t, a t (k) Input the image and obtain the sedimentation acceleration-time curve; Determine whether the curve converges. If so, obtain the convergence value of the settlement acceleration If not, a settlement alarm signal is generated; If the settlement acceleration converges to If the value is greater than the preset threshold, a settlement warning signal is generated.

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